Multi-omics and Single-cell Omics: New Tools in Drug Target Discovery

Biological systems are inherently noisy, with noise caused by the measurement process (i.e., technical noise) and by inherent biological variability (i.e., biological noise). Biological noise accounts for phenotypic differences within a population of individuals, as well as differences between two different cells of the same lineage within an organ of a single individual. Biomedical investigators typically strive to eliminate technical noise as much as possible to optimize the signal-to-noise ratio of a measurement, and to mitigate the impact of biological noise by repeated measurements, thereby improving the statistical confidence in the mean signal.

With the advent of the genomic era, the biomedical research paradigm has moved from pure reductionism to a more holistic, integrated approach to system analysis. In this setting, biological noise plays a key role, accounting for heterogeneity in response to perturbations between cells and between individuals, and offering a mechanistic explanation for incomplete genetic penetrance (divergent pathophenotypes), variable functional phenotypes in subjects with identical disease-causing genetic variants, as well as variability in drug response. Clearly, understanding the bases for this type of biological heterogeneity is essential for defining individual disease risk, prognosis, and precision therapeutics.

Since the beginning of the Human Genome Project, we have moved from DNA sequence determination to a detailed assessment of other omic components. Technical approaches to measuring each omic level have evolved rapidly. More efficient, rapid, lower-cost DNA sequencing is now complemented by bulk transcriptomics, proteomics, epigenomics, and metabolomics, which, in turn, have been followed by single-cell transcriptomics and spatial transcriptomics, as well as single-cell proteomics, epigenomics, and metabolomics. This extraordinary growth in the detailed omic characterization of a biological system has led to analytical challenges that have yet to be resolved satisfactorily. These multi-omic systems are of high dimensionality and are over-determined from a dynamical systems perspective, which leads to the following questions: How can one assess the interactions among multi-omic layers? How can one reduce the dimensionality of the system to make it optimally tractable and biomedically useful? How can one exploit the biological noise inherent in these multi-omic systems to define disease expression within an individual and consequent precision therapeutics tailored to that individual’s multi-ome?

We and others have approached this problem through the lens of molecular interaction networks (Figure 1). Molecular networks (e.g., protein-protein interaction networks, metabolic networks, gene regulatory networks, Bayesian coexpression networks) provide graphical depictions of relationships between the elements within a given network. For example, in the typical protein-protein interaction network, each node represents a protein and its physical interaction with another protein in the proteome is depicted by a link or edge. We have shown that subnetworks are contained within the comprehensive protein-protein interaction network that are associated with specific diseases (disease modules) (1,2). By reducing the overall dimensionality of the network through a disease-focused perspective, these constructs offer a more detailed, causal view of the pathways and proteins that govern disease pathobiology. Disease modules that overlap provide insights into pathways and proteins that are common to different diseases. In addition, disease modules offer a mechanistic path toward drug target identification, making the target discovery process more efficient and rational. One can also use this same approach to repurpose approved drugs as we have done for coronary artery disease (3) and SARS-CoV-2 infection (4) by applying a variety of network-based statistical approaches including network-based AI-strategies (3–5).

Figure 1. Network integration of multi-omics data from the patient to the single cell level.

Analysis of integrated multi-omic networks provides a basis for resolving phenotypic heterogeneity in human pathobiology and in defining precise drug therapies.

(Reproduced with permission: Wang R et al., Arterioscler Thromb Vase Biol 2023;43:493-503)

Expanding this analysis of the protein-protein interaction network to include the transcriptome yields additional useful information, including which pathways within a disease module are present in a given cell type or tissue (6). Only tissues that express key components of pathways governing disease phenotype manifest disease, regardless of whether or not genetic variants associated with the disease are themselves expressed in that tissue. Furthermore, using a transcriptome-based differential analysis of gene expression pairs (pairwise correlation analysis) in diseased vs. normal tissue, one can generate patient-specific disease modules, or reticulotypes (after the Latin for network), that can provide unique information on patient-specific disease mechanisms and patient-specific drug targets, as we showed for hypertrophic cardiomyopathy (7).

It is important to point out that biological variability of complex multi-omic systems can be a reflection of deterministic genetic variability (variants, mutants) as well as (bio)chemical stochasticity (epigenetics, post-translational modification of the proteome or the transcriptome). This latter class of causes of omic noise is more challenging to assess owing to its randomness and incompleteness. Theoretically, these stochastic modifications of the proteome can serve as the basis for a multitude of instantiations of the protein-protein interaction network with possibly differing effects on the phenotypes of interest. In the case of exposome-based epigenomic modification, it is not always clear as to what the time-activity relationship is to the (patho)phenotypes of interest. Furthermore, natural, time-dependent variation in the epigenome as a result of stage of development coupled with trans-generational effects on epigenetic marks make this level of omic analysis even more complicated, with no widely accepted approach available as yet.

Are there evolutionary advantages to multi-omic networks that can inform our analysis of them? One answer to this question lies in an analysis of the dynamical behavior of such networks. Coupling among omic layers appears to increase the stable operating range of the system, mitigating the consequences of (adverse) perturbations, rendering the system more resilient that it would be were one omic layer to operate in isolation (8). It is likely that variance in network structure, protein expression, or protein function (heritable or stochastic) yields biological noise that influences this resilience. Variation in the concentration of metabolites in metabolic networks coupled with variation in the catalytic constants of their coupled reactions provide a stabler operating range than limited or no variation, providing the system with the ability to utilize, for example, energy sources effectively in conditions of dearth or abundance (9). Allosteric modulation of enzyme systems provides a similar functional advantage (10).

In addition to molecular-level omics, cell-based immunophenotyping has been utilized in conjunction with single-cell RNA sequencing to characterize in more detail subsets of cells of ostensibly similar lineage. This approach, coupled with multi-organ and single-cell analyses of gene expression, has provided insight into patient-specific regulation of the immune response and patient-specific drug target identification in allergic (11) and autoimmune disorders (12).

Detailed immunophenotyping is one method for characterizing relevant phenotypic features that govern disease expression (13). Robust phenotyping in general, including orthogonal phenotyping (i.e., characterizing phenotypic features that are not believed or known to play a role in the disease of interest), is essential for identifying disease mechanisms and potential drug targets. Coupled with multi-omic data, in-depth phenotyping can be utilized to ascertain key mechanisms of disease in a patient-specific manner, as well as potential therapeutic targets. Furthermore, similar approaches can be used to identify network-based, multi-omic-derived biomarkers that can be used to predict disease course and response to therapy.

To achieve these goals effectively requires a specific computational workflow linked to newer network-based analytical algorithms (14) (Figure 2). Typically, the analytical exercise requires first a feature selection step (i.e., which elements in the phenotypic and multi-omic datasets may be important for outcome prediction), followed by data integration and representation (i.e., dimensionality reduction and newly transformed data rendering), and a data clustering methodology. This approach is designed to yield information on outcome associations, biomarker generation, and risk or response prediction. There are numerous methods that have been developed for use in this workflow that include supervised vs. unsupervised feature selection, linear vs. nonlinear feature extraction and data representation, linear vs. network-based multi-omic data integration, and partitioning vs. hierarchical clustering algorithms. There are many examples of specific algorithms under each of these feature selection or model types, the choice of which depends upon the nature of the dataset and the goal of the analysis.

Figure 2. Computational strategy for disease subtyping.

Upper figure: progression from one-size-fits-all medicine to individualized medicine.

Lower figure: Computational pipeline for analysis of multi-omic datasets

(Reproduced with permission: Maiorino E, Loscalzo J. Arterioscler Thromb Vase Biol 2023;43:1111-1123)

Recent advances in network-based machine learning and artificial intelligence utilize a combination of physicochemical features of small-molecule drugs, estimated binding constants to protein targets, and network features of disease modules and their multi-omic context to predict drug-target interactions (5,15). This type of analysis coupled with AI-based functional assessment of the disease pathway or module within which the drug target is localized can then be used to minimize the number of drug-target interactions that require experimental testing in vitro and in vivo, decreasing development time and offering the promise of accelerating implementation in human clinical trials.

Biomedical investigators have spent the past 200 years attempting to reduce the complexity of the biological systems they study. In the current era of large datasets of multi-omic systems, we can no longer afford to avoid their intrinsic complexity. In fact, that complexity and the natural biological variation (noise) that further complicates it should be viewed as a means to move the field of precision medicine forward. Recognizing that many, if not most, pathophenotypes are convergent, that deeper phenotyping will unveil the subtle distinctions that discriminate between individuals, and that a robust analysis of the molecular complexity underlying these phenotypes will provide a deeper individualized understanding of disease mechanism and a path to more precises and effective therapies.