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Cells are the vehicles that allows genetic code to proliferate in the world, taking on various forms – as illustrated by the tree of life. The cell features are determined by the manufacturing of proteins, a process that has its blueprints encoded as genes in the genome. It is crucial for all cells to have the right amount of protein, regardless of context (part of a multicellular organism or self-sustained). The protein landscape (amount and type) vary depending on the environment. Cells of the multicellular organism should maintain the protein balance to provide its’ intended function in the organism tissue. The cells of multicellular organisms are faced with an imbalance due to sex-related chromosomal imbalances and other genome effects that change the number of gene copies. Restoration from the imbalance is done by dosage compensation systems. Cells that are isolated from the organism and grown inside the lab are common in research, known as cell lines. Cancer cells are similar to cell lines and have lost their original function in the organism in favor of a self-sustained lifestyle. The new environment (context) for these isolated cells impose a challenge; the cells must reorganize their genomes (holding the blueprints for proteins) to obtain autonomy. In this thesis, the genome evolution of isolated cells, cell lines, has been studied using Drosophila melanogaster (the fruit fly). Compared to normal cells of the host organism, cell line genomes are highly mutated and rearranged. With the emergence of novel sequencing technologies that can read long fragments of the genome, this complexity of cell line genomes can be captured. On the topic of novel sequencing technologies, new software implementations are presented and the future of software for long reads and complex genomes is discussed. The main focus of this thesis is to describe how an established and commonly used cell line has reorganized its’ genome to sustain a culture environment. Important information about the genome structure is provided to the research community. The thesis also describes the genome reorganization in new cell lines, covering the early adaptations to cell autonomy. Together, these investigations are of high relevance to human cancer research. This thesis has also studied the fundamentals for regulation of protein balance in organismal cells. Specifically, a recognition sequence to the X chromosome of the protein Painting of Fourth. This protein is related to dosage compensation and primarily enhance transcription from the 4 thchromosome in Drosophila melanogaster, but has been observed tooccasionally bind to the X chromosome.

Additional file 1: Figure S1. Copy-number comparison between cell lines. Comparison of copy-number calls (scored in 1 kbp bins of non-repetitive regions) between the S2-DRSC line sequenced here (PS) and (A) two other S2-DRSC lines (DM & BO) from [5], and (B) an Sg4 line (male karyotype) as well as a Kc167 line (female karyotype) from [5]. The comparison between other cell lines serve as a reference point and shows how divergent cell-line stocks can be. Although the S2-DRSC are the same stock they show a copy-number agreement of 74% and thus a discrepancy of 26%. The discrepancy observed between other S2-DRSC datasets (BM and BO, showing a 82% copy-number agreement) is similar to our dataset (PS, showing 81 and 82% agreement to BM and BO, respectively) and thus confirms that our stock is S2-DRSC. Figure S2. Rearrangement calling logic using long-reads. (A) Description of rearrangement calling logic using long reads. (1) Reads are classified as informative or contained (redundant); Informative reads (thick black arrow) remain after filtering out contained reads that are fully mapped (thin black arrows) onto a longer read. (2) Multiple alignments to the reference genome of an informative read (shown as colored blocks with origin from the first track indicated as colored dotted lines) are interpreted as rearrangements. An insertion of sequence (black alignment block) relative to the reference genome is indicated by a black triangle. A duplication of sequence (shown as a black horizontal sparse dotted line between two hollow arrow-heads) is indicated by the overlap of alignments (overlapping regions of red, yellow, and blue blocks). Dotted black lines indicate alignments that are adjacent on the read. (3) The rearrangement haplotype frequency is estimated by counting the number of contained reads spanning the corresponding alignment breakpoint on the informative read. Using the algorithm, rearrangements were called using Pacbio and Nanopore reads. Bar plots show the abundance (X-axis) of (B) insertion sequence lengths classified by length intervals (Y-axis), (C) fraction of insertions > 1 kbp annotated as repetitive sequences, classified by fraction intervals (Y-axis), (D) rearrangement breakpoint distance relative to the reference genome for six classes (Y-axis, Transl. = Translocation), (E) rearrangement breakpoint density per Mbp and chromosome arm (Y-axis). Figure S3. Gain and loss of gene functions in S2-DRSC. Enriched gene functions by GO classification of genes that were (A) gained (one or more additional copies) or (B) disrupted (rearrangement breakpoint in all haplotypes). Figure S4. Insertions of mitochondrial genome sequence into the nuclear genome. Shown are three SVs which insert mitochondrial genome sequence. Each panel has two sections: Shown in the top is an ultra-long read onto which the mitochondrial genome sequence insertion context (including other SVs, e.g., insertions of LTR elements, shown in green) is revealed and other reads mapping onto the ultra-long read which support the event. This view is truncated to focus on the mitochondrial genome sequence. A (★) indicates that the alignment continues past clipping. Shown in the bottom is the ordered alignments to the reference genome (regions marked with color corresponding to the top section and genes shown above as blue bars) of the ultra-long read (non-truncated). Alignment scale is indicated to the left. Arrowheads denote alignment mapping to the reference forward or reverse strand. The dotted black line shows how the alignments are connected. Green triangles denote insertion of LTR elements and are sized according to the length of the inserted element. The LTR element insertions are labeled by the most abundant element class (the > 13 kbp insertions were sometimes composed of multiple segments classified to different LTR elements). Interestingly, the read sequence at the opposite side of the mitochondrial genome insertions on the 4:th chromosome (panel C, top section) is unknown: this sequence was unmapped and did not receive hits upon online BLAST at the NCBI webpage. Figure S5. Copy-number at translocation breakpoints. The abundance (Y-axis) of copy-number deviations above baseline (X-axis) at translocation breakpoints showed that all translocations occurred in regions with coverage above baseline (> 2 for X, > 4 for autosomes). Figure S6. De novo genome assembly comparison. S2-DRSC long-read datasets were assembled using Wtdbg2 (with and without removal of read repetitive sequences; see figure Legend). Fly datasets were assembled individually and as pooled datasets. The diagram shows assembly contiguity; contig length is shown on the Y-axis and the contig’s contribution to the total assembly is shown on the X-axis. Assembly contiguity of datasets comprised of phylogenetically divergent flies can be used to roughly evaluate the haplotype complexity of S2-DRSC. Assembly contiguity is expected to decrease as more datasets and more divergent datasets are pooled. Fly assemblies show high contiguity and pooled datasets show lower contiguity. S2-DRSC has low contiguity similar to that of pooled datasets regardless of the removal of repetitive regions (identified via alignment overlap to sequence annotated as repetitive sequence) from the read sequences. Figure S7. SNP haplotype-frequency from linked short-reads. Abundance (Y-axis) of SNPs classified to haplotype frequency (X-axis) in autosomal tetraploid regions not annotated as repetitive sequences. SNP haplotype frequencies were determined based on the fraction of reads supporting each SNP; 3: [87.5–100%]. SNPs supported by all reads were ignored since they were probably present in the progenitor fly stock. Figure S8. Read support fraction of SNPs at various cutoffs. Abundance (Y-axis) of SNP supportive read fractions (X-axis). The read support cutoff value is stated in the figure titles. The Y-scale in the N = 1 figure is maintained in the other figures. As the read support threshold is increased (for both reads supportive of SNP and reads supportive of reference sequence), the number of SNPs decreases, which is reflective of low-frequency cellular heterogeneity. Figure S9. Genome bin coverage. The figure shows the long-read coverage (X-axis) distribution density (Y-axis) in 50 bp genomic bins per chromosome (colored lines). Black vertical lines denote the coverage range for copy-number assignment. Shaded regions indicate the diploid (X) and tetraploid (autosome) copy-number coverage range. Figure S10. Recall ratio of transposable element insertions from short-reads. Repetitive sequence insertions called from long-reads were overlapped to calls from short-read data. The figure shows the recall ratio (Y-axis) per insertion haplotype frequency and chromosome (X-axis). Figure S11. Presence of local duplication, transposable element insertion and gain of copy number in cell lines. Abundance of local duplications (top), transposable element insertions (middle), and copy-number gains (bottom, data from [5]) in various cell-lines. Raw data from [1, 5, 32] was used for duplication and TE insertion analysis. The datasets vary in quality (read length and read depth) and the abundance of local duplication and TE insertion cannot be directly compared. Bars labeled with asterisks indicate that a different dataset was used due to low coverage in the data from [5]. A gray shading in duplication and TE insertion abundance indicate low coverage dataset. For details see Materials and Methods. Figure S12. SNP phylogenetic tree zoom-in on S-cluster. Shown is a zoom-in on S-cluster in Fig. 3A. Figure S13. Rearrangement strandedness. Alignments in SV calls were classified as having maintained (alignments have the same strand) or changed strands (alignments have different strands). A change of strand means an inversion event occurred. The plot shows the number (Y-axis) of rearrangements with maintained or changed strand (X-axis).