Genome-wide identification of replication fork stalling/pausing sites and the interplay between RNA Pol II transcription and DNA replication progression.

BACKGROUND: DNA replication progression can be affected by the presence of physical barriers like the RNA polymerases, leading to replication stress and DNA damage. Nonetheless, we do not know how transcription influences overall DNA replication progression. RESULTS: To characterize sites where DNA replication forks stall and pause, we establish a genome-wide approach to identify them. This approach uses multiple timepoints during S-phase to identify replication fork/stalling hotspots as replication progresses through the genome. These sites are typically associated with increased DNA damage, overlapped with fragile sites and with breakpoints of rearrangements identified in cancers but do not overlap with replication origins. Overlaying these sites with a genome-wide analysis of RNA polymerase II transcription, we find that replication fork stalling/pausing sites inside genes are directly related to transcription progression and activity. Indeed, we find that slowing down transcription elongation slows down directly replication progression through genes. This indicates that transcription and replication can coexist over the same regions. Importantly, rearrangements found in cancers overlapping transcription-replication collision sites are detected in non-transformed cells and increase following treatment with ATM and ATR inhibitors. At the same time, we find instances where transcription activity favors replication progression because it reduces histone density. CONCLUSIONS: Altogether, our findings highlight how transcription and replication overlap during S-phase, with both positive and negative consequences for replication fork progression and genome stability by the coexistence of these two processes.

A comprehensive AI-driven analysis of large-scale omic datasets reveals novel dual-purpose targets for the treatment of cancer and aging.

As aging and tumorigenesis are tightly interconnected biological processes, targeting their common underlying driving pathways may induce dual-purpose anti-aging and anti-cancer effects. Our transcriptomic analyses of 16,740 healthy samples demonstrated tissue-specific age-associated gene expression, with most tumor suppressor genes downregulated during aging. Furthermore, a large-scale pan-cancer analysis of 11 solid tumor types (11,303 cases and 4431 control samples) revealed that many cellular processes, such as protein localization, DNA replication, DNA repair, cell cycle, and RNA metabolism, were upregulated in cancer but downregulated in healthy aging tissues, whereas pathways regulating cellular senescence were upregulated in both aging and cancer. Common cancer targets were identified by the AI-driven target discovery platform-PandaOmics. Age-associated cancer targets were selected and further classified into four groups based on their reported roles in lifespan. Among the 51 identified age-associated cancer targets with anti-aging experimental evidence, 22 were proposed as dual-purpose targets for anti-aging and anti-cancer treatment with the same therapeutic direction. Among age-associated cancer targets without known lifespan-regulating activity, 23 genes were selected based on predicted dual-purpose properties. Knockdown of histone demethylase KDM1A, one of these unexplored candidates, significantly extended lifespan in Caenorhabditis elegans. Given KDM1A's anti-cancer activities reported in both preclinical and clinical studies, our findings propose KDM1A as a promising dual-purpose target. This is the first study utilizing an innovative AI-driven approach to identify dual-purpose target candidates for anti-aging and anti-cancer treatment, supporting the value of AI-assisted target identification for drug discovery.

Identification of lncRNAs associated with uterine corpus endometrial cancer prognosis based on the competing endogenous RNA network.

Uterine Corpus Endometrial Carcinoma (UCEC) is one of the major malignant tumors of the female reproductive system. However, there are limitations in the currently available diagnostic approaches for UCEC. Long non-coding RNAs (lncRNAs) play important roles in regulating biological processes as competitive endogenous RNA (ceRNA) in tumors. To study the potential of lncRNAs as non-invasive diagnostic tumor markers, RNA-sequencing dataset of UCEC patients from The Cancer Genome Atlas was used to identify differentially expressed genes. A lncRNA-miRNA-mRNA ceRNA network was constructed by differentially expressed lncRNAs, miRNAs and miRNAs. Pathway enrichment and functional analysis for the mRNAs in the constructed ceRNA network provide the direction of future research for UCEC by demonstrating the most affected processes and pathways. Seven potential lncRNA biomarkers (C20orf56, LOC100144604, LOC100190940, LOC151534, LOC727677, FLJ35390, LOC158572) were validated in UCEC patients by quantitative real-time PCR. Notably, LOC100190940 and LOC158572 were identified as novel RNA molecules with unknown functions. Receiver operating characteristic (ROC) curve analysis demonstrated that the combined 7 lncRNAs had a high diagnostic value for UCEC patients with area under curve (AUC) of 0.941 (95% CI: 0.875-0.947). Our study highlights the potential of the validated 7 lncRNAs panel as diagnostic biomarkers in UCEC, providing new insights into the UCEC pathogenesis.

Biomedical generative pre-trained based transformer language model for age-related disease target discovery.

Target discovery is crucial for the development of innovative therapeutics and diagnostics. However, current approaches often face limitations in efficiency, specificity, and scalability, necessitating the exploration of novel strategies for identifying and validating disease-relevant targets. Advances in natural language processing have provided new avenues for predicting potential therapeutic targets for various diseases. Here, we present a novel approach for predicting therapeutic targets using a large language model (LLM). We trained a domain-specific BioGPT model on a large corpus of biomedical literature consisting of grant text and developed a pipeline for generating target prediction. Our study demonstrates that pre-training of the LLM model with task-specific texts improves its performance. Applying the developed pipeline, we retrieved prospective aging and age-related disease targets and showed that these proteins are in correspondence with the database data. Moreover, we propose CCR5 and PTH as potential novel dual-purpose anti-aging and disease targets which were not previously identified as age-related but were highly ranked in our approach. Overall, our work highlights the high potential of transformer models in novel target prediction and provides a roadmap for future integration of AI approaches for addressing the intricate challenges presented in the biomedical field.

Circulating microRNAs as diagnostic biomarkers for ischemic stroke: evidence from comprehensive analysis and real-world validation.

Ischemic stroke (IS) is the majority of strokes which remain the second leading cause of deaths in the last two decades. Circulating microRNAs (miRNAs) have been suggested as potential diagnostic and therapeutic tools for IS by previous studies analyzing their differential expression. However, inconclusive and controversial conclusions of these results have to be addressed. In this study, comprehensive analysis and real-world validation were performed to assess the associations between circulating miRNAs and IS. 29 studies with 112 miRNAs were extracted after manual selection and filtering, 12 differentially expressed miRNAs were obtained from our results of meta-analysis. These miRNAs were evaluated in 20 IS patients, compared to 20 healthy subjects. 4 miRNAs (hsa-let-7e-5p, hsa-miR-124-3p, hsa-miR-17-5p, hsa-miR-185-5p) exhibited the significant expression level in IS patient plasma samples. Pathway and biological process enrichment analysis for the target genes of the 4 validated miRNAs identified cellular senescence and neuroinflammation as key post-IS response pathways. The results of our analyses closely correlated with the pathogenesis and implicated pathways observed in IS subjects suggested by the literature, which may provide aid in the development of circulating diagnostic or therapeutic targets for IS patients.

Precious1GPT: multimodal transformer-based transfer learning for aging clock development and feature importance analysis for aging and age-related disease target discovery.

Aging is a complex and multifactorial process that increases the risk of various age-related diseases and there are many aging clocks that can accurately predict chronological age, mortality, and health status. These clocks are disconnected and are rarely fit for therapeutic target discovery. In this study, we propose a novel approach to multimodal aging clock we call Precious1GPT utilizing methylation and transcriptomic data for interpretable age prediction and target discovery developed using a transformer-based model and transfer learning for case-control classification. While the accuracy of the multimodal transformer is lower within each individual data type compared to the state of art specialized aging clocks based on methylation or transcriptomic data separately it may have higher practical utility for target discovery. This method provides the ability to discover novel therapeutic targets that hypothetically may be able to reverse or accelerate biological age providing a pathway for therapeutic drug discovery and validation using the aging clock. In addition, we provide a list of promising targets annotated using the PandaOmics industrial target discovery platform.

Identification of dual-purpose therapeutic targets implicated in aging and glioblastoma multiforme using PandaOmics - an AI-enabled biological target discovery platform.

Glioblastoma Multiforme (GBM) is the most aggressive and most common primary malignant brain tumor. The age of GBM patients is considered as one of the disease's negative prognostic factors and the mean age of diagnosis is 62 years. A promising approach to preventing both GBM and aging is to identify new potential therapeutic targets that are associated with both conditions as concurrent drivers. In this work, we present a multi-angled approach of identifying targets, which takes into account not only the disease-related genes but also the ones important in aging. For this purpose, we developed three strategies of target identification using the results of correlation analysis augmented with survival data, differences in expression levels and previously published information of aging-related genes. Several studies have recently validated the robustness and applicability of AI-driven computational methods for target identification in both cancer and aging-related diseases. Therefore, we leveraged the AI predictive power of the PandaOmics TargetID engine in order to rank the resulting target hypotheses and prioritize the most promising therapeutic gene targets. We propose cyclic nucleotide gated channel subunit alpha 3 (CNGA3), glutamate dehydrogenase 1 (GLUD1) and sirtuin 1 (SIRT1) as potential novel dual-purpose therapeutic targets to treat aging and GBM.

Identification of Therapeutic Targets for Amyotrophic Lateral Sclerosis Using PandaOmics - An AI-Enabled Biological Target Discovery Platform.

Amyotrophic lateral sclerosis (ALS) is a severe neurodegenerative disease with ill-defined pathogenesis, calling for urgent developments of new therapeutic regimens. Herein, we applied PandaOmics, an AI-driven target discovery platform, to analyze the expression profiles of central nervous system (CNS) samples (237 cases; 91 controls) from public datasets, and direct iPSC-derived motor neurons (diMNs) (135 cases; 31 controls) from Answer ALS. Seventeen high-confidence and eleven novel therapeutic targets were identified and will be released onto ALS.AI (http://als.ai/). Among the proposed targets screened in the c9ALS Drosophila model, we verified 8 unreported genes (KCNB2, KCNS3, ADRA2B, NR3C1, P2RY14, PPP3CB, PTPRC, and RARA) whose suppression strongly rescues eye neurodegeneration. Dysregulated pathways identified from CNS and diMN data characterize different stages of disease development. Altogether, our study provides new insights into ALS pathophysiology and demonstrates how AI speeds up the target discovery process, and opens up new opportunities for therapeutic interventions.

Hallmarks of aging-based dual-purpose disease and age-associated targets predicted using PandaOmics AI-powered discovery engine.

Aging biology is a promising and burgeoning research area that can yield dual-purpose pathways and protein targets that may impact multiple diseases, while retarding or possibly even reversing age-associated processes. One widely used approach to classify a multiplicity of mechanisms driving the aging process is the hallmarks of aging. In addition to the classic nine hallmarks of aging, processes such as extracellular matrix stiffness, chronic inflammation and activation of retrotransposons are also often considered, given their strong association with aging. In this study, we used a variety of target identification and prioritization techniques offered by the AI-powered PandaOmics platform, to propose a list of promising novel aging-associated targets that may be used for drug discovery. We also propose a list of more classical targets that may be used for drug repurposing within each hallmark of aging. Most of the top targets generated by this comprehensive analysis play a role in inflammation and extracellular matrix stiffness, highlighting the relevance of these processes as therapeutic targets in aging and age-related diseases. Overall, our study reveals both high confidence and novel targets associated with multiple hallmarks of aging and demonstrates application of the PandaOmics platform to target discovery across multiple disease areas.