The autophagy proteome in the brain

As one of the most important cellular housekeepers, autophagy directly affects cellular health, homeostasis, and function. Even though the mechanisms behind autophagy are well described, how molecular alterations and dysfunctions can lead to pathology in disease contexts still demands deeper investigation. Proteomics is a widely employed tool used to investigate molecular alterations associated with pathological states and has proven useful in identifying alterations in protein expression levels and post-translational modifications in autophagy. In this narrative review, we expand on the molecular mechanisms behind autophagy and its regulation, and further compile recent literature associating autophagy disturbances in context of brain disorders, utilizing discoveries from varying models and species from rodents and cellular models to human post-mortem brain samples. To outline, the canonical pathways of autophagy, the effects of post-translational modifications on regulating each step of autophagy, and the future directions of proteomics in autophagy will be discussed. We further aim to suggest how advancing proteomics can help further unveil molecular mechanisms with regard to neurological disorders.

Maximizing Analytical Performance in Biomolecular Discovery with LC-MS: Focus on Psychiatric Disorders

In this review, we discuss the cutting-edge developments in mass spectrometry proteomics and metabolomics that have brought improvements for the identification of new disease-based biomarkers. A special focus is placed on psychiatric disorders, for example, schizophrenia, because they are considered to be not a single disease entity but rather a spectrum of disorders with many overlapping symptoms. This review includes descriptions of various types of commonly used mass spectrometry platforms for biomarker research, as well as complementary techniques to maximize data coverage, reduce sample heterogeneity, and work around potentially confounding factors. Finally, we summarize the different statistical methods that can be used for improving data quality to aid in reliability and interpretation of proteomics findings, as well as to enhance their translatability into clinical use and generalizability to new data sets.

Diving into the proteomic atlas of SARS-CoV-2 infected cells

The COVID-19 pandemic was initiated by the rapid spread of a SARS-CoV-2 strain. Though mainly classified as a respiratory disease, SARS-CoV-2 infects multiple tissues throughout the human body, leading to a wide range of symptoms in patients. To better understand how SARS-CoV-2 affects the proteome from cells with different ontologies, this work generated an infectome atlas of 9 cell models, including cells from brain, blood, digestive system, and adipocyte tissue. Our data shows that SARS-CoV-2 infection mainly trigger dysregulations on proteins related to cellular structure and energy metabolism. Despite these pivotal processes, heterogeneity of infection was also observed, highlighting many proteins and pathways uniquely dysregulated in one cell type or ontological group. These data have been made searchable online via a tool that will permit future submissions of proteomic data ( https://reisdeoliveira.shinyapps.io/Infectome_App/ ) to enrich and expand this knowledgebase.

Iron-based biomarkers for personalizing pharmacological ascorbate therapy in glioblastoma: insights from a phase 2 clinical trial

BACKGROUND

Pharmacological ascorbate (intravenous delivery reaching plasma concentrations ≈ 20 mM; P-AscH-) has emerged as a promising therapeutic strategy for glioblastoma. Recently, a single-arm phase 2 clinical trial demonstrated a significant increase in overall survival when P-AscH- was combined with temozolomide and radiotherapy. As P-AscH- relies on iron-dependent mechanisms, this study aimed to assess the predictive potential of both molecular and imaging-based iron-related markers to enhance the personalization of P-AscH- therapy in glioblastoma participants.

METHODS

Participants (n = 55) with newly diagnosed glioblastoma were enrolled in a phase 2 clinical trial conducted at the University of Iowa (NCT02344355). Tumor samples obtained during surgical resection were processed and stained for transferrin receptor and ferritin heavy chain expression. A blinded pathologist performed pathological assessment. Quantitative susceptibility mapping (QSM) measures were obtained from pre-radiotherapy MRI scans following maximal safe surgical resection. Circulating blood iron panels were evaluated prior to therapy through the University of Iowa Diagnostic Laboratory.

RESULTS

Through univariate analysis, a significant inverse association was observed between tumor transferrin receptor expression and overall and progression-free survival. QSM measures exhibited a significant, positive association with progression-free survival. Subjects were actively followed until disease progression and then were followed through chart review or clinical visits for overall survival.

CONCLUSIONS

This study analyzes iron-related biomarkers in the context of P-AscH- therapy for glioblastoma. Integrating molecular, systemic, and imaging-based markers offers a multifaceted approach to tailoring treatment strategies, thereby contributing to improved patient outcomes and advancing the field of glioblastoma therapy.

Gut microbiota from patients with COVID-19 cause alterations in mice that resemble post-COVID symptoms

Long-term sequelae of coronavirus disease (COVID)-19 are frequent and of major concern. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection affects the host gut microbiota, which is linked to disease severity in patients with COVID-19. Here, we report that the gut microbiota of post-COVID subjects had a remarkable predominance of Enterobacteriaceae strains with an antibiotic-resistant phenotype compared to healthy controls. Additionally, short-chain fatty acid (SCFA) levels were reduced in feces. Fecal transplantation from post-COVID subjects to germ-free mice led to lung inflammation and worse outcomes during pulmonary infection by multidrug-resistant Klebsiella pneumoniae. transplanted mice also exhibited poor cognitive performance. Overall, we show prolonged impacts of SARS-CoV-2 infection on the gut microbiota that persist after subjects have cleared the virus. Together, these data demonstrate that the gut microbiota can directly contribute to post-COVID sequelae, suggesting that it may be a potential therapeutic target.

Proteomic signatures of schizophrenia-sourced iPSC-derived neural cells and brain organoids are similar to patients' postmortem brains

BACKGROUND

Schizophrenia is a complex and severe neuropsychiatric disorder, with a wide range of debilitating symptoms. Several aspects of its multifactorial complexity are still unknown, and some are accepted to be an early developmental deficiency with a more specifically neurodevelopmental origin. Understanding the timepoints of disturbances during neural cell differentiation processes could lead to an insight into the development of the disorder. In this context, human brain organoids and neural cells differentiated from patient-derived induced pluripotent stem cells are of great interest as a model to study the developmental origins of the disease.

RESULTS

Here we evaluated the differential expression of proteins of schizophrenia patient-derived neural progenitors (NPCs), early neurons, and brain organoids in comparison to healthy individuals. Using bottom-up shotgun proteomics with a label-free approach for quantitative analysis, we found multiple dysregulated proteins since NPCs, modified, and disrupted the 21DIV neuronal differentiation, and cerebral organoids. Our experimental methods have shown impairments in pathways never before found in patient-derived induced pluripotent stem cells studies, such as spliceosomes and amino acid metabolism; but also, those such as axonal guidance and synaptogenesis, in line with postmortem tissue studies of schizophrenia patients.

CONCLUSION

In conclusion, here we provide comprehensive, large-scale, protein-level data of different neural cell models that may uncover early events in brain development, underlying several of the mechanisms within the origins of schizophrenia.

Cannabinoids modulate proliferation, differentiation, and migration signaling pathways in oligodendrocytes

Cannabinoid signaling, mainly via CB1 and CB2 receptors, plays an essential role in oligodendrocyte health and functions. However, the specific molecular signals associated with the activation or blockade of CB1 and CB2 receptors in this glial cell have yet to be elucidated. Mass spectrometry-based shotgun proteomics and in silico biology tools were used to determine which signaling pathways and molecular mechanisms are triggered in a human oligodendrocytic cell line (MO3.13) by several pharmacological stimuli: the phytocannabinoid cannabidiol (CBD); CB1 and CB2 agonists ACEA, HU308, and WIN55, 212-2; CB1 and CB2 antagonists AM251 and AM630; and endocannabinoids anandamide (AEA) and 2-arachidonoylglycerol (2-AG). The modulation of cannabinoid signaling in MO3.13 was found to affect pathways linked to cell proliferation, migration, and differentiation of oligodendrocyte progenitor cells. Additionally, we found that carbohydrate and lipid metabolism, as well as mitochondrial function, were modulated by these compounds. Comparing the proteome changes and upstream regulators among treatments, the highest overlap was between the CB1 and CB2 antagonists, followed by overlaps between AEA and 2-AG. Our study opens new windows of opportunities, suggesting that cannabinoid signaling in oligodendrocytes might be relevant in the context of demyelinating and neurodegenerative diseases. Proteomics data are available at ProteomeXchange (PXD031923).

UPLC-HDMSE to discover serum biomarkers in adults with type 1 diabetes

Type 1 diabetes (T1D) is a complex disease with metabolic and functional changes that can alter an individual's proteome. An LC-MS/MS analytical method, in an HDMS^E system, was used to identify differentially expressed proteins in the high abundance protein-depleted serum of T1D patients and healthy controls. Samples were processed in Progenesis QI for Proteomics software. A functional enrichment of the proteins was performed with Gene Ontology and ToppGene, and the interactions were visualized by STRING 11.5. As a result, 139 proteins were identified, 14 of which were downregulated in the serum of patients with T1D compared to controls. Most of the differentially expressed proteins were shown to be involved with the immune system, inflammation, and growth hormone stimulus response, and were associated with the progression of T1D. Differential protein expression data showed for the first-time changes in CPN2 expression levels in the serum of patients with T1D. Our findings indicate that these proteins are targets of interest for future investigations and for validation of protein biomarkers in T1D.

Plasma proteomic signature of major depressive episode in the elderly

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.

Morphological, cellular, and molecular basis of brain infection in COVID-19 patients

Although increasing evidence confirms neuropsychiatric manifestations associated mainly with severe COVID-19 infection, long-term neuropsychiatric dysfunction (recently characterized as part of “long COVID-19” syndrome) has been frequently observed after mild infection. We show the spectrum of cerebral impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, ranging from long-term alterations in mildly infected individuals (orbitofrontal cortical atrophy, neurocognitive impairment, excessive fatigue and anxiety symptoms) to severe acute damage confirmed in brain tissue samples extracted from the orbitofrontal region (via endonasal transethmoidal access) from individuals who died of COVID-19. In an independent cohort of 26 individuals who died of COVID-19, we used histopathological signs of brain damage as a guide for possible SARS-CoV-2 brain infection and found that among the 5 individuals who exhibited those signs, all of them had genetic material of the virus in the brain. Brain tissue samples from these five patients also exhibited foci of SARS-CoV-2 infection and replication, particularly in astrocytes. Supporting the hypothesis of astrocyte infection, neural stem cell–derived human astrocytes in vitro are susceptible to SARS-CoV-2 infection through a noncanonical mechanism that involves spike–NRP1 interaction. SARS-CoV-2–infected astrocytes manifested changes in energy metabolism and in key proteins and metabolites used to fuel neurons, as well as in the biogenesis of neurotransmitters. Moreover, human astrocyte infection elicits a secretory phenotype that reduces neuronal viability. Our data support the model in which SARS-CoV-2 reaches the brain, infects astrocytes, and consequently, leads to neuronal death or dysfunction. These deregulated processes could contribute to the structural and functional alterations seen in the brains of COVID-19 patients.

Human disease biomarker panels through systems biology

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.

Gaussian versus Non-Gaussian Filtering of Phase-Insensitive Nonclassicality

Measures of quantum properties are essential to understanding the fundamental differences between quantum and classical systems as well as quantifying resources for quantum technologies. Here, two broad classes of bosonic phase-space functions, which are filtered versions of the Glauber-Sudarshan P function, are compared with regard to their ability to uncover nonclassical effects of light through their negativities. Gaussian filtering of the P function yields the family of s-parametrized quasiprobabilities, while more powerful regularized nonclassicality quasiprobabilities are obtained by non-Gaussian filtering. A method is proposed to directly sample such phase-space functions for the restricted case of phase-independent quantum states from balanced homodyne measurements. This overcomes difficulties of previous approaches that manually append uniformly distributed optical phases to the measured quadrature data. We experimentally demonstrate this technique for heralded single- and two-photon states using balanced homodyne detection with varying efficiency. The s-parametrized quasiprobabilities, which can be directly sampled, are non-negative for detection efficiencies below 0.5. By contrast, we show that significant negativities of non-Gaussian filtered quasiprobabilities uncover nonclassical effects for arbitrarily low efficiencies.

Popliteal Cysts: A Systematic Review of Nonoperative and Operative Treatment

BACKGROUND

Treatment methods for popliteal cysts have varied over the past several decades and have posed challenges to providers as recurrences were frequent. With greater understanding of relevant anatomy, both operative and nonoperative treatment methods have evolved to appropriately target relevant pathology and improve outcomes. The purposes of this review were to outline the evolution of treatment methods and to qualitatively summarize clinical outcomes.

METHODS

We performed a systematic review on treatments for popliteal cysts to include publications from 1970 to 2019. Other inclusion criteria consisted of studies with ≥10 patients enrolled, studies with a patient age of ≥16 years, studies with an adequate description of the treatment technique, and studies with a Level of Evidence of IV or higher. The review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, and literature quality was assessed using a modified Coleman methodology score.

RESULTS

Thirty studies met inclusion criteria in this review. Nine studies discussed nonoperative treatment, and 21 studies discussed operative treatment. Eight of the 9 nonoperative treatment studies utilized corticosteroid injections. The most recent studies have advocated for ultrasound-guided intracystic injection with possible cyst fenestration. Most operative studies utilized an arthroscopic approach to enlarge the communication with the joint space. However, alternative treatment techniques are still utilized.

CONCLUSIONS

The current literature on the treatment of popliteal cysts indicates that intracystic corticosteroid injection with cyst fenestration is an effective nonoperative treatment method. Arthroscopic surgical procedures with enlargement of the communication have been most widely studied, with positive results; however, further studies are needed to confirm superiority over other treatment methods.

LEVEL OF EVIDENCE

Therapeutic Level IV. See Instructions for Authors for a complete description of levels of evidence.

A Guide to Mass Spectrometry-Based Quantitative Proteomics

Proteomics has become an attractive science in the postgenomic era, given its capacity to identify up to thousands of molecules in a single, complex sample and quantify them in an absolute and/or relative manner. The use of these techniques enables understanding of cellular and molecular mechanisms of diseases and other biological conditions, as well as identification and screening of protein biomarkers. Here we provide a straightforward, up-to-date compilation and comparison of the main quantitation techniques used in comparative proteomics such as in vitro and in vivo stable isotope labeling and label-free techniques. Additionally, this chapter includes common methods for data acquisition in proteomics and some appropriate methods for data processing. This compilation can serve as a reference for scientists who are new to, or already familiar with, quantitative proteomics.

Relationship of American Indian blood quantum with osteoporosis risk: a cross-sectional study of American Indian women in Oklahoma

Information regarding the prevalence and risk of osteoporosis among American Indian (AI) women is limited. This study showed that with increasing AI blood quantum, the prevalence of osteoporosis at the hip based on BMD T-scores decreased and this appeared to be independent of other risk factors.

INTRODUCTION

This study was designed to investigate the effects of AI blood quantum (BQ) on osteoporosis prevalence and risk in a cohort of AI women in Oklahoma.

METHODS

Women (n = 301), aged 50 years and older, were recruited to participate in the Oklahoma American Indian Women's Osteoporosis Study. Baseline bone density, fracture history, bone biochemical markers, and potential risk factors were assessed. Participants were stratified by AI BQ into BQ1 ≤ 25%, BQ2 = 25-49%, BQ3 = 50-74%, and BQ4 = 75-100%. The effects of BQ on the prevalence and risk of osteoporosis were evaluated.

RESULTS

Based on T-scores, one in approximately eight women in the study was osteoporotic at one or more sites. The prevalence of osteoporosis decreased (p < 0.05) with increasing BQ, especially at the hip, trochanteric, and intertrochanter regions. No differences in bone-specific alkaline phosphatase and C-telopeptide were observed across BQ that could account for the differences in bone density. 25-OH vitamin D decreased with increasing BQ, but mean for each BQ1-4 was > 40 ng/mL. Fracture history did not differ across BQ, and though 52% of the population consumed less than the RDA for calcium, no effect of BQ was observed.

CONCLUSIONS

In this cohort of women who identified as AI, greater Indian BQ was associated with a decrease in the prevalence of osteoporosis.

Organizational determinants of evaluation practice in Australian prevention agencies

Program evaluation is essential to inform decision making, contribute to the evidence base for strategies, and facilitate learning in health promotion and disease prevention organizations. Theoretical frameworks of organizational learning, and studies of evaluation capacity building describe the organization as central to evaluation capacity. Australian prevention organizations recognize limitations to current evaluation effectiveness and are seeking guidance to build evaluation capacity. This qualitative study identifies organizational facilitators and barriers to evaluation practice, and explores their interactions in Australian prevention organizations. We conducted semi-structured interviews with 40 experienced practitioners from government and non-government organizations. Using thematic analysis, we identified seven key themes that influence evaluation practice: leadership, organizational culture, organizational systems and structures, partnerships, resources, workforce development and training and recruitment and skills mix. We found organizational determinants of evaluation to have multi-level interactions. Leadership and organizational culture influenced organizational systems, resource allocation and support of staff. Partnerships were important to overcome resource deficits, and systems were critical to embed evaluation within the organization. Organizational factors also influenced the opportunities for staff to develop skills and confidence. We argue that investment to improve these factors would allow organizations to address evaluation capacity at multiple levels, and ultimately facilitate effective evaluation practice.

Erythritol and Lufenuron Detrimentally Alter Age Structure of Wild Drosophila suzukii (Diptera: Drosophilidae) Populations in Blueberry and Blackberry

We report on the efficacy of 0.5 M (61,000 ppm) erythritol (E) in Truvia Baking Blend, 10 ppm lufenuron (L), and their combination (LE) to reduce egg and larval densities of wild populations of Drosophila suzukii (Matsumura) infesting fields of rabbiteye blueberries (Vaccinium virgatum) and blackberries (Rubus sp.). Formulations included the active ingredients (lufenuron, erythritol, or both), sugar (in control and erythritol treatments), and Dawn hand-soap applied to plants with pressurized 3-gallon garden spray tanks. The three chemical treatments (E, L, and LE) had no effect on D. suzukii ovipositing in blackberry and blueberry fruit, but they did reduce larval infestation by 75%, particularly densities of first and second instars. Erythritol and lufenuron were equally efficacious compounds as a D. suzukii ovicide and larvicide, but they did not display additive or synergistic activity. Extremely high larval mortality in control fruits show an age structure heavily skewed toward egg output.