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.

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.

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.

'Candidatus Oscillochloris fontis': a novel mesophilic phototrophic Chloroflexota bacterium belonging to the ubiquitous Oscillochloris genus.

We present the results of a study of mesophilic anoxygenic phototrophic Chloroflexota bacteria from Mechigmen hot spring (the Chukotka Peninsula) and Siberia. According to 16S rRNA phylogenetic analysis, these bacteria belong to Oscillochloris trichoides. However, sequencing the draft genome of the bacterium from the Chukotka and analysis of the average nucleotide identity, as well as in silico DNA-DNA hybridization, reveal that this bacterium belongs to a novel species within the Oscillochloris genus. We, therefore, propose 'Candidatus Oscillochloris fontis' as a novel taxon to represent this mesophilic alkaliphilic anaerobic anoxygenic phototrophic bacterium. Spectrophotometry and high-performance liquid chromatography analysis show that the bacterium possesses bacteriochlorophylls c and a, as well as lycopene, ß-carotene and γ-carotene. In addition, transmission electron microscopy shows the presence of chlorosomes, polyhydroxyalkanoate- and polyphosphate-like granules. The genome of 'Ca. Oscillochloris fontis' and the Siberian strains of Oscillochloris sp. possess the key genes for nitrogenase complex (nifH) and ribulose-1,5-bisphosphate carboxylase/oxygenase (cbbL), as previously described for O. trichoides DG-6. The results presented here, and previously published data, show that Oscillochloris bacteria from different aquatic environments have the potential for CO2 and N2 fixation. Additionally, we describe a new primer system for the detection of RuBisCo form I.