Proteomic profiling of postmortem prefrontal cortex tissue of suicide completers

Altered proteomics in brain extracellular vesicles from depressed individuals who died by suicide implicates synaptic processes

BACKGROUND

Major depressive disorder (MDD) is a common and debilitating disorder whose molecular neurobiology remains unclear. Extracellular vesicles (EVs) are small vesicles that are released by cells and are involved in intercellular communication. They carry bioactive molecules, such as proteins, that reflect the state of their cell of origin. In this study, we sought to investigate the proteomic cargo of brain EVs from depressed individuals as compared to EVs from matched neurotypical individuals. In addition, we investigated how the EV proteomic cargo compares to the proteomic profile of bulk tissue.

METHODS

Using mass spectrometry and label-free quantification, we investigated the EV and bulk tissue protein profile from anterior cingulate cortex samples from 86 individuals. We performed differential expression analysis to compare cases and controls, followed by in silico analysis to determine potential implicated functions of dysregulated proteins.

RESULTS

Extracellular vesicles display distinct proteomic profiles compared to bulk tissue. Differential expression analysis showed that 70 proteins were differentially packaged in EVs in MDD, while there was no significant difference in protein levels between groups in bulk tissue. In silico analysis points to a strong role of these differential EV proteins in synaptic functions.

CONCLUSION

To our knowledge, this is the first study to profile EV proteins in depression, providing novel information to better understand the pathophysiology of MDD. This work paves the way for discovering new therapeutic targets for MDD and prompts more investigations into EVs in MDD and other psychiatric disorders.

Proteome analysis of the prefrontal cortex and the application of machine learning models for the identification of potential biomarkers related to suicide

Introduction

Suicide is a significant public health problem, with increased rates in low- and middle-income countries such as Mexico; therefore, suicide prevention is important. Suicide is a complex and multifactorial phenomenon in which biological and social factors are involved. Several studies on the biological mechanisms of suicide have analyzed the proteome of the dorsolateral prefrontal cortex (DLPFC) in people who have died by suicide. The aim of this work was to analyze the protein expression profile in the DLPFC of individuals who died by suicide in comparison to age-matched controls in order to gain information on the molecular basis in the brain of these individuals and the selection of potential biomarkers for the identification of individuals at risk of suicide. In addition, this information was analyzed using machine learning (ML) algorithms to propose a model for predicting suicide.

Methods

Brain tissue (Brodmann area 9) was sampled from male cases (n=9) and age-matched controls (n=7). We analyzed the proteomic differences between the groups using two-dimensional polyacrylamide gel electrophoresis and mass spectrometry. Bioinformatics tools were used to clarify the biological relevance of the differentially expressed proteins. In addition, this information was analyzed using machine learning (ML) algorithms to propose a model for predicting suicide.

Results

Twelve differentially expressed proteins were also identified (t 14 ≤ 0.5). Using Western blotting, we validated the decrease in expression of peroxiredoxin 2 and alpha-internexin in the suicide cases. ML models were trained using densitometry data from the 2D gel images of each selected protein and the models could differentiate between both groups (control and suicide cases).

Discussion

Our exploratory pathway analysis highlighted oxidative stress responses and neurodevelopmental pathways as key processes perturbed in the DLPFC of suicides. Regarding ML models, KNeighborsClassifier was the best predicting conditions. Here we show that these proteins of the DLPFC may help to identify brain processes associated with suicide and they could be validated as potential biomarkers of this outcome.

High-throughput proteomics-guided biomarker discovery of hepatocellular carcinoma

Liver cancer stands as the fifth leading cause of cancer-related deaths globally. Hepatocellular carcinoma (HCC) comprises approximately 85%-90% of all primary liver malignancies. However, only 20-30% of HCC patients qualify for curative therapy, primarily due to the absence of reliable tools for early detection and prognosis of HCC. This underscores the critical need for molecular biomarkers for HCC management. Since proteins reflect disease status directly, proteomics has been utilized in biomarker developments for HCC. In particular, proteomics coupled with liquid chromatography-mass spectrometer (LC-MS) methods facilitate the process of discovering biomarker candidates for diagnosis, prognosis, and therapeutic strategies. In this work, we investigated LC-MS-based proteomics methods through recent reference reviews, with a particular focus on sample preparation and LC-MS methods appropriate for the discovery of HCC biomarkers and their clinical applications. We classified proteomics studies of HCC according to sample types, and we examined the coverage of protein biomarker candidates based on LC-MS methods in relation to study scales and goals. Comprehensively, we proposed protein biomarker candidates categorized by sample types and biomarker types for appropriate clinical use. In this review, we summarized recent LC-MS-based proteomics studies on HCC and proposed potential protein biomarkers. Our findings are expected to expand the understanding of HCC pathogenesis and enhance the efficiency of HCC diagnosis and prognosis, thereby contributing to improved patient outcomes.

A multi-tissue longitudinal proteomics study to evaluate the suitability of post-mortem samples for pathophysiological research

Recent developments in mass spectrometry-based proteomics have established it as a robust tool for system-wide analyses essential for pathophysiological research. While post-mortem samples are a critical source for these studies, our understanding of how body decomposition influences the proteome remains limited. Here, we have revisited published data and conducted a clinically relevant time-course experiment in mice, revealing organ-specific proteome regulation after death, with only a fraction of these changes linked to protein autolysis. The liver and spleen exhibit significant proteomic alterations within hours post-mortem, whereas the heart displays only modest changes. Additionally, subcellular compartmentalization leads to an unexpected surge in proteome alterations at the earliest post-mortem interval (PMI). Additionally, we have conducted a comprehensive analysis of semi-tryptic peptides, revealing distinct consensus motifs for different organs, indicating organ-specific post-mortem protease activity. In conclusion, our findings emphasize the critical importance of considering PMI effects when designing proteomics studies, as these effects may significantly overshadow the impacts of diseases. Preferably, the samples should be taken in the operation room, especially for studies including subcellular compartmentalization or trans-organ comparison. In single-organ studies, the planning should involve careful control of PMI.

Altered transcriptomes, cell type proportions, and dendritic spine morphology in hippocampus of suicide decedents

BACKGROUND

Suicide is a manner of death resulting from complex environmental and genetic risks that affect millions of people globally. Both structural and functional studies identified the hippocampus as one of the vulnerable brain regions contributing to suicide risk.

METHODS

We have identified the hippocampal tissue transcriptomes, gene ontology, cell type proportions, and dendritic spine morphology in controls (n = 28) and suicide decedents (n = 22). In addition, the transcriptomic signature in iPSC-derived neuronal precursor cells (NPCs) and neurons were also investigated in controls (n = 2) and suicide decedents (n = 2).

RESULTS

The hippocampal tissue transcriptomic data revealed that NPAS4 gene expression was downregulated while ALDH1A2, NAAA, and MLXIPL gene expressions were upregulated in hippocampal tissue of suicide decedents. The gene ontology identified 29 significant pathways including NPAS4-associated gene ontology terms "excitatory post-synaptic potential", "regulation of postsynaptic membrane potential" and "long-term memory" indicating alteration of glutamatergic synapses in the hippocampus of suicide decedents. The cell type deconvolution identified decreased excitatory neuron proportion and an increased inhibitory neuron proportion providing evidence of excitation/inhibition imbalance in the hippocampus of suicide decedents. In addition, suicide decedents had increased dendric spine density in the hippocampus, due to an increase of thin (relatively unstable) dendritic spines, compared to controls. The transcriptomes of iPSC-derived hippocampal-like NPCs and neurons revealed 31 and 33 differentially expressed genes in NPC and neurons, respectively, of suicide decedents.

CONCLUSIONS

Our findings will provide new insights into the hippocampal neuropathology of suicide.

Depicting the molecular features of suicidal behavior: a review from an "omics" perspective

Background Suicide is one of the leading global causes of death. Behavior patterns from suicide ideation to completion are complex, involving multiple risk factors. Advances in technologies and large-scale bioinformatic tools are changing how we approach biomedical problems. The "omics" field may provide new knowledge about suicidal behavior to improve identification of relevant biological pathways associated with suicidal behavior. Methods We reviewed transcriptomic, proteomic, and metabolomic studies conducted in blood and post-mortem brains from individuals who experienced suicide or suicidal behavior. Omics data were combined using systems biology in silico, aiming at identifying major biological mechanisms and key molecules associated with suicide. Results Post-mortem samples of suicide completers indicate major dysregulations in pathways associated with glial cells (astrocytes and microglia), neurotransmission (GABAergic and glutamatergic systems), neuroplasticity and cell survivor, immune responses and energy homeostasis. In the periphery, studies found alterations in molecules involved in immune responses, polyamines, lipid transport, energy homeostasis, and amino and nucleic acid metabolism. Limitations We included only exploratory, non-hypothesis-driven studies; most studies only included one brain region and whole tissue analysis, and focused on suicide completers who were white males with almost none confounding factors. Conclusions We can highlight the importance of synaptic function, especially the balance between the inhibitory and excitatory synapses, and mechanisms associated with neuroplasticity, common pathways associated with psychiatric disorders. However, some of the pathways highlighted in this review, such as transcriptional factors associated with RNA splicing, formation of cortical connections, and gliogenesis, point to mechanisms that still need to be explored.

Network analysis of plasma proteomes in affective disorders

The conventional differentiation of affective disorders into major depressive disorder (MDD) and bipolar disorder (BD) has insufficient biological evidence. Utilizing multiple proteins quantified in plasma may provide critical insight into these limitations. In this study, the plasma proteomes of 299 patients with MDD or BD (aged 19-65 years old) were quantified using multiple reaction monitoring. Based on 420 protein expression levels, a weighted correlation network analysis was performed. Significant clinical traits with protein modules were determined using correlation analysis. Top hub proteins were determined using intermodular connectivity, and significant functional pathways were identified. Weighted correlation network analysis revealed six protein modules. The eigenprotein of a protein module with 68 proteins, including complement components as hub proteins, was associated with the total Childhood Trauma Questionnaire score (r = -0.15, p = 0.009). Another eigenprotein of a protein module of 100 proteins, including apolipoproteins as hub proteins, was associated with the overeating item of the Symptom Checklist-90-Revised (r = 0.16, p = 0.006). Functional analysis revealed immune responses and lipid metabolism as significant pathways for each module, respectively. No significant protein module was associated with the differentiation between MDD and BD. In conclusion, childhood trauma and overeating symptoms were significantly associated with plasma protein networks and should be considered important endophenotypes in affective disorders.

Altered transcriptomes, cell type proportions, and dendritic spine morphology in hippocampus of suicide deaths

Suicide is a condition resulting from complex environmental and genetic risks that affect millions of people globally. Both structural and functional studies identified the hippocampus as one of the vulnerable brain regions contributing to suicide risk. Here, we have identified the hippocampal transcriptomes, gene ontology, cell type proportions, dendritic spine morphology, and transcriptomic signature in iPSC-derived neuronal precursor cells (NPCs) and neurons in postmortem brain tissue from suicide deaths. The hippocampal tissue transcriptomic data revealed that NPAS4 gene expression was downregulated while ALDH1A2, NAAA, and MLXIPL gene expressions were upregulated in tissue from suicide deaths. The gene ontology identified 29 significant pathways including NPAS4-associated gene ontology terms "excitatory post-synaptic potential", "regulation of postsynaptic membrane potential" and "long-term memory" indicating alteration of glutamatergic synapses in the hippocampus of suicide deaths. The cell type deconvolution identified decreased excitatory neuron proportion and an increased inhibitory neuron proportion providing evidence of excitation/inhibition imbalance in the hippocampus of suicide deaths. In addition, suicide deaths had increased dendric spine density, due to an increase of thin (relatively unstable) dendritic spines, compared to controls. The transcriptomes of iPSC-derived hippocampal-like NPCs and neurons revealed 31 and 33 differentially expressed genes in NPC and neurons, respectively, of suicide deaths. The suicide-associated differentially expressed genes in NPCs were RELN, CRH, EMX2, OXTR, PARM1 and IFITM2 which overlapped with previously published results. The previously-known suicide-associated differentially expressed genes in differentiated neurons were COL1A1, THBS1, IFITM2, AQP1, and NLRP2. Together, these findings would help better understand the hippocampal neurobiology of suicide for identifying therapeutic targets to prevent suicide.