Advances in neuroproteomics for neurotrauma: unraveling insights for personalized medicine and future prospects

Neuroproteomics, an emerging field at the intersection of neuroscience and proteomics, has garnered significant attention in the context of neurotrauma research. Neuroproteomics involves the quantitative and qualitative analysis of nervous system components, essential for understanding the dynamic events involved in the vast areas of neuroscience, including, but not limited to, neuropsychiatric disorders, neurodegenerative disorders, mental illness, traumatic brain injury, chronic traumatic encephalopathy, and other neurodegenerative diseases. With advancements in mass spectrometry coupled with bioinformatics and systems biology, neuroproteomics has led to the development of innovative techniques such as microproteomics, single-cell proteomics, and imaging mass spectrometry, which have significantly impacted neuronal biomarker research. By analyzing the complex protein interactions and alterations that occur in the injured brain, neuroproteomics provides valuable insights into the pathophysiological mechanisms underlying neurotrauma. This review explores how such insights can be harnessed to advance personalized medicine (PM) approaches, tailoring treatments based on individual patient profiles. Additionally, we highlight the potential future prospects of neuroproteomics, such as identifying novel biomarkers and developing targeted therapies by employing artificial intelligence (AI) and machine learning (ML). By shedding light on neurotrauma's current state and future directions, this review aims to stimulate further research and collaboration in this promising and transformative field.

Recent advances in autophagic machinery: a proteomic perspective

INTRODUCTION

Autophagy is an evolutionarily conserved cellular clearance process, by which cytosolic components are delivered to autolysosomes for breakdown and recycling to maintain cellular homeostasis. During the past decades, autophagy has been found to be tightly implicated in various physiological and pathological progresses. Unraveling the regulatory mechanisms of the autophagy process will contribute to the development of emerging autophagy-targeting strategies for the treatment of various diseases. Recently, the rapid development of proteomics approaches has enabled the use of large-scale unbiased strategies to unravel autophagy machinery.

AREAS COVERED

In this review, we will highlight the recent contributions of proteomics strategies in clarifying the autophagy machinery, with an emphasis on the three different types of autophagy (namely macroautophagy, microautophagy, and chaperone-mediated autophagy). We will also discuss the emerging role of proteomics approaches in investigating the mechanism of the autophagy-based unconventional secretory pathway (secretory autophagy).

EXPERT OPINION

Proteomics has provided an effective strategy for the comprehensive analysis of the autophagy process, which will broaden our understanding of autophagy machinery, and holds great promise for developing clinical therapies targeting autophagy.

Mass spectrometry based proteomics and metabolomics in personalized oncology

Precision medicine (PM) means the customization of healthcare with decisions and practices adjusted to the individual patient. It includes personalized diagnostics, patients' sub-classification, individual treatment selection and the monitoring of its effectiveness. Currently, in oncology, PM is based on the molecular and cellular features of a tumor, its microenvironment and the patient's genetics and lifestyle. Surprisingly, the available targeted therapies were found effective only in a subset of patients. An in-depth understanding of tumor biology is crucial to improve their effectiveness and develop new therapeutic targets. Completion of genetic information with proteomics and metabolomics can give broader knowledge about tumor biology which consequently provides novel biomarkers and indicates new therapeutic targets. Recently, metabolomics and proteomics have extensively been applied in the field of oncology. In the context of PM, human studies, with the use of mass spectrometry (MS) which allows the detection of thousands of molecules in a large number of samples, are the most valuable. Such studies, focused on cancer biomarkers discovery or patients' stratification, are presented in this review. Moreover, the technical aspects of MS-based clinical proteomics and metabolomics are described.

Proteomic investigations into hypertension: what's new and how might it affect clinical practice?

Introduction: Hypertension is a multifactorial disease that has, thus far, proven to be a difficult target for pharmacological intervention. The application of proteomic strategies may help to identify new biomarkers for the early diagnosis and prompt treatment of hypertension, in order to control blood pressure and prevent organ damage. Areas covered: Advances in proteomics have led to the discovery of new biomarkers to help track the pathophysiological processes implicated in hypertension. These findings not only help to better understand the nature of the disease, but will also contribute to the clinical needs for a timely diagnosis and more precise treatment. In this review, we provide an overview of new biomarkers identified in hypertension through the application of proteomic techniques, and we also discuss the difficulties and challenges in identifying biomarkers in this clinical setting. We performed a literature search in PubMed with the key words 'hypertension' and 'proteomics', and focused specifically on the most recent literature on the utility of proteomics in hypertension research. Expert opinion: There have been several promising biomarkers of hypertension identified by proteomics, but too few have been introduced to the clinic. Thus, further investigations in larger cohorts are necessary to test the feasibility of this strategy for patients. Also, this emerging field would profit from more collaboration between clinicians and researchers.

Whole-Genome Sequencing in Cancer

Genome sequencing of cancer has fundamentally advanced our understanding of the underlying biology of this disease, and more recently has provided approaches to characterize and monitor tumors in the clinic, guiding and evaluating treatment. Although cancer research is relying more on whole-genome characterization, the clinical application of genomics is largely limited to targeted sequencing approaches, tailored to capture specific clinically relevant biomarkers. However, as sequencing costs reduce, and the tools to effectively analyze complex and large-scale data improve, the ability to effectively characterize whole genomes at scale in a clinically relevant time frame is now being piloted. This ability effectively blurs the line between clinical cancer research and the clinical management of the disease. This leads to a new paradigm in cancer management in which real-time analysis of an individual's disease can have a rapid and lasting impact on our understanding of how clinical practices need to change to exploit novel therapeutic rationales. In this article, we will discuss how whole-genome sequencing (WGS), often combined with transcriptome analysis, has been used to understand cancer and how this approach is uniquely positioned to provide a comprehensive view of an evolving disease in response to therapy.

Toward multiomics-based next-generation diagnostics for precision medicine

Our healthcare system is experiencing a paradigm shift to precision medicine, aiming at an early prediction of individual disease risks and targeted interventions. Whole-genome sequencing is currently gaining momentum, as it has the potential to capture all classes of genetic variation, thus providing a more complete picture of the individual's genetic makeup, which could be utilized in genetic testing; however, this will also lead to difficulties in interpreting the test results, necessitating careful integration of genomic data with other layers of information, both molecular multiomics measurements of epigenome, transcriptome, proteome, metabolome and even microbiome, as well as comprehensive information on diet, lifestyle and environment. Overall, the translation of patient-specific data into actionable diagnostic tools will be a challenging task, requiring expertise from multiple disciplines, secure data sharing in large reference databases and a strong computational infrastructure.

Analyzing the Secretome of Gut Microbiota as the Next Strategy For Early Detection of Colorectal Cancer

Dysbiosis of gut microbiome can contribute to inflammation, and subsequently initiation and progression of colorectal cancer (CRC). Throughout these stages, various proteins and metabolites are secreted to the external environment by microorganisms or the hosts themselves. Studying these proteins may help enhance our understanding of the host-microorganism relationship or they may even serve as useful biomarkers for CRC. However, secretomic studies of gut microbiome of CRC patients, until now, are scarcely performed. In this review article, the focus is on the roles of gut microbiome in CRC, the current findings on CRC secretome are highlighted, and the emerging challenges and strategies to drive forward this area of research are addressed.

Proteomics and the microbiome: pitfalls and potential

Introduction: Human symbiotic microbiota are now known to play important roles in human health and disease. Significant progress in our understanding of the human microbiome has been driven by recent technological advances in the fields of genomics, transcriptomics, and proteomics. As a complementary method to metagenomics, proteomics is enabling detailed protein profiling of the microbiome to decipher its structure and function and to analyze its relationship with the human body. Fecal proteomics is being increasingly applied to discover and validate potential health and disease biomarkers, and Therapeutic Goods Administration (TGA)-approved instrumentation and a range of clinical assays are being developed that will collectively play key roles in advancing personalized medicine. Areas covered: This review will introduce the complexity of the microbiome and its role in health and disease (in particular the gastrointestinal tract or gut microbiome), discuss current genomic and proteomic methods for studying this system, including the discovery of potential biomarkers, and outline the development of clinically accepted protocols leading to personalized medicine. Expert commentary: Recognition of the important role the microbiome plays in both health and disease is driving current research in this key area. A proteogenomics approach will be essential to unravel the biologies underlying this complex network.

Recent insights into human bronchial proteomics - how are we progressing and what is next?

The human respiratory system is highly prone to diseases and complications. Many lung diseases, including lung cancer (LC), tuberculosis (TB), and chronic obstructive pulmonary disease (COPD) have been among the most common causes of death worldwide. Cystic fibrosis (CF), the most common genetic disease in Caucasians, has adverse impacts on the lungs. Bronchial proteomics plays a significant role in understanding the underlying mechanisms and pathogenicity of lung diseases and provides insights for biomarker and therapeutic target discoveries. Areas covered: We overview the recent achievements and discoveries in human bronchial proteomics by outlining how some of the different proteomic techniques/strategies are developed and applied in LC, TB, COPD, and CF. Also, the future roles of bronchial proteomics in predictive proteomics and precision medicine are discussed. Expert commentary: Much progress has been made in bronchial proteomics. Owing to the advances in proteomics, we now have better ability to isolate proteins from desired cellular compartments, greater protein separation methods, more powerful protein detection technologies, and more sophisticated bioinformatic techniques. These all contributed to our further understanding of lung diseases and for biomarker and therapeutic target discoveries.

The cancer proteomic landscape and the HUPO Cancer Proteome Project

The Human Cancer Proteome Project (Cancer-HPP) is an international initiative organized by HUPO whose key objective is to decipher the human cancer proteome through a coordinated effort by cancer proteome researchers around the world. The ultimate goal is to map the entire human cancer proteome to disclose tumor biology and drive improved diagnostics, treatment and management of cancer. Here we report the progress in the cancer proteomics field to date, and discuss future proteomic developments that will be needed to optimally delineate cancer phenotypes and advance the molecular characterization of this significant disease that is one of the leading causes of death worldwide.

Transforming big data into computational models for personalized medicine and health care

Health care systems generate a huge volume of different types of data. Due to the complexity and challenges inherent in studying medical information, it is not yet possible to create a comprehensive model capable of considering all the aspects of health care systems. There are different points of view regarding what the most efficient approaches toward utilization of this data would be. In this paper, we describe the potential role of big data approaches in improving health care systems and review the most common challenges facing the utilization of health care big data.

Mining the fecal proteome: from biomarkers to personalised medicine

INTRODUCTION

Fecal proteomics has gained increased prominence in recent years. It can provide insights into the diagnosis and surveillance of many bowel diseases by both identifying potential biomarkers in stool samples and helping identify disease-related pathways. Fecal proteomics has already shown its potential for the discovery and validation of biomarkers for colorectal cancer screening, and the analysis of fecal microbiota by MALDI-MS for the diagnosis of a range of bowel diseases is gaining clinical acceptance. Areas covered: Based on a comprehensive analysis of the current literature, we introduce the range of sensitive and specific proteomics methods which comprise the current 'Proteomics Toolbox', explain how the integration of fecal proteomics with data processing/bioinformatics has been used for the identification of potential biomarkers for both CRC and other gut-related pathologies and analysis of the fecal microbiome, outline some of the current fecal assays in current clinical practice and introduce the concept of personalised medicine which these technologies will help inform. Expert commentary: Integration of fecal proteomics with other proteomics and genomics strategies as well as bioinformatics is paving the way towards personalised medicine, which will bring with it improved global healthcare.

Mass Spectrometry-Based Analysis for the Discovery and Validation of Potential Colorectal Cancer Stool Biomarkers

Colorectal cancer (CRC) is the third leading cause of cancer mortality for both men and women, and the second leading cause of cancer death for men and women combined. If detected early, before metastasis has occurred, survival following surgical resection of the tumor is >90%. Early detection is therefore critical for effective disease surveillance. Unfortunately, current biomarker assays lack the necessary sensitivity and specificity for reliable early disease detection. Development of new robust, non- or minimally invasive specific and sensitive biomarkers or panels with improved compliance and performance is therefore urgently required. The use of fecal samples offers several advantages over other clinical biospecimens (e.g., plasma or serum) as a source of CRC biomarkers, including: collection is noninvasive, the test can be performed at home, one is not sample limited, and the stool effectively samples the entire length of the inner bowel wall contents (including tumor) as it passes down the gastrointestinal tract. Recent advances in mass spectrometry now facilitate both the targeted discovery and validation of potential CRC biomarkers. We describe, herein, detailed protocols that can be used to mine deeply into the fecal proteome to reveal candidate proteins, identify proteotypic/unitypic peptides (i.e., peptides found in only a single known human protein that serve to identify that protein) suitable for sensitive and specific quantitative multiplexed analysis, and undertake high-throughput analysis of clinical samples. Finally, we discuss future directions that may further position this technology to support the current switch in translation research toward personalized medicine.

Quality by design (QbD) approach of pharmacogenomics in drug designing and formulation development for optimization of drug delivery systems

Conventional approaches of drug discovery are very complex, costly and time consuming. But after the completion of human genome project, applications of pharmacogenomics in this area completely revolutionize the drug discovery and development process to produce a quality by design (QbD) approach based products. The applications of two areas of pharmacogenomics i.e. structural and functional pharmacogenomics excel the drug discovery process by employing genomic data in drug target identification and evaluation, lead optimization via high throughput screening, evaluation of drug metabolizing enzymes, drug transporters and drug receptors using computer aided technique and bioinformatics library data base. Pharmacogenomics also provides an important and reliable basis for evaluation and optimization of the dosage forms as well as repositioning of failed drugs for the treatment of new disease. Various dosage forms of category of drugs such as anticancer drugs, vaccines, gene and DNA delivery systems and immunological agents can be easily evaluated based on the genetic markers of the related disease. The effect of different formulation polymers on pharmacokinetic and pharmacodynamic properties of drugs can be assessed easily and therefore it plays an important role in formulation optimization. However, current applications of pharmacogenomics in drug discovery and formulation optimization are very limited because of costly and non accessible techniques for everyone, but in future, with the advancement in the technology; the application of genomic data in drug discovery will provide us with innovative, safer and more efficacious medicines.