: Multi-omics (genomics, transcriptomics, epigenomics, proteomics, metabolomics, etc.) research approaches are vital for understanding the hierarchical complexity of human biology and have proven to be extremely valuable in cancer research and precision medicine. Emerging scientific advances in recent years have made high-throughput genome-wide sequencing a central focus in molecular research by allowing for the collective analysis of various kinds of molecular biological data from different types of specimens in a single tissue or even at the level of a single cell. Additionally, with the help of improved computational resources and data mining, researchers are able to integrate data from different multi-omics regimes to identify new prognostic, diagnostic, or predictive biomarkers, uncover novel therapeutic targets, and develop more personalized treatment protocols for patients. For the research community to parse the scientifically and clinically meaningful information out of all the biological data being generated each day more efficiently with less wasted resources, being familiar with and comfortable using advanced analytical tools, such as Google Cloud Platform becomes imperative. This project is an interdisciplinary, cross-organizational effort to provide a guided learning module for integrating transcriptomics and epigenetics data analysis protocols into a comprehensive analysis pipeline for users to implement in their own work, utilizing the cloud computing infrastructure on Google Cloud. The learning module consists of three submodules that guide the user through tutorial examples that illustrate the analysis of RNA-sequence and Reduced-Representation Bisulfite Sequencing data. The examples are in the form of breast cancer case studies, and the data sets were procured from the public repository Gene Expression Omnibus. The first submodule is devoted to transcriptomics analysis with the RNA sequencing data, the second submodule focuses on epigenetics analysis using the DNA methylation data, and the third submodule integrates the two methods for a deeper biological understanding. The modules begin with data collection and preprocessing, with further downstream analysis performed in a Vertex AI Jupyter notebook instance with an R kernel. Analysis results are returned to Google Cloud buckets for storage and visualization, removing the computational strain from local resources. The final product is a start-to-finish tutorial for the researchers with limited experience in multi-omics to integrate transcriptomics and epigenetics data analysis into a comprehensive pipeline to perform their own biological research. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [16] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses. Highlights:

In healthy older adults, the immune system generally preserves its response and contributes to a long, healthy lifespan. However, rapid deterioration in immune regulation can lead to chronic inflammation, termed inflammaging, which accelerates pathological aging and diminishes the quality of life in older adults with frailty. A significant limitation in current aging research is the predominant focus on comparisons between young and older populations, often overlooking the differences between healthy older adults and those experiencing pathological aging. Our study elucidates the intricate immunological dynamics of the CD4/Treg axis in frail older adults compared to comparable age-matched healthy older adults. By utilizing publicly available RNA sequencing and single-cell RNA sequencing (scRNAseq) data from peripheral blood mononuclear cells (PBMCs), we identified a specific Treg cell subset and transcriptional landscape contributing to the dysregulation of CD4⁺ T-cell responses. We explored the molecular mechanisms underpinning Treg dysfunction, revealing that Tregs from frail older adults exhibit reduced mitochondrial protein levels, impairing mitochondrial oxidative phosphorylation. This impairment is driven by the TNF/NF-kappa B pathway, leading to cumulative inflammation. Further, we gained a deeper understanding of the CD4/Treg axis by predicting the effects of gene perturbations on cellular signaling networks. Collectively, these findings highlight the age-related relationship between mitochondrial dysfunction in the CD4/Treg axis and its role in accelerating aging and frailty in older adults. Targeting Treg dysfunction offers a critical basis for developing tailored therapeutic strategies aimed at improving the quality of life in older adults.

Although there has been a reduction in head and neck squamous cell carcinoma occurrence, it continues to be a serious global health concern. The lack of precise early diagnostic biomarkers and postponed diagnosis in the later stages are notable constraints that contribute to poor survival rates and emphasize the need for innovative diagnostic methods. In this study, we employed machine learning alongside weighted gene co-expression network analysis (WGCNA) and network biology to investigate the gene expression patterns of blood platelets, identifying transcriptomic markers for HNSCC diagnosis. Our comprehensive examination of publicly available gene expression datasets revealed nine genes with significantly elevated expression in samples from individuals diagnosed with HNSCC. These potential diagnostic markers were further assessed using TCGA and GTEx datasets, demonstrating high accuracy in distinguishing between HNSCC and non-cancerous samples. The findings indicate that these gene signatures could revolutionize early HNSCC identification. Additionally, the study highlights the significance of tumor-educated platelets (TEPs), which carry RNA signatures indicative of tumor-derived material, offering a non-invasive source for early-detection biomarkers. Despite using platelet and tumor samples from different individuals, our results suggest that TEPs reflect the transcriptomic and epigenetic landscape of tumors. Future research should aim to directly correlate tumor and platelet samples from the same patients to further elucidate this relationship. This study underscores the potential of these biomarkers in transforming early diagnosis and personalized treatment strategies for HNSCC, advocating for further research to validate their predictive and therapeutic potential.

The human kidney is known to possess renal progenitor cells (RPCs) that can assist in the repair of acute tubular injury. The RPCs are sparsely located as single cells throughout the kidney. We recently generated an immortalized human renal progenitor cell line (HRTPT) that co-expresses PROM1/CD24 and expresses features expected on RPCs. This included the ability to form nephrospheres, differentiate on the surface of Matrigel, and undergo adipogenic, neurogenic, and osteogenic differentiation. These cells were used in the present study to determine how the cells would respond when exposed to nephrotoxin. Inorganic arsenite (iAs) was chosen as the nephrotoxin since the kidney is susceptible to this toxin and there is evidence of its involvement in renal disease. Gene expression profiles when the cells were exposed to iAs for 3, 8, and 10 passages (subcultured at 1:3 ratio) identified a shift from the control unexposed cells. The cells exposed to iAs for eight passages were then referred with growth media containing no iAs and within two passages the cells returned to an epithelial morphology with strong agreement in differential gene expression between control and cells recovered from iAs exposure. Results show within three serial passages of the cells exposed to iAs there was a shift in morphology from an epithelial to a mesenchymal phenotype. EMT was suggested based on an increase in known mesenchymal markers. We found RPCs can undergo EMT when exposed to a nephrotoxin and undergo MET when the agent is removed from the growth media.

In natural language processing, named entity recognition (NER) is a crucial task involving finding and categorizing text entities. The biomedical domain presents substantial hurdles due to the complex structure of the language and the existence of nested entities. This paper introduces an innovative method for Nested NER by utilizing a multilayer bidirectional encoder representation transformer (BERT)-based model, notably employing pretrained PubMedBERT. Our proposed model is designed to manage nested entities’ complexities effectively. We combined the robust contextual embeddings from PubMedBERT with a multilayer tagging process. This approach allowed the model to precisely differentiate between overlapping items, a frequent occurrence in biomedical literature. To assess the effectiveness of our Multilayer NER Model (MultilayerNERModel), we conducted thorough experiments on the BioNNE English Dataset, a dataset for a shared task of BioASQ competition. The findings suggest that employing a multilayer approach enhances the model’s ability to identify nested entities, resulting in the thorough detection of entities in biomedical texts. It earned the highest overall performance in English oriented track, with an F1 score of 67.30% and a macro F1 score of 56.36%. These results demonstrate the significant impact of utilizing a multilayer approach in Nested NER tasks, especially in the biomedical domain. The use of UMLS dictionaries, along with the MultilayerNERModel, further enhances the model’s performance in biomedical entity recognition.