ChIP- based nuclear DNA isolation for genome sequencing in Pyropia to remove bacterial and cytosol DNA contamination

doi:10.21203/rs.3.rs-2586340/v1

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ChIP- based nuclear DNA isolation for genome sequencing in Pyropia to remove bacterial and cytosol DNA contamination

https://doi.org/10.21203/rs.3.rs-2586340/v1

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Contamination from epiphytic bacteria and cytosolic DNA (plastid and mitochondrion) is challenging the accuracy of genome-wide analysis of nori-producing marine seaweed Pyropia yezoensis. Unlike bacteria and organellar DNA, Pyropia nuclear DNA is tightly associated with histone proteins. In this study, we applied Chromatin Immuno-precipitation (ChIP) of histone H3 to isolate nuclear DNA followed by high-throughput sequencing. More than 99.5% of ChIP-sequencing data are successfully aligned to the reference nuclear genome, remarkably higher than the ones from direct-extraction and nuclei-extraction data in which 40%-50% are from plastid. The proportion of data that mapped to the bacterial database when using ChIP extraction was very low. Additionally, ChIP-data can cover up to 89% of the nuclear genome, higher than direct-extraction data at equal data size and comparable to the latter at equal sequencing depth. The uncovered regions from the three methods are mostly overlapping, suggesting that incomplete sequencing accounts for the missing data, rather than failed chromatin-antibody binding in ChIP-extraction method. This ChIP-extraction method can successfully separate nuclear DNA from cytosolic DNA and bacterial DNA, thus overwhelmingly reducing the sequencing cost in genome resequencing project and provides a strictly purified reference data for genome assembly. The applicability to other macroalgae would makes it a valuable contribution to the algal research community.

bacterial contamination

Chromatin Immuno-precipitation

genome sequencing

histone H3

Pyropia yezoensis

nuclear DNA

plastid DNA contamination

red algae

Pyropia is a typical genus in the rhodophyte order Bangiales and is of fundamental commercial and social importance as the edible seaweed “nori” (Cho and Rhee 2019). Pyropia species, mainly Pyropia yezoensis (also named as Neopyropia yezoensis) and Pyropia haitanensis (also named as Neoporphyra haitanensis), are widely cultivated in Asian countries(Cao et al. 2016, Yang et al. 2020). Genetic breeding of new cultivars with improved economic traits, such as higher productivity and better quality, is critical to the commercial development of Pyropia sp. as a multibillion-dollar, world-wide aquaculture industry(Abdelrahman et al. 2017). Meanwhile, as intertidal red seaweed, Pyropia has attracted numerous research interests in multicellularity, stress adaptation, cellular development and evolution (Wang et al. 2020, Guan et al. 2022). Genome-associated analysis in Pyropia species is required by both genetic dissection of economic traits and basic biological studies. Recently, it was revolutionized by the high throughput and the low cost of next-generation sequencing (NGS) technologies, which have increased the amount of genome sequencing data (McCombie et al. 2019).

A big challenge in Pyropia genome sequencing is bacterial contamination, as Pyropia, like other seaweeds, is colonized by widely diverse bacteria that interact with them throughout their life cycle (Wang et al. 2022). Antibiotic treatments (usually using a mix of several kinds of antibiotics) will affect the growth of seaweed and fail to remove bacteria completely. Researchers used quartz sands polishing to remove bacteria on algal surface before isolating genomic DNA, but still found some contamination remained (Liu et al. 2022). Once the bacterial sequences were included in the data, it is impossible to identify and remove them completely, as the reference genomes for marine bacteria are not sufficient nowadays (Wang et al. 2022). Therefore, technology that can avoid bacterial DNA is in urgent need.

Another problem in Pyropia genome sequencing is that Pyropia species usually have high copies of chloroplast genome and mitochondrion genome. In sequencing data of directly extracted DNA from P. yezoensis thalli, chloroplast and mitochondrion genome data can account for up to 70%-80% (Wang et al. 2013, Wang et al. 2015). Thus, a vast amount of sequencing data is uninformative and more sequencing depth is required. This strongly leads to uncomplete genomic analysis in Pyropia.

Chromatin Immuno-precipitation (ChIP), using specific antibodies to bind to chromatin, is widely applied to identify protein-associated genomic loci (Ueda et al. 2022). As a eukaryote, nuclear DNA is tightly bound to a group of basic histone proteins and packaged into a structure called the nucleosome(Li and Zhu 2015). As a contrary, bacterial DNA, as well as the plastid and mitochondrion DNA, that originated from ancient bacterial endosymbionts, are naked(Burki 2017, Hołówka and Płachetka 2017). Based on the absence/presence of histone proteins associated with nuclear DNA only, we used the histone H3 antibody in ChIP to specifically isolate nucleosome DNA from P. yezoensis thalli for high-throughput sequencing. The effect of diminishing bacterial and plastid DNA, and the genome-wide coverage were examined through comparing to the direct-extraction and nuclei-extraction methods.

2.1 Algal culture and sample collection

The thallus of Pyropia yezoensis (accession GCA_009829735.1) was used in this study. Thallus was cultivated in boiled natural seawater with Provasoli's enrichment solution medium (PES) at 10 ℃ in an incubator, with a light con-centration of 20 µmol photons · m^− 2 · s^− 1 and a 12/12-h light/dark cycle (Ismail and El-Sheekh 2018). The medium was refreshed every three days. Thalli at the age of 30–45 days were used for genomic DNA extraction. Fresh thalli were collected directly from the culture medium and wiped out using autoclaved gauze to remove surface water. They were then immediately frozen in liquid nitrogen for direct-extraction or nuclei-extraction of genomic DNA, or put into 1.0% formaldehyde solution for ChIP-extraction (detailed procedures were described below). For dehydrated treatment, wiped thalli were placed in a glass dish and exposed to air in the incubator. They were collected when their absolute water contents (AWCs) reaching to 50% as described in Mao et al(Mao et al. 2019). Two biological replicates were collected for each genomic DNA extraction method.

2.2 Chromatin Immuno-precipitation (ChIP) and Hi-Seq sequencing

ChIP experiments were performed following the procedures described by Wei et al. in 2018 (Wei and Xu 2018). Briefly, we collected thalli at 30 ~ 45 days age and treated them with 1.0% formaldehyde for DNA/histone cross-linking for 30 min. After quenching the cross-linking reaction with 2M glycine, we froze the cross-linked thalli samples with liquid nitrogen and ground. Chromatin was isolated by sucrose density gradient centrifugation. Histone H3 antibody (ab176842; Abcam) and Magna ChIP™ Protein A + G Magnetic Beads (16–663, Sigma-Aldrich) were used for immuno-precipitation in this study. ChIP-DNA was purified by Universal DNA Purification Kit (DP214-02, Tiangen). The purity and integrity of ChIP-extracted DNA were analyzed by agarose gel electrophoresis. The concentrations of ChIP-DNA samples were quantified by Qubit. Paired-end sequencing libraries were prepared as instructed by the standard Illumina protocol and then loaded on the Illumina HiSeq 2000 platform for sequencing. For one of the dehydrated sample, we generated more ChIP-data (about 13.7 Gb) to investigate the effect of data size on genome coverage.

2.3 ChIP-Seq data analysis.

Raw data were trimmed by Fastp to get the clean data (Chen et al. 2018). FastQc was used for statistic quality control (Andrews 2010). Clean data were mapped to the Pyropia yezoensis genome (GCA_009829735.1) and gene database by BWA (Jung and Han 2022). Sorting alignment by the 5' position on the chromosome and filtering out alignments with low mapping quality were done by samtools (Li et al. 2009). Depth and coverage were counted by bamdst (https://github.com/shiquan/bamdst). Covered regions were plotted by karyoplotR (Gel and Serra 2017). VEN-plots were performed on TBtools (Chen et al. 2020). Tandem repeats were identified by Tandem Repeats Finder (https://tandem.bu.edu/trf/trf.html). Randomly picking reads from a dataset was done by seqtk (https://github.com/lh3/seqtk).

Transcriptomic data of dehydration samples were downloaded from a published work(Sun et al. 2019). Gene expression level was measured by TPM (Transcripts per kilobase of exon model per million mapped reads) and calculated by stringtie (Li and Dewey 2011, Pertea et al. 2016).

2.4 Genomic DNA preparation by direct-extraction or nuclei-extraction

P. yezoensis thalli were frozen in liquid nitrogen and ground into a powder. Then DNA was isolated following the instruction of the one-step-lysis plant DNA kit (Nobelab, Beijing). This method is called direct-extraction. For nuclei-extraction, thalli were first frozen in liquid nitrogen and ground. Nuclei were then isolated by sucrose density gradient centrifugation as described by Zhang et al. (Zhang et al. 1995), followed by DNA purification via the Universal DNA Purification Kit (DP214-02, Tiangen, Beiing).

3.1 Histone H3-based ChIP-Seq data collection

We carried out ChIP experiments for P. yezoensis thalli and dehydrated thalli using H3 antibody, with two replicated samples, respectively. The extracted DNA samples were then used for Illumina sequencing. For every sample, we also performed nuclei-extraction method to obtain genomic DNA for sequencing. Around 6.5 Gb Hi-Seq sequencing raw data were obtained for both ChIP-extracted DNA (called as ChIP-data) and nuclei-extracted DNA (called as nuclei-data) for each sample (Table 1). After preliminary quality control, about 6.0 Gb clean data were aligned to the reference nuclear genome of P. yezoensis. For ChIP-data from both normal and dehydrated thalli, the mapping rates ranged from 99.41 to 99.81%, while for nuclei-data, the numbers are lower than 50% (47%-50%), but still higher than the one of the direct-extraction data (38.76%) (Table 1). We then retrieved the unmappable reads and aligned them to both plastid and mitochondrial genomes. More than 81.0% of unmappable reads of nuclei-data (corresponding to 40.96% of total clean reads) were successfully mapped onto plastid genomes and 10.0% (corresponding to 5.26% of total clean reads) onto mitochondrial genome, suggesting that severe cytosol DNA contamination still exists during nuclei extraction and that plastid contamination accounts for the majority (Table 1). Among the ChIP-data, only 1.0%-4.0% of nuclear-genome-unmappable reads were from cytosol DNA (Table 1). The dramatically dropped proportion in ChIP-data underlines the high efficiency of this ChIP-method in removing the cytosolic DNA contamination in Pyropia thalli.

Table 1

Statistics of sequencing data and mapping rates.
	Raw data (Gb)	Clean data (Gb)	Nuclear mapping rate	Plastid mapping rate	Mitochondrion mapping rate	Bacteria mapping rate
ChIP-data1*	7.95	7.32	99.81%	0.0055%	0.0014%	0.11%
ChIP-data2	8.12	7.54	99.41%	0.0087%	0.0016%	0.40%
ChIP-dehydrated-data1	13.71	12.77	99.76%	0.0049%	0.0010%	0.14%
ChIP-dehydrated-data2	7.32	6.80	99.62%	0.012%	0.0019%	0.18%
Nuclei-data1	8.10	7.83	49.91%	40.96%	5.26%	3.27%
Nuclei-data2	6.14	6.20	47.36%	42.95%	5.42%	3.52%
Nuclei-dehydrated-data1	11.10	10.72	48.63%	42.00%	5.37%	3.42%
Nuclei-dehydrated-data2	6.39	6.11	49.21%	41.18%	5.42%	3.51%
Direct-extracted-data	11.20	10.70	38.76%	53.41%	6.64%	0.48%
*, data1 and data2 of each method were from the two biological replicates.

To examine the capability to remove epiphytic bacterial contamination of ChIP-method, we aligned the nuclear/plastid/mitochondrion-unmappable reads to the non-redundant protein sequence database (NR). For nuclei-data, 82.0%-86.0% of reads (accounting for less than 3.5% of total clean reads) can map to bacterial sequences. The bacterial mapping rate of ChIP-data varied from 48.0–69.0%, accounting for 0.11%-0.40% of total clean reads. For direct-extraction data, bacterial contamination went up to 40.68%, accounting for 0.48% of the total clean reads. Though the ratios were not overwhelmingly lower, the practical number of bacterial reads for each ChIP-data was only 1/7 or 1/36 of the one for its nuclei-extraction counterpart, indicating the effectiveness in filtering bacterial contamination of ChIP-extraction method (Table 1).

3.2 Relationship of transcriptional activities and aligned depth by ChIP-data

The compactness of DNA and histone proteins affects the accessibility of transcription-related proteins (e.g., transcriptional factors) to DNA and thus determines the transcriptional activity of related genes (Hayashi and Masukata 2011). Hypothetically, DNA with higher transcriptional activities may have a loose chromatin structure and lower probability of being extracted and sequenced in ChIP-extraction method. We calculated the aligned depth of each protein-coding gene in ChIP-data of P. yezoensis thalli under either normal or dehydrated condition, and then investigated its correlation relationship the transcriptional level as detected in the previously published transcriptomic data. Normal distribution patterns were observed in both the normal and dehydrated samples. Genes with low transcriptional levels (TPM < 10) had a widely ranged aligned depth, from zero to 1000, while genes with higher transcription had a relative moderate aligned depth (Fig. 1). Similar patterns were also observed in direct-extraction-data and nuclei-data. Apparently, there’s no direct correlation between transcriptional activities of genomic loci and their chances to be detected via ChIP-method, which is inconsistent with our hypothesis.

3.3 Genome coverages of the three methods

In addition to the capability of ChIP-extraction method to remove contamination, another concern of this method is whether some genomic regions would be missed due to the incomplete combination of nucleosomes and H3 antibody. Starting from the same amount of clean data, we investigated the distribution of mappable reads on the reference nuclear genome. Overall, for ChIP-data, 86.0%-89.0% of genome region had at least four reads successfully aligned (aligned depth > 4X). The coverage ratio of corresponding nuclei-data was 2.0%-3.5% lower, and the ratio of direct-extraction data was only 79.76%. If we raised the depth threshold to more than 10X, the difference in the coverage ratio between ChIP-data and nuclei-data was enhanced and even more when the threshold set as 30X. However, when we randomly picked up a portion of clean nuclei-data to make the number of mappable reads from ChIP-data and nuclei-data equal (overall sequencing depth as 21X), the difference in the coverage ratio between the two sets of data declined to less than 1.0%. This suggested that better genome coverage could be achieved by increasing the amount of effective data (mappable data), though at the cost of increasing costs. Therefore, we generated 12.77 Gb clean ChIP-data for one dehydrated thalli sample to enlarge the coverage in the nuclear genome. The genome mapping depth increased to 98X and the coverage ratio increased to 88.12%, 1.55% higher than the previous result of 4.0Gb data.

For a better view of the genomic distribution of covered regions by different datasets, we loaded the alignment results in the karyoplotR program. As shown in Fig. 2, except some ups and downs at the chromosomal end, generally the covered regions from the three datasets are all distributed evenly along chromosome 1 and large missing regions were not observed. We also noticed some peaks in all the three datasets. Their patterns in direct-extraction-data are similar to that one in nuclei-data, albeit some peaks are apparently missed in ChIP-data.

3.4 Feature of the uncovered region

There’re three possible reasons why the above uncovered regions were not detected in sequencing: 1, These regions are part of N. yezoensis chromosomes, and were successfully collected by either of the DNA extraction methods, but hard to be sequenced by the Illumina sequencing platform or any platforms, due to extreme GC content, high sequence complexity, or other unknown reasons. In this situation, the uncovered regions in each method are supposed to mostly overlap. 2, Some regions are difficult to collect using ChIP-extraction method due to the lack of nucleosome structure or low affinity of H3 and its antibody. Therefore, the ChIP-method should have generated some specific uncovered regions. 3, Some regions in the current reference genome are from bacterial contamination. They were generated in initial genome de novo sequencing and assembly and should also be included in these direct-extraction data but less in nuclei-extraction data and barely in ChIP-data. To figure out which of the above reason(s) is possible as far as this study is concerned, we retrieved the DNA sequences of uncovered regions by each method and performed a mutual alignment. About 95% of uncovered regions in ChIP-method overlapped with those in the other two methods (Fig. 3a). Then we collected protein-coding genes linked to these regions. 190, 197, and 230 genes located within or partially overlapping with the uncovered regions of the combined ChIP-data, combined nuclei-data and the 15Gb control-data respectively, thus named as uncovered genes of corresponding method. Interestingly, all the uncovered genes of the combined ChIP-data and nuclei-data were included in the control group and 188 genes were shared in all three groups, stressing the similarity in uncovered genomic regions in the three DNA extraction methods (Fig. 3b). The prominent overlap of uncovered regions supported the attribution to incomplete sequencing on the Illumina sequencing platform. We did observe that about 99.4% of uncovered regions were successfully aligned by a previously reported PacBio sequencing data (Fig. 3c).

To further characterize the biological features of the uncovered regions, we first investigated the GC contents. GC contents of ChIP-uncovered and nuclei-extraction-uncovered regions were about 59.0% and 60.0% respectively, slightly lower than the nuclear genome (64.63%). N-containing regions accounted for about 3.6%-4.6%. About 8000 tandem repeats (TRs, about 0.8 Mb) were identified, and the total length accounted for 5.94%-8.34% of uncovered regions, comparable to the genome-wide proportion of repeat sequences (Table S1). We then looked at the transcriptional levels and taxonomies of homologs in the NR database of the uncovered genes. Among the 188 shared uncovered genes, 116 (61.7%) exhibited no or scarce transcriptional activity (TPM < 5) in thalli under both normal and dehydrated conditions. In addition to the 21 genes with no blast hits at all, 163 genes showed hits in eukaryotes. Among them, 75 genes have BLAST hits solely in Porphyra umbilicalis (another Bangiales genera) and 16 have the best hit in P. umbilicalis followed by other hits in bacteria or eukaryotes, suggesting they were Bangiales-specific genes.

Genome de novo sequencing and re-sequencing of Pyropia species suffered significantly from DNA contamination from both epiphytic bacteria and cytosol organelles including plastid and mitochondrion. In this study, we developed a ChIP method to specifically isolate nuclear DNA that can be subsequently used for the next-generation sequencing. Compared to the direct-extraction and nuclei-extraction methods, this method can overwhelmingly lower the proportion of bacterial, plastid and mitochondrial data in total sequencing data, without sacrificing genome coverage, therefore will make important contributions in two ways. First, it is more economical in terms of sequencing cost and produced data with few extra experimental procedures, as it produces less garbage data (bacteria, organelles). Second, serves as a curation-reference for higher accuracy in de novo genome sequencing and assembly. Due to the high diversity and limited data of marine bacterial sequences, even a small amount in nuclei-data for the assembly will bring severe contamination and currently it is impossible to distinguish them and remove afterwards. This ChIP-method can strongly lower the content of bacterial DNA and generate “clean” DNA for either the Illumina short-reads sequencing platform and long-reads platform-like PacBio. For genomic regions and genes in a finished genome assembly, being successfully aligned by ChIP-extracted “clean” data will provide strong evidence to be free of contaminants. Moreover, the ChIP-extraction method can also be applied to other organisms that are suffering from bacterial or plastid DNA contamination, especially marine seaweeds.We failed to obtain sequencing data for more than 10% (17.66Mb) regions in the P. yezoensis genome through ChIP-method, most of which were also not detected by both direct-extraction method and nuclei-extraction method, suggesting the incomplete sequencing mainly accounts for the missing data. This hypothesis is supported by higher coverage ratio when raising the sequencing depth. For further improvement, combined antibodies of histone H3 and H4 to increase affinity to the nucleosome, and mixed biological samples from different developmental stages or stress-treatments to exclude the effect of transcriptional activities on chromatin structure, as well as improved experimental procedures of ChIP protocol, such as CUT&Tag (Kaya-Okur et al. 2019), should be helpful to achieve higher genome coverage.

Previous ChIP-Seq analysis for a transcription factor or the histone modification mark in animals and plants revealed false positive genomic regions and enrichment of high-affinity binding sites(Pickrell et al. 2011, Jain et al. 2015). Sequencing bias such as GC-rich regions also exists in ChIP-Seq(Benjamini and Speed 2012). In this study, though the overall genomic distribution of the covered region for each DNA extraction method was similar, we noticed varied sequencing depths on certain genomic regions. Since sequencing data were generated by the same platform, we think the main cause lies in the different accessibility and affinity of antibodies to histone H3, which is dictated by varying chromatin structure.

Additionally, P. yezoensis also harbor epiphytic fungi, such as ascomycetes and oomycetes, on marine seaweeds (Mo et al. 2015, Klochkova et al. 2016). Histone proteins are the key players in separating nuclear DNA from cytosol and bacterial DNA in ChIP-method. Since histone H3 and H4 also exist in fungi with well-conserved protein sequences, our method cannot distinguish this fungal DNA.

The authors have no competing interests to declare that are relevant to the content of this article.

Funding

This work was supported by the Natural Science Foundation of China (32172970), the Shandong Natural Science Foundation (ZR2019MC012), the Fundamental Research Funds for the Central Universities (202112024).

Availability of data and material

Data in this work it is available on reasonable request, please contact Z.Z at [email protected] or D.W at [email protected]

Authors' contributions

D. W. designed the research; Z. Z. and X. G. performed research; Z. Z. and X. Z. prepared samples; D. W., Y. M and A. P. contributed in funding and paper editing; Z. Z., J. W. and D. W. analyzed data; D. W, Z. Z. and A. P. wrote the paper.

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No competing interests reported.

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ChIP- based nuclear DNA isolation for genome sequencing in Pyropia to remove bacterial and cytosol DNA contamination

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Abstract

Figures

1 Introduction

2 Material And Methods

2.1 Algal culture and sample collection

2.2 Chromatin Immuno-precipitation (ChIP) and Hi-Seq sequencing

2.3 ChIP-Seq data analysis.

2.4 Genomic DNA preparation by direct-extraction or nuclei-extraction

3 Results

3.1 Histone H3-based ChIP-Seq data collection

3.2 Relationship of transcriptional activities and aligned depth by ChIP-data

3.3 Genome coverages of the three methods

3.4 Feature of the uncovered region

4 Discussion

Declarations

Funding

Availability of data and material

Authors' contributions

References

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Supplementary Files

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