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As more uses for biomarkers are sought after for an increasing number of disease targets, single-target biomarkers are slowly giving way for biomarker panels. These panels incorporate various sources of biomolecular and clinical data to guarantee a higher robustness and power of separation for a clinical test. Multifactorial diseases such as psychiatric disorders show great potential for clinical use, assisting medical professionals during the analysis of risk and predisposition, disease diagnosis and prognosis, and treatment applicability and efficacy. More specific tests are also being developed to assist in ruling out, distinguishing between, and confirming suspicions of multifactorial diseases, as well as to predict which therapy option may be the best option for a given patient’s biochemical profile. As more complex datasets are entering the field, involving multi-omic approaches, systems biology has stepped in to facilitate the discovery and validation steps during biomarker panel generation. Filtering biomolecules and clinical data, pre-validating and cross-validating potential biomarkers, generating final biomarker panels, and testing the robustness and applicability of those panels are all beginning to rely on machine learning and systems biology and research in this area will only benefit from advances in these approaches.

Several omic fields have been used in the development of biomarker panels, most traditionally involving genetics and proteomics. The post-translational modification of proteins, however, is an important regulatory system of many biological processes, affecting a wide range of biochemical properties of proteins, including their binding, localization, activity, and stability. These modifications are not analyzed if not specifically searched for in proteomic workflows, making them an underrepresented source of important information in the field of biomarker research. Biomarkers can particularly benefit the diagnosis and prognosis of neurological and psychiatric diseases due to the difficulty of accessing tissue and distinguishing between multiple possible conditions. In this article, post-translational modifications in the context of brain disease are compiled, highlighting the potential that this data source holds for improving the field of medicine.

Major depressive disorder (MDD) is a complex and multifactorial psychiatric disorder that causes serious health, social, and economic concerns worldwide. The main treatment of the symptoms is through antidepressant (AD) drugs. However, not all patients respond properly to these drugs. Omic sciences are widely used to analyze not only biomarkers for the AD response but also their molecular mechanism. In this review, we aimed to focus on omics data to better understand the molecular mechanisms involving AD effects on MDD. We consistently found, from preclinical to clinical data, that glutamatergic transmission, immune/inflammatory processes, energy metabolism, oxidative stress, and lipid metabolism were associated with traditional and potential new ADs. Despite efforts of studies investigating biomarkers of response to ADs, which could contribute to personalized treatment, there is no biomarker panel available for clinical application. From clinical genomic studies, we found that the main findings contribute to the development of pharmacogenomic tests for AD efficacy for each patient. Several studies pointed at DRD2, PXDNL, CACNA1E, and CACNA2D1 genes as potential targets for MDD treatment and the efficacy and rapid-antidepressant effect of ketamine. Finally, more in-depth studies of the molecular targets pointed here are needed to determine the clinical relevance and provide further evidence for precision MDD treatment.

Depression is a complex and multifactorial disease, affecting about 6.5% of the elderly population in what is referred to as late-life depression (LLD). Despite its public health relevance, there is still limited information about the molecular mechanisms of LLD. We analyzed the blood plasma of 50 older adults, 19 with LLD and 31 controls, through untargeted mass spectrometry, and used systems biology tools to identify biochemical pathways and biological processes dysregulated in the disease. We found 96 differentially expressed proteins between LLD patients and control individuals. Using elastic-net regression, we generated a panel of 75 proteins that comprises a potential model for determining the molecular signature of LLD. We also showed that biological pathways related to vesicle-mediated transport and voltage-dependent calcium channels may be dysregulated in LLD. These data can help to build an understanding of the molecular basis of LLD, offering an integrated view of the biomolecular alterations that occur in this disorder. SIGNIFICANCE: Major depressive disorder in the elderly, called late-life depression (LLD), is a common and disabling disorder, with recent prevalence estimates of 6.5% in the general population. Despite the public health relevance, there is still limited information about the molecular mechanisms of LLD. The findings in this paper shed light on LLD heterogeneous biological mechanisms. We uncovered a potential novel biomolecular signature for LLD and biological pathways related to this condition which can be targets for the development of novel interventions for prevention, early diagnosis, and treatment of LLD.

Proteomic tools are especially useful when it comes to investigating complex samples such as human blood plasma, in which protein quantities can span across up to ten orders of magnitude. Ultra definition mass spectrometry, in combination with two-dimensional liquid chromatography, provides better coverage of complex proteomes and allows for better control of collision energy, keeping the fragmentation benefits of high collision energy associated with drift time measurements from ion mobility separation. Here, we present a protocol to assist in the identification of proteins in human blood plasma and other similar samples with a large dynamic range.

Proteomic tools are especially useful when it comes to investigating complex samples such as human blood plasma, in which protein quantities can span across up to ten orders of magnitude. Ultra definition mass spectrometry, in combination with two-dimensional liquid chromatography, provides better coverage of complex proteomes and allows for better control of collision energy, keeping the fragmentation benefits of high collision energy associated with drift time measurements from ion mobility separation. Here, we present a protocol to assist in the identification of proteins in human blood plasma and other similar samples with a large dynamic range.

COVID-19 patients may exhibit neuropsychiatric and neurological symptoms. We found that anxiety and cognitive impairment are manifested by 28-56% of SARS-CoV-2-infected individuals with mild respiratory symptoms and are associated with altered cerebral cortical thickness. Using an independent cohort, we found histopathological signs of brain damage in 25% of individuals who died of COVID-19. All of the affected brain tissues exhibited foci of SARS-CoV-2 infection and replication, particularly in astrocytes. Infection of neural stem cell-derived astrocytes changed energy metabolism, altered key proteins and metabolites used to fuel neurons and for biogenesis of neurotransmitters, and elicited a secretory phenotype that reduces neuronal viability. Our data support the model where SARS-CoV-2 reaches the brain, infects astrocytes and triggers neuropathological changes that contribute to the structural and functional alterations in the brain of COVID-19 patients.

Major depressive disorder is a multifactorial disease, with molecular mechanisms not fully understood. A breakthrough could be reached with a panel of diagnostic biomarkers, which could be helpful to stratify patients and guide physicians to a better therapeutic choice, reducing the time between diagnostic and remission. This review brings the most recent works in proteomic biomarkers and highlights several potential proteins that could compose a panel of biomarkers to diagnostic and response to medication. These proteins are related to immune, inflammatory, and coagulatory systems and may also be linked to energy metabolism, oxidative stress, cell communication, and oligodendrogenesis.

Brain disorders are among the most complex and difficult to understand of human disorders in terms of pathophysiology and etiology. Differently from other human diseases such as cancer, which uses biomarkers in clinical practice, there are no prognostic and diagnostic biomarkers available for psychiatric disorders. Those associated with the likelihood of a successful medication are also not existent, impairing treatment strategies. Proteomics is a suitable tool for identifying such biomarkers to be validated and further implemented in the clinic. Here we present a protocol for the proteome analyses of blood plasma and serum collected in vivo, aiming for the discovery of potential biomarkers and the comprehension of the molecular bases of diseases and treatments.

Brain disorders are among the most complex and difficult to understand of human disorders in terms of pathophysiology and etiology. Differently from other human diseases such as cancer, which uses biomarkers in clinical practice, there are no prognostic and diagnostic biomarkers available for psychiatric disorders. Those associated with the likelihood of a successful medication are also not existent, impairing treatment strategies. Proteomics is a suitable tool for identifying such biomarkers to be validated and further implemented in the clinic. Here we present a protocol for the proteome analyses of blood plasma and serum collected in vivo, aiming for the discovery of potential biomarkers and the comprehension of the molecular bases of diseases and treatments.