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25 October 2017

How Next-Generation Sequencing Has Aided Our Understanding of the Sequence Composition and Origin of B Chromosomes

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Leibniz Institute of Plant Genetics and Crop Plant Research Gatersleben, 06466 Seeland, Germany
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Author to whom correspondence should be addressed.
This article belongs to the Special Issue Chromosomal Evolution

Abstract

Accessory, supernumerary, or—most simply—B chromosomes, are found in many eukaryotic karyotypes. These small chromosomes do not follow the usual pattern of segregation, but rather are transmitted in a higher than expected frequency. As increasingly being demonstrated by next-generation sequencing (NGS), their structure comprises fragments of standard (A) chromosomes, although in some plant species, their sequence also includes contributions from organellar genomes. Transcriptomic analyses of various animal and plant species have revealed that, contrary to what used to be the common belief, some of the B chromosome DNA is protein-encoding. This review summarizes the progress in understanding B chromosome biology enabled by the application of next-generation sequencing technology and state-of-the-art bioinformatics. In particular, a contrast is drawn between a direct sequencing approach and a strategy based on a comparative genomics as alternative routes that can be taken towards the identification of B chromosome sequences.

2. The Acquisition of Sequences Enriched in B Chromosome

Differences between the A and B chromosomes with respect to their size, structure, and pattern of meiotic pairing behavior offer the opportunity to isolate the B chromosomes via micro-dissection. In plant species where it is difficult to synchronize mitotic division across many cells, advantage can be taken of the natural synchrony associated with meiosis, particularly in the anthers, where large numbers of pollen mother cells passage through meiosis simultaneously. In the earliest reported use of micro-dissection to obtain B chromosome-specific sequences, Sandery et al. [32] attempted to clone into lambda phage DNA obtained from a very large number of rye B chromosomes; although the approach was rather inefficient. The introduction of PCR was responsible for a quantum leap in efficiency, and this technology lies behind most current protocols for chromosome micro-dissection and the subsequent handling of the DNA [33,34,35,36]. Successful in situ painting of B chromosomes (e.g., rye [37], Brachycome dichromosomatica [38]) with labelled DNA generated after microdissection was possible because of the enrichment of chromosome-specific repetitive sequences, rather than the chromosome specific low- and single-copy sequences. A list of B chromosomes successfully isolated via micro-dissection is given in Table 1. An alternative route to acquiring chromosome-specific DNA takes advantage of the power of flow-sorting to separate chromosomes on the basis of their size [39]. A major advantage of this approach is that it isolates orders of magnitude higher numbers of chromosomes than is feasible using micro-dissection. The resulting DNA can be amplified using either degenerate oligonucleotide primed PCR [40] or Phi29 multiple displacement amplification [41]. The latter technique is more effective where longer amplicons (5–30 Kbp) are preferred [42]. Species for which flow-sorting has been successfully used to purify B chromosome DNA are listed in Table 2.
Table 1. Isolation of B chromosomes by microdissection.
Table 2. Isolation of B chromosomes by flow sorting.

3. The In Silico-Based Identification of B Chromosome-Enriched Sequences

Various strategies have been elaborated to identify B chromosome sequences from NGS-acquired data. This section summarizes the differences between the direct and indirect (comparative) methods (Figure 1).
Figure 1. Direct and indirect methods used to identify B chromosome sequences using next generation sequencing (NGS). Strategy 1: the direct method. This approach requires a prior step, in which the B chromosomes are isolated either by micro-dissection or by flow-sorting. Strategy 2: the indirect method. This method requires the acquisition of sequence data from both an individual carrying a B chromosome(s) (+B dataset) and a related individual lacking any B chromosome(s) (0B dataset). The two datasets are compared using three alternative methods. In “similarity-based read clustering”, a graphically based analysis is performed using, for example, the RepeatExplorer pipeline. Sequence information is transformed into graphical structures (vertices correspond to sequence reads and edges characterize the overlap between reads). Differences (presence/absence of sequence reads) in the 0B and +B datasets affect the clusters, and are used to distinguish B chromosome sequences. The two-colored circles indicate reads containing sequences from 0B and +B probes. The “coverage ratio analysis” requires an initial alignment of reads, using an alignment pipeline such as Burrows-Wheeler Alignment tool (BWA). Differences in the read coverage ratio indicate B chromosome-derived candidate regions. The pink section illustrates an example of a putative candidate region, which features the absence of reads in the 0B dataset and their presence in the +B dataset. In the “k-mer frequency ratio analysis” approach, a program such as the Kmasker pipeline identifies differences in the k-mer frequency ratio. The illustration shows an example of a B chromosome segment (shown in pink) in which the k-mer frequency is low or zero in the 0B dataset, but high in the +B dataset. Both the coverage ratio and k-mer frequency ratio analyses, but not the similarity-based read clustering approach, require a reference sequence.

3.1. Strategy 1—The Direct Route: Isolating, then Sequencing Micro-Dissected or Flow-Sorted B Chromosomes

Once B chromosomes have been isolated by either micro-dissection (Table 1) or flow-sorting (Table 2), it is possible to derive their nucleotide content by standard DNA sequencing approaches. The benefit of this direct method is that there is an a priori assurance that most of the sequences generated are harbored by a B chromosome complement. Employing sufficient sequencing depth, in conjunction with the deployment of advanced bioinformatic tools such as the “targeted chromosome-based cloning via long-range assembly” method [58] can generate sequence assemblies of high quality. Data acquired from a low sequencing depth experiment cannot produce sufficient sequence coverage to allow for a reliable assembly. The major problem encountered with sequencing DNA from micro-dissected material is the noise generated by contamination from non-target chromosomes, from non-target species and from PCR amplification bias. Thus, sequence reads should always be tested (where possible) against reference genome sequences. Here, high specificity and sequence uniqueness is required to identify B chromosome-specific fragments.
Similar to the micro-dissection approach, flow-sorted chromosomes offer a significant reduction in sample complexity, since a specific chromosome can be purified for sequencing. An effective method of sequencing flow-sorted material platform is the so-called “Chicago Hi-C scaffolding” approach, since it requires only small amounts of template DNA [59]. The ability to assemble long sequence scaffolds aids in assessing co-linearity and synteny between B and A chromosomes, and in addressing the origin of B chromosomes sequences. The major limitation encountered with flow-sorting is the difficulty of discriminating between B and fragmented A chromosomes. Measurable progress has been made in recent years towards minimizing this source of contamination [60].

3.2. Strategy 2—The Indirect Route: Comparing Whole Genome Sequence Acquired from Individuals Carrying and Not Carrying B Chromosomes

Inferring a B chromosome location for a given sequence from whole genome sequence data requires a comparison between datasets from a pair of (preferably related) accessions, one of which carries one or more B chromosomes (+B) and the other does not (0B). In principle, the approach identifies peaks where the ratio of aligned sequences is significantly higher in the +B dataset than in the 0B dataset. These regions are identified as putative candidates that are enriched in B chromosome sequences. Here, three different methods have been suggested to identify B chromosome-enriched sequences. The use of several independent +B and 0B identification methods helps to reduce the number of false positives.

3.2.1. Similarity-Based Read Clustering

B chromosome-enriched sequences, such as satellite DNA, retrotransposons, and organelle-derived sequences, can be identified by the similarity-based clustering of NGS reads, as attempted by the RepeatExplorer pipeline, which identifies clusters of frequently overlapping reads, and interprets these as parts of repetitive elements [61]. In addition, the pipeline estimates copy numbers, based on the frequency of duplicate reads. It is able to connect adjacent sequence clusters via the use of paired-end sequence reads. Furthermore, it performs BLAST nucleotide and protein sequence (BLASTN and BLASTX) similarity searches [62] against specialized databases of repetitive elements and repeat-encoded conserved protein domains, which supports the annotation of repetitive elements. To reveal the presence of repetitive elements on a B chromosome, the analysis can be run in a comparative mode, performing a simultaneous clustering of reads from the +B and 0B samples. The structure of the clusters can be investigated using the SeqGrapheR program [61]. The approach has been applied with some success in both rye [3] and Plantago lagopus [63].

3.2.2. Coverage Ratio Analysis

The “coverage ratio analysis” can be performed by mapping genomic reads against a reference genome [22], as is cited in the manuscript. However, it could be also performed by mapping genomic reads against a reference transcriptome as performed by Navarro-Dominguez et al. [14]. The method works by aligning the +B and 0B dataset, looking for differences in the sequence read coverage ratio (Figure 1). Alignment software such as Burrows-Wheeler Alignment tool (BWA) [64] and Bowtie [65] can be used to construct sequence alignment/maps [66]. Subsequently, the constructed SAM/BAM files are investigated for regions with different numbers of aligned reads. The B chromosome sequence content of the cichlid fish A. latifasciata was determined from high coverage whole genome sequence (acquired with an Illmuina HiSeq platform, San Diego, CA, USA) of individuals with and without the B chromosomes, and the reads were mapped onto a reference genome—in this case, that of the related cichlid species M. zebra [22]. The coverage ratio analysis revealed that the B chromosomes contain thousands of sequences which have copies on almost every A chromosome. Although most of the genic sequences on the B chromosomes have been fragmented, a few do appear to be intact. Subsequent sequence analysis of micro-dissected A. latifasciata B chromosomes has confirmed this conclusion [22].

3.2.3. k-mer Frequency Ratio Analysis

A third possible approach is referred to “k-mer frequency ratio analysis.” Here, the critical variable is the k-mer frequency ratio (Figure 1). A k-mer is defined as a sequence fragment of length k. The method relies on the construction of a set of such k-mer indices covering all sequence motifs occurring in the dataset. Two programs designed to perform this task are Tallymer [67] and Jellyfish [68]. The Kmasker tool [69] can be applied to run the k-mer frequency ratio analysis. In addition to its core functionality of masking repetitive elements and identifying low copy sequences, Kmasker can also be used to design both probes for in situ hybridization [70] and single nucleotide polymorphism markers.
The approach was applied in the carnivorous plant species Genlisea to study its divergent genome size evolution [71]. In this regard, when comparing the two-sister species Genlisea nigrocaulis and Genlisea hispidula in their repeat composition, the approach revealed sequence candidates that were involved in the genome size expansion, which is a similar experiment as comparing 0B and +B datasets.

3.3. Benefits and Merits of Indirect and Direct Strategies

The major advantage of the indirect over the direct strategy lies in its not requiring a technical intervention (micro-dissection or flow-sorting), which not only incurs cost, but also introduces an unavoidable degree of contamination by off-target material. While most of the unwanted sequence can be excluded using bioinformatics approaches, this further intervention adds yet another intermediate step. Nevertheless, the direct approach gains from the fact that the bulk of the sequence acquired is relevant, while in the indirect approach, the opposite is the case, since most of the sequence acquired originates from the A chromosome complement or from the organellar genomes. Contamination in the template acquired by micro-dissection is likely to derive from off-target species (microorganisms, human) rather than from the host, whereas for the flow-sorted template, the major source of contaminating DNA is likely to be the host’s A chromosome complement and/or organellar DNA. Where a reference genome sequence has been established, much of the contamination should be identifiable using homology searches, except for sequences that are shared between the B and A chromosomes. This is less obviously the case for a template acquired from micro-dissected chromosomes, as in this case, the source of the contamination is unknown. The challenge for the indirect method is to set an appropriate threshold that minimizes type I error, while still retaining a sufficient number of sequences. Defining this threshold depends on the sequencing depth, the sequence diversity to reference genome sequence and the probability of assembly error. Thus, all sequences identified via the indirect route are associated with a level of uncertainty. In general, the indirect approach is most effective for the discovery of sequences that are abundant on the B chromosomes. In some situations, technical considerations can suggest one method as more suitable than the alternative. For instance, where it is not possible to boost the number of somatic cells undergoing mitosis, flow-sorting becomes inefficient. Similarly, micro-dissection is difficult to carry out where the target chromosome cannot be readily identified on the basis of its morphology. Combining direct and indirect approaches can be an effective strategy, since the outcome of one can be used to validate the outcome of the other.

3.4. An In Silico Method Used to Identify B Chromosome Sequences

One way of assigning the origin of specific sequences to the B chromosome is to make use of synteny between closely related species, a phenomenon whereby interspecific gene order is maintained, at least within relatively short genomic segments. The “genome zipper approach” [72], which exploits this conservation of gene order, has been used in a number of plant species to order and structure NGS sequences [3,72]. As demonstrated in rye [3], the “genome zipper approach” can be extended to B chromosome sequences, once candidate sequences have been identified by a BLASTN analysis against an appropriate reference genome sequence.

4. Conclusions

Combining NGS with state-of-the-art bioinformatics is providing new ways of identifying sequences specific to B chromosomes, revealing a wealth of molecular data relevant for the study of their origin and evolution. Based on sequence data obtained from animal, plant and fungal B chromosomes, the present consensus is that the B chromosomes are composed of duplicated segments derived from potentially multiple A chromosomes, with the addition of some organellar DNA (see review by Houben et al. [73]). Some B chromosomes contain paralogs of A-chromosome-located genes, either as intact or as degenerate sequences. Genic sequences on the B chromosomes do make some contribution to the host transcriptome [74,75]. B-chromosome-specific repeats tend to be derived from the amplification of A chromosome coding and non-coding sequences [76]. The similarities between the B chromosomes and both the so-called “double minute” chromosomes and homogeneously staining regions has suggested that these structures were formed in a comparable manner [76]. There may be parallels between B chromosomes and marker chromosomes in tumor tissue formed by chromothripsis, a process in which several distinct chromosomal regions simultaneously fragment and subsequently are imperfectly reassembled [77]. Human small supernumerary marker chromosomes may serve as an appropriate model for the early evolution of the B chromosomes [78], although these do not share the drive mechanism characteristic of the B chromosomes. Taking into account the growing number of species for which B chromosome-located genic sequences with possible functions have been reported, B chromosomes cannot be considered as “genetically inert” any more. However, their physiological importance still remains at best sketchily understood.
Modern sequencing and bioinformatics methods can be expected to shed new light on the B chromosomes and thereby improve our knowledge of their genomic dynamics. A detailed understanding of the workings of the (peri)centromere will be needed before the mechanistic basis of their characteristic drive can be unraveled. Further progress in RNA sequencing technology will allow for a more rounded picture of the effect on the transcriptome of the B chromosomes to be generated. Additional analysis of the B chromosomes can be expected to provide exciting information relevant to the rapid genome changes that can occur in higher eukaryotes.

Acknowledgments

We thank the Deutsche Forschungsgemeinschaft (HO 1779/261 and SCHO 1420/2-1) for the financial support. Uwe Scholz acknowledges support from the German Ministry of Education and Research (BMBF) for grant 031A536 “de.NBI”.

Author Contributions

A.R., T.S., U.S., and A.H. wrote the paper.

Conflicts of Interest

The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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