Proteomics Is Not an Island: Multi-omics Integration Is the Key to Understanding Biological Systems

Transforming Clinical Research: The Power of High-Throughput Omics Integration

High-throughput omics technologies have dramatically changed biological research, providing unprecedented insights into the complexity of living systems. This review presents a comprehensive examination of the current landscape of high-throughput omics pipelines, covering key technologies, data integration techniques and their diverse applications. It looks at advances in next-generation sequencing, mass spectrometry and microarray platforms and highlights their contribution to data volume and precision. In addition, this review looks at the critical role of bioinformatics tools and statistical methods in managing the large datasets generated by these technologies. By integrating multi-omics data, researchers can gain a holistic understanding of biological systems, leading to the identification of new biomarkers and therapeutic targets, particularly in complex diseases such as cancer. The review also looks at the integration of omics data into electronic health records (EHRs) and the potential for cloud computing and big data analytics to improve data storage, analysis and sharing. Despite significant advances, there are still challenges such as data complexity, technical limitations and ethical issues. Future directions include the development of more sophisticated computational tools and the application of advanced machine learning techniques, which are critical for addressing the complexity and heterogeneity of omics datasets. This review aims to serve as a valuable resource for researchers and practitioners, highlighting the transformative potential of high-throughput omics technologies in advancing personalized medicine and improving clinical outcomes.

Mapping the human brain proteome: opportunities, challenges, and clinical potential

INTRODUCTION

Due to the segmented functions and complexity of the human brain, the characterization of molecular profiles within specific areas such as brain structures and biofluids is essential to unveil the molecular basis for structure specialization as well as the molecular imbalance associated with neurodegenerative and psychiatric diseases.

AREAS COVERED

Much of our knowledge about brain functionality derives from neurophysiological, anatomical, and transcriptomic approaches. More recently, laser capture and imaging proteomics, technological and computational developments in LC-MS/MS, as well as antibody/aptamer-based platforms have allowed the generation of novel cellular, spatial, and posttranslational dimensions as well as innovative facets in biomarker validation and druggable target identification.

EXPERT OPINION

Proteomics is a powerful toolbox to functionally characterize, quantify, and localize the extensive protein catalog of the human brain across physiological and pathological states. Brain function depends on multi-dimensional protein homeostasis, and its elucidation will help us to characterize biological pathways that are essential to properly maintain cognitive functions. In addition, comprehensive human brain pathological proteomes may be the basis in computational drug-repositioning methods as a strategy for unveiling potential new therapies in neurodegenerative and psychiatric disorders.

Elevated Nuclear PHGDH Synergistically Functions with cMyc to Reshape the Immune Microenvironment of Liver Cancer

Herein, we observed that nuclear localization of phosphoglycerate dehydrogenase (PHGDH) is associated with poor prognosis in liver cancer, and Phgdh is required for liver cancer progression in a mouse model. Unexpectedly, impairment of Phgdh enzyme activity exerts a slight effect in a liver cancer model. In liver cancer cells, the aspartate kinase-chorismate mutase-tyrA prephenate dehydrogenase (ACT) domain of PHGDH binds nuclear cMyc to form a transactivation axis, PHGDH/p300/cMyc/AF9, which drives chemokine CXCL1 and IL8 gene expression. Then, CXCL1 and IL8 promote neutrophil recruitment and enhance tumor-associated macrophage (TAM) filtration in the liver, thereby advancing liver cancer. Forced cytosolic localization of PHGDH or destruction of the PHGDH/cMyc interaction abolishes the oncogenic function of nuclear PHGDH. Depletion of neutrophils by neutralizing antibodies greatly hampers TAM filtration. These findings reveal a nonmetabolic role of PHGDH with altered cellular localization and suggest a promising drug target for liver cancer therapy by targeting the nonmetabolic region of PHGDH.

3PNMF-MKL: A non-negative matrix factorization-based multiple kernel learning method for multi-modal data integration and its application to gene signature detection

In this current era, biomedical big data handling is a challenging task. Interestingly, the integration of multi-modal data, followed by significant feature mining (gene signature detection), becomes a daunting task. Remembering this, here, we proposed a novel framework, namely, three-factor penalized, non-negative matrix factorization-based multiple kernel learning with soft margin hinge loss (3PNMF-MKL) for multi-modal data integration, followed by gene signature detection. In brief, limma, employing the empirical Bayes statistics, was initially applied to each individual molecular profile, and the statistically significant features were extracted, which was followed by the three-factor penalized non-negative matrix factorization method used for data/matrix fusion using the reduced feature sets. Multiple kernel learning models with soft margin hinge loss had been deployed to estimate average accuracy scores and the area under the curve (AUC). Gene modules had been identified by the consecutive analysis of average linkage clustering and dynamic tree cut. The best module containing the highest correlation was considered the potential gene signature. We utilized an acute myeloid leukemia cancer dataset from The Cancer Genome Atlas (TCGA) repository containing five molecular profiles. Our algorithm generated a 50-gene signature that achieved a high classification AUC score (viz., 0.827). We explored the functions of signature genes using pathway and Gene Ontology (GO) databases. Our method outperformed the state-of-the-art methods in terms of computing AUC. Furthermore, we included some comparative studies with other related methods to enhance the acceptability of our method. Finally, it can be notified that our algorithm can be applied to any multi-modal dataset for data integration, followed by gene module discovery.

Rapid Multi-Omics Sample Preparation for Mass Spectrometry

Multi-omics analysis is a powerful and increasingly utilized approach to gain insight into complex biological systems. One major hindrance with multi-omics, however, is the lengthy and wasteful sample preparation process. Preparing samples for mass spectrometry (MS)-based multi-omics involves extraction of metabolites and lipids with organic solvents, precipitation of proteins, and overnight digestion of proteins. These existing workflows are disparate and laborious. Here, we present a simple, efficient, and unified approach to prepare lipids, metabolites, and proteins for MS analysis. Our approach, termed the Bead-enabled Accelerated Monophasic Multi-omics (BAMM) method, combines an n-butanol-based monophasic extraction with unmodified magnetic beads and accelerated protein digestion. We demonstrate that the BAMM method affords comparable depth, quantitative reproducibility, and recovery of biomolecules as state-of-the-art multi-omics methods (e.g., Matyash extraction and overnight protein digestion). However, the BAMM method only requires about 3 h to perform, which saves 11 steps and 19 h on average compared to published multi-omics methods. Furthermore, we validate the BAMM method for multiple sample types and formats (biofluid, culture plate, and pellet) and show that in all cases, it produces high biomolecular coverage and data quality.

A novel subtype to predict prognosis and treatment response with DNA driver methylation-transcription in ovarian cancer

Aims: To identify a novel subtype with DNA driver methylation-transcriptomic multiomics and predict prognosis and therapy response in serous ovarian cancer (SOC). Methods: SOC cohorts with both mRNA and methylation were collected, and DNA driver methylation (DNAme) was identified with the MithSig method. A novel prognostic subtype was developed by integrating the information on DNAme and prognosis-regulated DNAme-associated mRNA by similarity network fusion. Results: 43 overlapped DNAme were identified in three independent cohorts. SOC patients were categorized into three distinct subtypes by integrated multiomics. There were differences in prognosis, tumor microenvironment and response to therapy among the subtypes. Conclusion: This study identified 43 DNAmes and proposes a novel subtype toward personalized chemotherapy and immunotherapy for SOC patients based on multiomics.

Single-cell multi-omics sequencing and its applications in studying the nervous system

Single-cell sequencing has become one of the most powerful and popular techniques in dissecting molecular heterogeneity and modeling the cellular architecture of a biological system. During the past twenty years, the throughput of single-cell sequencing has increased from hundreds of cells to over tens of thousands of cells in parallel. Moreover, this technology has been developed from sequencing transcriptome to measure different omics such as DNA methylome, chromatin accessibility, and so on. Currently, multi-omics which can analyze different omics in the same cell is rapidly advancing. This work advances the study of many biosystems, including the nervous system. Here, we review current single-cell multi-omics sequencing techniques and describe how they improve our understanding of the nervous system. Finally, we discuss the open scientific questions in neural research that may be answered through further improvement of single-cell multi-omics sequencing technology.

Life sciences and mass spectrometry: some personal reflections

Molecular analysis of biological systems by mass spectrometry was in focus of technological developments in the second half of the 20th century, in which the issues of chemical identification of high molecular diversity by biophysical instrumental methods appeared as a mission impossible. By developing dialogs between researchers dealing with life sciences and medicine on one side and technology developers on the other, new horizons toward deciphering, identifying and quantifying of complex systems became a reality. Contributions toward this goal can be today considered as pioneering efforts delivered by a number of researchers, including generations of motivated students and associates.

Analysing omics data sets with weighted nodes networks (WNNets)

Current trends in biomedical research indicate data integration as a fundamental step towards precision medicine. In this context, network models allow representing and analysing complex biological processes. However, although effective in unveiling network properties, these models fail in considering the individual, biochemical variations occurring at molecular level. As a consequence, the analysis of these models partially loses its predictive power. To overcome these limitations, Weighted Nodes Networks (WNNets) were developed. WNNets allow to easily and effectively weigh nodes using experimental information from multiple conditions. In this study, the characteristics of WNNets were described and a proteomics data set was modelled and analysed. Results suggested that degree, an established centrality index, may offer a novel perspective about the functional role of nodes in WNNets. Indeed, degree allowed retrieving significant differences between experimental conditions, highlighting relevant proteins, and provided a novel interpretation for degree itself, opening new perspectives in experimental data modelling and analysis. Overall, WNNets may be used to model any high-throughput experimental data set requiring weighted nodes. Finally, improving the power of the analysis by using centralities such as betweenness may provide further biological insights and unveil novel, interesting characteristics of WNNets.

Feature Selection Methods for Protein Biomarker Discovery from Proteomics or Multiomics Data

Untargeted mass spectrometry (MS)-based proteomics provides a powerful platform for protein biomarker discovery, but clinical translation depends on the selection of a small number of proteins for downstream verification and validation. Due to the small sample size of typical discovery studies, protein markers identified from discovery data may not be generalizable to independent datasets. In addition, a good protein marker identified using a discovery platform may be difficult to implement in verification and validation platforms. Moreover, although multiomics characterization is being increasingly used in discovery cohort studies, there is no existing method for multiomics-facilitated protein biomarker selection. Here, we present ProMS, a computational algorithm for protein marker selection. The algorithm is based on the hypothesis that a phenotype is characterized by a few underlying biological functions, each manifested by a group of coexpressed proteins. A weighted k-medoids clustering algorithm is applied to all univariately informative proteins to identify both coexpressed protein clusters and a representative protein for each cluster as markers. In two clinically important classification problems, ProMS shows superior performance compared with existing feature selection methods. ProMS can be extended to the multiomics setting (ProMS_mo) through a constrained weighted k-medoids clustering algorithm, and the protein panels selected by ProMS_mo show improved performance on independent test data compared with ProMS. In addition to superior performance, ProMS and ProMS_mo also have two unique strengths. First, the feature clusters enable functional interpretation of the selected protein markers. Second, the feature clusters provide an opportunity to select replacement protein markers, facilitating a robust transition to the verification and validation platforms. In summary, this study provides a unified and effective computational framework for selecting protein biomarkers using proteomics or multiomics data. The software implementation is publicly available at https://github.com/bzhanglab/proms.

Reflections on the HUPO Human Proteome Project, the Flagship Project of the Human Proteome Organization, at 10 Years

We celebrate the 10th anniversary of the launch of the HUPO Human Proteome Project (HPP) and its major milestone of confident detection of at least one protein from each of 90% of the predicted protein-coding genes, based on the output of the entire proteomics community. The Human Genome Project reached a similar decadal milestone 20 years ago. The HPP has engaged proteomics teams around the world, strongly influenced data-sharing, enhanced quality assurance, and issued stringent guidelines for claims of detecting previously "missing proteins." This invited perspective complements papers on "A High-Stringency Blueprint of the Human Proteome" and "The Human Proteome Reaches a Major Milestone" in special issues of Nature Communications and Journal of Proteome Research, respectively, released in conjunction with the October 2020 virtual HUPO Congress and its celebration of the 10th anniversary of the HUPO HPP.

The genetic proteome: Using genetics to inform the proteome of mycobacterial pathogens

Mycobacterial pathogens pose a sustained threat to human health. There is a critical need for new diagnostics, therapeutics, and vaccines targeting both tuberculous and nontuberculous mycobacterial species. Understanding the basic mechanisms used by diverse mycobacterial species to cause disease will facilitate efforts to design new approaches toward detection, treatment, and prevention of mycobacterial disease. Molecular, genetic, and biochemical approaches have been widely employed to define fundamental aspects of mycobacterial physiology and virulence. The recent expansion of genetic tools in mycobacteria has further increased the accessibility of forward genetic approaches. Proteomics has also emerged as a powerful approach to further our understanding of diverse mycobacterial species. Detection of large numbers of proteins and their modifications from complex mixtures of mycobacterial proteins is now routine, with efforts of quantification of these datasets becoming more robust. In this review, we discuss the “genetic proteome,” how the power of genetics, molecular biology, and biochemistry informs and amplifies the quality of subsequent analytical approaches and maximizes the potential of hypothesis-driven mycobacterial research. Published proteomics datasets can be used for hypothesis generation and effective post hoc supplementation to experimental data. Overall, we highlight how the integration of proteomics, genetic, molecular, and biochemical approaches can be employed successfully to define fundamental aspects of mycobacterial pathobiology.

Galactolipid and Phospholipid Profile and Proteome Alterations in Soybean Leaves at the Onset of Salt Stress

Salinity is a major environmental factor that constrains soybean yield and grain quality. Given our past observations using the salt-sensitive soybean (Glycine max [L.] Merr.) accession C08 on its early responses to salinity and salt-induced transcriptomic modifications, the aim of this study was to assess the lipid profile changes in this cultivar before and after short-term salt stress, and to explore the adaptive mechanisms underpinning lipid homeostasis. To this end, lipid profiling and proteomic analyses were performed on the leaves of soybean seedlings subjected to salt treatment for 0, 0.5, 1, and 2 h. Our results revealed that short-term salt stress caused dynamic lipid alterations resulting in recycling for both galactolipids and phospholipids. A comprehensive understanding of membrane lipid adaption following salt treatment was achieved by combining time-dependent lipidomic and proteomic data. Proteins involved in phosphoinositide synthesis and turnover were upregulated at the onset of salt treatment. Salinity-induced lipid recycling was shown to enhance jasmonic acid and phosphatidylinositol biosyntheses. Our study demonstrated that salt stress resulted in a remodeling of membrane lipid composition and an alteration in membrane lipids associated with lipid signaling and metabolism in C08 leaves.

Genomics, Proteomics and Metabolomics Approaches for Predicting Diabetic Nephropathy in Type 2 Diabetes Mellitus Patients

BACKGROUND

There is a continuous rise in the prevalence of type 2 diabetes mellitus (T2DM) worldwide and most patients are unaware of the presence of this chronic disease at the early stages. T2DM is associated with complications related to long-term damage and failure of multiple organ systems caused by vascular changes associated with glycated end products, oxidative stress, mild inflammation, and neovascularization. Among the most frequent complications of T2DM observed in about 20-40% of T2DM patients is diabetes nephropathy (DN).

METHODS

A literature search was made in view of highlighting the novel applications of genomics, proteomics and metabolomics, as the new prospective strategy for predicting DN in T2DM patients.

RESULTS

The complexity of DN requires a comprehensive and unbiased approach to investigate the main causes of disease and identify the most important mechanisms underlying its development. With the help of evolving throughput technology, rapidly evolving information can now be applied to clinical practice.

DISCUSSION

DN is also the leading cause of end-stage renal disease and comorbidity independent of T2DM. In terms of the comorbidity level, DN has many phenotypes; therefore, timely diagnosis is required to prevent these complications. Currently, urine albumin-to-creatinine ratio and estimated glomerular filtration rate (eGFR) are gold standards for assessing glomerular damage and changes in renal function. However, GFR estimation based on creatinine is limited to hyperfiltration status; therefore, this makes albuminuria and eGFR indicators less reliable for early-stage diagnosis of DN.

CONCLUSION

The combination of genomics, proteomics, and metabolomics assays as suitable biological systems can provide new and deeper insights into the pathogenesis of diabetes, as well as discover prospects for developing suitable and targeted interventions.

Fitness variation across subtle environmental perturbations reveals local modularity and global pleiotropy of adaptation

Building a genotype-phenotype-fitness map of adaptation is a central goal in evolutionary biology. It is difficult even when adaptive mutations are known because it is hard to enumerate which phenotypes make these mutations adaptive. We address this problem by first quantifying how the fitness of hundreds of adaptive yeast mutants responds to subtle environmental shifts. We then model the number of phenotypes these mutations collectively influence by decomposing these patterns of fitness variation. We find that a small number of inferred phenotypes can predict fitness of the adaptive mutations near their original glucose-limited evolution condition. Importantly, inferred phenotypes that matter little to fitness at or near the evolution condition can matter strongly in distant environments. This suggests that adaptive mutations are locally modular - affecting a small number of phenotypes that matter to fitness in the environment where they evolved - yet globally pleiotropic - affecting additional phenotypes that may reduce or improve fitness in new environments.

Multimodal vibrational studies of drug uptake in vitro: Is the whole greater than the sum of their parts?

Herein, we investigated the use of multimodal Raman and infrared (IR) spectroscopic microscopy for the elucidation of drug uptake and subsequent cellular responses. Firstly, we compared different methods for the analysis of the combined data. Secondly, we evaluated whether the combined analysis provided enough benefits to justify the fusion of the data. A459 cells inoculated with doxorubicin (DOX) at different times were fixed and analysed using each technique. Raman spectroscopy provided high sensitivity to DOX and enabled an accurate estimation of the drug uptake at each time point, whereas IR provided a better insight into the resultant changes in the biochemical composition of the cell. In terms of benefits of data fusion, 2D correlation analysis allowed the study of the relationship between IR and Raman variables, whereas the joint analysis of IR and Raman enabled the correlation of the different variables to be monitored over time. In summary, the complementary nature of IR and Raman makes the combination of these vibrational techniques an appealing tool to follow drug kinetics and cellular response.

Conjugating immunoassays to mass spectrometry: Solutions to contemporary challenges in clinical diagnostics

Developments in immunoassays and mass spectrometry have independently influenced diagnostic technology. However, both techniques possess unique strengths and limitations, which define their ability to meet evolving requirements for faster, more affordable and more accurate clinical tests. In response, hybrid techniques, which combine the accessibility and ease-of-use of immunoassays with the sensitivity, high throughput and multiplexing capabilities of mass spectrometry are continually being explored. Developments in antibody conjugation methodology have expanded the role of these biomolecules to applications outside of conventional colorimetric assays and histology. Furthermore, the range of different mass spectrometry ionisation and analysis technologies has enabled its successful adaptation as a detection method for numerous clinically relevant immunological assays. Several recent examples of combined mass spectrometry-immunoassay techniques demonstrate the potential of these methods as improved diagnostic tests for several important human diseases. The present challenges are to continue technological advancements in mass spectrometry instrumentation and develop improved bioconjugation methods, which can overcome their existing limitations and demonstrate the clinical significance of these hybrid approaches.

Proteomics approaches to understand cell biology and virulence of Entamoeba histolytica protozoan parasite

Entamoeba histolytica is the primitive eukaryotic parasite responsible of human amoebiasis, a disease characterized by bloody intestinal diarrhea and invasive extraintestinal illness. The knowledge of the complete genome sequence of virulent E. histolytica and related non-pathogenic species allowed the development of novel genome-wide methodological approaches including protein expression profiling and cellular proteomics in the so called post-genomic era. Proteomics studies have greatly increased our understanding of the cell biology of this ancient parasite. This review summarizes the current works concerning proteomics studies on cell biology, life cycle, virulence and pathogenesis, novel therapies, and protein expression regulation mechanisms in E. histolytica parasite. Also, we discuss the use of proteomics data for the development of novel therapies, the identification of potential disease biomarkers and differential diagnosis between species. SIGNIFICANCE: Entamoeba histolytica is the unicellular protozoan parasite responsible of human amoebiasis, a serious disease with worldwide distribution characterized by bloody intestinal diarrhea and invasive extraintestinal illness including peritonitis and liver, pulmonary and brain abscesses. The post-genomic era allowed the development of proteomic studies including protein expression profiling and cellular proteomics. These proteomics studies have greatly increased our understanding on cell biology, life cycle (cyst-trophozoite conversion), virulence, pathogenesis, novel therapies, and protein expression regulation mechanisms in E. histolytica. Importantly, proteomics has revealed the identity of proteins related to novel therapies, and the identification of potential disease biomarkers and proteins with use in diagnosis between species. Hopefully in the coming years, and through the use of more sophisticated omics tools, including deep proteomics, a more complete set of proteins involved in the aforementioned cellular processes can be obtained to understand the biology of this ancient eukaryote.

Potential of multiomics technology in precision medicine

PURPOSE OF REVIEW

The 'precision medicine' refers to the generation of identification and classification criteria for advanced taxonomy of patients, exploiting advanced models to infer optimized clinical decisions for each disease phenotype.

RECENT FINDINGS

The current article reviews new advances in the past 18 months on the microbiomics science intended as new discipline contributing to advanced 'precision medicine'. Recently published data highlight the importance of multidimensional data in the description of deep disease phenotypes, including microbiome and immune profiling, and support the efficacy of the systems medicine to better stratify patients, hence optimizing diagnostics, clinical management and response to treatments.

SUMMARY

The articles referenced in this review help inform the reader on new decision-support systems that can be based on multiomics patients' data including microbiome and immune profiling. These harmonized and integrated data can be elaborated by artificial intelligence to generate optimized diagnostic pipelines and clinical interventions.

Chasing Intracellular Zika Virus Using Proteomics

Flaviviruses are the most medically relevant group of arboviruses causing a wide range of diseases in humans and are associated with high mortality and morbidity, as such posing a major health concern. Viruses belonging to this family can be endemic (e.g., dengue virus), but can also cause fulminant outbreaks (e.g., West Nile virus, Japanese encephalitis virus and Zika virus). Intense research efforts in the past decades uncovered shared fundamental strategies used by flaviviruses to successfully replicate in their respective hosts. However, the distinct features contributing to the specific host and tissue tropism as well as the pathological outcomes unique to each individual flavivirus are still largely elusive. The profound footprint of individual viruses on their respective hosts can be investigated using novel technologies in the field of proteomics that have rapidly developed over the last decade. An unprecedented sensitivity and throughput of mass spectrometers, combined with the development of new sample preparation and bioinformatics analysis methods, have made the systematic investigation of virus-host interactions possible. Furthermore, the ability to assess dynamic alterations in protein abundances, protein turnover rates and post-translational modifications occurring in infected cells now offer the unique possibility to unravel complex viral perturbations induced in the infected host. In this review, we discuss the most recent contributions of mass spectrometry-based proteomic approaches in flavivirus biology with a special focus on Zika virus, and their basic and translational potential and implications in understanding and characterizing host responses to arboviral infections.