WO2021206192A1

WO2021206192A1 - A method for detecting transposable genetic material in a biological sample based on codon optimality

Info

Publication number: WO2021206192A1
Application number: PCT/KR2020/004782
Authority: WO
Inventors: Eun Yu Kim; Jungnam CHO
Original assignee: Cas Center For Excellence In Molecular Plant Sciences
Priority date: 2020-04-08
Filing date: 2020-04-08
Publication date: 2021-10-14

Abstract

The present invention relates to a novel method for detecting transposable or non-self genetic material in a biological sample. The method of the present invention may be effectively used for detecting transposons with highly enhanced accuracy, based on the reduced codon optimality of transposons. The method of the present invention may be applied as reliable research tool for investigating genetic transpositional issue in an organism, or a methodology for diagnosing a disease accompanying with genetic transposition or infectious issues with foreign genome invasion.

Description

A METHOD FOR DETECTING TRANSPOSABLE GENETIC MATERIAL IN A BIOLOGICAL SAMPLE BASED ON CODON OPTIMALITY

The present invention relates to a novel method for detecting a transposable element or non-self genetic material in a biological sample using codon optimality.

Transposable elements (TEs) pose a significant threat to genome integrity (1, 2). Particularly, plant genomes are heavily populated by TEs bearing continuous danger of genomic instability caused by transpositions. RNA-dependent RNA polymerase 6 (RDR6) in plants acts as a first line of host defence against transposons by templating TE RNAs to form double-stranded RNAs (3). The duplexed RNAs are then sliced to 21- or 22-nucleotide siRNAs (also referred to as epigenetically activated siRNAs, easiRNAs) by DICER-LIKE 3 or 4 (DCL3/4). The siRNAs trigger the post-transcriptional gene silencing by cleaving the source RNAs and later establish rather stable epigenetic silencing by recruiting DNA methyltransferases to the target chromatins (4-7). Apart from TEs, RDR6 also targets the RNAs of virus and transgenes (8-10), while the host genome's protein-coding genes are protected from the RDR6-mediated process by the RNA decay pathways (11-13). Several studies so far have attempted to answer how RDR6 specifically recognizes non-self and invasive genetic elements suggesting that miRNA-mediated (14) or mRNA turnover pathway (15) induces the initial RNA cleavage, which is essential for RDR6 targeting (16, 17). However, it is still far from understanding the precise mechanism for the initiation of siRNA biogenesis pathway. Previously, we reported that CpG dinucleotide-rich sequences exhibit epiallelic behaviour that is unable to regain DNA methylation once lost, whereas transposons are CpG-poor, associated with active siRNA production and readily remethylatable (18). Here in this study, we suggest a novel model that ribosome-coupled pathway detects transposons and guide them to siRNA-generating cellular compartments that are formed by liquid-liquid phase separation (LLPS).

Throughout this application, various publications and patents are referred and citations are provided in parentheses. The disclosures of these publications and patents in their entities are hereby incorporated by references into this application in order to fully describe this invention and the state of the art to which this invention pertains.

The present inventors have made intensive studies to develop reliable methods for detecting a transposable genetic material that may change its position within a genome. As results, the present inventors have discovered that said transposable genetic material showed significantly reduced codon optimality and translational activity compared to other loci, leading to translation slowdown and consequential ribosome stalling. Thus, codon optimality and the values related thereto may serve as a quantitative indicator for the existence of transposable genetic materials in the biological samples.

Accordingly, it is an object of this invention to provide a method for detecting transposable or non-self genetic material in a biological sample.

It is another object of this invention to provide a method for measuring a transposing activity of a transposable genetic material in a biological sample.

Other objects and advantages of the present invention will become apparent from the following detailed description together with the appended claims and drawings.

The features and advantages of the present invention will be summarized as follows:

(a) The present invention provides a method for detecting transposable or non-self genetic material in a biological sample.

(b) The method of the present invention may be effectively used for detecting transposons with highly enhanced accuracy, based on the reduced codon optimality of transposons discovered by the present inventors for the first time.

(c) The method of the present invention may be applied as reliable research tool for investigating genetic transpositional issue in an organism, or a methodology for diagnosing a disease accompanying with genetic transposition or infectious issues with foreign genome invasion.

Fig. 1 shows the codon optimality and translation efficiency. Fig. 1a represents GC contents at different codon nucleotide positions. Fig. 1b shows translation efficiency indices (TEI), determined as the log2 ratio of TRAP-seq to RNA-seq. Fig. 1c represents codon translation coefficient (CTC). The optimal and sub-optimal codons are marked in red and blue, respectively. Inlets are the base compositions by the codon 5 nucleotide positions of optimal (red) and sub-optimal (blue) codons. Fig. 1d shows correlation of codon optimality and TEI. Figs. 1e and 1f show codon optimality between genes and transposons (Fig. 1e) and easiRNA-producing and random loci (Fig. 1f). Fig. 1g indicates the easiRNA levels in the sub-optimal and optimal TEs. Pearson's product-moment correlation (Fig. 1d) and Wilcoxon rank-sum test was used for statistical analyses (Figs. 1b, 1e-1f).

Fig. 2 represents ribosome stalling, RNA truncation and easiRNA production. Fig. 2a shows number of ribo-seq reads by lengths. Reads ranging from 40 to 65 nucleotides were selected as disome fraction. Fig. 2b shows genomic features of disome loci compared with the whole transcriptome of Arabidopsis. Figs. 2c-2e represent comparison of disome RNAs for codon optimality (Fig. 2c), degradability, defined as the log2 ratio of degradome-seq to RNA-seq (Fig. 2d) and easiRNA levels (Fig. 2e). Fig. 2F shows codon optimality of RDR6-target transposons. RDR6 targets were identified for those with reduced easiRNA levels in ddm1 rdr6 double mutants than in ddm1. Wilcoxon rank-sum test was carried out for statistical analyses (Figs. 2c-2f).

Fig. 3 shows localization of transposon RNAs to stress granules (SGs). Fig. 3a represents MA plot for SG-RNA-seq. SG-enriched (red) and depleted (blue) transcripts are defined as those above 2 and below -2 of fold change, respectively. Fig. 3b shows fraction of transposons in SG-enriched and depleted transcripts. Genes with FPKM value above 1 were defined as being expressed. Figs. 3c-3f show comparison of SG-5 enriched and depleted RNAs for the mRNA levels (Fig. 3c) and codon optimality (Fig. 3d), translation efficiency (Fig. 3e) and easiRNA levels (Fig. 3f). Wilcoxon rank-sum test was performed for statistical analyses (Fig. 3b-3f). Fig. 3g shows genomic loci showing the RNA-seq, ribo-seq, SG-RNA-seq and easiRNA-seq of the representative SG-enriched (left) and depleted (right) TE.

Fig. 4 represents the result of in vitro phase separation assay of SGS3. Fig. 4 a shows protein domain structure (upper) and PrD-like score (lower) of SGS3. ZF-XS, zinc finger-rice gene X and SGS3; CC, coiled coil. Fig. 4b shows bright field (upper) and fluorescence (lower) microscopy of GFP and GFP-tagged SGS3 proteins. Scale bars, 10 μm. Fig. 4c represents fluorescence time-lapse microscopy of GFP-SGS3. Scale bar, 2 μm. Figs. 4d and 4e represent FRAP of GFP-SGS3 shown as the time-lapse fluorescence microscopy (Fig. 4d) and the plot of the time course recovery after photobleaching (Fig. 4 E). Scale bar, 5 μm. Data are presented as mean ± s.d. (n = 13).

Fig. 5 represents rice genomic loci showing different translation efficiency. RNA-seq (upper) and TRAP-seq (lower) data showing Tos17 retrotransposon and its neighboring gene (LOC_Os07g44710).

Fig. 6 shows codon frequency and translation efficiency in rice. Fig. 6a and 6b represent GCC and GCU codon frequency in highly (Fig. 6a) and lowly translating genes (Fig. 6b). Fig. 6c shows codon optimality in high and low TEI and random genes. High and low TEI genes are top 1,000 genes when ranked from the highest and lowest TEI, respectively. Figs. 6d-6e show the results of comparison of translation efficiency to codon 5 optimality calculated for the out-framed, frame +1 (Fig. 6d) and +2 (Fig. 6e), coding sequences. Wilcoxon rank-sum test was carried out for statistical analyses (Figs. 6a-6e).

Fig. 7 shows translation efficiency and codon optimality of Arabidopsis. Fig. 7a represents genomic loci of Arabidopsis showing Evade retrotransposon and its neighboring gene (AT5G17160) for RNA-seq (upper) and ribo-seq (lower). Figs. 7b and 7c represents comparison of genes and transposons in Arabidopsis for translation efficiency (Fig. 7b) and codon optimality (Fig. 7c). Fig. 7d shows translation efficiency of ddm1 and 5 ddm1 rdr6 double mutant. Wilcoxon rank-sum test was carried out for statistical analyses (Figs. 7b-7d).

Fig. 8 shows prion-like domain prediction of the epigenetic factors in Arabidopsis. Fig. 8a indicates prion-like domains of UBP1b. Figs. 8b-8g indicate prion-like domains of RDR family proteins. Figs. 8h-8k indicate prion-like domains of DCL family proteins. Figs. 8l-8u indicate prion-like domains of AGO family proteins.

Fig. 9 represents a schematic model for the role of stress granule in transposon silencing. Unlike ordinary genes, transposon RNAs exhibit reduced translational efficiency because of the unfavored codon usage. Ribosome stalling leads to RNA truncation and SG localization which collectively contribute to selective processing of transposon RNAs to siRNAs.

In one aspect of this invention, there is provided a method for detecting transposable or non-self genetic material in a biological sample comprising:

measuring codon optimality of nucleic acid molecule in the biological sample isolated from a subject; and

determining existence of the transposable or the non-self genetic material in the biological sample based on the measured codon optimality.

The term "detecting" as used herein, has comprehensive meaning including any action of accessing information regarding the existence, level or activity of a target of interest in a sample, e.g., transposable genetic material. Therefore, the phrase "detecting transposable genetic material" is used interchangeably with "measuring a level of transposable genetic material" or "assessing an activity of transposable genetic material".

The term "transposable genetic material" as used herein, refers to any genetic material, i.e. nucleic acid molecule, that has an ability to move from one location on the genome to another. Thus, a genetic material may be "transposable" only if it has an ability to transpose, even it remains in its original location.

The term "nucleic acid molecule" as used herein has comprehensive meaning including DNA (gDNA and cDNA) and RNA molecule. A nucleotide, which is a basic construct unit of nucleic acid molecule, includes nucleotide analogues with modified sugar or base, as well as natural-occurring nucleotides (Scheit, Nucleotide Analogs, John Wiley, New York (1980); Uhlman and Peyman, Chemical Reviews, 90:543-584(1990)).

It would be obvious to the skilled artisan that the nucleotide sequences used in this invention are not limited to those listed in the appended Sequence Listings.

For nucleotides, the variations may be purely genetic, i.e., ones that do not result in changes in the protein product. This includes nucleic acids that contain functionally equivalent codons, or codons that encode the same amino acid, such as six codons for arginine or serine, or codons that encode biologically equivalent amino acids.

Considering biologically equivalent variations described hereinabove, the nucleic acid molecule of this invention may encompass sequences having substantial identity to them. Sequences having the substantial identity show at least 60%, concretely at least 70%, more concretely at least 80%, even more concretely at least 90%, and most concretely at least 95% similarity to the nucleic acid molecule of this invention, as measured using one of the sequence comparison algorithms. Methods of alignment of sequences for comparison are well-known in the art.

The term "biological sample" as used herein, refers to not only samples that are obtainable from an organism, but also any samples that may contain genetic material. Examples of the biological sample include human and animal blood, plant body fluids, human and animal waste, microbial culture liquid, cell culture liquid, virus culture liquid, biopsy culture liquid, soil and air, but are not limited thereto.

The term "genetic material" as used herein, refers to any material storing genetic information in the nuclei or an organism's cells. The genetic material includes DNA or RNA that is passed along from one generation to the next. Extra chromosomal including organelle or plasmid DNA, can also be a part of the "genetic material" that determines genetic properties of the organism.

The term "codon optimality" as used herein, refers to an ability of a given codon to affect mRNA stability in a translation-dependent manner. mRNAs enriched in optimal codons tend to be more stable, display greater abundance, and higher translation efficiency. Codon optimality is varying due to differences in the frequency of occurrence of synonymous codons in coding DNA. Since codon optimality reflect the translation rates and accuracy, it has positive correlations with codon frequency and translation efficiency.

In some embodiment, it may be determined that the transposable or the non-self genetic material exists in the biological sample where the measured codon optimality is less than 85%, concretely 80%, more concretely 70%, even more concretely 60%, most concretely 50% of those of control

According to a concrete embodiment, the codon optimality is determined by a correlation coefficient between codon frequency and translation efficiency.

The term "correlation coefficient between codon frequency and translation efficiency" as used herein, refers to any coefficient representing the statistic correlation of two variables, codon frequency and translation efficiency. According to a concrete embodiment, said correlation coefficient is Pearson's correlation coefficient. The present inventors termed the Pearson's correlation coefficient between codon frequency and translation efficiency as "codon translation coefficient (CTC)", of which a higher value indicates higher optimality of a codon.

According to a concrete embodiment, the translation efficiency is determined by relative level of translation to transcription.

According to a concrete embodiment, the codon optimality is determined by relative ratio of a frequency of codon with the Pearson's correlation coefficient above first cut-off value to a frequency of codon with the Pearson's correlation coefficient below second cut-off value.

According to a concrete embodiment, the relative ratio is a log2 ratio.

In some embodiment, the first cut-off value may be identical to the second cut-off value. Concretely, the first cut-off value is higher than the second cut-off value.

More concretely, the first cut-off value is 0.1-0.2. Even more concretely, the indicated cut-off value is 0.12-0.18, and most concretely, 0.14-0.16.

More concretely, the second cut-off value is -0.2~-0.1. Even more concretely, the indicated cut-off value is -0.18~0.12, and most concretely, -0.16~0.14.

According to a concrete embodiment, the transposable genetic material is transposon.

The term "transposon" as used herein, also known as "jumping genes" or "transposable element", refers to nucleic acid sequence that can move from one genomic location to another by a cut-and-paste mechanism. Transposons sometimes alter the genetic identity and genome size of a cell, causing fatal mutations.

It is well known in the art that in various eukaryotes, including mammals, transposons are transcriptionally activated by certain diseases or at particular pathogenic stages. It was also suggested that transposition might be an important component of disease progression. Therefore, in a sense that the method of the present invention could also be applied to non-plant systems, the method of the present invention provides a crucial diagnostic or prognostic information regarding the transposon-involved genetic disorder in animals. Therefore, the phrase "method for detecting transposable material" as used herein may be used interchangeably with "method for diagnosing transposon-related diseases".

According to a concrete embodiment, the non-self genetic material is viral nucleic acid.

Non-self genetic elements such as nucleic acids of invading bacteria or viruses, which are mostly pathogenic, are known to be recognized by similar siRNA-mediated mechanisms that works on transposon. Therefore, they may also be recognized by the method of the present invention at the translation stage.

Particularly, viral genome share similar features with transposon and other non-self invaders in an inconsistency of codon usage with those of host cells and resulting reduced translational activity. Therefore, the present invention may provide a critical information regarding viral infection in a host animal including a human, thus be applied as a reliable tool for diagnosing a disease related to viral infection.

The term "diagnosing" as used herein, includes the following matters: (a) to determine susceptibility of a subject to a particular disease or disorder; (b) to evaluate whether a subject has a particular disease or disorder; (c) to assess a prognosis of a subject suffering from a specific disease or disorder; or (d) therametrics (e.g., monitoring conditions of a subject to provide an information to treatment efficacy).

According to a concrete embodiment, the biological sample is derived from plant or plant cell.

The term "plant(s)" as used herein, is understood by a meaning including a plant cell, a plant tissue and a plant seed as well as a mature plant.

The plants applicable of the present method include, but not limited to, most dicotyledonous plants including lettuce, chinese cabbage, potato and radish, and most monocotyledonous plants including rice plant, barley and banana tree. Preferably, the present method can be applied to the plants selected from the group consisting of food crops such as rice plant, wheat, barley, corn, bean, potato, Indian bean, oat and Indian millet; vegetable crops such as Arabidopsis sp., Chinese cabbage, radish, red pepper, strawberry, tomato, watermelon, cucumber, cabbage, melon, pumpkin, welsh onion, onion and carrot; crops for special use such as ginseng, tobacco plant, cotton plant, sesame, sugar cane, sugar beet, Perilla sp., peanut and rape; fruit trees such as apple tree, pear tree, jujube tree, peach tree, kiwi fruit tree, grape tree, citrus fruit tree, persimmon tree, plum tree, apricot tree and banana tree; flowering crops such as rose, gladiolus, gerbera, carnation, chrysanthemum, lily and tulip; and fodder crops such as ryegrass, red clover, orchardgrass, alfalfa, tallfescue and perennial ryograss.

In another aspect of this invention, there is provided a method for measuring a transposing activity of a transposable genetic material in a biological sample comprising:

measuring codon optimality of the transposable genetic material; and

determining the transposing activity of the transposable genetic material based on the measured codon optimality.

As the transposable genetic material, the biological samples and the concrete method for measuring codon optimality are already explained in detail above, they are omitted herein to avoid excessive overlaps.

The term "transposing activity" as used herein, refers to a quantitative value regarding an extent or a strength of ability to move from one location on the genome to another. The term "transposing activity" encompasses the frequency and/or distance of moving. The present invention provides a method for evaluating the transposing activity of specific transposable genetic material, e.g. transposon, as well as a method for determining the existence of it in a sample, based on the negative correlation between the codon optimality and the frequency and/or the distance of transposition.

Throughout this specification and the claims, unless the context requires otherwise, the word "comprise", and variations such as "comprises" and "comprising", will be understood to imply the inclusion of a stated member, integer or step or group of members, integers or steps but not the exclusion of any other member, integer or step or group of members, integers or steps. The terms "a" and "an" and "the" and similar reference used in the context of describing the invention (especially in the context of the claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by the context. Recitation of ranges of values herein is merely intended to serve as a shorthand method of referring individually to each separate value falling within the range. Unless otherwise indicated herein, each individual value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., "such as", "for example"), provided herein is intended merely to better illustrate the invention and does not pose a limitation on the scope of the invention otherwise claimed. No language in the specification should be construed as indicating any non-claimed element essential to the practice of the invention.

The present invention will now be described in further detail by examples. It would be obvious to those skilled in the art that these examples are intended to be more concretely illustrative and the scope of the present invention as set forth in the appended claims is not limited to or by the examples.

EXAMPLES

MATERIALS AND METHODS

1. Plant materials and growth condition

Arabidopsis seeds of Columbia-0 (Col-0), ddm1-2 (selfed for five generations) and ddm1-2 rdr6-11 double mutants (genotyped and collected from F2 segregants derived from ddm1-2 and rdr6-11 crosses) were surface-sterilized in 75% ethanol and germinated on half-strength 5 Murashige and Skoog media. Plants grown for 10 days under 16 h light/8 h dark cycling at 22 ℃ and were collected for RNA-seq, SG-RNA-seq and ribo-seq.

2. Codon sequence analysis

Codon frequency was calculated for the whole set of coding sequences of rice and Arabidopsis using the R package "seqinr". Codon translation coefficient was defined as the Pearson's correlation coefficient between the codon frequency and translation efficiency of expressed genes. Optimal codons and sub-optimal codons (Fig. 1C) are those above 0.15 and below -0.15 of CTC, respectively. The log2 ratio of optimal to sub-optimal codon frequency of individual transcript is defined as codon optimality.

3. Next-generation sequencing (NGS) library construction

For RNA-seq, the mRNAs were purified from 3 μg of total RNA using poly-T oligo-attached magnetic beads. Library preparation was carried out using the NEBNext® UltraTM RNA Library Prep Kit (NEB) following the manufacturer's instruction. Sequencing was performed on an Illumina Hiseq platform and 150 bp paired-end reads were generated.

For ribo-seq, the plant samples were lysed and digested by RNase I, then ribosome protected fragments (RPFs) were purified using the MicroSpin S-400 columns (GE Healthcare). After rRNA depletion, the RPFs were purified by polyacrylamide gel electrophoresis (PAGE). Then, the 5' and 3' adapters were ligated followed by the end-repair and dA-tailing. The adapter-ligated cDNAs were obtained by the one-step reverse transcription and PAGE purification. After PCR amplification and PAGE purification, the sequencing library was prepared using the NEBNext® Multiplex Small RNA Library Prep Kit (NEB) and the resulting library was loaded onto an Illumina HiSeq X machine for PE150 sequencing.

4. NGS data analysis

For RNA-seq data analysis, the raw data were first processed through the in-house Perl scripts to remove reads containing adapter, ploy-N and low-quality sequences. Clean reads were then aligned to the Arabidopsis reference genome (TAIR10) in default settings using Hisat2 (version 2.0.5). The FPKM of gene and transposons were calculated by StringTie (version 1.3.5) guided by the gene annotation file (TAIR10) downloaded from TAIR (ftp://ftp.arabidopsis.org/home/tair/Genes/TAIR10_genome_release/). Visualization of the sequencing data was performed using the Integrative Genomics Viewer (IGV).

For ribo-seq data analysis, the software Cutadapt (version 1.12) was first used to trim adapter sequences and the reads between 20-50 bp were retained. FASTX_toolkit (version 0.0.14) was used to filter out the low-quality reads and Bowtie (version1.0.1, parameter -l 20) to filter out the structural and ribosomal RNA reads. The kept reads were aligned to the genome by Tophat2 and the cufflinks (version 2.2.1) were employed to calculate FPKM. For disome analysis, the reads between 40-65 bp after removal of adapters were selected.

Public datasets used in this study are from PRJNA298638 (rice TRAP-seq) (31), SRP043448 (rice small RNA-seq) (32) and GSE52952 (Arabidopsis small RNA-seq and degradome-seq) (33).

5. Stress granule (SG) enrichment

The isolation of SG was modified from previous study (34). Briefly, 2 g of samples was ground with a precooled mortar and pestle in liquid nitrogen. The ground samples were collected into 50 ml conical tube and resuspended in 5 mL of SG lysis buffer (50 mM Tris-HCl pH 7.4, 100 mM KOAc, 2 mM MgOAc, 0.5 mM DTT, 0.5% NP40, Complete EDTA-free Protease Inhibitor Cocktail (Roche), 1 U/mL of RNasin Plus RNase Inhibitor (Promega)). The resulting slurry was centrifuged at 4,000 g for 10 min at 4 ℃, the supernatant was removed, and the pellet was resuspended in 2 ml of lysis buffer. The samples were again centrifuged at 18,000 g for 10 min at 4℃. The pellets were resuspended in 2 ml lysis buffer, vortexed and centrifuged at 18,000 g at 4 ℃ for 10 min. The supernatant was discarded and the pellets were resuspended in 1 ml of lysis buffer. After a final centrifugation at 850 g for 10 min at 4℃, the supernatant (enriched with SGs) was transferred to a new 1.5 ml microcentrifuge tube and purified using the RNeasy Plant Mini Kit (QIAGEN).

6. Protein sequence analysis

Protein domains were predicted by SMART (http://smart.embl-heidelberg.de/) using the full-length amino acid sequence of SGS3. Prediction of prion-like domains was performed using the web-based tool, PLAAC (http://plaac.wi.mit.edu/).

7. Protein expression and purification

To produce the recombinant protein of SGS3, the coding sequence of SGS3 gene was PCR amplified using the specific primers listed in the Table 1. The amplified DNA was then cloned into the modified pET28a (Novagen) expression vector containing the eGFP at the N terminus. The expression of GFP-SGS3 protein was induced in Escherichia coli Rosetta (DE3) (Novagen) by adding 0.1 mM isopropyl β-d-1-thiogalactopyranoside (IPTG) at 16 ℃ overnight. The collected cells were resuspended in lysis buffer (20 mM Tris-HCl pH 7.6, 200 mM NaCl, 10 % Glycerol, 0.1 % Tween20, 1 mM PMSF) and lysed by sonication, then centrifuged at 20,000 g for 45 min at 4℃. The supernatants were purified with Ni-NTA (Qiagen) in the elution buffer (250 mM imidazole in lysis buffer) according to the manufacturer's instructions and further purified using the Superdex 200 increase 10/300 column. The purified proteins were stored in storage buffer (20mM HEPES pH 7.4, 150 mM KCl, 1 mM DTT) at 100 μM of protein concentration until used.

Oligos used in this invention

Name	Sequence (5’ → 3’)
eGFP-BamHI-F	CGGGATCCATGGTGAGCAAGGGCGAGGA
eGFP-EcoRI-R	GGAATTCGTACAGCTCGTCCATGCCGT
SGS3-FL-SacI-F	CGAGCTCATGAGTTCTAGGGCTGGTCC
SGS3-FL-SalI-R	GCGTCGACTCAATCATCTTCATTGTGAAGGC

8. In vitro phase separation assay

For in vitro liquid droplet assembly, 10 μM of GFP-SGS3 protein mixed with PEG8000 (NEB) at 10% (w/v) was used. GFP fluorescence was imaged using a Zeiss LSM880 confocal microscopy equipped with 40Х/1.1 water immersion objective and the GaAsP spectral detector. The GFP was excited at 488 nm and detected at 491-535 nm.

9. Microscopy analysis

For time-lapse microscopy, GFP fluorescence was observed under Zeiss LSM880 confocal microscopy. Images were acquired every 3 sec for 5 min. At each time point, maximum projections from z-stack of 14 steps with step size of 0.6 μm were applied. Image analysis was performed with FIJI/ImageJ. FRAP assay of GFP-SGS3 was performed on a Zeiss LSM880 Airy scan confocal microscope. Photobleaching was done using a 488 nm laser pulse. Recovery was recorded every second for 5 min.

RESULTS

1. Coding sequence analysis

Since DNA methylation was reported to be negatively correlated with GC3 contents (GC contents at the third nucleotide positions of codons)(10, 19), we first interrogated the base compositions of coding sequences in the rice genome. As shown in Fig. 1a, while the GC contents at first and second nucleotide position of codons are similar between genes and transposons, the GC3 contents of transposons are remarkably lower than those of genes. Such divergence of codon sequence usage prompted us to study the translatome of transposons. We re-analysed the public Translating Ribosome Affinity Purification (TRAP)-seq translatome data (PRJNA298638) generated from rice callus because in vitro tissue cultured callus samples express high levels of transposons. By assessing the translation efficiency index (TEI) defined as the relative level of translation to transcription, we observed that TEs are significantly weaker in translation than genes in general (Fig. 1b and Fig. 5). This may suggest that translation-coupled pathway might be involved in the discrimination of transposons from genes, possibly serving as an initial sorting step for further siRNA biogenesis.

2. Assessment of codon optimality

Since TEs with no expression could not be determined for their TEIs and therefore the TEIs and easiRNAs could not be correlated for those TEs, we wanted to assign sequence-derived measure indicative to translation potential but regardless of their transcription. Given that the unequal usage of synonymous codons has been observed in various organisms and such codon sequence bias impacts on RNA expression and stability (20, 21), we analyzed the codon usage bias in relation to the translation efficiency in the rice transcriptome. The Pearson's correlation coefficient between codon frequency and TEI was defined as the codon translation coefficient (CTC) and provided a reference for further determination of the codon optimality. Figure 1c shows that the codons used in the rice transcriptome exhibited varying levels of CTC. Noticeably, the codons ending with G or C showed positive CTC values meaning that those codons are more frequently used in the actively translating RNAs. On the other hand, A or U-ending codons showed low CTC values. Based on this, we selected those above 0.15 of CTC values as the optimal codons and those below -0.15 as sub-optimal codons. We then calculated the relative ratio of the optimal to sub-optimal codon frequencies for each transcript that will be hereinafter referred to as codon optimality. The codon optimality showed positive correlation with TEIs (Fig. 1d and Figs. 6a-6c), whereas the out-of-frame codon optimality showed less and insignificant correlation (Figs. 6d and 6e), collectively confirming that codon optimality determined only by sequence information is a reliable proxy representing the translatability.

We then compared the codon optimality of the whole set of annotated genes and transposons in the rice genome. Fig. 1e shows that transposons are lower in the codon optimality, which is consistent with the reduced translational activity shown in Fig. 1b. We next selected for the loci generating easiRNAs in the rice osmet1-2 mutant and compared their codon optimality with randomly selected loci. The easiRNA-producing loci are lower in codon optimality (Fig. 1f), likely exhibiting weaker translational activities. Oppositely, the optimal and sub-optimal transposons were selected based on their codon optimality and compared for their easiRNA levels. Consistently, the sub-optimal TEs produced higher levels of easiRNAs (Fig. 1g). It is worth mentioning that ribosome stalling induces RNA cleavage through the so called No-go RNA decay (NGD) pathway (22). In addition, the core NGD complex Pelo-Hbs1 was previously reported to suppress transposon activity in Drosophila (23). Given that RNA truncation is an essential prerequisite for RDR6 targeting (16, 17) and subsequent easiRNA biogenesis, the NGD pathway may serve as an initial entry point for TE RNAs to be guided to the siRNA production pathway.

In order to test if the reduced translational activity of transposons is conserved in other species, we tested decrease in dna methylation 1 (ddm1) mutant of Arabidopsis for the ribosome footprint profiling (ribo-seq) experiment. Similar to rice, Arabidopsis transposons were drastically reduced in translation and lower in codon optimality compared to genes (Figs. 7a-7c). Since siRNAs can inhibit the translational process (24), we wanted to test whether the weak translation of transposons is the cause or consequence of siRNA production. For this, we carried out additional ribo-seq experiments using the ddm1 rdr6 double mutant which does express transposons but does not produce easiRNAs. Interestingly, we were not able to detect any noticeable changes of translational activities between ddm1 and ddm1 rdr6 double mutants (Fig. 7d), indicating that the reduced translation of TEs is not caused by siRNAs. Taken altogether, transposons are rich in translationally unfavourable codons, thus triggering the easiRNA production presumably through NGD pathway.

3. Disome RNA analysis

Ribosome stalling or queuing frequently occurs when translation slows down and two ribosomes are stacked at the stalled site. A previous study showed that collision of stacked ribosome is critical for NGD pathway (25). In order to profile the RNAs containing the queued ribosome, we selected the di-ribosome (disome) fragment reads ranging 40 to 65 nucleotides from our ribo-seq data generated from ddm1 mutant (Fig. 2a). Disome fragments were strongly enriched with the non-protein-coding RNAs including tRNAs and rRNAs as well as organellar RNAs, while only around 20 % was protein-coding genes, which is consistent with the previous reports (Fig. 2b) (26). We retrieved the sequences of the disome-containing protein-coding genes and compared the codon optimality with those of randomly selected RNAs. As shown in fig. 2C, disome RNAs showed drastically reduced codon optimality suggesting that ribosome stalling might be caused by codon sequence usage unfavourable for translation.

Ribosome stalling often causes RNA cleavage at the 5' end of ribosome-protected regions (22). To test if disome RNAs undergo frequent truncation, we analysed the degradome-seq data generated from ddm1 mutant. Degradome-seq maps the 5' end of the truncated RNAs and by normalizing its levels by the RNA-seq values we determined the degradability of each transcript. Figure 2D shows that disome RNAs are significantly more prone to RNA cleavage than the randomly selected RNAs. We then looked at the easiRNA levels of disome RNAs that produced considerably more easiRNAs than the random RNAs (Fig. 2e). As an opposite approach, we selected for the RDR6 target transposons by their dependency of easiRNA production on RDR6 and determined their codon optimality. Consistently, we were able to observe that RDR6 targets have lower codon optimality as compared with the randomly selected non-RDR6 targets (Fig. 2f). In conclusion, ribosome stalling caused by sub-optimal codons triggers RNA cleavage and subsequently easiRNA production.

4. Localization of TE RNAs to Stress granule

It has been reported that slowly translating RNAs locate to cytoplasmic compartments known as stress granules (SGs) (27). Importantly, RDR6 and SGS3, two essential factors of easiRNA pathway, colocalize with the major SG component UBP1b in the stressed and the DNA methylation-deficient mutant plants (24, 28). Given these knowledges, we reasoned that transposon RNAs preferably locate to SGs possibly due to their weak translational activities and are therefore selectively taken over to the easiRNAs production pathway. In order to demonstrate this hypothesis, we performed SG enrichment followed by RNA-seq experiment (SG-RNA-seq) using the ddm1 mutant of Arabidopsis. By normalizing to the total RNA-seq levels we assessed the SG-enrichment of each transcript that gave us 863 SG-enriched and 891 SG-depleted RNAs (Fig. 3a). Intriguingly, the fraction of transposons in the SG-enriched RNAs were more than 35 percent, while those of SG-depleted RNAs and total transcriptome were only around 5 percent (Fig. 3b). SG-RNA-seq also revealed that SG-enriched RNAs are remarkably lower in the RNA levels, codon optimality and translational efficiency, but associated with higher levels of easiRNAs (Figs. 3c-3g). These data collectively indicate that TE RNAs are preferentially localized to SGs where easiRNA pathway is present.

It is well documented that membrane-less cellular compartments are formed by the liquid-liquid phase separation of ribonucleoproteins (29, 30). Of those, the plants SG core component UBP1b contain prion-like domains (Fig. S4A), the critical protein domain triggering LLPS, and forms cytoplasmic foci in ddm1 mutant background (24). Although RDR6 and SGS3 were known to form cytoplasmic puncta and colocalize with UBP1b (28), their biophysical property of LLPS has not been studied. We first paid attention to SGS3 because it is predicted to contain prion-like domains (Fig. 4a). The GFP-tagged SGS3 protein was expressed in E. coli and purified to test its phase separation activity in vitro. Shown in fig. 4b is the microscopy images of full-length SGS3 protein forming the liquid droplets in vitro, which is a hallmark of LLPS. To further demonstrate the fluidity and dynamicity of SGS3 protein droplets, which is a typical characteristic of phase-separating proteins, we carried out the time-lapse microscopy imaging analysis. Figure 4c shows the fluorescence microscopy images of two adjacent protein droplets which are fusing together in only several seconds. Additionally, we performed FRAP assay and observed that the lesions of the photobleached SGS3 protein droplets recovered almost completely in around 30 seconds (Figs. 4d and 4e). In summary, the reduced translation detected in transposons results in the localization of TE RNAs to SGs, where the easiRNA pathway locates presumably through the LLPS of SGS3. The cellular compartmentalization of TE RNAs to SGs provides additional selectivity of easiRNA pathway towards transposon RNAs.

As RDR6 and AGO7 are known to function in conjunction with SGS3 and colocalize together forming cytoplasmic foci, we checked if they contain prion-like domains. The PLAAC (Prion-Like Amino Acid Composition) algorithm predicted no noticeable prion-like domains in RDR6 and AGO7 (Figs. 8g and 8r), suggesting that they might be guided to SGs via the physical interaction with SGS3. Our interrogation of prion-like domains in the small RNA pathway factors revealed that AGO1, 2, 3 and 5 contain prion-like domains at their N termini (Fig. 8). This may suggest that apart from the easiRNA pathway other cellular processes involving small RNAs can also be mediated by LLPS. Taken altogether, transposon RNAs are detected by their reduced translational status and exclusively funnelled to easiRNA pathway. The selective processing to easiRNAs is governed by the frequent RNA truncation and SG localization of TE RNAs which are both caused by ribosome stalling (illustrated in Fig. 9). Given that viral and transgene RNAs are recognized and processed by the similar siRNA-mediated epigenetic silencing machineries (10), they are also likely recognized as genome invaders at the translation stage. Our work thus demonstrates the recognition mechanism of non-self genetic elements which is essential for genome maintenance.

Having described a preferred embodiment of the present invention, it is to be understood that variants and modifications thereof falling within the spirit of the invention may become apparent to those skilled in this art, and the scope of this invention is to be determined by appended claims and their equivalents.

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Claims

A method for detecting transposable or non-self genetic material in a biological sample comprising:

measuring codon optimality of nucleic acid molecule in the biological sample isolated from a subject; and

determining existence of the transposable or the non-self genetic material in the biological sample based on the measured codon optimality.
The method according to claim 1, wherein the codon optimality is determined by a correlation coefficient between codon frequency and translation efficiency.
The method according to claim 2, wherein the correlation coefficient is Pearson's correlation coefficient between codon frequency and translation efficiency.
The method according to claim 3, wherein the translation efficiency is determined by relative level of translation to transcription.
The method according to claim 4, wherein the codon optimality is determined by relative ratio of a frequency of codon with the Pearson's correlation coefficient above first cut-off value to a frequency of codon with the Pearson's correlation coefficient below second cut-off value.
The method according to claim 5, wherein the relative ratio is a log2 ratio.
The method according to claim 5, wherein the first cut-off value is 0.1-0.2.
The method according to claim 5, wherein the second cut-off value and is -0.2~-0.1.
The method according to claim 1, wherein the transposable genetic material is transposon.
The method according to claim 1, wherein the non-self genetic material is viral nucleic acid.
The method according to claim 1, wherein the biological sample is derived from plant or plant cell.
A method for measuring a transposing activity of a transposable genetic material in a biological sample comprising:

measuring codon optimality of the transposable genetic material; and

determining the transposing activity of the transposable genetic material based on the measured codon optimality.