Systematic screening and mapping of regulatory elements in non-coding genomic regions, methods, compositions, and applications thereof

Patent metadata

Field	Value
Publication number	US-12499971-B2
Application number	US-201716337846-A
Country	US
Kind code	B2
Filing date	Sep 27, 2017
Priority date	Sep 28, 2016
Publication date	Dec 16, 2025
Grant date	Dec 16, 2025

Abstract

The application relates to methods for identifying putative regulatory elements that regulates a gene, comprising: obtaining a measure of intrinsic activity of a plurality of genomic elements; obtaining a measure of proximity between each of the genomic elements and the gene; scoring a predicted impact of each of the genomic elements on the gene as a function of the measure of intrinsic activity and the measure of proximity, wherein a plurality of predicted impacts scored are ranked to identify at least one genomic element as a putative regulatory element that regulates the gene; and optionally, training, optimizing, and/or validating the scoring of predicted impact using experimental or computational data describing functional interactions between the genomic elements and the gene. The application also relates to methods for identification of transcriptional enhancers and repressors regulating a gene associated with an agricultural trait of interest in plants or a disease phenotype in mammalians.

First claim

What is claimed is:

1 . A method for identifying a noncoding putative regulatory element that regulates a gene, comprising:
(a) performing a chromatin accessibility assay, a DNA-associated protein binding assay, an enhancer activity assay, or a combination thereof and obtaining a measure of intrinsic activity of a plurality of genomic elements; (b) performing a proximity ligation assay and obtaining a measure of proximity between each of the genomic elements and the gene; (c) identifying noncoding putative regulatory elements that regulate the gene using an Activity×Proximity model based on the measure of intrinsic activity obtained in (a) and the measure of proximity obtained in (b) as input; (d) altering a noncoding putative regulatory element identified in (c) in a population of T cells when the noncoding putative regulatory element identified in (c) is an enhancer associated with T cell dysfunction; and (e) administering the population of T cells of (d) to a subject having T cell dysfunction.

2 . The method of claim 1 , further comprising training, optimizing, and/or validating the Activity×Proximity model using experimental or computational data describing functional interactions between the putative regulatory elements and the gene or using perturbation date obtained from a perturbation-based screening.

3 . The method of claim 2 , wherein said perturbation-based screening is carried out using a DNA binding protein.

4 . The method of claim 3 , wherein the DNA binding protein is selected from a Cas protein, a zinc finger, a zinc finger nuclease (ZFN), a transcription activator-like effector (TALE), a transcription activator-like effector nuclease (TALEN), a meganuclease, or a modified version thereof.

5 . The method of claim 2 , wherein the perturbation-based screening comprises:
introducing a library of guide RNAs into a population of cells, said cells either expressing an RNA-guided DNA binding protein or having the RNA-guided DNA binding protein or a coding sequence thereof introduced simultaneously or sequentially with the guide RNAs, wherein the guide RNAs target different non-coding genomic sequences within at least one genomic region; selecting cells based on a phenotype; and determining (i) relative representation of the guide RNAs present in the selected cells or (ii) deletion of a genomic sequence targeted by pairs of the guide RNAs from the selected cells, wherein (i) the relative representation of the guide RNAs or (ii) the deletion of the genomic sequence indicates the targeted genomic sequence as a regulatory element of a gene associated with the phenotype.

6 . The method of claim 5 , wherein the RNA-guided DNA binding protein is a CRISPR effector protein.

7 . The method of claim 6 , wherein the CRISPR effector protein is a catalytically active Cas protein, and wherein the guide RNAs are introduced as pairs of guide RNAs, each pair designed for targeted deletion of the non-coding genomic sequence.

8 . The method of claim 7 , wherein each pair of guide RNAs target 20-5,000 bp of the genomic sequence for deletion.

9 . The method of claim 6 , wherein the CRISPR effector protein is a modified Cas protein.

10 . The method of claim 9 , wherein the modified Cas protein comprises one or more mutations compared to a wild-type Cas protein, and wherein the modified Cas protein is not catalytically competent.

11 . The method of claim 10 , wherein the modified Cas protein is a modified Cas9 or Cpf1.

12 . The method of claim 9 , wherein the guide RNAs are introduced using a vector encoding two or more guide RNAs, wherein each of said guide RNAs targets a different non-coding genomic sequence for multiplex perturbation.

13 . The method of claim 9 , wherein the modified Cas is fused to a transcriptional repressor domain or a transcriptional activator domain.

14 . The method of claim 13 , wherein the transcriptional repressor domain is a KRAB domain, a NuE domain, NcoR domain, SID domain, or a SID4X domain, or a DMNT domain (DNA methylation).

15 . The method of claim 9 , wherein at least one of the guide RNAs comprises a loop modified by insertion of at least one distinct aptamer RNA sequence adapted to bind to an adaptor protein that comprises a transcriptional repressor domain.

16 . The method of claim 1 , wherein the chromatin accessibility assay consists of one or more of DNase I hypersensitivity, ATAC-Seq, FAIRE-Seq, or NOMe-Seq; wherein the DNA-associated protein binding assay consists of H3K27ac ChIP-Seq, histone modification ChIP-seq, transcription factor ChIP-seq, or p300 ChIP-Seq; or wherein the enhancer activity assay consists of MPRA or STARR-Seq.

17 . The method of claim 1 , wherein the measure of proximity is further determined using one of or a function of genomic distance between a regulatory element and its target promoter.

18 . The method of claim 1 , wherein the Activity×Proximity model uses:
a function of quantitative DHS, H3K27ac, and Hi-C values, or
log 2 (H3K27ac RPM×DHS RPM×Hi-C contact×Hi-C contact).

19 . The method of claim 1 , wherein the gene is associated with a disease phenotype in mammals.

20 . The method of claim 1 , wherein the Activity×Proximity model is further weighted by one or more factors related to a local regulatory landscape.

21 . The method of claim 20 , wherein the factors related to the local regulatory landscape are selected from gene density, enhancer density, presence of promoter-proximal regulatory elements, and rank thereof.

22 . The method of claim 1 , wherein the regulatory elements that regulate expression of the gene are selected from the group consisting of CTLA4, CMTM6, CMTM4, LAG3, BTLA, PTGER2, CD160, KLRG1, BCL2, IL7R, and KLRC1.

23 . The method of claim 1 , wherein the T cells are chimeric antigen receptor (CAR) expressing T cells or T-cell receptor (TCR) modified T cells.

24 . A method for identifying a noncoding putative regulatory element that regulates an immune regulatory gene, comprising:
(a) performing a chromatin accessibility assay, a DNA-associated protein binding assay, an enhancer activity assay, or a combination thereof and obtaining a measure of intrinsic activity of a plurality of genomic elements; (b) performing a proximity ligation assay and obtaining a measure of proximity between each of the genomic elements and the immune regulatory gene; (c) identifying noncoding putative regulatory elements that regulate the immune regulatory gene using an Activity×Proximity model based on the measure of intrinsic activity obtained in (a) and the measure of proximity obtained in (b) as input; (d) altering a noncoding putative regulatory element identified in (c) in a population of T cells; and (e) administering the population of T cells of (d) to a subject having T cell dysfunction.

25 . The method of claim 24 , further comprising training, optimizing, and/or validating the Activity×Proximity model using experimental or computational data describing functional interactions between the putative regulatory elements and the gene or using perturbation data obtained from a perturbation-based screening.

26 . The method of claim 25 , wherein said perturbation-based screening is carried out using a DNA binding protein.

27 . The method of claim 26 , wherein the DNA binding protein is selected from a Cas protein, a zinc finger, a zinc finger nuclease (ZFN), a

Assignees

• Broad Inst Inc
• Harvard College
• Massachusetts Inst Technology

Inventors

• Engreitz Jesse
• Lander Eric S
• Fulco Charles

Classifications

• C12N15/1089

  Design, preparation, screening or analysis of libraries using computer algorithms · CPC title

• C12N15/1079 Primary

  Screening libraries by altering the phenotype or phenotypic trait of the host (reporter assays C12N15/1086) · CPC title

• C12N15/1072

  Differential gene expression library synthesis, e.g. subtracted libraries, differential screening · CPC title

• G16B20/00

  ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations · CPC title

• G16H50/30

  for calculating health indices; for individual health risk assessment · CPC title