Copy number variants (CNVs), duplications and deletions of genomic sequences, contribute to evolutionary adaptation but can also confer deleterious effects and cause disease. Whereas the effects of amplifying individual genes or whole chromosomes (i.e., aneuploidy) have been studied extensively, much less is known about the genetic and functional effects of CNVs of differing sizes and structures. Here, we investigated Saccharomyces cerevisiae (yeast) strains that acquired adaptive CNVs of variable structures and copy numbers following experimental evolution in glutamine-limited chemostats. Although beneficial in the selective environment, CNVs result in decreased fitness compared with the euploid ancestor in rich media. We used transposon mutagenesis to investigate mutational tolerance and genome-wide genetic interactions in CNV strains. We find that CNVs increase mutational target size, confer increased mutational tolerance in amplified essential genes, and result in novel genetic interactions with unlinked genes. We validated a novel genetic interaction between different CNVs and BMH1 that was common to multiple strains. We also analyzed global gene expression and found that transcriptional dosage compensation does not affect most genes amplified by CNVs, although gene-specific transcriptional dosage compensation does occur for ∼12% of amplified genes. Furthermore, we find that CNV strains do not show previously described transcriptional signatures of aneuploidy. Our study reveals the extent to which local and global mutational tolerance is modified by CNVs with implications for genome evolution and CNV-associated diseases, such as cancer.

Changes in gene regulation are thought to underlie most phenotypic differences between species. For subterranean rodents such as the naked mole-rat, proposed phenotypic adaptations include hypoxia tolerance, metabolic changes and cancer resistance. However, it is largely unknown what regulatory changes may associate with these phenotypic traits, and whether these are unique to the naked mole-rat, the mole-rat clade or also present in other mammals. Here, we investigate regulatory evolution in heart and liver from two African mole-rat species and two rodent outgroups using genome-wide epigenomic profiling. First, we adapted and applied a phylogenetic modeling approach to quantitatively compare epigenomic signals at orthologous regulatory elements, and identified thousands of promoter and enhancer regions with differential epigenomic activity in mole-rats. These elements associate with known mole-rat adaptation in metabolic and functional pathways, and suggest candidate genetic loci that may underlie mole-rat innovations. Second, we evaluated ancestral and species-specific regulatory changes in the study phylogeny, and report several candidate pathways experiencing stepwise remodeling during the evolution of mole-rats, such as the insulin and hypoxia response pathways. Third, we report nonorthologous regulatory elements overlap with lineage-specific repetitive elements and appear to modify metabolic pathways by rewiring of HNF4 and RAR/RXR transcription factor binding sites in mole-rats. These comparative analyses reveal how mole-rat regulatory evolution informs previously reported phenotypic adaptations. Moreover, the phylogenetic modeling framework we propose here improves upon the state-of-the-art by addressing known limitations of inter-species comparisons of epigenomic profiles, and has broad implications in the field of comparative functional genomics.

Principal Component Analysis (PCA) is widely utilized in statistics, machine learning, and genomics for dimensionality reduction and uncovering low-dimensional latent structure. To address the challenges posed by ever-growing data size, fast and memory-efficient PCA methods have gained prominence. In this paper, we propose a novel Randomized Singular Value Decomposition (RSVD) algorithm implemented in PCAone, featuring a window-based optimization scheme that enables accelerated convergence while improving the accuracy. Additionally, PCAone incorporates out-of-core and multithreaded implementations for the existing Implicitly Restarted Arnoldi Method (IRAM) and RSVD. Through comprehensive evaluations using multiple large-scale real-world datasets in different fields, we demonstrate the advantage of PCAone over existing methods. The new algorithm achieves significantly faster computation time while maintaining accuracy comparable to the slower IRAM method. Notably, our analyses of UK Biobank, comprising around 0.5 million individuals and 6.1 million common SNPs, demonstrate that PCAone accurately computes the top 40 principal components within 9 hours. This analysis effectively captures population structure, signals of selection, structural variants, and low recombination regions, utilizing less than 20 GB of memory and 20 CPU threads. Furthermore, when applied to single-cell RNA sequencing data featuring 1.3 million cells, PCAone, accurately capturing the top 40 principal components in 49 minutes. This performance represents a 10-fold improvement over state-of-the-art tools.

Bacterial genomes differ in both gene content and sequence mutations, which underlies extensive phenotypic diversity, including variation in susceptibility to antimicrobials or vaccine-induced immunity. To identify and quantify important variants, all genes within a population must be predicted, functionally annotated and clustered, representing the pangenome. Despite the volume of genome data available, gene prediction and annotation are currently conducted in isolation on individual genomes, which is computationally inefficient and frequently inconsistent across genomes. Here, we introduce the open-source software graph-gene-caller (ggCaller). ggCaller combines gene prediction, functional annotation, and clustering into a single workflow using population-wide de Bruijn Graphs, removing redundancy in gene annotation, and resulting in more accurate gene predictions and orthologue clustering. We applied ggCaller to simulated and real-world bacterial datasets containing hundreds or thousands of genomes, comparing it to current state-of-the-art tools. ggCaller has considerable speed-ups with equivalent or greater accuracy, particularly with datasets containing complex sources of error, such as assembly contamination or fragmentation. ggCaller is also an important extension to bacterial genome-wide association studies, enabling querying of annotated graphs for functional analyses. We highlight this application by functionally annotating DNA sequences with significant associations to tetracycline and macrolide resistance in Streptococcus pneumoniae, identifying key resistance determinants that were missed when using only a single reference genome. ggCaller is a novel bacterial genome analysis tool with applications in bacterial evolution and epidemiology.

The cytidine deaminases APOBEC3A (A3A) and APOBEC3B (A3B) are prominent mutators of human cancer genomes. However, tumor-specific genetic modulators of APOBEC-induced mutagenesis are poorly defined. Here, we utilized a screen to identify 61 gene deletions that increase A3B-induced mutations in yeast. We also determined whether each deletion was epistatic with Ung1 loss, which indicated whether the encoded factors participate in the HR-dependent bypass of A3B/Ung1-dependent abasic sites or suppress A3B-catalyzed deamination by protecting against aberrant formation of single stranded DNA (ssDNA). We found that the mutation spectra of A3B-induced mutations revealed genotype-specific patterns of strand-specific ssDNA formation and nucleotide incorporation across APOBEC-induced lesions. Combining these three metrics, we were able to establish a multifactorial signature of APOBEC-induced mutations specific to (1) failure to remove H3K56 acetylation, (2) defective CTF18-RFC complex function, and (3) defective HR-mediated bypass of APOBEC-induced lesions. We extended these results by analyzing mutation data for human tumors and found BRCA1/2-deficient breast cancers display 3- to 4-fold more APOBEC-induced mutations. Mirroring our results in yeast, Rev1-mediated C-to-G substitutions are mainly responsible for increased APOBEC-signature mutations in BRCA1/2-deficient tumors, and these mutations associate with lagging strand synthesis during replication. These results identify important factors that influence DNA replication-dynamics and likely the abundance of APOBEC-induced mutation during tumor progression. They also highlight a novel role for BRCA1/2 during HR-dependent lesion bypass of APOBEC-induced lesions during cancer cell replication.

MicroRNAs (miRNAs) pair to sites in mRNAs to direct the degradation of these RNA transcripts. Conversely, certain RNA transcripts can direct the degradation of particular miRNAs. This target-directed miRNA degradation (TDMD) requires the ZSWIM8 E3 ubiquitin ligase. Here, we report the function of ZSWIM8 in the mouse embryo. Zswim8 -/- embryos were smaller than their littermates and died near the time of birth. This highly penetrant perinatal lethality was apparently caused by a lung sacculation defect attributed to failed maturation of alveolar epithelial cells. Some mutant individuals also had heart ventricular septal defects. These developmental abnormalities were accompanied by aberrant accumulation of more than 50 miRNAs observed across 12 tissues, which often led to enhanced repression of their mRNA targets. These ZSWIM8-sensitive miRNAs were preferentially produced from genomic miRNA clusters, and in some cases, ZSWIM8 caused a switch in the dominant strand or isoform that accumulated from a miRNA hairpin-observations suggesting that TDMD provides a mechanism to uncouple coproduced miRNAs from each other. Overall, our findings indicate that the regulatory influence of ZSWIM8, and presumably TDMD, in mammalian biology is widespread and consequential, and posit the existence of many yet-unidentified transcripts that trigger miRNA degradation.

Genomic studies of vertebrate chromosome evolution have long been hindered by the scarcity of chromosome-scale DNA sequences of some key taxa. One of those limiting taxa has been the elasmobranchs (sharks and rays), which harbor species often with numerous chromosomes and enlarged genomes. Here, we report the chromosome-scale genome assembly for the zebra shark Stegostoma tigrinum, an endangered species that has a relatively small genome among sharks (3.71 Gb), as well as for the whale shark Rhincodon typus Our analysis employing a male-female comparison identified an X Chromosome, the first genomically characterized shark sex chromosome. The X Chromosome harbors the Hox C cluster whose intact linkage has not been shown for an elasmobranch fish. The sequenced shark genomes exhibit a gradualism of chromosome length with remarkable length-dependent characteristics-shorter chromosomes tend to have higher GC-content, gene density, synonymous substitution rate, and simple tandem repeat content as well as smaller gene length and lower interspersed repeat content. We challenge the traditional binary classification of karyotypes as with and without so-called microchromosomes. Even without microchromosomes, the length-dependent characteristics persist widely in some other vertebrate lineages. Our investigation of elasmobranch karyotypes underpins their unique characteristics and provides clues for understanding how vertebrate karyotypes accommodate intragenomic heterogeneity to realize a complex readout. It also paves the way to dissecting more genomes with variable sizes to be sequenced on high quality.

Neurons are morphologically complex cells that rely on the compartmentalization of protein expression to develop and maintain their cytoarchitecture. Targeting of RNA transcripts to axons is one of the mechanisms that allows rapid local translation of proteins in response to extracellular signals. 3'; untranslated regions (UTRs) of mRNA are noncoding sequences that play a critical role in determining transcript localization and translation by interacting with specific RNA-binding proteins (RBPs). However, how 3' UTRs contribute to mRNA metabolism and the nature of RBP complexes responsible for these functions remain elusive. We performed 3' end sequencing of RNA isolated from cell bodies and axons of sympathetic neurons exposed to either Nerve Growth factor (NGF) or Neurotrophin 3 (NT-3). NGF and NT-3 are growth factors essential for sympathetic neuron development through distinct signalling mechanisms. Whereas NT-3 acts mostly locally, NGF signal is retrogradely transported from axons to cell bodies. We discovered that both NGF and NT-3 affect transcription and alternative polyadenylation in the nucleus and induce the localization of specific 3'UTR isoforms to axons, including short 3'UTR isoforms found exclusively in axons. The integration of our data with CLIP sequencing data supports a model whereby long 3'UTR isoforms associate with RBP complexes in the nucleus, and upon reaching the axons, are remodelled locally into shorter isoforms. Our findings shed new light into the complex relationship between nuclear polyadenylation, mRNA localisation and local 3'UTR remodelling in developing neurons.

Telomeres and subtelomeres, the genomic regions located at chromosome extremities, are essential for genome stability in eukaryotes. In the absence of the canonical maintenance mechanism provided by telomerase, telomere shortening induces genome instability. The landscape of the ensuing genome rearrangements is not accessible by short-read sequencing. Here, we leverage Oxford Nanopore Technologies long-read sequencing to survey the extensive repertoire of genome rearrangements in telomerase mutants of the model green microalga Chlamydomonas reinhardtii In telomerase mutant strains grown for hundreds of generations, most chromosome extremities were capped by short telomere sequences that were either recruited de novo from other loci or maintained in a telomerase-independent manner. Other extremities did not end with telomeres but only with repeated subtelomeric sequences. The subtelomeric elements, including rDNA, were massively rearranged and involved in breakage-fusion-bridge cycles, translocations, recombinations and chromosome circularization. These events were established progressively over time and displayed heterogeneity at the subpopulation level. New telomere-capped extremities composed of sequences originating from more internal genomic regions were associated with high DNA methylation, suggesting that de novo heterochromatin formation contributes to restore chromosome end stability in C. reinhardtii The diversity of alternative strategies present in the same organism to maintain chromosome integrity and the variety of rearrangements found in telomerase mutants illustrate genome plasticity at short timescales.

Single-cell transcriptome data has been widely used to reconstruct gene regulatory networks (GRNs) controlling critical biological processes such as development and differentiation. While a growing list of algorithms has been developed to infer GRNs using such data, achieving an inference accuracy consistently higher than random guessing has remained challenging. To address this, it is essential to delineate how the accuracy of regulatory inference is limited. Here, we systematically characterized factors limiting the accuracy of inferred GRNs and demonstrated that using pre-mRNA information can help improve regulatory inference compared to the typically used information (i.e., mature mRNA). Using kinetic modeling and simulated single-cell datasets, we showed that mature mRNA levels of target genes often fail to accurately report upstream regulatory activities due to gene-level and network-level factors, which can be improved by using pre-mRNA levels. We tested this finding on public single-cell RNA-seq datasets using intronic reads as proxies of pre-mRNA levels and can indeed achieve a higher inference accuracy compared to using exonic reads (corresponding to mature mRNAs). Using experimental datasets, we further validated findings from the simulated datasets and identified factors such as transcription factor activity dynamics influencing the accuracy of pre-mRNA-based inference. This work delineates the fundamental limitations of gene regulatory inference and helps improve GRN inference using single-cell RNA-seq data.