Precious2GPT: the combination of multiomics pretrained transformer and conditional diffusion for artificial multi-omics multi-species multi-tissue sample generation.

Synthetic data generation in omics mimics real-world biological data, providing alternatives for training and evaluation of genomic analysis tools, controlling differential expression, and exploring data architecture. We previously developed Precious1GPT, a multimodal transformer trained on transcriptomic and methylation data, along with metadata, for predicting biological age and identifying dual-purpose therapeutic targets potentially implicated in aging and age-associated diseases. In this study, we introduce Precious2GPT, a multimodal architecture that integrates Conditional Diffusion (CDiffusion) and decoder-only Multi-omics Pretrained Transformer (MoPT) models trained on gene expression and DNA methylation data. Precious2GPT excels in synthetic data generation, outperforming Conditional Generative Adversarial Networks (CGANs), CDiffusion, and MoPT. We demonstrate that Precious2GPT is capable of generating representative synthetic data that captures tissue- and age-specific information from real transcriptomics and methylomics data. Notably, Precious2GPT surpasses other models in age prediction accuracy using the generated data, and it can generate data beyond 120 years of age. Furthermore, we showcase the potential of using this model in identifying gene signatures and potential therapeutic targets in a colorectal cancer case study.

A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models.

Idiopathic pulmonary fibrosis (IPF) is an aggressive interstitial lung disease with a high mortality rate. Putative drug targets in IPF have failed to translate into effective therapies at the clinical level. We identify TRAF2- and NCK-interacting kinase (TNIK) as an anti-fibrotic target using a predictive artificial intelligence (AI) approach. Using AI-driven methodology, we generated INS018_055, a small-molecule TNIK inhibitor, which exhibits desirable drug-like properties and anti-fibrotic activity across different organs in vivo through oral, inhaled or topical administration. INS018_055 possesses anti-inflammatory effects in addition to its anti-fibrotic profile, validated in multiple in vivo studies. Its safety and tolerability as well as pharmacokinetics were validated in a randomized, double-blinded, placebo-controlled phase I clinical trial (NCT05154240) involving 78 healthy participants. A separate phase I trial in China, CTR20221542, also demonstrated comparable safety and pharmacokinetic profiles. This work was completed in roughly 18 months from target discovery to preclinical candidate nomination and demonstrates the capabilities of our generative AI-driven drug-discovery pipeline.

Defining the progeria phenome.

Progeroid disorders are a heterogenous group of rare and complex hereditary syndromes presenting with pleiotropic phenotypes associated with normal aging. Due to the large variation in clinical presentation the diseases pose a diagnostic challenge for clinicians which consequently restricts medical research. To accommodate the challenge, we compiled a list of known progeroid syndromes and calculated the mean prevalence of their associated phenotypes, defining what we term the 'progeria phenome'. The data were used to train a support vector machine that is available at https://www.mitodb.com and able to classify progerias based on phenotypes. Furthermore, this allowed us to investigate the correlation of progeroid syndromes and syndromes with various pathogenesis using hierarchical clustering algorithms and disease networks. We detected that ataxia-telangiectasia like disorder 2, spastic paraplegia 49 and Meier-Gorlin syndrome display strong association to progeroid syndromes, thereby implying that the syndromes are previously unrecognized progerias. In conclusion, our study has provided tools to evaluate the likelihood of a syndrome or patient being progeroid. This is a considerable step forward in our understanding of what constitutes a premature aging disorder and how to diagnose them.

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.

High-confidence cancer patient stratification through multiomics investigation of DNA repair disorders.

Multiple cancer types have limited targeted therapeutic options, in part due to incomplete understanding of the molecular processes underlying tumorigenesis and significant intra- and inter-tumor heterogeneity. Identification of novel molecular biomarkers stratifying cancer patients with different survival outcomes may provide new opportunities for target discovery and subsequent development of tailored therapies. Here, we applied the artificial intelligence-driven PandaOmics platform ( https://pandaomics.com/ ) to explore gene expression changes in rare DNA repair-deficient disorders and identify novel cancer targets. Our analysis revealed that CEP135, a scaffolding protein associated with early centriole biogenesis, is commonly downregulated in DNA repair diseases with high cancer predisposition. Further screening of survival data in 33 cancers available at TCGA database identified sarcoma as a cancer type where lower survival was significantly associated with high CEP135 expression. Stratification of cancer patients based on CEP135 expression enabled us to examine therapeutic targets that could be used for the improvement of existing therapies against sarcoma. The latter was based on application of the PandaOmics target-ID algorithm coupled with in vitro studies that revealed polo-like kinase 1 (PLK1) as a potential therapeutic candidate in sarcoma patients with high CEP135 levels and poor survival. While further target validation is required, this study demonstrated the potential of in silico-based studies for a rapid biomarker discovery and target characterization.

CD44+ and CD133+ Non-Small Cell Lung Cancer Cells Exhibit DNA Damage Response Pathways and Dormant Polyploid Giant Cancer Cell Enrichment Relating to Their p53 Status.

Cancer stem cells (CSCs) play a critical role in the initiation, progression and therapy relapse of many cancers including non-small cell lung cancer (NSCLC). Here, we aimed to address the question of whether the FACS-sorted CSC-like (CD44 + &CD133 +) vs. non-CSC (CD44-/CD133- isogenic subpopulations of p53wt A549 and p53null H1299 cells differ in terms of DNA-damage signaling and the appearance of "dormant" features, including polyploidy, which are early markers (predictors) of their sensitivity to genotoxic stress. X-ray irradiation (IR) at 5 Gy provoked significantly higher levels of the ATR-Chk1/Chk2-pathway activity in CD44-/CD133- and CD133+ subpopulations of H1299 cells compared to the respective subpopulations of A549 cells, which only excited ATR-Chk2 activation as demonstrated by the Multiplex DNA-Damage/Genotoxicity profiling. The CD44+ subpopulations did not demonstrate IR-induced activation of ATR, while significantly augmenting only Chk2 and Chk1/2 in the A549- and H1299-derived cells, respectively. Compared to the A549 cells, all the subpopulations of H1299 cells established an increased IR-induced expression of the γH2AX DNA-repair protein. The CD44-/CD133- and CD133+ subpopulations of the A549 cells revealed IR-induced activation of ATR-p53-p21 cell dormancy signaling-mediated pathway, while none of the CD44+ subpopulations of either cell line possessed any signs of such activity. Our data indicated, for the first time, the transcription factor MITF-FAM3C axis operative in p53-deficient H1299 cells, specifically their CD44+ and CD133+ populations, in response to IR, which warrants further investigation. The p21-mediated quiescence is likely the predominant surviving pathway in CD44-/CD133- and CD133+ populations of A549 cells as indicated by single-cell high-content imaging and analysis of Ki67- and EdU-coupled fluorescence after IR stress. SA-beta-galhistology revealed that cellular-stress-induced premature senescence (SIPS) likely has a significant influence on the temporary dormant state of H1299 cells. For the first time, we demonstrated polyploid giant and/or multinucleated cancer-cell (PGCC/MGCC) fractions mainly featuring the progressively augmenting Ki67low phenotype in CD44+ and CD133+ A549 cells at 24-48 h after IR. In contrast, the Ki67high phenotype enrichment in the same fractions of all the sorted H1299 cells suggested an increase in their cycling/heterochromatin reorganization activity after IR stress. Our results proposed that entering the "quiescence" state rather than p21-mediated SIPS may play a significant role in the survival of p53wt CSC-like NSCLC cells after IR. The results obtained are important for the selection of therapeutic schemes for the treatment of patients with NSCLC, depending on the functioning of the p53 system in tumor cells.