Turning omics data into therapeutic insights

Advancing personalized medicine: Integrating statistical algorithms with omics and nano-omics for enhanced diagnostic accuracy and treatment efficacy

Medical laboratory services enable precise measurement of thousands of biomolecules and have become an inseparable part of high-quality healthcare services, exerting a profound influence on global health outcomes. The integration of omics technologies into laboratory medicine has transformed healthcare, enabling personalized treatments and interventions based on individuals' distinct genetic and metabolic profiles. Interpreting laboratory data relies on reliable reference values. Presently, population-derived references are used for individuals, risking misinterpretation due to population heterogeneity, and leading to medical errors. Thus, personalized references are crucial for precise interpretation of individual laboratory results, and the interpretation of omics data should be based on individualized reference values. We reviewed recent advancements in personalized laboratory medicine, focusing on personalized omics, and discussed strategies for implementing personalized statistical approaches in omics technologies to improve global health and concluded that personalized statistical algorithms for interpretation of omics data have great potential to enhance global health. Finally, we demonstrated that the convergence of nanotechnology and omics sciences is transforming personalized laboratory medicine by providing unparalleled diagnostic precision and innovative therapeutic strategies.

Epigenetic Studies of Chinese Herbal Medicine: Pleiotropic Role of DNA Methylation

Accumulating knowledge has been achieved on DNA methylation participating in numerous cellular processes and multiple human diseases; however, few studies have addressed the pleiotropic role of DNA methylation in Chinese herbal medicine (CHM). CHM has been used worldwide for the prevention and treatment of multiple diseases. Newly developed epigenetic techniques have brought great opportunities for the development of CHM. In this review, we summarize the DNA methylation studies and portray the pleiotropic role of DNA methylation in CHM. DNA methylation serves as a mediator participating in plant responses to environmental factors, and thus affecting CHM medicinal plants growth and bioactive compound biosynthesis which are vital for therapeutic effects. Furthermore, DNA methylation helps to uncover the pharmaceutical mechanisms of CHM formulae, herbs, and herbal-derived compounds. It also provides scientific validation for constitution theory and other essential issues of CHM. This newly developed field of DNA methylation is up-and-coming to address many complicated scientific questions of CHM; it thus not only promotes disease treatment but also facilitates health maintenance.

Phosphoproteomics: a valuable tool for uncovering molecular signaling in cancer cells

INTRODUCTION

Many pathologies, including cancer, have been associated with aberrant phosphorylation-mediated signaling networks that drive altered cell proliferation, migration, metabolic regulation, and can lead to systemic inflammation. Phosphoproteomics, the large-scale analysis of protein phosphorylation sites, has emerged as a powerful tool to define signaling network regulation and dysregulation in normal and pathological conditions.

AREAS COVERED

We provide an overview of methodology for global phosphoproteomics as well as enrichment of specific subsets of the phosphoproteome, including phosphotyrosine and phospho-motif enrichment of kinase substrates. We review quantitative methods, advantages and limitations of different mass spectrometry acquisition formats, and computational approaches to extract biological insight from phosphoproteomics data. Throughout, we discuss various applications and their challenges in implementation.

EXPERT OPINION

Over the past 20 years the field of phosphoproteomics has advanced to enable deep biological and clinical insight through the quantitative analysis of signaling networks. Future areas of development include Clinical Laboratory Improvement Amendments (CLIA)-approved methods for analysis of clinical samples, continued improvements in sensitivity to enable analysis of small numbers of rare cells and tissue microarrays, and computational methods to integrate data resulting from multiple systems-level quantitative analytical methods.

The comparison of miRNAs that respond to anti-breast cancer drugs and usnic acid for the treatment of breast cancer

This study was designed to compare usnic acid with anti-breast cancer drug molecules (A-BCDM) routinely used in the treatment of breast cancer. The miRNA information of 17 anti-breast cancer drug used in breast cancer treatment was obtained from the Small Molecule-miRNA Network-Based Inferance (SMIR-NBI) tool. We had been determined common and different expressed miRNAs between 17 A-BCDM & usnic acid and were classified according to the common miRNAs to reveal molecular similarity. As a result of the bioinformatic analyzes, 20 common miRNAs were determined between 17 A-BCDM and usnic acid. The common miRNAs were analyzed with bioinformatic tolls for determining pathways and targets. The most common miRNAs for 6 of 17 A-BCDM and usnic acid were determined as miR-374a-5p and miR-26a-5p. We compared the anti-proliferative effect of usnic acid and one of the 17 A-BCDM that tamoxifen on MDA-MB-231 triple negative breast cancer cell with real-time cell analysis system. The real time PCR assay was carried out with miR-26a-5p for evaluate to expression level of MDA-MB-231 breast cancer cell and MCF-12A non-cancerous epithelial breast cell. As a result of study, usnic acid as novel candidate drug molecule showed high similarity ratio with 5-Fluorouracil, Sulindac Sulfide, Curcumin and Cisplatin A-BCDM used in treatment of breast cancer. miR-26a-5p as common response miRNA of usnic acid and tamoxifen was showed a decreased level of expression by validated qRT-PCR assay. The obtained from study, in addition to 17 A-BCDM, usnic acid has also the potential to be used as a candidate molecule in the treatment of breast cancer. Moreover, miR-26a-5p might be used as a biomarker in the treatment of breast cancer but further analysis is required.

Proteome-Scale Mapping of Perturbed Proteostasis in Living Cells

Proteinopathies are degenerative diseases in which specific proteins adopt deleterious conformations, leading to the dysfunction and demise of distinct cell types. They comprise some of the most significant diseases of aging-from Alzheimer's disease to Parkinson's disease to type 2 diabetes-for which not a single disease-modifying or preventative strategy exists. Here, we survey approaches in tractable cellular and organismal models that bring us toward a more complete understanding of the molecular consequences of protein misfolding. These include proteome-scale profiling of genetic modifiers, as well as transcriptional and proteome changes. We describe assays that can capture protein interactomes in situ and distinct protein conformational states. A picture of cellular drivers and responders to proteotoxicity emerges from this work, distinguishing general alterations of proteostasis from cellular events that are deeply tied to the intrinsic function of the misfolding protein. These distinctions have consequences for the understanding and treatment of proteinopathies.

A Multi-Omics Interpretable Machine Learning Model Reveals Modes of Action of Small Molecules

High-throughput screening and gene signature analyses frequently identify lead therapeutic compounds with unknown modes of action (MoAs), and the resulting uncertainties can lead to the failure of clinical trials. We developed an approach for uncovering MoAs through an interpretable machine learning model of transcriptomics, epigenomics, metabolomics, and proteomics. Examining compounds with beneficial effects in models of Huntington's Disease, we found common MoAs for compounds with unrelated structures, connectivity scores, and binding targets. The approach also predicted highly divergent MoAs for two FDA-approved antihistamines. We experimentally validated these effects, demonstrating that one antihistamine activates autophagy, while the other targets bioenergetics. The use of multiple omics was essential, as some MoAs were virtually undetectable in specific assays. Our approach does not require reference compounds or large databases of experimental data in related systems and thus can be applied to the study of agents with uncharacterized MoAs and to rare or understudied diseases.

Massive integrative gene set analysis enables functional characterization of breast cancer subtypes

The availability of large-scale repositories and integrated cancer genome efforts have created unprecedented opportunities to study and describe cancer biology. In this sense, the aim of translational researchers is the integration of multiple omics data to achieve a better identification of homogeneous subgroups of patients in order to develop adequate diagnostic and treatment strategies from the personalized medicine perspective. So far, existing integrative methods have grouped together omics data information, leaving out individual omics data phenotypic interpretation. Here, we present the Massive and Integrative Gene Set Analysis (MIGSA) R package. This tool can analyze several high throughput experiments in a comprehensive way through a functional analysis strategy, relating a phenotype to its biological function counterpart defined by means of gene sets. By simultaneously querying different multiple omics data from the same or different groups of patients, common and specific functional patterns for each studied phenotype can be obtained. The usefulness of MIGSA was demonstrated by applying the package to functionally characterize the intrinsic breast cancer PAM50 subtypes. For each subtype, specific functional transcriptomic profiles and gene sets enriched by transcriptomic and proteomic data were identified. To achieve this, transcriptomic and proteomic data from 28 datasets were analyzed using MIGSA. As a result, enriched gene sets and important genes were consistently found as related to a specific subtype across experiments or data types and thus can be used as molecular signature biomarkers.