Genetic-variant hotspots and hotspot clusters in the human genome facilitating adaptation while increasing instability

Modelling DNA damage-repair and beyond

The paper presents a review of mechanistic modelling studies of DNA damage and DNA repair, and consequences to follow in mammalian cell nucleus. We hypothesize DNA deletions are consequences of repair of double strand breaks leading to the modifications of genome that play crucial role in long term development of genetic inheritance and diseases. The aim of the paper is to review formation mechanisms underlying naturally occurring DNA deletions in the human genome and their potential relevance for bridging the gap between induced DNA double strand breaks and deletions in damaged human genome from endogenous and exogenous events. The model of the cell nucleus presented enables simulation of DNA damage at molecular level identifying the spectrum of damage induced in all chromosomal territories and loops. Our mechanistic modelling of DNA repair for double stand breaks (DSB), single strand breaks (SSB) and base damage (BD), shows the complexity of DNA damage is responsible for the longer repair times and the reason for the biphasic feature of mammalian cells repair curves. In the absence of experimentally determined data, the mechanistic model of repair predicts the in vivo rate constants for the proteins involved in the repair of DSB, SSB, and of BD.

YY1 is involved in homologous recombination inhibition at guanine quadruplex sites in human cells

Homologous recombination (HR) is a key process for repairing DNA double strand breaks and for promoting genetic diversity. However, HR occurs unevenly across the genome, and certain genomic features can influence its activity. One such feature is the presence of guanine quadruplexes (G4s), stable secondary structures widely distributed throughout the genome. These G4s play essential roles in gene transcription and genome stability regulation. Especially, elevated G4 levels in cells deficient in the Bloom syndrome helicase (BLM) significantly enhance HR at G4 sites, potentially threatening genome stability. Here, we investigated the role of G4-binding protein Yin Yang-1 (YY1) in modulating HR at G4 sites in human cells. Our results show that YY1's binding to G4 structures suppresses sister chromatid exchange after BLM knockdown, and YY1's chromatin occupancy negatively correlates with the overall HR rate observed across the genome. By limiting RAD51 homolog 1 (RAD51) access, YY1 preferentially binds to essential genomic regions, shielding them from excessive HR. Our findings unveil a novel role of YY1-G4 interaction, revealing novel insights into cellular mechanisms involved in HR regulation.

Biomarkers of Aging and Relevant Evaluation Techniques: A Comprehensive Review

The risk of developing chronic illnesses and disabilities is increasing with age. To predict and prevent aging, biomarkers relevant to the aging process must be identified. This paper reviews the known molecular, cellular, and physiological biomarkers of aging. Moreover, we discuss the currently available technologies for identifying these biomarkers, and their applications and potential in aging research. We hope that this review will stimulate further research and innovation in this emerging and fast-growing field.

Extended Enrichment for Ultrasensitive Detection of Low-Frequency Mutations by Long Blocker Displacement Amplification

Detecting low-frequency DNA mutations hotspots cluster is critical for cancer diagnosis but remains challenging. Quantitative PCR (qPCR) is constrained by sensitivity, and allele-specific PCR is restricted by throughput. Here we develop a long blocker displacement amplification (LBDA) coupled with qPCR for ultrasensitive and multiplexed variants detection. By designing long blocker oligos to perfectly match wildtype sequences while mispairing with mutants, long blockers enable 14-44 nt enrichment regions which is 2-fold longer than normal BDA in the experiments. For wild template with a specific nucleotide, LBDA can detect different mutation types down to 0.5 % variant allele frequency (VAF) in one reaction, with median enrichment fold of 1,000 on 21 mutant DNA templates compared to the wild type. We applied LBDA-qPCR to detect KRAS and NRAS mutation hotspots, utilizing a single plex assay capable of covering 81 mutations and tested in synthetic templates and colorectal cancer tissue samples. Moreover, the mutation types were verified through Sanger sequencing, demonstrating concordance with results obtained from next generation sequencing. Overall, LBDA-qPCR provides a simple yet ultrasensitive approach for multiplexed detection of low VAF mutations hotspots, presenting a powerful tool for cancer diagnosis and monitoring.

Identification of a founder variant AAGAB c.370C>T, p.Arg124Ter in patients with punctate palmoplantar keratoderma in Southern Denmark

Palmoplantar keratoderma (PPK) is a heterogeneous group of rare skin diseases characterized by hyperkeratosis on the palms or soles. The subtype isolated punctate PPK is caused by heterozygous variants in AAGAB. We investigated if the variant AAGAB c.370C>T, p.Arg124Ter in patients with punctate PPK in the Region of Southern Denmark represented a founder variant and estimated the age to the most recent common ancestor. We performed haplotype analysis on samples from 20 patients diagnosed with punctate PPK and the AAGAB c.370C>T, p.Arg124Ter variant. Using the Gamma Method, we calculated the years to the most recent common ancestor. We also explored the presence of the variant in other populations through literature and databases (HGMD, ClinVar, and gnomAD). Our analysis revealed a shared haplotype of 3.0 Mb, suggesting shared ancestry. The ancestral haplogroup was estimated to an age of 12.1 generations (CI: 4.9-20.3) equivalent to approximately 339 years (CI: 137-568). This study confirms that the frequently observed variant AAGAB c.370C>T, p.Arg124Ter in punctate PPK among patients in the Region of Southern Denmark is caused by a founder variant. We recommend testing for the variant as initial screening in our region and potentially for all Danish patients presenting with punctate PPK.

Association Analysis Provides Insights into Plant Mitonuclear Interactions

Cytonuclear interaction refers to the complex and ongoing process of coevolution between nuclear and organelle genomes, which are responsible for cellular respiration, photosynthesis, lipid metabolism, etc. and play a significant role in adaptation and speciation. There have been a large number of studies to detect signatures of cytonuclear interactions. However, identification of the specific nuclear and organelle genetic polymorphisms that are involved in these interactions within a species remains relatively rare. The recent surge in whole genome sequencing has provided us an opportunity to explore cytonuclear interaction from a population perspective. In this study, we analyzed a total of 3,439 genomes from 7 species to identify signals of cytonuclear interactions by association (linkage disequilibrium) analysis of variants in both the mitochondrial and nuclear genomes across flowering plants. We also investigated examples of nuclear loci identified based on these association signals using subcellular localization assays, gene editing, and transcriptome sequencing. Our study provides a novel perspective on the investigation of cytonuclear coevolution, thereby enriching our understanding of plant fitness and offspring sterility.

Mutation Hotspots Found in Bladder Cancer Aid Prediction of Carcinogenic Risk in Normal Urothelium

More than 80,000 new cases of bladder cancer are estimated to be diagnosed in 2023. However, the 5-year survival rate for bladder cancer has not changed in decades, highlighting the need for prevention. Numerous cancer-causing mutations are present in the urothelium long before signs of cancer arise. Mutation hotspots in cancer-driving genes were identified in non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC) tumor samples. Mutation burden within the hotspot regions was measured in normal urothelium with a low and high risk of cancer. A significant correlation was found between the mutation burden in normal urothelium and bladder cancer tissue within the hotspot regions. A combination of measured hotspot burden and personal risk factors was used to fit machine learning classification models. The efficacy of each model to differentiate between adjacent benign urothelium from bladder cancer patients and normal urothelium from healthy donors was measured. A random forest model using a combination of personal risk factors and mutations within MIBC hotspots yielded the highest AUC of 0.9286 for the prediction of high- vs. low-risk normal urothelium. Currently, there are no effective biomarkers to assess subclinical field disease and early carcinogenic progression in the bladder. Our findings demonstrate novel differences in mutation hotspots in NMIBC and MIBC and provide the first evidence for mutation hotspots to aid in the assessment of cancer risk in the normal urothelium. Early risk assessment and identification of patients at high risk of bladder cancer before the clinical presentation of the disease can pave the way for targeted personalized preventative therapy.

HotSPOT: A Computational Tool to Design Targeted Sequencing Panels to Assess Early Photocarcinogenesis

Mutations found in skin are acquired in specific patterns, clustering around mutation-prone genomic locations. The most mutation-prone genomic areas, mutation hotspots, first induce the growth of small cell clones in healthy skin. Mutations accumulate over time, and clones with driver mutations may give rise to skin cancer. Early mutation accumulation is a crucial first step in photocarcinogenesis. Therefore, a sufficient understanding of the process may help predict disease onset and identify avenues for skin cancer prevention. Early epidermal mutation profiles are typically established using high-depth targeted next-generation sequencing. However, there is currently a lack of tools for designing custom panels to capture mutation-enriched genomic regions efficiently. To address this issue, we created a computational algorithm that implements a pseudo-exhaustive approach to identify the best genomic areas to target. We benchmarked the current algorithm in three independent mutation datasets of human epidermal samples. Compared to the sequencing panel designs originally used in these publications, the mutation capture efficacy (number of mutations/base pairs sequenced) of our designed panel improved 9.6-12.1-fold. Mutation burden in the chronically sun-exposed and intermittently sun-exposed normal epidermis was measured within genomic regions identified by hotSPOT based on cutaneous squamous cell carcinoma (cSCC) mutation patterns. We found a significant increase in mutation capture efficacy and mutation burden in cSCC hotspots in chronically sun-exposed vs. intermittently sun-exposed epidermis (p < 0.0001). Our results show that our hotSPOT web application provides a publicly available resource for researchers to design custom panels, enabling efficient detection of somatic mutations in clinically normal tissues and other similar targeted sequencing studies. Moreover, hotSPOT also enables the comparison of mutation burden between normal tissues and cancer.

DeepSom: a CNN-based approach to somatic variant calling in WGS samples without a matched normal

MOTIVATION

Somatic mutations are usually called by analyzing the DNA sequence of a tumor sample in conjunction with a matched normal. However, a matched normal is not always available, for instance, in retrospective analysis or diagnostic settings. For such cases, tumor-only somatic variant calling tools need to be designed. Previously proposed approaches demonstrate inferior performance on whole-genome sequencing (WGS) samples.

RESULTS

We present the convolutional neural network-based approach called DeepSom for detecting somatic single nucleotide polymorphism and short insertion and deletion variants in tumor WGS samples without a matched normal. We validate DeepSom by reporting its performance on five different cancer datasets. We also demonstrate that on WGS samples DeepSom outperforms previously proposed methods for tumor-only somatic variant calling.

AVAILABILITY AND IMPLEMENTATION

DeepSom is available as a GitHub repository at https://github.com/heiniglab/DeepSom.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Copy number variation profile-based genomic typing of premenstrual dysphoric disorder in Chinese

Premenstrual dysphoric disorder (PMDD) affects nearly 5% women of reproductive age. Symptomatic heterogeneity, together with largely unknown genetics, have greatly hindered its effective treatment. In the present study, analysis of genomic sequencing-based copy-number-variations (CNVs) called from 100-kb white blood cell DNA sequence windows by means of semi-supervised clustering led to the segregation of patient genomes into the D and V groups, which correlated with the depression and invasion clinical types respectively with 89.0% consistency. Application of diagnostic CNV features selected using the correlation-based machine-learning method enabled the classification of the CNVs obtained into the D group, V group, total-patient group and control group with an average accuracy of 83.0%. The power of the diagnostic CNV features was 0.98 on average, suggesting that these CNV features could be employed for the molecular diagnosis of the major clinical types of PMDD. This demonstrated concordnce between the CNV profiles and clinical types of PMDD supported the validity of symptom-based diagnosis of PMDD for differentiating between its two major clinical types, as well as the predominanly genetic nature of PMDD with a host of overlaps between multiple susceptibility genes/pathways and the diagnostic CNV features as indicators of involvement in PMDD etiology.