Multiplexed Cre-dependent selection yields systemic AAVs for targeting distinct brain cell types

Multiplexed Cre-dependent selection yields systemic AAVs for

targeting distinct brain cell types

Sripriya Ravindra Kumar

Timothy F. Miles

Xinhong Chen

David Brown

Tatyana

Dobreva

Qin Huang

1,4

Xiaozhe Ding

Yicheng Luo

Pétur H. Einarsson

Alon

Greenbaum

1,2,3

Min J. Jang

Benjamin E. Deverman

1,4

Viviana Gradinaru

Division of Biology and Biological Engineering, California Institute of Technology, Pasadena,

California, USA.

Present addresses: Joint Department of Biomedical Engineering, North Carolina State

University, Raleigh, North Carolina, USA.

Present addresses: University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.

Present addresses: Stanley Center for Psychiatric Research, Broad Institute, Cambridge,

Massachusetts, USA.

Abstract

Recombinant adeno-associated viruses (rAAVs) are efficient, non-invasive gene delivery vectors

via intravenous delivery, however, natural serotypes display a finite set of tropisms. To expand

their utility, we evolved AAV capsids to efficiently transduce specific cell types in adult mouse

brains. Building upon our previous Cre recombination-based AAV targeted evolution (CREATE)

platform, we developed Multiplexed-CREATE (M-CREATE) to quickly and accurately identify

variants of interest in a given selection landscape through multiple positive and negative selection

criteria by incorporating next-generation sequencing, synthetic library generation, and a novel

analysis pipeline.

In vivo

selections for brain endothelial cell-, astrocyte-, and neuron-transducing

capsids have identified variants that can transduce the central nervous system broadly, exhibit bias

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Corresponding author statement:

Correspondence should be addressed to: Viviana Gradinaru., address for corresponding author:

viviana@caltech.edu.

AUTHOR CONTRIBUTIONS:

S.R.K., B.E.D., T.F.M., and V.G. designed the experiments. S.R.K., B.E.D., X.C., T.F.M., Y.L., A.G., Q.H., and M.J.J. performed

experiments. X.C. assisted with virus production and characterization of AAV-PHP variants in mice. Q.H. assisted with method

optimization, cloning, virus production and tissue harvest. Y.L. assisted with method optimization and processed tissues for deep

sequencing for 3-mer-s library. T.F.M performed the clustering analysis, contributed to experiments related to NGS data validation,

variant assessment across mice strains and amino acid bias heatmap analysis. A.G. processed and imaged cleared brain hemisphere,

and compiled the Supplementary Video 1 with input from S.R.K., and V.G. D.B., T.D., P.E., built the software to process the NGS raw

data for analysis with input from B.E.D., T.F.M., V.G., and S.R.K. M.J.J. performed the HCR experiments. X.D. produced structural

models for AAV9 and contributed to the data analysis pipeline. S.R.K. prepared the figures with input from all authors. S.R.K., T.F.M.,

B.E.D., and V.G. wrote the manuscript with input from all authors. V.G. supervised all aspects of the work.

Equal contributions statement:

These authors contributed equally: Timothy F. Miles, Xinhong Chen.

COMPETING FINANCIAL INTERESTS STATEMENT:

The California Institute of Technology has filed and licensed a patent application for the work described in this manuscript with

S.R.K., B.E.D., and V.G. listed as inventors (Caltech disclosure reference no. CIT 8198).

ETHICAL COMPLIANCE:

We have complied with all relevant ethical regulations.

HHS Public Access

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Published in final edited form as:

Nat Methods

. 2020 May ; 17(5): 541–550. doi:10.1038/s41592-020-0799-7.

Author Manuscript

toward vascular cells and astrocytes, target neurons with greater specificity, or cross the blood-

brain barrier across diverse murine strains. Collectively, M-CREATE methodology accelerates the

discovery of novel capsids for use in neuroscience and gene therapy applications.

INTRODUCTION:

Recombinant adeno-associated viruses (rAAVs) are widely used as gene delivery vectors in

scientific research and therapeutic applications due to their ability to transduce both dividing

and non-dividing cells, their long-term persistence as episomal DNA in infected cells, and

their low immunogenicity

–

. However, gene delivery by natural AAV serotypes is limited

by dose-limiting safety constraints and largely overlapping tropisms. AAV capsids

engineered by rational design

–

or directed evolution

–

have yielded vectors with

improved efficiencies for select cell populations

–

, yet much work remains. Previously,

we evolved AAV-PHP.B/eB variants from AAV9 using a selection method called CREATE:

Cre recombination-based AAV targeted evolution

. This method relies on applying positive

selective pressure for functional capsids by pairing Cre expression in defined cell

populations with Cre-Lox recombination-dependent PCR amplification of capsid variants.

To more efficiently expand the AAV toolbox, we developed Multiplexed-CREATE (M-

CREATE) (Fig. 1a, Supplementary Fig. 1a, b), named for its ability to accurately compare

the enrichment profiles of thousands of capsid variants across multiple cell types and organs

within a single experiment. This method improves upon its predecessor by capturing the

breadth of capsid variants at every stage of the selection process. M-CREATE supports: (1)

the calculation of a true enrichment score for each variant by using next generation

sequencing (NGS) to correct for biases in viral production prior to selection, (2) reduced

propagation of bias in successive rounds of selection through the creation of a post-round 1

synthetic pool library with equal variant representation, (3) the reduction of false positives

by including codon replicates of each selected variant in the pool. Combined, these

improvements allow confident interpretation across a broad range of enrichments in multiple

positive selections and enable post-hoc negative screening by comparing deep sequencing of

recovered capsid libraries among multiple targets (cells types or organs). Collectively, these

features transform our ability to identify variants worthy of validation and characterization

in vivo

To demonstrate the ability of M-CREATE to reveal interesting variants missed by its

predecessor (CREATE), we used the capsid library design that yielded AAV-PHP.B,

identifying several AAV9 variants with distinct tropisms including ones that have biased

transduction of brain vascular cells or that can cross the blood-brain barrier (BBB) without

strain specificity.

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RESULTS:

Multiplexed-CREATE allows detailed characterization of the capsid libraries during

Round-1 selection.

During DNA and virus library generation there is potential for accumulation of biases that

over-represent certain capsid variants, obscuring their true enrichment during

in vivo

selection. These biases may result from PCR amplification bias in the DNA library or

sequence bias in the efficiency of virus production across various steps: capsid assembly,

genome packaging and stability during purification. We investigated this with a

7-mer-i (i-

insertion)

library, a randomized 7-mer library inserted between positions 588–589 of AAV9

(Fig. 1a,b) in rAAV-ΔCap9-in-cis-Lox2 plasmid (Supplementary Fig. 1a; theoretical library

size: 3.4×10

unique nucleotides, and an estimated ~1×10

upon transfection; see

Methods). Sequencing libraries after DNA assembly and virus purification to a depth of 10 –

20 million (M) reads was adequate to capture the bias among variants during virus

production (Fig. 1c; despite ~1% variant overlap among these libraries; Supplementary Fig.

1c,d), demonstrating that even permissive sites like 588–589 will impose biological

constraints on sampled sequence space. The DNA library had a uniform distribution of 9.6

M unique variants within ~10 M total reads (read count (RC) mean = 1.0, S.D. = 0.074),

indicating minimal bias. In contrast, the virus library had 3.6 M unique variants within ~20

M depth (RC mean = 4.59, S.D. = 11.15) indicating enrichment of a subset of variants

during viral production.

For

in vivo

selection, we intravenously administered the

7-mer-i

viral library at a dose of

2×10

vg per adult transgenic mouse expressing Cre in different brain cell types: GFAP-Cre

mice for astrocytes, SNAP25-Cre mice for neurons, and Tek-Cre mice for endothelial cells

(

= 2 mice per Cre transgenic line, see Methods). Two weeks after intravenous (IV)

injection, we harvested brain, spinal cord, and liver tissues. We extracted the rAAV genomes

from tissues and selectively amplified the capsids that transduced Cre-expressing cells

(Supplementary Fig. 1e–i). Upon deep sequencing, we observed ~8×10

unique nucleotide

variants recovered from brain tissues and < 50 variants in spinal cords (~48% of which were

identified in virus library) across the transgenic lines, and each variant was represented with

an enrichment score reflecting the change in relative abundance between the brain and the

starting virus library (Fig. 1d, see Methods).

Two features of this dataset stand out. First, the recovered variants in brain tissue were

disproportionately represented among the fraction of the transformed capsid library observed

by sequencing after viral production demonstrating how production biases skew selection

results. Second, the distribution of capsid read counts (RCs) reveals that more than half of

the unique recovered variants after selection appear at remarkably low read counts. These

variants may either be unintended mutants from experimental manipulation or AAV9-like

variants with low basal level of CNS transduction (Supplementary Fig. 1e, see Methods).

A novel Round-2 library design improves the selection outcome

Concerned that the sequence bias during viral production and recovery would propagate

across selection rounds despite our post-hoc enrichment scoring, we designed an unbiased

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library based on the round-1 (R1) output (

synthetic pool

library) via oligo pools (Twist

Bioscience). This library was compared to a library PCR amplified directly from the

recovered R1 DNA (

PCR pool

library) (Fig. 2a, Supplementary Table 1, see Methods).

The

synthetic pool

library design comprised: (1) equimolar amounts of ~8950 capsid

variants present at high read counts in at least one of the R1 selections from brain and spinal

cord (Supplementary Fig. 1e, see Methods); (2) alternative codon replicates of those ~8950

variants (optimized for mammalian codons) to reduce false positives; and (3) a “

spike-in

”

library of controls (Supplementary Note 1, Supplementary Dataset 1), resulting in a total

library size of 18,000 nucleotide variants.

As anticipated, both round-2 (R2) virus libraries produced a high titer (~6×10

vg per 10 ng

of R2 DNA library per 150 mm dish; Supplementary Fig. 2a), and ~99% of variants from the

R2 DNA were found after viral production (Fig. 2b). However, the distribution of the DNA

and virus libraries from both designs differed significantly. The

PCR pool

library carries

forward the R1 selection biases (Fig. 2c, Supplementary Fig. 2b,c) where the abundance

reflects prior enrichment across tissues in R1 as well as bias from viral production and

sample mixing. Comparatively, the

synthetic pool

DNA library is more evenly distributed,

minimizing bias amplification across selection rounds.

For

in vivo

selection, we intravenously administered a dose of 1×10

vg per adult

transgenic mouse into three of the previously used lines (

= 2 mice per Cre transgenic line –

GFAP, SNAP25, Tek), as well as the Syn-Cre line (for neurons). Two weeks after IV

injection, rAAV genomes from brain samples were extracted, selectively amplified, and deep

sequenced (as in R1). The

synthetic pool

library produced a greater number of positively

enriched capsid variants than the

PCR pool

brain library (e.g. ~1700 versus ~700 variants/

tissue library at amino acid (AA) level in GFAP-Cre) (Fig. 2d, Supplementary Fig. 2d). In

the

synthetic pool

, ~90% of the variants from the

spike-in

library were positively enriched as

expected (Supplementary Fig. 2d, middle panel; Supplementary Dataset 1).

The degree of correlation for enrichment scores of variants recovered from both

PCR

and

synthetic pool

libraries varies in each Cre transgenic line, demonstrating the presence of

noise within experiments (Supplementary Fig. 2e, Supplementary Note 2). The

synthetic

pool

’s codon replicate feature addresses this predicament by pinpointing the level of

enrichment needed within each selection to rise above noise (Fig. 2e, Supplementary Fig.

3a,b). This is a significant advantage over the PCR pool design, allowing researchers to

confidently interpret enrichment scores in a given selection.

Analysis of AAV capsid libraries after Round-2 selections

Whereas the AA distribution of the DNA library closely matched the Oligopool design,

virus production selected for a motif with Asn (N) at position 2,

-branched AAs (I, T, V) at

position 4, and positively charged AAs (K, R) at position 5 (Fig. 2f, Supplementary Fig. 3c).

Fitness for BBB crossing resulted in a very different pattern. In comparison to the R2 virus

library, highly enriched variants share preferences, for example, proline (P) in position 5,

and phenylalanine (F) in position 6.

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Confident in our ability to assess enrichment score reproducibility within the

synthetic pool

design

, we

determined the distribution of the positively enriched variants from brain across

all peripheral organs (Fig. 2g, left). About 60 variants that are highly enriched in brain are

comparatively depleted across all other organs (Fig. 2g, middle). Encouraged by the

expected behavior of

spike-in

control variants (AAV9, PHP.B, PHP.eB), eleven novel

variants were chosen for further validation (Fig. 2g, right), including several that would have

been overlooked if the choice had been based on

PCR pool

or CREATE (Supplementary

Table 2).

These variants were chosen due to their enrichments and where they fall in sequence space.

We noticed that the positively enriched variants cluster into distinct families based on

sequence similarity (see Methods). In agreement with the heatmaps discussed above, the

most enriched variants form a distinct family across selections that share a common motif: T

in position 1, L in position 2, P in positive 5, F in position 6, and K or L in position 7 (Fig.

3a, Supplementary Fig. 3d). This AA pattern closely resembles the previously identified

variant, AAV-PHP.B – TLAVPFK (Supplementary Note 3). Given the sequence similarity

among members, we predicted that they may similarly cross the BBB and target the central

nervous system.

Capsid recovery from Round-2 selection yields a pool of AAV9 variants with enhanced

BBB entry and CNS transduction

Given the dominance of the PHP.B-family in this particular selection, we tested its most

enriched member, TALKPFL (Fig. 3a,b) henceforth referred to as AAV-PHP.V1. Somewhat

surprisingly given its sequence similarity to AAV.PHP.B, the tropism of AAV-PHP.V1 is

biased toward transducing brain vascular cells (Fig. 3c, Supplementary Fig. 4a). When

delivered intravenously, AAV-PHP.V1 carrying a fluorescent reporter under the control of

the ubiquitous CAG promoter transduces ~60% of GLUT1

cortical brain vasculature

compared to ~20% with AAV-PHP.eB and almost no transduction with AAV9 (Fig. 3c,d). In

addition to the vasculature, AAV-PHP.V1 also transduced ~60% of cortical S100

astrocytes

(Fig. 3e). However, AAV-PHP.V1 is not as efficient for astrocyte transduction as the

previously reported AAV-PHP.eB (when packaged with an astrocyte specific GfABC1D

promoter

, Supplementary Fig. 4b).

For applications requiring endothelial cell-restricted transduction via intravenous delivery,

AAV-PHP.V1 vectors can be used in three different systems: (1) in endothelial cell-type

specific Tek-Cre

mice with a Cre-dependent expression vector (Fig. 3f (left), 3g,

Supplementary Video 1), (2) in fluorescent reporter mice where Cre is delivered with an

endothelial cell-type specific MiniPromoter (Ple261)

(Fig. 3f (right), 3h, Supplementary

Fig. 4c–e), and (3) in wild-type mice by packaging a self-complementary genome (scAAV)

containing a ubiquitous promoter (Supplementary Fig. 4f). The mechanism of endothelial

cell-specific transduction by AAV-PHP.V1 using scAAV genomes is unclear, but shifts in

vector tropism when packaging scAAV genomes have been reported for another capsid

Given the dramatic difference in tropism between AAV-PHP.V1 and AAV-PHP.B/eB, we

tested several additional variants within the PHP.B-like family. One variant, AAV-PHP.V2 –

TTLKPFL, differed by only one AA from AAV-PHP.V1, has a similar tropism

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(Supplementary Fig. 5, Supplementary Note 4). Three other variants with sequences of

roughly equal deviation from both AAV.PHP.V1 and AAV.PHP.B, AAV-PHP.B4 –

TLQIPFK, AAV-PHP.B7 – SIERPFK, and AAV-PHP.B8 – TMQKPFI (Fig. 3a,b, 4a,b), have

PHP.B-like tropism with biased transduction toward neurons and astrocytes (Fig. 4b,

Supplementary Fig. 6a–c). Similar variants among the spike-in library, AAV-PHP.B5 –

TLQLPFK and AAV-PHP.B6 – TLQQPFK, also shared this tropism (Fig. 3b, 4a,b;

Supplementary Fig. 6a; Supplementary Note 5).

We next investigated a series of variants selected to verify M-CREATE’s predictive power

outside this family: (1) A highly enriched variant with a completely unrelated sequence,

AAV-PHP.C1 – RYQGDSV (Fig. 3a,b, 4a,b), transduced astrocytes at a similar efficiency

and neurons at lower efficiency compared to other tested variants from B-family (Fig. 4b).

(2) Two variants found in high abundance in the R2

synthetic pool

virus library and

negatively enriched in brain (with both codon replicates in agreement), AAV-PHP.X1 –

ARQMDLS and AAV-PHP.X2 – TNKVGNI (Supplementary Fig. 2b, right), poorly

transduced the CNS (Supplementary Fig. 6b). (3) Two variants that were found in higher

abundance in brain libraries from the

PCR pool

R2, AAV-PHP.X3 – QNVTKGV and AAV-

PHP.X4 - LNAIKNI also failed to outperform AAV9 in the brain (Supplementary Fig. 6d).

Collectively, our characterization of these AAV variants demonstrates several key points.

First, within a diverse sequence family, there is room for both functional redundancy and the

emergence of novel tropisms. Second, highly enriched sequences outside the dominant

family are also likely to possess enhanced function. Third, buoyed by codon replicate

agreement in the synthetic pool, a variant’s enrichment across tissues may be predictive.

Fourth, while the

synthetic pool

R2 library contains a subset of the sequences that are in the

PCR pool

R2 and may thereby lack some enhanced variants, the excluded

PCR pool

population is enriched in false positives.

The ability to confidently predict

in vivo

transduction from a pool of 18,000 variants across

mice is a significant advance in the selection process and demonstrates the power of M-

CREATE for the evolution of individual vectors.

Re-investigation of capsid selection that yielded AAV.PHP.eB reveals variant that

specifically transduces neurons

Using NGS, we re-investigated a

3-mer-s (s-substitution)

PHP.B library generated by the

prior CREATE methodology that yielded AAV-PHP.eB

(Fig. 5a, Supplementary Note 6).

We deep sequenced the brain libraries using Cre-dependent PCR and a R2 liver library from

wild-type mice (processed via PCR for all capsid sequences regardless of Cre-mediated

inversion) and identified 150 – 200 positively enriched capsids in brain tissue (Fig. 5b,

Supplementary Fig.7a,b).

Variants that were positively enriched in brain and negatively enriched in liver show a

significant bias towards certain AAs: G, D, E at position 1; G, S at position 2 (which

includes the AAV-PHP.eB motif, DG); and S, N, P at position 9, 10, 11 (Fig. 5c,

Supplementary Fig. 7c, see Methods). Variants that were positively enriched in the brain

were clustered according to their sequence similarities and ranked by their negative

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enrichment in liver (represented by node size in clusters; see Methods). A distinct family

referred to as N emerged with a common motif “SNP” at positions 9–11 on PHP.B backbone

(Fig. 5d, Supplementary Fig. 7d).

The core variant of the N-family cluster: AQTLAVPFSNP was found in high abundance in

R1 and R2 selections, had higher enrichment score in Vglut2 and Vgat brain tissues

compared to GFAP, and had negative enrichment in liver tissue (Fig. 5b, Supplementary Fig.

7a–d). Unlike AAV-PHP.eB, this variant (AAV-PHP.N) specifically transduced NeuN

neurons even when packaged with a ubiquitous CAG promoter, although the transduction

efficiency varied across brain regions (from ~10–70% in NeuN

neurons, including both

VGLUT1

excitatory and GAD1

inhibitory neurons, Fig. 5e,f; Supplementary Fig. 7e,f).

Thus, by re-examining the

3-mer-s

library we were able to identify several novel variants,

including one with notable cell-type-specific tropism (Supplementary Note 7).

Investigation of capsid families beyond C57BL/6J mouse strain

The enhanced CNS tropism of AAV-PHP.eB is absent in a subset of mouse strains. It is

highly efficient in C57BL/6J, FVB/NCrl, DBA/2, and SJL/J, with intermediate enhancement

in 129S1/SvimJ, and no enhancement in BALB/cJ and several additional strains

–

. This

pattern holds for the two newly identified variants from the PHP.B family, AAV-PHP.V1 and

AAV-PHP.N (Fig. 6a, Supplementary Table 3), which did not transduce the CNS in

BALB/cJ, yet transduced the FVB/NJ strain (Fig. 6b). AAV-PHP.V1 transduced Human

Brain Microvascular Endothelial Cell (HBMEC) culture, resulting in increased mean

fluorescent intensity compared to AAV9 and AAV-PHP.eB (Supplementary Fig. 8a)

however, suggesting the potential for mechanistic complexity.

Importantly, M-CREATE revealed many non-PHP.B-like sequence families that enriched

through selection for transduction of cells in the CNS. We tested the previously mentioned

AAV-PHP.C1: RYQGDSV, as well as AAV-PHP.C2: WSTNAGY, and AAV-PHP.C3:

ERVGFAQ (Fig. 6a). These showed enhanced BBB crossing irrespective of mouse strain,

with roughly equal CNS transduction in BALB/cJ and C57BL/6J (Fig. 6c, Supplementary

Fig. 8b). Collectively, these preliminary studies suggest that M-CREATE is capable of

finding capsid variants with diverse mechanisms of BBB entry that lack strain-specificity.

DISCUSSION:

This work outlines the development and validation of an improved platform, M-CREATE,

for multiplexed viral capsid selection. M-CREATE incorporates multiple internal controls to

monitor sequence progression, minimize bias, and accelerate the discovery of capsid variants

with novel tropisms. Utilizing M-CREATE, we have identified both individual capsids and

distinct families of capsids that are biased toward different cell-types of the adult brain. The

outcome from

7-mer-i

selection demonstrates the possibility of finding AAV capsids with

improved efficiency and specificity towards one or more cell types. Patterns of CNS

infectivity across mouse strains suggest that M-CREATE may also identify multiple capsids

with distinct mechanisms of BBB crossing. With additional rounds of evolution as shown in

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the

3-mer-s

selection, the specificity or efficiency of

7-mer-i

library variants may be

improved, as was observed with AAV-PHP.N. or AAV-PHP.eB (from prior study).

We believe that the variants tested

in vivo

and their families will find broad application in

neuroscience, including studies involving the BBB

, neural circuits

, neuropathologies

and therapeutics

. AAV-PHP.V1 or AAV-PHP.N are well-suited for studies requiring gene

delivery for optogenetic or chemogenetic manipulations

, or rare monogenic disorders

(targeting brain endothelial cells: e.g., GLUT1-deficiency syndrome, NLS1-microcephaly

;

or targeting neurons: e.g., mucopolysaccharidosis type IIIC (MPSIIIC)

The outcome from M-CREATE will open several promising lines of inquiry: (1) assessment

of identified capsid families across species, (2) investigation of the mechanistic properties

that underlie the ability to cross specific barriers (BBB) or target specific cell populations,

(3) further evolution of the identified variants for improved efficiency and specificity, and (4)

using the datasets generated by M-CREATE as training sets for

in silico

selection by

machine learning models. M-CREATE is presently limited by the low throughput of vector

characterization

in vivo

, however RNA sequencing technologies

offer hope in this regard.

In summary, M-CREATE will serve as a next-generation capsid selection platform that can

open new directions in vector engineering and potentially broaden the AAV toolbox for

various applications in science and in therapeutics.

ONLINE METHODS

Plasmids

A. Library generation—

The rAAV-ΔCap-in-cis-Lox2 plasmid (Supplementary Fig. 1a,

plasmid available upon request at Caltech CLOVER Center) is a modification of the rAAV-

ΔCap-in-cis-Lox plasmid

. For

7-mer-i

library fragment generation, we used the

pCRII-9Cap-XE plasmid

as a template. The AAV2/9 REP-AAP-ΔCap plasmid

(Supplementary Fig. 1a, plasmid available upon request at Caltech CLOVER Center) was

modified from the AAV2/9 REP-AAP plasmid

(See Supplementary Note 8).

B. Capsid characterization

(i)

AAV capsids:

The AAV capsid variants with 7-mer insertions or 11-mer substitutions

were made between positions 587–597 of AAV-PHP.B capsid using the pUCmini-iCAP-

PHP.B backbone

(Addgene ID: 103002).

(ii)

ssAAV genomes:

To characterize the AAV capsid variants, we used the single stranded

(ss) rAAV genomes. We used genomes such as pAAV:CAG-mNeonGreen

(equivalent

plasmid, pAAV: CAG-eYFP

; Addgene ID: 104055), pAAV:CAG-NLS-EGFP

(equivalent version with one NLS is on Addgene ID 104061), pAAV:CAG-DIO-EYFP

(Addgene ID: 104052), pAAV: GfABC1D-2xNLS-mTurquoise2

(Addgene ID: 104053),

and pAAV-Ple261-iCre

(Addgene ID 49113) (See Supplementary Note 9).

(iii)

scAAV genomes:

To characterize the AAV capsid variant, AAV-PHP.V1, using self-

complementary (sc) rAAV genomes, we used scAAV genomes from different sources.

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scAAV:CB6-EGFP was a gift from Dr. Guangping Gao and scAAV:CAG-EGFP

from

Addgene (Addgene ID:83279) (See Supplementary Note 9).

AAV capsid library generation

A. Round-1 AAV capsid DNA library

(i)

Mutagenesis strategy:

The 7-mer randomized insertion was designed using the NNK

saturation mutagenesis strategy, involving degenerate primers containing mixed bases

(Integrated DNA Technologies, Inc.). N can be A, C, G, or T bases and K can be G, or T.

Using this strategy, we obtained combinations of all 20 AAs at each position of the 7-mer

peptide using 33 codons, resulting in a theoretical library size of 1.28 billion at the level of

AA combinations. The mutagenesis strategy for the

3-mer-s

PHP.B library is described in

our prior work

(ii)

Library cloning:

The 480 bp AAV capsid fragment (450–592 AAs) with the 7-mer

randomized insertion between AAs 588 and 589 was generated by conventional PCR

methods using the pCRII-9Cap-XE template by Q5 Hot Start High-Fidelity 2X Master Mix

(NEB; M0494S) with forward primer, XF: 5’-

ACTCATCGACCAATACTTGTACTATCTCTCTAGAAC-3’ and reverse primer,

7xMNN-588i: 5’-

GTATTCCTTGGTTTTGAACCCAACCGGTCTGCGCCTGTGCMNNMNNMNNMNNM

NNMNNMNNTTGGGCACTCTGGTGGTTTGTG-3’ (See Supplementary Note 10).

The rAAV-ΔCap-in-cis-Lox2 plasmid (6960 bp) was linearized with the restriction enzymes

AgeI and XbaI, and the amplified library fragment was assembled into the linearized vector

at 1:2 molar ratio using the NEBuilder HiFi DNA Assembly Master Mix (NEB; E2621S) by

following the NEB recommended protocol.

(iii)

Library purification:

The assembled library was then subjected to Plasmid Safe (PS)

DNase I (Epicentre; E3105K) treatment, or alternatively, Exonuclease V (RecBCD) (NEB;

M0345S) following the recommended protocols, to purify the assembled product by

degrading the un-assembled DNA fragments from the mixture. The resulting mixture was

purified with a PCR purification kit (DNA Clean and Concentrator kit, Zymo Research;

D4013).

(iv)

Library yield:

With an assembly efficiency of 15% – 20% post-PS treatment, we

obtained a yield of about 15 – 20 ng per 100 ng of input DNA per 20 μL reaction.

(v)

Quality control:

See Supplementary Note 11.

B. Round-2 AAV capsid DNA library

(i)

PCR pool

design:

To maintain proportionate pooling, we mathematically determined

the fraction of each sample/library that needs to be pooled based on an individual library’s

diversity (see Supplementary Note 12).

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The pooled sample was used as a template for further amplification with 12 cycles of 98°C

for 10 s, 60°C for 20 s, and 72°C for 30 s by Q5 polymerase, using the primers 588-R2lib-F:

5’-CACTCATCGACCAATACTTGTACTATCTCTCT-3’ and 588-R2lib-R: 5’-

GTATTCCTTGGTTTTGAACCCAACCG-3’. Similar to R1 library generation, the PCR

product was assembled into the rAAV-ΔCap-in-cis-Lox2 plasmid and the virus was

produced (see Supplementary Note 13).

(ii)

Synthetic pool

design:

As described in the PCR pool strategy, we chose high-

confidence variants whose RCs were above the error-dominant noise slope from the plot of

library distribution (see Supplementary Fig. 1e and Supplementary Note 12). This came to

about 9000 sequences from all brain and spinal cord samples of all Cre lines. We used

similar primer design as mentioned in the description of the R1 library generation. Primers

XF: 5’-ACTCATCGACCAATACTTGTACTATCTCTCTAGAAC-3’ and 11-mer-588i: 5’-

GTATTCCTTGGTTTTGAACCCAACCGGTCTGCGCxrefMNNMNNMNNMNNMNNM

NNMNNxrefACTCTGGTGGTTTGTG-3’, where

“xrefMNNMNNMNNMNNMNNMNNMNNxref” was replaced with unique nucleotide

sequence of a 7-mer tissue recovered variant (7xMNN) along with modification of two

adjacent codons flanking on either end of the 7-mer insertion site (6xX), which are residues

587–588 “AQ” and residues 589–590 “AQ” on AAV9 capsid. Since

spike-in

library has 11-

mer mutated variants, we used the same primer design where

“xrefMNNMNNMNNMNNMNNMNNMNNxref” was replaced with a specific nucleotide

sequence of a 11-mer variant. A duplicate of each sequence in this library was designed with

different codons optimized for mammals. The primers were designed using a custom-built

Python based script. The custom-designed oligopool was synthesized in an equimolar ratio

by Twist Biosciences. The oligopool was used to minimally amplify the pCRII-XE Cap9

template over 13 cycles of 98°C for 10 s, 60°C for 20 s, and 72°C for 30 s. To obtain a

higher yield for large-scale library preparation, the product of the first PCR was used as a

template for the second PCR using the primers XF and 588-R2lib-R (described above) and

minimally amplified for 13 cycles. Following PCR, we assembled the R2

synthetic pool

DNA library and produced the virus as described in R1 (see Supplementary Note 13).

C. AAV virus library production, purification and genome extraction—

prevent capsid mosaic formation of the

7-mer-i

library in 293T producer cells, we

transfected only 10 ng of assembled library per 150 mm dish along with other required

reagents for AAV vector production (see Supplementary Note 14). For the rAAV DNA

extraction from purified rAAV viral library, ~10% of the purified viral library was used to

extract the viral genome by proteinase K treatment (see Supplementary Note 15).

Animals

All animal procedures performed in this study were approved by the California Institute of

Technology Institutional Animal Care and Use Committee (IACUC), and we have complied

with all relevant ethical regulations. C57BL/6J (000664), Tek-Cre

(8863), SNAP25-Cre

(23525), GFAP-Cre

(012886), Syn1-Cre

(3966), and Ai14

(007908) mice lines used in

this study were purchased from the Jackson Laboratory (JAX). The IV injection of rAAVs

was into the retro-orbital sinus of adult mice. For testing the transduction phenotypes of

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novel rAAVs, 6- to 8-week-old C57BL/6J or Tek-Cre or Ai14 adult male mice were

randomly assigned. The experimenter was not blinded for any of the experiments performed

in this study.

In vivo

selection

The

7-mer-i

viral library selections were carried out in different lines of Cre transgenic adult

mice: Tek-Cre, SNAP25-Cre, and GFAP-Cre for the R1 selections, and those three plus

Syn1-Cre for the R2 selections. Male and female adult mice were intravenously

administered with a viral vector dose of 2×10

vg/mouse for the R1 selection, and a dose of

1×10

vg/mouse for the R2 selection. The dose was determined based on the virus yield

which was different across selection rounds (Supplementary Fig. 2a). Both genders were

used to recover capsid variants with minimal gender bias. Two weeks post-injection, mice

were euthanized and all organs including brain were collected, snap frozen on dry ice, and

stored at −80°C.

A. rAAV genome extraction from tissue

(i) Optimization—

See Supplementary Note 16.

(ii) rAAV genome extraction with the Trizol method—

Half of a frozen brain

hemisphere (0.3 g approx.) was homogenized with a 2 ml glass homogenizer (Sigma

Aldrich; D8938) or a motorized plastic pestle (Fisher Scientific;12-141-361, 12-141-363)

(for smaller tissues) or beads using BeadBug homogenizers (1.5–3.0 mm zirconium or steel

beads per manufacturer recommendations) (Homogenizers, Benchmark Scientific, D1032–

15, D1032–30, D1033–28) and processed using Trizol as described in our prior work

(also

see Supplementary Note 17). From deep sequencing data analysis, we observed that the

amount of tissue processed was sufficient for rAAV genome recovery.

(iii) rAAV genome recovery by Cre-dependent PCR—

rAAV genomes with Lox

sites flipped by Cre recombination were selectively recovered and amplified using PCR with

primers that yield a PCR product only if the Lox sites are flipped (see Supplementary Fig.

1b). We used the primers 71F: 5’-CTTCCAGTTCAGCTACGAGTTTGAGAAC-3’ and

CDF/R: 5’- CAAGTAAAACCTCTACAAATGTGGTAAAATCG-3’ and amplified the Cre-

recombined genomes over 25 cycles of 98°C for 10 s, 58°C for 30 s, and 72°C for 1 min,

using Q5 DNA polymerase.

(iv) Total rAAV genome recovery by PCR (Cre-independent)—

To recover all

rAAV genomes from a tissue, we used the primers XF (5’-

ACTCATCGACCAATACTTGTACTATCTCTCTAGAAC-3’) and 588-R2lib-R (5’-

GTATTCCTTGGTTTTGAACCCAACCG-3’) to amplify the genomes over 25 cycles of

98°C for 10 s, 60°C for 30 s, and 72°C for 30 min, using Q5 DNA polymerase.

Sample preparation for NGS

We processed the DNA library, the virus library, and the tissue libraries post-

in vivo

selection to add flow cell adaptors around the diversified 7-mer insertion region (see

Supplementary Fig. 1b).

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A. Preparation of rAAV DNA and Viral DNA library—

The Gibson-assembled rAAV

DNA library and the DNA extracted from the viral library were amplified by Q5 DNA

polymerase using the primers 588i-lib-PCR1–6bpUID-F: 5’-

CACGACGCTCTTCCGATCTAANNNNNNAGTCCTATGGACAAGTGGCCACA-3’ and

588i-lib-PCR1-R: 5’-

GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTTCCTTGGTTTTGAACCCAACC

G-3’ that are positioned around 50 bases from the randomized 7-mer insertion on the capsid,

and that contain the Read1 and Read2 flow cell sequences on the 5’ end (See Supplementary

Note 18). Using 5–10 ng of template DNA in a 50 μl reaction, the DNA was minimally

amplified for 4 cycles of 98°C for 10 s, 60°C for 30 s, and 72°C for 10 s. The mixture was

then purified with a PCR purification kit. The eluted DNA was then used as a template in a

second PCR to add the unique indices (single or dual) via the recommended primers (NEB;

E7335S, E7500S, E7600S) in a 12-cycle reaction using the same temperature cycle as

described above. The samples were then sent for deep sequencing following additional

processing and validation (see Supplementary Note 19).

B. Preparation of rAAV tissue DNA library—

The PCR-amplified rAAV DNA library

from tissue (see section A: iii and iv) was further amplified with a 1:100 dilution of this

DNA as a template to the primers 1527: 5’-

ACACTCTTTCCCTACACGACGCTCTTCCGATCTGACAAGTGGCCACAAACCACCA

G-3’ and 1532: 5’-

GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCCTTGGTTTTGAACCCAACCG-

3’ that are positioned around 50 bases from the randomized 7-mer insertion on the capsid,

and that contain the Read1 and Read2 sequences on the 5’ end. The DNA was amplified by

Q5 DNA polymerase for 10 cycles of 98°C for 10 s, 59°C for 30 s, and 72°C for 10 s. The

mixture was purified with a PCR purification kit. The eluted DNA was then used as a

template in a second PCR to add the unique indices (single or dual) using the recommended

primers (NEB; E7335S, E7500S, E7600S) in a 10-cycle reaction with the same temperature

cycle as described above (for DNA and virus library preparation), and followed additional

processing and validation before sequencing (see Supplementary Note 19).

In vivo

characterization of AAV vectors

A. Cloning AAV capsid variants—

The AAV capsid variants were cloned into a

pUCmini-iCAP-PHP.B backbone (Addgene ID: 103002) using overlapping forward and

reverse primers with 11-mer substitution (in case of

7-mer-i

variants, the flanking AA from

AAV9 capsid AA587–588 “AQ” and AA589–590 “AQ” were subjected to codon

modification) that spans from the MscI site (at position 581 AA) to the AgeI site (at position

600 AA) on the pUCmini plasmid. The primers were designed for all capsid variants using a

custom Python script and cloned using standard molecular techniques (see Supplementary

Note 20). List of primers used to clone AAV-PHP variants is provided (Supplementary Table

4).

B. AAV vector production—

Using an optimized protocol

, we produced AAV vectors

from 5–10 150 mm plates, which yielded sufficient amounts for administration to adult

mice.

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C. AAV vector administration, dosage and expression time.—

AAV vectors were

administered intravenously to adult male mice (6 – 8 weeks of age) via retro-orbital injection

at doses of 1 – 10×10

vg with 3–4 weeks of

in vivo

expression times unless mentioned

otherwise in the figures/legends (also see Supplementary Note 21).

D. Tissue processing—

After 3 weeks of expression (unless noted otherwise), the mice

were anesthetized with Euthasol (pentobarbital sodium and phenytoin sodium solution,

Virbac AH) and transcardially perfused with 30 – 50 mL of 0.1 M phosphate buffered saline

(PBS) (pH 7.4), followed by 30 – 50 ml of 4% paraformaldehyde (PFA) in 0.1 M PBS. After

this procedure, all organs were harvested and post-fixed in 4% PFA at 4°C overnight. The

tissues were then washed and stored at 4°C in 0.1 M PBS and 0.05% sodium azide. All

solutions used for this procedure were freshly prepared. For the brain and liver, 100-μm

thick sections were cut on a Leica VT1200 vibratome.

For vascular labeling, the mice were anesthetized and transcardially perfused with 20 mL of

ice-cold PBS, followed by 10 mL of ice-cold PBS containing Texas Red-labeled

Lycopersicon Esculentum (Tomato) Lectin (1:100, Vector laboratories, TL-1176) or DyLight

594 labeled Tomato Lectin (1:100, Vector laboratories, DL-1177), and then placed in 30 mL

of ice-cold 4% PFA for fixation.

E. Immunohistochemistry—

Immunohistochemistry was performed on 100-μm thick

tissue sections to label different cell-type markers such as NeuN (1:400, Abcam, ab177487)

for neurons, S100 (1:400, Abcam, ab868) for astrocytes, Olig2 (1:400; Abcam, ab109186)

for oligodendrocyte lineage cells, and GLUT-1 (1:400; Millipore Sigma, 07–1401) for brain

endothelial cells using optimized protocols (See Supplementary Note 22).

F. Hybridization chain reaction (HCR) based RNA labeling in tissues—

Fluorescence

in situ

hybridization chain reaction (FITC-HCR) was used to label excitatory

neurons with VGLUT1 and inhibitory neurons with GAD1 to characterize the AAV capsid

variant AAV-PHP.N in brain tissue using an adapted third-generation HCR

protocol (See

Supplementary Note 23).

G. Imaging and image processing—

All images in this study were acquired either

with a Zeiss LSM 880 confocal microscope using the objectives Fluar 5× 0.25 M27, Plan-

Apochromat 10× 0.45 M27 (working distance 2.0 mm), and Plan-Apochromat 25× 0.8 Imm

Corr DIC M27 multi-immersion; or with a Keyence BZ-X700 microscope (see

Supplementary Note 24). The acquired images were processed in the respective microscope

softwares Zen Black 2.3 SP1 (Zeiss), BZ-X Analyzer (Keyence), Keyence Hybrid Cell

Count software (BZ-H3C), ImageJ, Imaris (Bitplane) and with Photoshop CC 2018 (Adobe).

The images were compiled in Illustrator CC 2018 (Adobe).

H. Tissue clearing—

Brain hemispheres were cleared using iDISCO

method and

tissues over 500 μm thickness were optically cleared using ScaleS4(0)

(See Supplementary

Note 25).

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I. Tissue processing and imaging for quantification of rAAV transduction

vivo

—

For quantification of rAAV transduction, 6- to 8-week-old male mice were

intravenously injected with the virus, which was allowed to express for 3 weeks (unless

specified otherwise). The mice were randomly assigned to groups and the experimenter was

not blinded. The mice were perfused and the organs were fixed in PFA. The brains and livers

were cut into 100-μm thick sections and immunostained with different cell-type-specific

antibodies, as described above. The images were acquired either with a 25× objective on a

Zeiss LSM 880 confocal microscope or with a Keyence BZ-X700 microscope; images that

are compared directly across groups were acquired and processed with the same microscope

and settings (See Supplementary Note 26).

In vitro

characterization of AAV vectors

Human Brain Microvascular Endothelial Cells (HBMEC) (ScienCell Research Laboratories,

Cat. 1000) were cultured as per the instructions provided by the vendor (also see

Supplementary Note 27 for AAV transduction protocol).

Data analysis

A. Quantification of rAAV vector transduction—

Manual counting was performed

with the Adobe Photoshop CC 2018 Count Tool for cell types in which expression and/or

antibody staining covered the whole cell morphology. The Keyence Hybrid Cell Count

software (BZ-H3C) was used where the software could reliably detect distinct cells in an

entire dataset. To maintain consistency in counting across different markers and groups, one

person was assigned to quantify across all groups in all brain areas (see Supplementary Note

28). The experimenter was not blinded during any of the analysis.

B. NGS data alignment and processing—

The raw fastq files from NGS runs were

processed with custom built scripts that align the data to AAV9 template DNA fragment

containing the diversified region 7xNNK (for R1) or 11xNNN (for R2 since it was

synthesized as 11xNNN) (see Supplementary Note 29).

C. NGS data analysis—

The aligned data were then further processed via a custom

data-processing pipeline, with scripts written in Python.

The enrichment scores of variants (Total = N) across different libraries were calculated from

the read counts (RCs) according to the following formula:

Enrichment score = log10

Variant 1 RC in tissue library1/Sum of variants N RC in library1

Variant 1 RC in virus library/Sum of variants N RC in virus library

To consistently represent library recovery between R1 and R2 selected variants, we

estimated the enrichment score of the variants in R1 selection (see Supplementary Note 30).

The standard score of variants in a specific library was calculated using this formula:

Standard score =

read count_i–mean

/standard deviation

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Where read count_i is raw copy number of a variant i,

Mean is the mean of read counts of all variants across a specific library,

Standard deviation is the standard deviation of read counts of all variants across a specific

library.

The plots generated in this article were using the following software - Plotly, GraphPad

PRISM 7.05, Matplotlib, Seaborn, and Microsoft Excel 2016. The AAV9 capsid structure

(PDB 3UX1)

was modeled in PyMOL.

D. Heatmap generation—

The relative AA distributions of the diversified regions are

plotted as heatmaps. The plots were generated using the Python Plotly plotting library. The

heatmap values were generated from custom scripts written in Python, using functions in the

custom “pepars” Python package (see Supplementary Note 31).

E. Clustering analysis—

Using custom scripts written in MATLAB (version R2017b;

MathWorks) the reverse Hamming distances representing the number of shared AAs

between two peptides was determined. Cytoscape (version 3.7.1

) software was then used

to cluster the variants. The AA frequency plot representing the highlighted cluster was

created using Weblogo (Version 2.8.2)

(see Supplementary Note 32).

Statistics and reproducibility:

Statistical tests were performed using GraphPad PRISM or Python scripts. All correlation

analyses reported were carried out using a linear least-squares regression method by an

inbuilt Python function from SciPy library “

scipy.stats.linregress

”, and the coefficient of

determination (R

) is reported. Tests evaluating the significance of amino acid bias were

done using statsmodels Python library. A one-proportion z-test for a library vs known

template frequency (NNK), and two-proportion z-test for two library comparisons were

performed. P-values are corrected for multiple comparisons using Bonferroni correction. For

datasets with two experimental group comparisons, a Mann-Whitney test was used and two-

tailed exact P-values are reported. For more than two experimental group comparisons with

one variable, a one-way ANOVA non-parametric Kruskal-Wallis test was performed and

correction for multiple comparisons using uncorrected Dunn’s test was performed. Exact P-

values are reported from both tests (unless indicated otherwise). For experimental group

comparisons with two variables, a two-way ANOVA with Tukey’s test for multiple

comparisons reporting corrected P-values were performed with 95% confidence interval

(CI).

All quantitative data reported in graphs are from biological replicates (mouse or tissue

culture replicates), where each data point from a biological replicate is the mean from

technical replicates (raw data such as images of a specific brain region). Statistical analyses

were performed on datasets with at least three biological replicates. Error bars in the figures

denote standard errors of mean (S.E.M.). All experiments were validated in more than one

independent trial unless otherwise noted.

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Reporting Summary:

Includes additional information on the methods and reproducibility.

ACCESSION CODES:

GenBank:

AAV-PHP.V1

AAV-PHP.N

AAV-PHP.V2

AAV-PHP.B4

AAV-PHP.B5

AAV-

PHP.B6

AAV-PHP.B7

AAV-PHP.B8

AAV-PHP.C1

AAV-PHP.C2

, and

AAV-PHP.C3

DATA AVAILABILITY STATEMENT:

Data beyond what has been provided in the article and supplementary documents are

available from the corresponding author upon request. The following vector plasmids are

deposited on Addgene for distribution (

http://www.addgene.org

) AAV-PHP.V1: 127847,

AAV-PHP.V2: 127848, AAV-PHP.B4: 127849, and AAV-PHP.N: 127851. Requests for other

reagents can be made at Caltech – CLOVER Center (

http://clover.caltech.edu/

CODE AVAILABILITY STATEMENT:

The codes used for M-CREATE data analysis were written in python or MATLAB and are

made available on GitHub:

https://github.com/GradinaruLab/mCREATE

. The custom

MATLAB scripts to generate HCR probes is accessible through GitHub on a different

repository:

https://github.com/GradinaruLab/HCRprobe

Supplementary Material

Refer to Web version on PubMed Central for supplementary material.

ACKNOWLEDGEMENTS:

We thank K. Y. Chan and R. Challis for performing mouse injections, R. Hurt for performing preliminary

characterization of vectors, Y. Lei for assistance with cloning, K. Beadle for vector production, E. Sullivan for

tissue sectioning, and E. Mackey for tissue sectioning and mouse colony management. We thank L. V. Sibener for

sharing the Matlab scripts used in amino acid clustering analysis. We thank N. Flytzanis and N. Goeden for their

contributions towards histology, imaging, data analysis and manuscript preparation. We thank the Biological

Imaging Facility at Caltech (supported by Caltech Beckman Institute and the Arnold and Mabel Beckman

Foundation). We also thank the Millard and Muriel Jacobs Genetics and Genomics Laboratory at Caltech; and

Integrative Genomics Core at City of Hope for providing sequencing service, P. Anguiano for administrative

assistance, and the entire Gradinaru group for discussions. This work was primarily supported by grants from the

National Institutes of Health (NIH) to V.G.: NIH Director’s New Innovator DP2NS087949 and PECASE, NIH

BRAIN R01MH117069, NIH Pioneer DP1OD025535, and SPARC 1OT2OD024899. Additional funding includes

the Vallee Foundation (V.G.), the Moore Foundation (V.G.), the CZI Neurodegeneration Challenge Network (V.G.),

and the NSF NeuroNex Technology Hub grant 1707316 (V.G.), the Heritage Medical Research Institute (V.G.), and

the Beckman Institute for CLARITY, Optogenetics and Vector Engineering Research (CLOVER) for technology

development and dissemination (V.G.).

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