iDeLUCS: a deep learning interactive tool for alignment-free clustering of DNA sequences.

SUMMARY: We present an interactive Deep Learning-based software tool for Unsupervised Clustering of DNA Sequences (iDeLUCS), that detects genomic signatures and uses them to cluster DNA sequences, without the need for sequence alignment or taxonomic identifiers. iDeLUCS is scalable and user-friendly: its graphical user interface, with support for hardware acceleration, allows the practitioner to fine-tune the different hyper-parameters involved in the training process without requiring extensive knowledge of deep learning. The performance of iDeLUCS was evaluated on a diverse set of datasets: several real genomic datasets from organisms in kingdoms Animalia, Protista, Fungi, Bacteria, and Archaea, three datasets of viral genomes, a dataset of simulated metagenomic reads from microbial genomes, and multiple datasets of synthetic DNA sequences. The performance of iDeLUCS was compared to that of two classical clustering algorithms (k-means++ and GMM) and two clustering algorithms specialized in DNA sequences (MeShClust v3.0 and DeLUCS), using both intrinsic cluster evaluation metrics and external evaluation metrics. In terms of unsupervised clustering accuracy, iDeLUCS outperforms the two classical algorithms by an average of ∼20%, and the two specialized algorithms by an average of ∼12%, on the datasets of real DNA sequences analyzed. Overall, our results indicate that iDeLUCS is a robust clustering method suitable for the clustering of large and diverse datasets of unlabeled DNA sequences. AVAILABILITY AND IMPLEMENTATION: iDeLUCS is available at https://github.com/Kari-Genomics-Lab/iDeLUCS under the terms of the MIT licence.

RHAMM regulates MMTV-PyMT-induced lung metastasis by connecting STING-dependent DNA damage sensing to interferon/STAT1 pro-apoptosis signaling.

BACKGROUND: RHAMM is a multifunctional protein that is upregulated in breast tumors, and the presence of strongly RHAMM+ve cancer cell subsets associates with elevated risk of peripheral metastasis. Experimentally, RHAMM impacts cell cycle progression and cell migration. However, the RHAMM functions that contribute to breast cancer metastasis are poorly understood. METHODS: We interrogated the metastatic functions of RHAMM using a loss-of-function approach by crossing the MMTV-PyMT mouse model of breast cancer susceptibility with Rhamm-/- mice. In vitro analyses of known RHAMM functions were performed using primary tumor cell cultures and MMTV-PyMT cell lines. Somatic mutations were identified using a mouse genotyping array. RNA-seq was performed to identify transcriptome changes resulting from Rhamm-loss, and SiRNA and CRISPR/Cas9 gene editing was used to establish cause and effect of survival mechanisms in vitro. RESULTS: Rhamm-loss does not alter initiation or growth of MMTV-PyMT-induced primary tumors but unexpectedly increases lung metastasis. Increased metastatic propensity with Rhamm-loss is not associated with obvious alterations in proliferation, epithelial plasticity, migration, invasion or genomic stability. SNV analyses identify positive selection of Rhamm-/- primary tumor clones that are enriched in lung metastases. Rhamm-/- tumor clones are characterized by an increased ability to survive with ROS-mediated DNA damage, which associates with blunted expression of interferon pathway and target genes, particularly those implicated in DNA damage-resistance. Mechanistic analyses show that ablating RHAMM expression in breast tumor cells by siRNA knockdown or CRISPR-Cas9 gene editing blunts interferon signaling activation by STING agonists and reduces STING agonist-induced apoptosis. The metastasis-specific effect of RHAMM expression-loss is linked to microenvironmental factors unique to tumor-bearing lung tissue, notably high ROS and TGFB levels. These factors promote STING-induced apoptosis of RHAMM+ve tumor cells to a significantly greater extent than RHAMM-ve comparators. As predicted by these results, colony size of Wildtype lung metastases is inversely related to RHAMM expression. CONCLUSION: RHAMM expression-loss blunts STING-IFN signaling, which offers growth advantages under specific microenvironmental conditions of lung tissue. These results provide mechanistic insight into factors controlling clonal survival/expansion of metastatic colonies and has translational potential for RHAMM expression as a marker of sensitivity to interferon therapy.

SomaticSiMu: a mutational signature simulator.

SUMMARY: SomaticSiMu is an in silico simulator of single and double base substitutions, and single base insertions and deletions in an input genomic sequence to mimic mutational signatures. SomaticSiMu outputs simulated DNA sequences and mutational catalogues with imposed mutational signatures. The tool is the first mutational signature simulator featuring a graphical user interface, control of mutation rates and built-in visualization tools of the simulated mutations. Simulated datasets are useful as a ground truth to test the accuracy and sensitivity of DNA sequence classification tools and mutational signature extraction tools under different experimental scenarios. The reliability of SomaticSiMu was affirmed by (i) supervised machine learning classification of simulated sequences with different mutation types and burdens, and (ii) mutational signature extraction from simulated mutational catalogues. AVAILABILITY AND IMPLEMENTATION: SomaticSiMu is written in Python 3.8.3. The open-source code, documentation and tutorials are available at https://github.com/HillLab/SomaticSiMu under the terms of the CreativeCommonsAttribution4.0InternationalLicense. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Differential Gene Expression of Brachypodium distachyon Roots Colonized by Gluconacetobacter diazotrophicus and the Role of BdCESA8 in the Colonization.

Alternatives to synthetic nitrogen fertilizer are needed to reduce the costs of crop production and offset environmental damage. Nitrogen-fixing bacterium Gluconacetobacter diazotrophicus has been proposed as a possible biofertilizer for monocot crop production. However, the colonization of G. diazotrophicus in most monocot crops is limited and deep understanding of the response of host plants to G. diazotrophicus colonization is still lacking. In this study, the molecular response of the monocot plant model Brachypodium distachyon was studied during G. diazotrophicus root colonization. The gene expression profiles of B. distachyon root tissues colonized by G. diazotrophicus were generated via next-generation RNA sequencing, and investigated through gene ontology and metabolic pathway analysis. The RNA sequencing results indicated that Brachypodium is actively involved in G. diazotrophicus colonization via cell wall synthesis. Jasmonic acid, ethylene, gibberellin biosynthesis. nitrogen assimilation, and primary and secondary metabolite pathways are also modulated to accommodate and control the extent of G. diazotrophicus colonization. Cellulose synthesis is significantly downregulated during colonization. The loss of function mutant for Brachypodium cellulose synthase 8 (BdCESA8) showed decreased cellulose content in xylem and increased resistance to G. diazotrophicus colonization. This result suggested that the cellulose synthesis of the secondary cell wall is involved in G. diazotrophicus colonization. The results of this study provide insights for future research in regard to gene manipulation for efficient colonization of nitrogen-fixing bacteria in Brachypodium and monocot crops.[Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

MLDSP-GUI: an alignment-free standalone tool with an interactive graphical user interface for DNA sequence comparison and analysis.

SUMMARY: Machine Learning with Digital Signal Processing and Graphical User Interface (MLDSP-GUI) is an open-source, alignment-free, ultrafast, computationally lightweight, and standalone software tool with an interactive GUI for comparison and analysis of DNA sequences. MLDSP-GUI is a general-purpose tool that can be used for a variety of applications such as taxonomic classification, disease classification, virus subtype classification, evolutionary analyses, among others. AVAILABILITY AND IMPLEMENTATION: MLDSP-GUI is open-source, cross-platform compatible, and is available under the terms of the Creative Commons Attribution 4.0 International license (http://creativecommons.org/licenses/by/4.0/). The executable and dataset files are available at https://sourceforge.net/projects/mldsp-gui/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

ML-DSP: Machine Learning with Digital Signal Processing for ultrafast, accurate, and scalable genome classification at all taxonomic levels.

BACKGROUND: Although software tools abound for the comparison, analysis, identification, and classification of genomic sequences, taxonomic classification remains challenging due to the magnitude of the datasets and the intrinsic problems associated with classification. The need exists for an approach and software tool that addresses the limitations of existing alignment-based methods, as well as the challenges of recently proposed alignment-free methods. RESULTS: We propose a novel combination of supervised Machine Learning with Digital Signal Processing, resulting in ML-DSP: an alignment-free software tool for ultrafast, accurate, and scalable genome classification at all taxonomic levels. We test ML-DSP by classifying 7396 full mitochondrial genomes at various taxonomic levels, from kingdom to genus, with an average classification accuracy of >97%. A quantitative comparison with state-of-the-art classification software tools is performed, on two small benchmark datasets and one large 4322 vertebrate mtDNA genomes dataset. Our results show that ML-DSP overwhelmingly outperforms the alignment-based software MEGA7 (alignment with MUSCLE or CLUSTALW) in terms of processing time, while having comparable classification accuracies for small datasets and superior accuracies for the large dataset. Compared with the alignment-free software FFP (Feature Frequency Profile), ML-DSP has significantly better classification accuracy, and is overall faster. We also provide preliminary experiments indicating the potential of ML-DSP to be used for other datasets, by classifying 4271 complete dengue virus genomes into subtypes with 100% accuracy, and 4,710 bacterial genomes into phyla with 95.5% accuracy. Lastly, our analysis shows that the "Purine/Pyrimidine", "Just-A" and "Real" numerical representations of DNA sequences outperform ten other such numerical representations used in the Digital Signal Processing literature for DNA classification purposes. CONCLUSIONS: Due to its superior classification accuracy, speed, and scalability to large datasets, ML-DSP is highly relevant in the classification of newly discovered organisms, in distinguishing genomic signatures and identifying their mechanistic determinants, and in evaluating genome integrity.

Genomic copy number variation in Mus musculus.

BACKGROUND: Copy number variation is an important dimension of genetic diversity and has implications in development and disease. As an important model organism, the mouse is a prime candidate for copy number variant (CNV) characterization, but this has yet to be completed for a large sample size. Here we report CNV analysis of publicly available, high-density microarray data files for 351 mouse tail samples, including 290 mice that had not been characterized for CNVs previously. RESULTS: We found 9634 putative autosomal CNVs across the samples affecting 6.87% of the mouse reference genome. We find significant differences in the degree of CNV uniqueness (single sample occurrence) and the nature of CNV-gene overlap between wild-caught mice and classical laboratory strains. CNV-gene overlap was associated with lipid metabolism, pheromone response and olfaction compared to immunity, carbohydrate metabolism and amino-acid metabolism for wild-caught mice and classical laboratory strains, respectively. Using two subspecies of wild-caught Mus musculus, we identified putative CNVs unique to those subspecies and show this diversity is better captured by wild-derived laboratory strains than by the classical laboratory strains. A total of 9 genic copy number variable regions (CNVRs) were selected for experimental confirmation by droplet digital PCR (ddPCR). CONCLUSION: The analysis we present is a comprehensive, genome-wide analysis of CNVs in Mus musculus, which increases the number of known variants in the species and will accelerate the identification of novel variants in future studies.

HD-CNV: hotspot detector for copy number variants.

SUMMARY: Copy number variants (CNVs) are a major source of genetic variation. Comparing CNVs between samples is important in elucidating their potential effects in a wide variety of biological contexts. HD-CNV (hotspot detector for copy number variants) is a tool for downstream analysis of previously identified CNV regions from multiple samples, and it detects recurrent regions by finding cliques in an interval graph generated from the input. It creates a unique graphical representation of the data, as well as summary spreadsheets and UCSC (University of California, Santa Cruz) Genome Browser track files. The interval graph, when viewed with other software or by automated graph analysis, is useful in identifying genomic regions of interest for further study. AVAILABILITY AND IMPLEMENTATION: HD-CNV is an open source Java code and is freely available, with tutorials and sample data from http://daleylab.org. CONTACT: jcamer7@uwo.ca

Leveraging machine learning for taxonomic classification of emerging astroviruses.

Astroviruses are a family of genetically diverse viruses associated with disease in humans and birds with significant health effects and economic burdens. Astrovirus taxonomic classification includes two genera, Avastrovirus and Mamastrovirus. However, with next-generation sequencing, broader interspecies transmission has been observed necessitating a reexamination of the current host-based taxonomic classification approach. In this study, a novel taxonomic classification method is presented for emergent and as yet unclassified astroviruses, based on whole genome sequence k-mer composition in addition to host information. An optional component responsible for identifying recombinant sequences was added to the method's pipeline, to counteract the impact of genetic recombination on viral classification. The proposed three-pronged classification method consists of a supervised machine learning method, an unsupervised machine learning method, and the consideration of host species. Using this three-pronged approach, we propose genus labels for 191 as yet unclassified astrovirus genomes. Genus labels are also suggested for an additional eight as yet unclassified astrovirus genomes for which incompatibility was observed with the host species, suggesting cross-species infection. Lastly, our machine learning-based approach augmented by a principal component analysis (PCA) analysis provides evidence supporting the hypothesis of the existence of human astrovirus (HAstV) subgenus of the genus Mamastrovirus, and a goose astrovirus (GoAstV) subgenus of the genus Avastrovirus. Overall, this multipronged machine learning approach provides a fast, reliable, and scalable prediction method of taxonomic labels, able to keep pace with emerging viruses and the exponential increase in the output of modern genome sequencing technologies.

Decoding mitochondrial-nuclear (epi)genome interactions: the emerging role of ncRNAs.

Bidirectional communication between the mitochondria and the nucleus is required for several physiological processes, and the nuclear epigenome is a key mediator of this relationship. ncRNAs are an emerging area of discussion for their roles in cellular function and regulation. In this review, we highlight the role of mitochondrial-encoded ncRNAs as mediators of communication between the mitochondria and the nuclear genome. We focus primarily on retrograde signaling, a process in which the mitochondrion relays ncRNAs to translate environmental stress signals to changes in nuclear gene expression, with implications on stress responses that may include disease(s). Other biological roles of mitochondrial-encoded ncRNAs, such as mitochondrial import of proteins and regulation of cell signaling, will also be discussed.

Communication between the nucleus (the cell control center) and the mitochondria (the energy-producing factories of the cell) is important for keeping cells working properly. Though communication goes both ways, signals sent from the mitochondria to the nucleus have become a big topic of discussion because they have been found to affect disease. ncRNAs are another topic that has been gaining traction. These are RNA transcripts that, instead of coding for proteins, have other roles in controlling our cells. Here we discuss ncRNAs that come from the mitochondria, called mt-ncRNAs. By sending mt-ncRNAs to the nucleus, mitochondria can send messages to the nucleus to help cells adapt to stress or changes in the environment. These mt-ncRNAs demonstrate the importance of mitochondria in controlling our cells. By studying this process, we gain information that helps in treating diseases.

Environment and taxonomy shape the genomic signature of prokaryotic extremophiles.

This study provides comprehensive quantitative evidence suggesting that adaptations to extreme temperatures and pH imprint a discernible environmental component in the genomic signature of microbial extremophiles. Both supervised and unsupervised machine learning algorithms were used to analyze genomic signatures, each computed as the k-mer frequency vector of a 500 kbp DNA fragment arbitrarily selected to represent a genome. Computational experiments classified/clustered genomic signatures extracted from a curated dataset of [Formula: see text] extremophile (temperature, pH) bacteria and archaea genomes, at multiple scales of analysis, [Formula: see text]. The supervised learning resulted in high accuracies for taxonomic classifications at [Formula: see text], and medium to medium-high accuracies for environment category classifications of the same datasets at [Formula: see text]. For [Formula: see text], our findings were largely consistent with amino acid compositional biases and codon usage patterns in coding regions, previously attributed to extreme environment adaptations. The unsupervised learning of unlabelled sequences identified several exemplars of hyperthermophilic organisms with large similarities in their genomic signatures, in spite of belonging to different domains in the Tree of Life.

DeLUCS: Deep learning for unsupervised clustering of DNA sequences.

We present a novel Deep Learning method for the Unsupervised Clustering of DNA Sequences (DeLUCS) that does not require sequence alignment, sequence homology, or (taxonomic) identifiers. DeLUCS uses Frequency Chaos Game Representations (FCGR) of primary DNA sequences, and generates "mimic" sequence FCGRs to self-learn data patterns (genomic signatures) through the optimization of multiple neural networks. A majority voting scheme is then used to determine the final cluster assignment for each sequence. The clusters learned by DeLUCS match true taxonomic groups for large and diverse datasets, with accuracies ranging from 77% to 100%: 2,500 complete vertebrate mitochondrial genomes, at taxonomic levels from sub-phylum to genera; 3,200 randomly selected 400 kbp-long bacterial genome segments, into clusters corresponding to bacterial families; three viral genome and gene datasets, averaging 1,300 sequences each, into clusters corresponding to virus subtypes. DeLUCS significantly outperforms two classic clustering methods (K-means++ and Gaussian Mixture Models) for unlabelled data, by as much as 47%. DeLUCS is highly effective, it is able to cluster datasets of unlabelled primary DNA sequences totalling over 1 billion bp of data, and it bypasses common limitations to classification resulting from the lack of sequence homology, variation in sequence length, and the absence or instability of sequence annotations and taxonomic identifiers. Thus, DeLUCS offers fast and accurate DNA sequence clustering for previously intractable datasets.

Differential Selection on Caste-Associated Genes in a Subterranean Termite.

Analyzing the information-rich content of RNA can help uncover genetic events associated with social insect castes or other social polymorphisms. Here, we exploit a series of cDNA libraries previously derived from whole-body tissue of different castes as well as from three behaviourally distinct populations of the Eastern subterranean termite Reticulitermes flavipes. We found that the number (~0.5 M) of single nucleotide variants (SNVs) was roughly equal between nymph, worker and soldier caste libraries, but dN/dS (ratio of nonsynonymous to synonymous substitutions) analysis suggested that some of these variants confer a caste-specific advantage. Specifically, the dN/dS ratio was high (~4.3) for genes expressed in the defensively specialized soldier caste, relative to genes expressed by other castes (~1.7−1.8) and regardless of the North American population (Toronto, Raleigh, Boston) from which the castes were sampled. The populations, meanwhile, did show a large difference in SNV count but not in the manner expected from known demographic and behavioural differences; the highly invasive unicolonial population from Toronto was not the least diverse and did not show any other unique substitution patterns, suggesting any past bottleneck associated with invasion or with current unicoloniality has become obscured at the RNA level. Our study raises two important hypotheses relevant to termite sociobiology. First, the positive selection (dN/dS > 1) inferred for soldier-biased genes is presumably indirect and of the type mediated through kin selection, and second, the behavioural changes that accompany some social insect urban invasions (i.e., 'unicoloniality') may be detached from the loss-of-diversity expected from invasion bottlenecks.

Mutational Patterns Observed in SARS-CoV-2 Genomes Sampled From Successive Epochs Delimited by Major Public Health Events in Ontario, Canada: Genomic Surveillance Study.

BACKGROUND: The emergence of SARS-CoV-2 variants with mutations associated with increased transmissibility and virulence is a public health concern in Ontario, Canada. Characterizing how the mutational patterns of the SARS-CoV-2 genome have changed over time can shed light on the driving factors, including selection for increased fitness and host immune response, that may contribute to the emergence of novel variants. Moreover, the study of SARS-CoV-2 in the microcosm of Ontario, Canada can reveal how different province-specific public health policies over time may be associated with observed mutational patterns as a model system. OBJECTIVE: This study aimed to perform a comprehensive analysis of single base substitution (SBS) types, counts, and genomic locations observed in SARS-CoV-2 genomic sequences sampled in Ontario, Canada. Comparisons of mutational patterns were conducted between sequences sampled during 4 different epochs delimited by major public health events to track the evolution of the SARS-CoV-2 mutational landscape over 2 years. METHODS: In total, 24,244 SARS-CoV-2 genomic sequences and associated metadata sampled in Ontario, Canada from January 1, 2020, to December 31, 2021, were retrieved from the Global Initiative on Sharing All Influenza Data database. Sequences were assigned to 4 epochs delimited by major public health events based on the sampling date. SBSs from each SARS-CoV-2 sequence were identified relative to the MN996528.1 reference genome. Catalogues of SBS types and counts were generated to estimate the impact of selection in each open reading frame, and identify mutation clusters. The estimation of mutational fitness over time was performed using the Augur pipeline. RESULTS: The biases in SBS types and proportions observed support previous reports of host antiviral defense activity involving the SARS-CoV-2 genome. There was an increase in U>C substitutions associated with adenosine deaminase acting on RNA (ADAR) activity uniquely observed during Epoch 4. The burden of novel SBSs observed in SARS-CoV-2 genomic sequences was the greatest in Epoch 2 (median 5), followed by Epoch 3 (median 4). Clusters of SBSs were observed in the spike protein open reading frame, ORF1a, and ORF3a. The high proportion of nonsynonymous SBSs and increasing dN/dS metric (ratio of nonsynonymous to synonymous mutations in a given open reading frame) to above 1 in Epoch 4 indicate positive selection of the spike protein open reading frame. CONCLUSIONS: Quantitative analysis of the mutational patterns of the SARS-CoV-2 genome in the microcosm of Ontario, Canada within early consecutive epochs of the pandemic tracked the mutational dynamics in the context of public health events that instigate significant shifts in selection and mutagenesis. Continued genomic surveillance of emergent variants will be useful for the design of public health policies in response to the evolving COVID-19 pandemic.

Vision deficits precede structural losses in a mouse model of mitochondrial dysfunction and progressive retinal degeneration.

Current animal models of retinal disease often involve the rapid development of a retinal disease phenotype; however, this is at odds with age-related diseases that take many years to manifest clinical symptoms. The present study was performed to examine an apoptosis-inducing factor (Aif)-deficient model, the harlequin carrier mouse (X(hq)X), and determine how mitochondrial dysfunction and subsequent accelerated aging affect the function and structure of the mouse retina. Vision and eye structure for cohorts of 6 X(hq)X and 6 wild type mice at 3, 11, and 15 months of age were studied using in vivo electroretinography (ERG), and optical coherence tomography (OCT). Retinal superoxide levels were determined in situ using dihydroethidium (DHE) histochemistry. Retinal cell counts were quantified post mortem using hematoxylin and eosin (H&E) staining. ERG analysis of X(hq)X retinal function indicated a reduction in b-wave amplitude significant at 3 months of age (p < 0.05), declining further with age. However, retinal neuron counts demonstrated the absence of physical degeneration at 3 and 11 months of age despite significant reduction in ERG b-wave amplitude. Superoxide anion levels were elevated in the ganglion cell, inner nuclear and outer nuclear layers of the retina (p < 0.01, p < 0.01, and p < 0.001, respectively) of 11-month-old X(hq)X mice in comparison to wild type, preceding the structural losses observed at 15 mos. Early onset of retinal function deficits occurred independently of neuron loss. Changes in neurotransmitter localization in the stressed retina may account for the early and significant reduction in retinal function. This remodeling of retinal neurochemistry in response to stress may be a relevant mechanism in the progression of normal retinal aging and early stages of some retinal degenerative diseases.

Mutation frequency is not elevated in the cerebellum of harlequin/Big Blue(®) mice but Class II deletions occur preferentially in young harlequin cerebellum.

The harlequin (hq)/Big Blue(®) mouse is a model of premature aging with neurodegeneration and a transgenic mutation target (λcII gene) for in vivo mutation analysis in individual tissues. The hq mutation results in downregulation of the apoptosis-inducing factor (Aif) gene with conflicting reports of elevated oxidative stress in neurons. Previously, the cerebellum of hq disease mice was reported to have a three-fold elevated frequency of -1bp deletions at a monobasic run (G(11)), a common mutation signature of reactive oxygen species [1]. To examine a diversity of mutations types associated with oxidative DNA damage, the frequency and nature of spontaneous cII mutations were determined in the cerebellum of three- and seven-month-old hq disease and Aif-proficient Big Blue(®) mice. Unlike the previous report, no elevation in spontaneous mutation frequency was observed in the cerebellum of hq disease compared to Aif-proficient mice. The G(11) sequence used in the previous study may be particularly susceptible to oxidative DNA damage leading to replication errors and the previous assay may detect transcriptional errors. We observed intrachromosomal deletions (10-21bp) associated with oxidative DNA damage exclusively in the cerebellum of four out of five young hq disease mice (p=0.0001). The pattern of mutation in the cerebellum of hq disease and Aif-proficient mice was similar. This mutation pattern is typical of previous reports of spontaneous mutations across multiple tissues and ages of wild type mice. Finally, an Aif-proficient mouse had the heaviest mutation shower yet reported with seven point mutations over 21bp in a single mutant, consistent with transient hypermutability, a phenomenon relevant to disease and genome evolution.

Somatic microindels in human cancer: the insertions are highly error-prone and derive from nearby but not adjacent sense and antisense templates.

Somatic microindels (microdeletions with microinsertions) have been studied in normal mouse tissues using the Big Blue lacI transgenic mutation detection system. Here we analyze microindels in human cancers using an endogenous and transcribed gene, the TP53 gene. Microindel frequency, the enhancement of 1-2 microindels and other features are generally similar to that observed in the non-transcribed lacI gene in normal mouse tissues. The current larger sample of somatic microindels reveals recurroids: mutations in which deletions are identical and the co-localized insertion is similar. The data reveal that the inserted sequences derive from nearby but not adjacent sequences in contrast to the slippage that characterizes the great majority of pure microinsertions. The microindel inserted sequences derive from a template on the sense or antisense strand with similar frequency. The estimated error rate of the insertion process of 13% per bp is by far the largest reported in vivo, with the possible exception of somatic hypermutation in the immunoglobulin gene. The data constrain possible mechanisms of microindels and raise the question of whether microindels are 'scars' from the bypass of large DNA adducts by a translesional polymerase, e.g. the 'Tarzan model' presented herein.

Spontaneous mutation frequency is elevated in skin of harlequin (hq)/Big Blue mice.

The harlequin (hq)/Big Blue mouse is a novel model of premature ageing distinguished by a patchy coat, early-onset neurodegeneration, stress-induced heart disease and a mutation detection assay applicable to individual tissues. The hq mutation causes down-regulation of apoptosis-inducing factor and an elevation of reactive oxygen species (ROS). Neural tissues have elevated mutant frequency and early-onset degeneration. This is the first examination of mutations and histology in the skin of hq disease mice. The frequency and pattern of cII mutations in skin from adult hq disease and wild-type (WT) mice 15 days after a single intraperitoneal (i.p.) injection of paraquat (PQ; 10 mg/kg) or vehicle control (VC) were determined to assess spontaneous mutagenesis and sensitivity to an exogenous ROS-inducing mutagen. Skin of hq disease mice shows elevated levels of ROS (P < 0.001) and reduced numbers of hair follicles and associated epidermal cells (P < 0.001) compared to WT control. Acute PQ exposure did not produce detectable skin histopathology. Spontaneous and PQ-induced mutation frequency is elevated in hq skin (P = 0.03 and P = 0.01, respectively) compared to VC-treated WT mice. Despite elevated mutation frequency, mutation patterns were unaltered. Acute PQ exposure resulted in a 1.6-fold increase in mutation frequency in WT mice compared to the level of spontaneous mutations but no significant impact on mutation frequency in hq disease mice. Increased mutation frequency in skin of hq disease mice may be relevant to mechanisms underlying the patchy coat and useful as a biomarker in tests of antioxidant efficacy in preventing the hq disease phenotype. Unaltered mutation patterns with hq disease are consistent with the multiple mutation types associated with ROS. Acute PQ exposure had only subtle effects in WT mice and reduced mitochondrial complex I activity and elevated antioxidant enzyme activity in hq disease mice may lead to PQ resistance.

Heart tissue of harlequin (hq)/Big Blue mice has elevated reactive oxygen species without significant impact on the frequency and nature of point mutations in nuclear DNA.

Age is a major risk factor for heart disease, and cardiac aging is characterized by elevated mitochondrial reactive oxygen species (ROS) with compromised mitochondrial and nuclear DNA integrity. To assess links between increased ROS levels and mutations, we examined in situ levels of ROS and cII mutation frequency, pattern and spectrum in the heart of harlequin (hq)/Big Blue mice. The hq mouse is a model of premature aging with mitochondrial dysfunction and increased risk of oxidative stress-induced heart disease with the means for in vivo mutation detection. The hq mutation produces a significant downregulation in the X-linked apoptosis-inducing factor gene (Aif) impairing both the antioxidant and oxidative phosphorylation functions of AIF. Brain and skin of hq disease mice have elevated frequencies of point mutations in nuclear DNA and histopathology characterized by cell loss. Reports of associated elevations in ROS in brain and skin have mixed results. Herein, heart in situ ROS levels were elevated in hq disease compared to AIF-proficient mice (p<0.0001) yet, mutation frequency and pattern were similar in hq disease, hq carrier and AIF-proficient mice. Heart cII mutations were also assessed 15 days following an acute exposure to an exogenous ROS inducer (10 mg paraquat/kg). Acute paraquat exposure with a short mutant manifestation period was insufficient to elevate mutation frequency or alter mutation pattern in the post-mitotic heart tissue of AIF-proficient mice. Paraquat induction of ROS requires mitochondrial complex I and thus is likely compromised in hq mice. Results of this preliminary survey and the context of recent literature suggest that determining causal links between AIF deficiency and the premature aging phenotypes of specific tissues is better addressed with assay of mitochondrial ROS and large-scale changes in mitochondrial DNA in specific cell types.

Postzygotic Somatic Mutations in the Human Brain Expand the Threshold-Liability Model of Schizophrenia.

The search for what causes schizophrenia has been onerous. This research has included extensive assessment of a variety of genetic and environmental factors using ever emerging high-resolution technologies and traditional understanding of the biology of the brain. These efforts have identified a large number of schizophrenia-associated genes, some of which are altered by mutational and epi-mutational mechanisms in a threshold liability model of schizophrenia development. The results, however, have limited predictability and the actual cause of the disease remains unknown. This current state asks for conceptualizing the problem differently in light of novel insights into the nature of mutations, the biology of the brain and the fine precision and resolution of emerging technologies. There is mounting evidence that mutations acquired during postzygotic development are more common than germline mutations. Also, the postzygotic somatic mutations including epimutations (PZMs), which often lead to somatic mosaicism, are relatively common in the mammalian brain in comparison to most other tissues and PZMs are more common in patients with neurodevelopmental mental disorders, including schizophrenia. Further, previously inaccessible, detection of PZMs is becoming feasible with the advent of novel technologies that include single-cell genomics and epigenomics and the use of exquisite experimental designs including use of monozygotic twins discordant for the disease. These developments allow us to propose a working hypothesis and expand the threshold liability model of schizophrenia that already encompasses familial genetic, epigenetic and environmental factors to include somatic de novo PZMs. Further, we offer a test for this expanded model using currently available genome sequences and methylome data on monozygotic twins discordant for schizophrenia (MZD) and their parents. The results of this analysis argue that PZMs play a significant role in the development of schizophrenia and explain extensive heterogeneity seen across patients. It also offers the potential to convincingly link PZMs to both nervous system health and disease, an area that has remained challenging to study and relatively under explored.