Ubiquitination of DNA Damage-Stalled RNAPII Promotes Transcription-Coupled Repair.

Transcription-coupled nucleotide excision repair (TC-NER) is initiated by the stalling of elongating RNA polymerase II (RNAPIIo) at DNA lesions. The ubiquitination of RNAPIIo in response to DNA damage is an evolutionarily conserved event, but its function in mammals is unknown. Here, we identified a single DNA damage-induced ubiquitination site in RNAPII at RPB1-K1268, which regulates transcription recovery and DNA damage resistance. Mechanistically, RPB1-K1268 ubiquitination stimulates the association of the core-TFIIH complex with stalled RNAPIIo through a transfer mechanism that also involves UVSSA-K414 ubiquitination. We developed a strand-specific ChIP-seq method, which revealed RPB1-K1268 ubiquitination is important for repair and the resolution of transcriptional bottlenecks at DNA lesions. Finally, RPB1-K1268R knockin mice displayed a short life-span, premature aging, and neurodegeneration. Our results reveal RNAPII ubiquitination provides a two-tier protection mechanism by activating TC-NER and, in parallel, the processing of DNA damage-stalled RNAPIIo, which together prevent prolonged transcription arrest and protect against neurodegeneration.

SIRT6 Is Responsible for More Efficient DNA Double-Strand Break Repair in Long-Lived Species.

DNA repair has been hypothesized to be a longevity determinant, but the evidence for it is based largely on accelerated aging phenotypes of DNA repair mutants. Here, using a panel of 18 rodent species with diverse lifespans, we show that more robust DNA double-strand break (DSB) repair, but not nucleotide excision repair (NER), coevolves with longevity. Evolution of NER, unlike DSB, is shaped primarily by sunlight exposure. We further show that the capacity of the SIRT6 protein to promote DSB repair accounts for a major part of the variation in DSB repair efficacy between short- and long-lived species. We dissected the molecular differences between a weak (mouse) and a strong (beaver) SIRT6 protein and identified five amino acid residues that are fully responsible for their differential activities. Our findings demonstrate that DSB repair and SIRT6 have been optimized during the evolution of longevity, which provides new targets for anti-aging interventions.

Transcription-coupled global genomic repair in E. coli.

The nucleotide excision repair (NER) pathway removes helix-distorting lesions from DNA in all organisms. Escherichia coli has long been a model for understanding NER, which is traditionally divided into major and minor subpathways known as global genome repair (GGR) and transcription-coupled repair (TCR), respectively. TCR has been assumed to be mediated exclusively by Mfd, a DNA translocase of minimal NER phenotype. This review summarizes the evidence that shaped the traditional view of NER in bacteria, and reviews data supporting a new model in which GGR and TCR are inseparable. In this new model, RNA polymerase serves both as the essential primary sensor of bulky DNA lesions genome-wide and as the delivery platform for the assembly of functional NER complexes in living cells.

High UV damage and low repair, but not cytosine deamination, stimulate mutation hotspots at ETS binding sites in melanoma.

Noncoding mutation hotspots have been identified in melanoma and many of them occur at the binding sites of E26 transformation-specific (ETS) proteins; however, their formation mechanism and functional impacts are not fully understood. Here, we used UV (Ultraviolet) damage sequencing data and analyzed cyclobutane pyrimidine dimer (CPD) formation, DNA repair, and CPD deamination in human cells at single-nucleotide resolution. Our data show prominent CPD hotspots immediately after UV irradiation at ETS binding sites, particularly at sites with a conserved TTCCGG motif, which correlate with mutation hotspots identified in cutaneous melanoma. Additionally, CPDs are repaired slower at ETS binding sites than in flanking DNA. Cytosine deamination in CPDs to uracil is suggested as an important step for UV mutagenesis. However, we found that CPD deamination is significantly suppressed at ETS binding sites, particularly for the CPD hotspot on the 5' side of the ETS motif, arguing against a role for CPD deamination in promoting ETS-associated UV mutations. Finally, we analyzed a subset of frequently mutated promoters, including the ribosomal protein genes RPL13A and RPS20, and found that mutations in the ETS motif can significantly reduce the promoter activity. Thus, our data identify high UV damage and low repair, but not CPD deamination, as the main mechanism for ETS-associated mutations in melanoma and uncover important roles of often-overlooked mutation hotspots in perturbing gene transcription.

Protein Dynamics in Complex DNA Lesions.

A single mutagen can generate multiple different types of DNA lesions. How different repair pathways cooperate in complex DNA lesions, however, remains largely unclear. Here we measured, clustered, and modeled the kinetics of recruitment and dissociation of 70 DNA repair proteins to laser-induced DNA damage sites in HeLa cells. The precise timescale of protein recruitment reveals that error-prone translesion polymerases are considerably delayed compared to error-free polymerases. We show that this is ensured by the delayed recruitment of RAD18 to double-strand break sites. The time benefit of error-free polymerases disappears when PARP inhibition significantly delays PCNA recruitment. Moreover, removal of PCNA from complex DNA damage sites correlates with RPA loading during 5'-DNA end resection. Our systematic study of the dynamics of DNA repair proteins in complex DNA lesions reveals the multifaceted coordination between the repair pathways and provides a kinetics-based resource to study genomic instability and anticancer drug impact.

Deep intronic founder mutations identified in the ERCC4/XPF gene are potential therapeutic targets for a high-frequency form of xeroderma pigmentosum.

Xeroderma pigmentosum (XP) is a genodermatosis defined by cutaneous photosensitivity with an increased risk of skin tumors because of DNA repair deficiency. The worldwide prevalence of XP is ~1 to 4 in million, with higher incidence in some countries and regions including Japan (1 in 22,000) and North Africa due to founder mutations and a high degree of consanguinity. Among XP, the complementation group F (XP-F), is a rare form (1% of worldwide XP); however, this is underdiagnosed, because the ERCC4/XPF gene is essential for fetal development and most of previously reported ERCC4/XPF pathogenic variants are hypomorphs causing relatively mild phenotypes. From the largest Japanese XP cohort study, we report 17 XP-F cases bearing two pathogenic variants, both identified in deep intronic regions of the ERCC4/XPF gene. The first variant, located in intron 1, is a Japanese founder mutation, which additionally accounts for ~10% of the entire Japanese XP cases (MAF = 0.00196), causing an aberrant pre-mRNA splicing due to a miss-binding of U1snRNA. The second mutation located in intron eight induces an alternative polyadenylation. Both mutations cause a reduction of the ERCC4/XPF gene expression, resulting in XP clinical manifestations. Most cases developed early-onset skin cancers, indicating that these variants need critical attention. We further demonstrate that antisense oligonucleotides designed for the mutations can restore the XPF protein expression and DNA repair capacity in the patients' cells. Collectively, these pathogenic variants can be potential therapeutic targets for XP.

AlpaPICO: Extraction of PICO frames from clinical trial documents using LLMs.

In recent years, there has been a surge in the publication of clinical trial reports, making it challenging to conduct systematic reviews. Automatically extracting Population, Intervention, Comparator, and Outcome (PICO) from clinical trial studies can alleviate the traditionally time-consuming process of manually scrutinizing systematic reviews. Existing approaches of PICO frame extraction involves supervised approach that relies on the existence of manually annotated data points in the form of BIO label tagging. Recent approaches, such as In-Context Learning (ICL), which has been shown to be effective for a number of downstream NLP tasks, require the use of labeled examples. In this work, we adopt ICL strategy by employing the pretrained knowledge of Large Language Models (LLMs), gathered during the pretraining phase of an LLM, to automatically extract the PICO-related terminologies from clinical trial documents in unsupervised set up to bypass the availability of large number of annotated data instances. Additionally, to showcase the highest effectiveness of LLM in oracle scenario where large number of annotated samples are available, we adopt the instruction tuning strategy by employing Low Rank Adaptation (LORA) to conduct the training of gigantic model in low resource environment for the PICO frame extraction task. More specifically, both of the proposed frameworks utilize AlpaCare as base LLM which employs both few-shot in-context learning and instruction tuning techniques to extract PICO-related terms from the clinical trial reports. We applied these approaches to the widely used coarse-grained datasets such as EBM-NLP, EBM-COMET and fine-grained datasets such as EBM-NLPrev and EBM-NLPh. Our empirical results show that our proposed ICL-based framework produces comparable results on all the version of EBM-NLP datasets and the proposed instruction tuned version of our framework produces state-of-the-art results on all the different EBM-NLP datasets. Our project is available at https://github.com/shrimonmuke0202/AlpaPICO.git.

Generation of site-specifically labelled fluorescent human XPA to investigate DNA binding dynamics during nucleotide excision repair.

Nucleotide excision repair (NER) promotes genomic integrity by removing bulky DNA adducts introduced by external factors such as ultraviolet light. Defects in NER enzymes are associated with pathological conditions such as Xeroderma Pigmentosum, trichothiodystrophy, and Cockayne syndrome. A critical step in NER is the binding of the Xeroderma Pigmentosum group A protein (XPA) to the ss/ds DNA junction. To better capture the dynamics of XPA interactions with DNA during NER we have utilized the fluorescence enhancement through non-canonical amino acids (FEncAA) approach. 4-azido-L-phenylalanine (4AZP or pAzF) was incorporated at Arg-158 in human XPA and conjugated to Cy3 using strain-promoted azide-alkyne cycloaddition. The resulting fluorescent XPA protein (XPACy3) shows no loss in DNA binding activity and generates a robust change in fluorescence upon binding to DNA. Here we describe methods to generate XPACy3 and detail in vitro experimental conditions required to stably maintain the protein during biochemical and biophysical studies.

Wiskott Aldrich syndrome protein (WASp)-deficient Th1 cells promote R-loop-driven transcriptional insufficiency and transcription-coupled nucleotide excision repair factor (TC-NER)-driven genome-instability in the pathogenesis of T cell acute lymphoblastic leukemia.

BACKGROUND: T-ALL is an aggressive hematological tumor that develops as the result of a multi-step oncogenic process which causes expansion of hematopoietic progenitors that are primed for T cell development to undergo malignant transformation and growth. Even though first-line therapy has a significant response rate, 40% of adult patients and 20% of pediatric patients will relapse. Therefore, there is an unmet need for treatment for relapsed/refractory T-ALL to develop potential targeted therapies. METHODS: Pediatric T-ALL patient derived T cells were grown under either nonskewingTh0 or Th1-skewing conditions to further process for ChIP-qPCR, RDIP-qPCR and other RT-PCR assays. Endogenous WASp was knocked out using CRISPR-Cas9 and was confirmed using flow cytometry and western blotting. LC-MS/MS was performed to find out proteomic dataset of WASp-interactors generated from Th1-skewed, human primary Th-cells. DNA-damage was assessed by immunofluorescence confocal-imaging and single-cell gel electrophoresis (comet assay). Overexpression of RNaseH1 was also done to restore normal Th1-transcription in WASp-deficient Th1-skewed cells. RESULTS: We discovered that nuclear-WASp is required for suppressing R-loop production (RNA/DNA-hybrids) at Th1-network genes by ribonucleaseH2 (RNH2) and topoisomerase1. Nuclear-WASp is associated with the factors involved in preventing and dissolving R-loops in Th1 cells. In nuclear- WASp-reduced malignant Th1-cells, R-loops accumulate in vivo and are processed into DNA-breaks by transcription-coupled-nucleotide-excision repair (TC-NER). Several epigenetic modifications were also found to be involved at Th1 gene locus which are responsible for active/repressive marks of particular genes. By demonstrating WASp as a physiologic regulator of programmed versus unprogrammed R-loops, we suggest that the transcriptional role of WASp in vivo extends also to prevent transcription-linked DNA damage during malignancy and through modification of epigenetic dysregulations. CONCLUSION: Our findings present a provocative possibility of resetting R-loops as a therapeutic intervention to correct both immune deficiency and malignancy in T-cell acute lymphoblastic leukemia patients and a novel role of WASp in the epigenetic regulation of T helper cell differentiation in T-ALL patients, anticipating WASp's requirement for the suppression of T-ALL progression.

Deep learning joint models for extracting entities and relations in biomedical: a survey and comparison.

The rapid development of biomedicine has produced a large number of biomedical written materials. These unstructured text data create serious challenges for biomedical researchers to find information. Biomedical named entity recognition (BioNER) and biomedical relation extraction (BioRE) are the two most fundamental tasks of biomedical text mining. Accurately and efficiently identifying entities and extracting relations have become very important. Methods that perform two tasks separately are called pipeline models, and they have shortcomings such as insufficient interaction, low extraction quality and easy redundancy. To overcome the above shortcomings, many deep learning-based joint name entity recognition and relation extraction models have been proposed, and they have achieved advanced performance. This paper comprehensively summarize deep learning models for joint name entity recognition and relation extraction for biomedicine. The joint BioNER and BioRE models are discussed in the light of the challenges existing in the BioNER and BioRE tasks. Five joint BioNER and BioRE models and one pipeline model are selected for comparative experiments on four biomedical public datasets, and the experimental results are analyzed. Finally, we discuss the opportunities for future development of deep learning-based joint BioNER and BioRE models.

Artificial intelligence-powered pharmacovigilance: A review of machine and deep learning in clinical text-based adverse drug event detection for benchmark datasets.

OBJECTIVE: The primary objective of this review is to investigate the effectiveness of machine learning and deep learning methodologies in the context of extracting adverse drug events (ADEs) from clinical benchmark datasets. We conduct an in-depth analysis, aiming to compare the merits and drawbacks of both machine learning and deep learning techniques, particularly within the framework of named-entity recognition (NER) and relation classification (RC) tasks related to ADE extraction. Additionally, our focus extends to the examination of specific features and their impact on the overall performance of these methodologies. In a broader perspective, our research extends to ADE extraction from various sources, including biomedical literature, social media data, and drug labels, removing the limitation to exclusively machine learning or deep learning methods. METHODS: We conducted an extensive literature review on PubMed using the query "(((machine learning [Medical Subject Headings (MeSH) Terms]) OR (deep learning [MeSH Terms])) AND (adverse drug event [MeSH Terms])) AND (extraction)", and supplemented this with a snowballing approach to review 275 references sourced from retrieved articles. RESULTS: In our analysis, we included twelve articles for review. For the NER task, deep learning models outperformed machine learning models. In the RC task, gradient Boosting, multilayer perceptron and random forest models excelled. The Bidirectional Encoder Representations from Transformers (BERT) model consistently achieved the best performance in the end-to-end task. Future efforts in the end-to-end task should prioritize improving NER accuracy, especially for 'ADE' and 'Reason'. CONCLUSION: These findings hold significant implications for advancing the field of ADE extraction and pharmacovigilance, ultimately contributing to improved drug safety monitoring and healthcare outcomes.

Reduced levels of prostaglandin I2 synthase: a distinctive feature of the cancer-free trichothiodystrophy.

The cancer-free photosensitive trichothiodystrophy (PS-TTD) and the cancer-prone xeroderma pigmentosum (XP) are rare monogenic disorders that can arise from mutations in the same genes, namely ERCC2/XPD or ERCC3/XPB Both XPD and XPB proteins belong to the 10-subunit complex transcription factor IIH (TFIIH) that plays a key role in transcription and nucleotide excision repair, the DNA repair pathway devoted to the removal of ultraviolet-induced DNA lesions. Compelling evidence suggests that mutations affecting the DNA repair activity of TFIIH are responsible for the pathological features of XP, whereas those also impairing transcription give rise to TTD. By adopting a relatives-based whole transcriptome sequencing approach followed by specific gene expression profiling in primary fibroblasts from a large cohort of TTD or XP cases with mutations in ERCC2/XPD gene, we identify the expression alterations specific for TTD primary dermal fibroblasts. While most of these transcription deregulations do not impact on the protein level, very low amounts of prostaglandin I2 synthase (PTGIS) are found in TTD cells. PTGIS catalyzes the last step of prostaglandin I2 synthesis, a potent vasodilator and inhibitor of platelet aggregation. Its reduction characterizes all TTD cases so far investigated, both the PS-TTD with mutations in TFIIH coding genes as well as the nonphotosensitive (NPS)-TTD. A severe impairment of TFIIH and RNA polymerase II recruitment on the PTGIS promoter is found in TTD but not in XP cells. Thus, PTGIS represents a biomarker that combines all PS- and NPS-TTD cases and distinguishes them from XP.

Getting to Know Named Entity Recognition: Better Information Retrieval.

Named entity recognition (NER) is a powerful computer system that utilizes various computing strategies to extract information from raw text input, since the early 1990s. With rapid advancement in AI and computing, NER models have gained significant attention and been serving as foundational tools across numerus professional domains to organize unstructured data for research and practical applications. This is particularly evident in the medical and healthcare fields, where NER models are essential in efficiently extract critical information from complex documents that are challenging for manual review. Despite its successes, NER present limitations in fully comprehending natural language nuances. However, the development of more advanced and user-friendly models promises to improve work experiences of professional users significantly.

The Role of DNA Repair (XPC, XPD, XPF, and XPG) Gene Polymorphisms in the Development of Myeloproliferative Neoplasms.

Background and Objectives: Several polymorphisms have been described in various DNA repair genes. Nucleotide excision DNA repair (NER) detects defects of DNA molecules and corrects them to restore genome integrity. We hypothesized that the XPC, XPD, XPF, and XPG gene polymorphisms influence the appearance of myeloproliferative neoplasms (MPNs). Materials and Methods: We investigated the XPC 1496C>T (rs2228000, XPC Ala499Val), XPC 2920A>C (rs228001, XPC Lys939Gln), XPD 2251A>C (rs13181, XPD Lys751Gln), XPF-673C>T (rs3136038), XPF 11985A>G (rs254942), and XPG 3507G>C (rs17655, XPG Asp1104His) polymorphisms by polymerase chain reaction-restriction fragment length polymorphism analysis in 393 MPN patients [153 with polycythemia vera (PV), 201 with essential thrombocythemia (ET), and 39 with primary myelofibrosis (PMF)] and 323 healthy controls. Results: Overall, we found that variant genotypes of XPD 2251A>C were associated with an increased risk of MPN (OR = 1.54, 95% CI = 1.15-2.08, p = 0.004), while XPF-673C>T and XPF 11985A>G were associated with a decreased risk of developing MPN (OR = 0.56, 95% CI = 0.42-0.76, p < 0.001; and OR = 0.26, 95% CI = 0.19-0.37, p < 0.001, respectively). Conclusions: In light of our findings, XPD 2251A>C polymorphism was associated with the risk of developing MPN and XPF-673C>T and XPF 11985A>G single nucleotide polymorphisms (SNPs) may have a protective role for MPN, while XPC 1496C>T, XPC 2920A>C, and XPG 3507G>C polymorphisms do not represent risk factors in MPN development.

NAT10 regulates the repair of UVB-induced DNA damage and tumorigenicity.

Chemical modifications in messenger RNA (mRNA) regulate gene expression and play critical roles in stress responses and diseases. Recently we have shown that N6-methyladenosine (m6A), the most abundant mRNA modification, promotes the repair of UVB-induced DNA damage by regulating global genome nucleotide excision repair (GG-NER). However, the roles of other mRNA modifications in the UVB-induced damage response remain understudied. N4-acetylcytidine (ac4C) is deposited in mRNA by the RNA-binding acetyltransferase NAT10. This NAT10-mediated ac4C in mRNA has been reported to increase both mRNA stability and translation. However, the role of ac4C and NAT10 in the UVB-induced DNA damage response remains poorly understood. Here we show that NAT10 plays a critical role in the repair of UVB-induced DNA damage lesions through regulating the expression of the key GG-NER gene DDB2. We found that knockdown of NAT10 enhanced the repair of UVB-induced DNA damage lesions by promoting the mRNA stability of DDB2. Our findings are in contrast to the previously reported role of NAT10-mediated ac4C deposition in promoting mRNA stability and may represent a novel mechanism for ac4C in the UVB damage response. Furthermore, NAT10 knockdown in skin cancer cells decreased skin cancer cell proliferation in vitro and tumorigenicity in vivo. Chronic UVB irradiation increases NAT10 protein levels in mouse skin. Taken together, our findings demonstrate a novel role for NAT10 in the repair of UVB-induced DNA damage products by decreasing the mRNA stability of DDB2 and suggest that NAT10 is a potential novel target for preventing and treating skin cancer.

ADPG: Biomedical entity recognition based on Automatic Dependency Parsing Graph.

Named entity recognition is a key task in text mining. In the biomedical field, entity recognition focuses on extracting key information from large-scale biomedical texts for the downstream information extraction task. Biomedical literature contains a large amount of long-dependent text, and previous studies use external syntactic parsing tools to capture word dependencies in sentences to achieve nested biomedical entity recognition. However, the addition of external parsing tools often introduces unnecessary noise to the current auxiliary task and cannot improve the performance of entity recognition in an end-to-end way. Therefore, we propose a novel automatic dependency parsing approach, namely the ADPG model, to fuse syntactic structure information in an end-to-end way to recognize biomedical entities. Specifically, the method is based on a multilayer Tree-Transformer structure to automatically extract the semantic representation and syntactic structure in long-dependent sentences, and then combines a multilayer graph attention neural network (GAT) to extract the dependency paths between words in the syntactic structure to improve the performance of biomedical entity recognition. We evaluated our ADPG model on three biomedical domain and one news domain datasets, and the experimental results demonstrate that our model achieves state-of-the-art results on these four datasets with certain generalization performance. Our model is released on GitHub: https://github.com/Yumeng-Y/ADPG.

XPG: a multitasking genome caretaker.

The XPG/ERCC5 endonuclease was originally identified as the causative gene for Xeroderma Pigmentosum complementation group G. Ever since its discovery, in depth biochemical, structural and cell biological studies have provided detailed mechanistic insight into its function in excising DNA damage in nucleotide excision repair, together with the ERCC1-XPF endonuclease. In recent years, it has become evident that XPG has additional important roles in genome maintenance that are independent of its function in NER, as XPG has been implicated in protecting replication forks by promoting homologous recombination as well as in resolving R-loops. Here, we provide an overview of the multitasking of XPG in genome maintenance, by describing in detail how its activity in NER is regulated and the evidence that points to important functions outside of NER. Furthermore, we present the various disease phenotypes associated with inherited XPG deficiency and discuss current ideas on how XPG deficiency leads to these different types of disease.

Methods for Assessment of Nucleotide Excision Repair Efficiency.

Nucleotide excision repair (NER) is responsible for removing a wide variety of bulky adducts from DNA, thus contributing to the maintenance of genome stability. The efficiency with which proteins of the NER system recognize and remove bulky adducts depends on many factors and is of great clinical and diagnostic significance. The review examines current concepts of the NER system molecular basis in eukaryotic cells and analyzes methods for the assessment of the NER-mediated DNA repair efficiency both in vitro and ex vivo.

Textual data transformations using natural language processing for risk assessment.

Underlying information about failure, including observations made in free text, can be a good source for understanding, analyzing, and extracting meaningful information for determining causation. The unstructured nature of natural language expression demands advanced methodology to identify its underlying features. There is no available solution to utilize unstructured data for risk assessment purposes. Due to the scarcity of relevant data, textual data can be a vital learning source for developing a risk assessment methodology. This work addresses the knowledge gap in extracting relevant features from textual data to develop cause-effect scenarios with minimal manual interpretation. This study applies natural language processing and text-mining techniques to extract features from past accident reports. The extracted features are transformed into parametric form with the help of fuzzy set theory and utilized in Bayesian networks as prior probabilities for risk assessment. An application of the proposed methodology is shown in microbiologically influenced corrosion-related incident reports available from the Pipeline and Hazardous Material Safety Administration database. In addition, the trained named entity recognition (NER) model is verified on eight incidents, showing a promising preliminary result for identifying all relevant features from textual data and demonstrating the robustness and applicability of the NER method. The proposed methodology can be used in domain-specific risk assessment to analyze, predict, and prevent future mishaps, ameliorating overall process safety.

DNA Alkylation Damage by Nitrosamines and Relevant DNA Repair Pathways.

Nitrosamines occur widespread in food, drinking water, cosmetics, as well as tobacco smoke and can arise endogenously. More recently, nitrosamines have been detected as impurities in various drugs. This is of particular concern as nitrosamines are alkylating agents that are genotoxic and carcinogenic. We first summarize the current knowledge on the different sources and chemical nature of alkylating agents with a focus on relevant nitrosamines. Subsequently, we present the major DNA alkylation adducts induced by nitrosamines upon their metabolic activation by CYP450 monooxygenases. We then describe the DNA repair pathways engaged by the various DNA alkylation adducts, which include base excision repair, direct damage reversal by MGMT and ALKBH, as well as nucleotide excision repair. Their roles in the protection against the genotoxic and carcinogenic effects of nitrosamines are highlighted. Finally, we address DNA translesion synthesis as a DNA damage tolerance mechanism relevant to DNA alkylation adducts.