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Comparative genomic and proteomic analysis reveals orthogroup structured evolution of tick protease inhibitors.

Protease inhibitors (PIs) play central roles in regulating endogenous proteolysis and host-parasite interactions in ticks. However, the evolutionary architecture underlying their diversification across tick lineages remains insufficiently resolved. Here, we performed a genome-wide comparative analysis of predicted proteomes from 14 tick species to systematically characterize PI repertoires. In total, 4931 putative PIs were identified and grouped into 20 families using the MEROPS classification system. Further, PI families such as Antistasin, WAP-type, and Pacifastin, which have not previously been systematically reported in tick genomes, were classified. Orthogroup inference demonstrated that PI expansion is structured at the level of evolutionary lineages rather than uniformly across families. By stratifying orthogroups according to duplication burden and taxonomic conservation, we identified a broadly conserved single-copy core under strong purifying selection. Motif level analysis of serpin reactive center loops further revealed conservation of inhibitory specificity within single copy orthogroups and diversification of key functional residues in duplication-associated lineages. Integration of secretion prediction and tissue-resolved proteomics from Hyalomma anatolicum and Rhipicephalus microplus demonstrated that evolutionary stratification is reflected at the protein level. Together, these findings provide an orthogroup-resolved evolutionary framework linking duplication dynamics, molecular evolution, and tissue-level protein deployment. This integrative approach offers a systematic basis for prioritizing conserved and diversified PI lineages for future functional and anti-tick intervention studies.

Animals

Transpulmonary proteomic gradient analysis in women with pulmonary arterial hypertension associated with systemic sclerosis.

This study investigated proteomic alterations in the pulmonary circulation of patients with pulmonary arterial hypertension associated with systemic sclerosis (PAH-SSc) by analyzing the transpulmonary protein gradient and comparing the proteomic profiles with systemic sclerosis (SSc) without PAH. Twenty women were included (10 PAH-SSc, 64.6 ± 10.8 years; 10 SSc, 62.8 ± 11.5 years). The transpulmonary gradient was defined as the difference in biomarker concentrations between wedge-position and pulmonary artery blood samples. Peptides were analysed using liquid chromatography-mass spectrometry, and differentially abundant proteins were identified with Proteome Discoverer. Protein-protein interaction networks were generated with STRING and visualized in Cytoscape. A total of 270 proteins were detected, with no significant transpulmonary gradient alterations. However, patients with PAH-SSc showed distinct proteomic profiles compared to SSc. Multivariate analysis identified 48 differentially abundant proteins in pulmonary artery plasma, with 15 overrepresented and 33 downregulated in PAH-SSc. Among these, the downregulation of transforming growth factor-beta-induced protein ig-h3 (TGFβI/ig-h3) points to a potential involvement of the TGF-β-related extracellular matrix remodelling pathway in PAH-SSc. However, further validation in larger and independent cohorts is required before its relevance as a biomarker or therapeutic target can be established. In conclusion, while no transpulmonary proteomic gradient was observed, the proteomic profiles of PAH-SSc and SSc were different. The profile in PAH-SSc was characterized by differences in immune response, lipid metabolism, and hemostatic proteins. SIGNIFICANCE: This study offers the first proteomic characterization of the transpulmonary gradient in PAH-SSc and SSc. Although no differences in the gradient were found, the pulmonary artery plasma proteome of PAH-SSc patients showed a distinct pattern compared to SSc. Several proteins associated with immune function, haemostasis, and cellular processes were altered, which may indicate specific pathophysiological features of PAH-SSc or suggest how lung dysfunction develops in SSc. Targeting dysregulated proteins like TGFβI/ig-h3 or addressing immune-coagulation imbalances may support future research studies. Overall, these findings refine the molecular profile of PAH-SSc and provide a basis for future large-scale studies aimed at clarifying disease mechanisms and identifying clinically relevant molecular signatures.

Humans

Spatial proteomic mapping of the human and mouse retina using IBEX.

We generated a comparative spatial proteomic atlas of the human and mouse retina using a highly multiplexed immunohistochemistry technique called iterative bleaching extends multiplexity (IBEX). We refined the IBEX workflow by integrating an antibody dissociation option alongside chemical bleaching. This dual strategy enabled removal of the entire antibody complex, permitting the flexible use of antibodies from the same host species across iterative cycles. We coupled this workflow with super-resolution imaging via deconvolution and applied it to the retina of healthy humans and WT mice and the Crb1rd8 mouse model. We successfully imaged over 25 protein markers on human and mouse tissue sections, generating spatial atlases of the major retinal cell populations. Cross-species protein expression was compared to scRNA-seq datasets to identify protein and transcript disparities. Super-resolution IBEX delineated the ultrastructural features of the outer limiting membrane (OLM), identifying CD44 as a core structural component tightly colocalized with a highly organized F-actin belt within Müller glial endfeet. Using the Crb1rd8 mouse model, disruption of this complex was spatially associated with rosette formation and OLM structural failure. In summary, spatial proteomic atlases of the human and mouse retina were used to reveal insights into the arrangement of major retinal cell populations and OLM structure.

Animals

Plasma Proteomic Profiles of Pediatric Patients With Human Herpesvirus 6B Encephalitis Following Umbilical Cord Blood Transplantation.

Human herpesvirus 6B (HHV-6B) encephalitis is a rare but severe complication of hematopoietic cell transplantation. This study investigated the pathogenesis of HHV-6B encephalitis by comparing plasma proteomic profiles of four pediatric patients with HHV-6B encephalitis to three with asymptomatic HHV-6B reactivation following umbilical cord blood transplantation (UCBT). Plasma proteomic profiling was conducted using liquid chromatography-mass spectrometry. Overall, 260 proteins were identified and quantified in plasma samples. At the onset of HHV-6B encephalitis and asymptomatic reactivation, 20 and 24 proteins, respectively, were significantly upregulated compared to their respective pre-onset levels. Of these, 11 proteins were uniquely upregulated in HHV-6B encephalitis. S100-A9 and S100-A8 were the most and second-most upregulated proteins in HHV-6B encephalitis, respectively. Elevated plasma S100A8/A9 heterodimer levels were confirmed via enzyme-linked immunosorbent assay in three of the four patients with HHV-6B encephalitis. Pathway analysis identified neutrophil degranulation as the most enriched category among upregulated proteins in HHV-6B encephalitis. Additionally, proteins related to the protein-lipid complex remodeling pathway were more prominently upregulated in HHV-6B encephalitis than in asymptomatic reactivation. Proteomic analysis revealed distinct plasma protein profiles between HHV-6B encephalitis and asymptomatic HHV-6B reactivation in pediatric UCBT recipients. The inflammatory response mediated by S100A8/A9 proteins may play a critical role in the pathogenesis of HHV-6B encephalitis. These findings indicate that proteomic analysis may provide novel insights into the host response to HHV-6B reactivation and the subsequent development of HHV-6B encephalitis.

Humans

Community-driven advances in computational mass spectrometry: The perspective of EuBIC-MS members.

Advances in data acquisition, artificial intelligence, and integrative bioinformatics are driving the rapid evolution of computational mass spectrometry, and in turn, transforming modern proteomics, metabolomics, and lipidomics. These developments have greatly increased the scale and complexity of mass spectrometry data, underscoring the importance of evolving accurate, transparent, efficient and reproducible data processing workflows. Addressing these challenges requires collaborative innovation that brings together expertise in software engineering, statistics, and biology. The European Bioinformatics Community for Mass Spectrometry (EuBIC-MS), an initiative of the European Proteomics Association (EuPA), fosters a culture of open, community-driven development through its biennial Developers Meetings and Winter Schools. This commentary summarizes the scientific background and outcomes of the EuBIC-MS Developers Meeting 2025, which took place in Novacella, Italy. Three keynote presentations highlighted major frontiers in the field: deep proteome and phosphoproteome profiling, text mining for protein-protein interaction extraction, and scalable proteomics for AI-driven drug discovery. Seven community-selected hackathons addressed emerging challenges such as single-cell proteomics data analysis, FAIR metadata extraction, deep learning frameworks, R-Python interoperability, and DIA validation. Together, these efforts demonstrate the potential for scientific and technical innovation to arise from open collaboration, and highlight how community-driven initiatives can accelerate progress in computational mass spectrometry. SIGNIFICANCE: Modern proteomics increasingly depends on computational advances to translate complex, high-dimensional data into biological knowledge. The EuBIC-MS Developers Meeting 2025 exemplifies how community-driven collaboration can directly accelerate this process by bringing together experts from bioinformatics, statistics, and experimental proteomics to co-develop open, interoperable, and reproducible analytical tools. By fostering shared software frameworks, transparent benchmarking, and collaborative problem solving, the EuBIC-MS community helps ensure that technological innovation translates into reliable biological insights. This collaborative model strengthens the foundation for quantitative, system-level understanding of proteomes and establishes a sustainable path for integrating artificial intelligence and next-generation data acquisition into routine biological discovery. This commentary shows some current highlights in the field of computational mass spectrometry and community-based approaches undertaken during the most recent Developers Meeting to solve these challenges. The approaches discussed and initiated during the meeting - ranging from deep proteome profiling and phosphosite mapping to text mining, single-cell data analysis, and FAIR metadata extraction - address key bottlenecks that currently limit the biological interpretability and comparability of proteomics data.

Mass Spectrometry

Oncoproteins E6/E7 of the human papillomavirus types 16 & 18 synergize in modulating oncogenes and tumor suppressor proteins in colorectal cancer.

OBJECTIVE: Our study presents a novel analysis of the oncogenes and tumor suppressor proteins directly modulated by E6/E7 of high-risk HPV types 16 and 18, in colorectal cancer (CRC). METHODS: HCT 116 (KRAS mutant) & HT-29 (TP53 mutant) cell models of CRC were transduced with E6/E7 of HPV16 and HPV18, individually and in combination. Further, we utilized a liquid chromatography mass spectrometry (LC-MS/MS) approach to analyze and compare the proteomes of both CRC cell models. RESULTS: We generated six stably transduced cell lines. Our data revealed a significantly higher, HPV-induced modulation of oncogenes and tumor suppressor proteins in the TP53 mutant model, as compared to the KRAS mutant model (p ≤ 0.01). Less than 1% of the genes were commonly modulated by HPV, between both models. We also report that HT-29 cells, expressing E6/E7 of both HPV types, significantly reduced the suppression of oncogenes as compared to cells expressing E6/E7 of either HPV types individually (p-value ≤0.00001). CONCLUSION: Our data imply that HPV coinfections leads to the sustenance of a pro-oncogenic environment in CRC. HPV modulates different oncogenes/tumor suppressor proteins in CRC of varying mutational backgrounds, thus highlighting the importance of personalized therapies for such diseases with mutational heterogeneity.

Humans

Elucidating the In Vitro Adverse Effect of Functionalized Single-Walled Carbon Nanotubes Against Breast Cancer Cells at the Proteomics Level.

The tremendous therapeutic potential of carbon-based nanomaterials (CNMs) has been limited by inconsistent data regarding the nanotoxicity assessment. Although a bulk of studies have been performed to assess the in vitro cytotoxicity mechanism of CNMs, the exact factors responsible for the cytotoxicity of CNMs have not been fully understood. With the rapid advancement of mass spectrometry technologies, proteomics has emerged as a powerful strategy for systematically investigating the molecular and cellular mechanisms underlying toxicity induced by nanomaterials. This study examined the in vitro cytotoxicity of single-walled carbon nanotubes (SWCNTs) in human MCF-7 breast cancer cells by conducting a comparative proteome-level analysis using mass spectrometry. Initially, the characterized SWCNTs were incubated with MCF-7 cells for 3, 6, and 24 h. Proteins were subsequently extracted from each treatment group and subjected to nano-liquid chromatography-tandem mass spectrometry (nLC-MS/MS) analysis. The relative abundance of the identified proteins was determined by comparison with the control group, and differential expression patterns, including upregulated and downregulated proteins, were assessed. A total of 3482 unique protein groups were identified across all exposure periods. Among these, 3466 protein groups were detected following 3 h of exposure, 3469 following 6 h of exposure, and 3480 following 24 h of exposure. Compared with the control group, the identified differentially expressed proteins exhibited fold changes ranging from 2-fold to 20-fold across the incubation periods. In total, 70 proteins were found to be significantly regulated following SWCNT exposure. Of the differentially expressed proteins, 45 were significantly upregulated, whereas 25 were significantly downregulated. Visualization of these regulations over time was shown in a heatmap of log2-transformed fold-change values to explore time-specific proteomic alterations. Functional enrichment analysis of these proteins also showed that the regulated proteins were significantly associated with Reactome pathways, including ER-to-Golgi anterograde transport, Golgi-to-ER retrograde transport, COPI-mediated vesicle trafficking, regulation of insulin-like growth factor transport and uptake by insulin-like growth factor-binding proteins, protein metabolism, and posttranslational protein modification. Furthermore, a systematic comparison of previous studies within the present findings was provided to situate our study within the broader context of understanding CNT-induced cellular toxicity. Collectively, these findings provided an important proteomic evidence of the adverse effects of SWCNTs on MCF-7 cells. Furthermore, this study showed a comprehensive proteomic landscape of cellular responses to SWCNT exposure, contributing to a better understanding of the molecular mechanisms underlying SWCNT-induced cytotoxicity and bridging the gap between protein regulation and the resulting cellular responses. In this study, we characterized the proteomic landscape of MCF-7 cells following SWCNT exposure, revealing molecular mechanisms associated with cellular responses and cytotoxicity. The identified differentially expressed proteins established a link between altered protein regulation and SWCNT-induced cellular effects. Moreover, these proteins need to be further validated in different cell models and would potentially represent promising candidates for the identification of novel molecular targets involved in SWCNT-induced cytotoxicity.

MCF‐7 cells

Proteomic insights into azoospermia: protein differences in testicular tissue between non-obstructive and obstructive azoospermia patients.

Non-obstructive azoospermia (NOA) and obstructive azoospermia (OA) are the main classifications of severe male infertility, but the molecular mechanism of NOA remains poorly understood. This study aimed to identify potential biomarkers and pathological mechanisms by comparing the proteomic differences in testicular tissues of NOA and OA patients. Through proteomic analysis based on liquid chromatography-tandem mass spectrometry (LC-MS/MS) of testicular samples from 5 NOA patients and 5 OA patients, we identified 5264 proteins, among which 717 differentially expressed proteins (DEPs) were found between the two groups (242 upregulated and 475 downregulated in NOA). Bioinformatics analysis indicated that these DEPs were significantly associated with reproductive development, gametogenesis, and cell structural stability. On the basis of this, six candidate proteins, including dysferlin (DYSF), myoferlin (MYOF), mitsugumin 53 (MG53), cluster of differentiation 63 (CD63), caveolin-3 (CAV3), and calpain-3 (CAPN3), were selected from the DEPs and verified in an expanded sample set (37 NOA cases and 28 OA cases) through quantitative real-time polymerase chain reaction (qRT-PCR) and Western blot, confirming their dysregulation in NOA. These findings provide new proteomic insights into NOA, highlighting the disruption of membrane repair and structural pathways, and offer potential biomarkers for understanding its pathogenesis.

Humans

Proteomic Analysis of Three Independent Series of Sequential Cystic Fibrosis Strains in an International Pseudomonas aeruginosa Reference Panel Indicates Positive Selection in Late Infection Strains.

Pseudomonas aeruginosa is a highly diverse, adaptable Gram-negative bacterium that thrives in many environments and is a frequent cause of chronic opportunistic infections in people with cystic fibrosis (CF). P. aeruginosa adapts over time of colonization to facilitate chronic infection, including loss of virulence factors; however, proteomic analyses of the adaptation to chronic infection have been limited. We previously assembled and characterized an international panel of P. aeruginosa strains from diverse clinical and geographical sources, including three sets of sequential CF isolates, enabling identification of conserved adaptations linked to chronic CF lung colonization. We compared the proteomes of eight strains (three early and five late infection) to assess whether common proteomic changes emerged during colonization across the sequential isolates. We identified 11 proteins showing increased abundance in late isolates in all three series, many of which are associated with virulence, regulation of virulence, or response to hypoxia. These include CF inhibitory factor repressor (CifR); WspR; 2 two-component response regulators (PA2572 and PA3702), and transcriptional regulator (PA2551). Moreover, we identified three proteins (PA2572, PA3819, and PA5028) that showed increased abundance in all five late isolates. The probability of this being random is 5.06 × 10-53 and therefore, strong evidence of positive selection. The increased abundance of PA2573 and PA3819 appears to improve the fitness of P. aeruginosa in response to antibiotics and oxidative stress. All three proteins share a tyrosine phosphorylation motif, suggesting a common regulatory mechanism. Overall, despite substantial diversity across P. aeruginosa, common adaptations occur in the CF lung.

Pseudomonas aeruginosa

Application of Proteomic Methods in Oomycete Biology.

The biochemical makeup of any organism provides insight into key factors regarding its biological functions. These factors can be explored using proteomics, which allows us to obtain a snapshot of the protein content and abundance in an organism, cell type or sub-cellular compartment. Here, we describe proteomic methodologies that can be used to dissect the biochemical mechanism of phytopathogenicity in oomycetes. These methodologies include protein extraction, purification, subsequent processing, mass spectrometry analysis, and qualitative and quantitative data processing of oomycete proteomes for comparative studies. Additionally, the use of mass spectra to assist in gene validation and modelling in unfinished oomycete genomes is also described.

Oomycetes

Chaperone-mediated autophagy regulates neuronal activity by sex-specific remodelling of the synaptic proteome.

Chaperone-mediated autophagy (CMA) declines in ageing and neurodegenerative diseases. Loss of CMA in neurons leads to neurodegeneration and behavioural changes in mice but the role of CMA in neuronal physiology is largely unknown. Here we show that CMA deficiency causes neuronal hyperactivity, increased seizure susceptibility and disrupted calcium homeostasis. Pre-synaptic neurotransmitter release and NMDA receptor-mediated transmission were enhanced in CMA-deficient females, whereas males exhibited elevated post-synaptic AMPA-receptor activity. Comparative quantitative proteomics revealed sexual dimorphism in the synaptic proteins degraded by CMA, with preferential remodelling of the pre-synaptic proteome in females and the post-synaptic proteome in males. We demonstrate that genetic or pharmacological CMA activation in old mice and an Alzheimer's disease mouse model restores synaptic protein levels, reduces neuronal hyperexcitability and seizure susceptibility, and normalizes neurotransmission. Our findings unveil a role for CMA in regulating neuronal excitability and highlight this pathway as a potential target for mitigating age-related neuronal decline.

Animals

From Variability to Consensus: Rescoring Harmonizes Peptide Identification across Diverse Search Engines and Data Sets.

Peptide-spectrum match (PSM) rescoring has become standard in proteomics workflows, improving peptide identification accuracy across diverse search engines. Despite the availability of multiple rescoring strategies, systematic comparisons spanning several search engines, data sets, and database configurations remain limited. Here, we benchmarked seven publicly available search engines, evaluating standard target-decoy-based false discovery rate (FDR) estimation alongside Percolator, MS2Rescore, and Oktoberfest across four data sets acquired on different mass spectrometry platforms in data-dependent mode and searched against protein databases of varying size and composition. Rescoring substantially increased identification consensus and reduced variability between search engines, with prediction-based approaches yielding the largest gains. While database size had limited impact for human data sets, it significantly affected identification rates on a metaproteomic data set. Entrapment-based evaluation indicated generally adequate FDR control across methods, although prediction-based rescoring exhibited a higher tendency toward FDR underestimation in specific configurations. Overall, advanced rescoring strategies harmonize peptide identification outcomes across search engines, thereby enhancing robustness and comparability in proteomics analyses. However, careful feature selection and appropriate database choice remain essential to ensure reliable FDR control and optimal performance across diverse experimental settings.

Search Engine

Integrative multi-omics analysis unravels the metabolic landscape and reveals serum biomarkers for early diagnosis of hyperuricemia.

BACKGROUND: Hyperuricemia (HUA) is a major risk factor for gout and multiple metabolic disorders. Although serum uric acid (UA) is the gold standard for HUA diagnosis, it fails to reflect early metabolic disturbances and shows limited predictive value for asymptomatic HUA. This study sought to elucidate the pathological mechanisms underlying HUA and identify novel diagnostic biomarkers beyond UA. METHODS: This study enrolled 195 patients with HUA and 98 healthy controls. Global metabolomics and proteomics profiling were performed to characterize molecular alterations underlying HUA. Based on the biological relevance of the shared dysregulated pathways, a pathway correlation network was constructed to elucidate the pathological mechanisms driving HUA initiation and progression. Furthermore, diagnostic biomarkers for HUA were identified using machine learning algorithms, and were validated with an external cohort. RESULTS: HUA patients exhibited distinct metabolic and proteomic profiles compared with healthy controls. Integrated multi-omics pathway analysis revealed that peroxisome proliferators-activated receptor signaling pathway, arachidonic acid metabolism, purine metabolism, pyrimidine metabolism and sphingolipid signaling pathway were significantly dysregulated in HUA. Among them, arachidonic acid metabolism was identified as a hub pathway involved in HUA progression. Furthermore, a metabolite panel consisting of cysteine-S-sulfate, glycerophosphocholine and 4-hydroxyphenylpyruvic acid was screened by machine learning and validated in an independent cohort, which showed slightly higher diagnostic performance for HUA than UA. CONCLUSIONS: This study reveals the core metabolic and protein regulatory networks of HUA, and identifies a novel serum metabolite panel for the diagnosis of HUA. These findings provide new insights for improved clinical diagnosis and management.

Humans

IL17 signaling promotes oocyte developmental competence acquisition during maturation.

BACKGROUND: Defects in the acquisition of oocyte developmental competence during the maturation process causes subfertility or infertility in animals and humans. Understanding the regulatory mechanisms of oocyte maturation is essential for reproductive biology and medicine. Follicular fluid (FF) is an important microenvironment governing oocyte maturation. METHODS: A tandem mass tags (TMT)-based comparative FF proteomic analysis was employed to identify FF proteins that are potentially crucial for oocyte maturation. A very large number of pig and mouse oocytes (approximately 20,000) and embryos (over 13,000, including somatic cell nuclear transfer, parthenogenetic activation, and in vitro fertilization embryos) were used to investigate the effects of identified FF proteins on in vitro oocyte maturation and subsequent in vitro and in vivo embryo development. RNA sequencing, quantitative PCR, enzyme-linked immunosorbent assays, and immunofluorescence were used to study the expression patterns and action mechanisms of identified FF proteins in oocytes. In addition, intra-oocyte levels of glutathione and reactive oxygen species were measured to assess redox homeostasis. RESULTS: Interleukin 17D (IL17D) was identified as an important FF protein and it is significantly upregulated in porcine FF during oocyte maturation. IL17D promotes oocyte maturation by enhancing bidirectional communication between oocytes and cumulus cells, via upregulating CX43 expression and transzonal projections, which helps to maintain oocyte redox homeostasis and nuclear-cytoplasmic synchrony. IL17D treatment of oocytes enhances subsequent in vitro and in vivo full-term embryo development by modulating lipid metabolism and histone modification reprogramming. IL17D exerts its function via activating IL17 signaling through binding to CD93. Two other IL17 family members, IL17A and IL17F, also enhance oocyte maturation quality. IL17D displays a conserved expression pattern and function in pig and mouse oocytes. CONCLUSIONS: This study reveals the critical roles of IL17D in regulating oocyte developmental competence acquisition during maturation by activating IL17 signaling. The findings provide valuable insights into the molecular mechanisms underlining oocyte developmental potential acquisition and may help to develop methods for efficient production of oocytes for assisted reproduction.

Animals

Plasma proteomic profiling characterizes candidate biomarkers of perimesencephalic non-aneurysmal subarachnoid hemorrhage.

OBJECT: This study aims to explore the plasma proteomic profiles of angiographically confirmed pmSAH and aSAH, and to identify candidate protein biomarkers for discriminating these subtypes on a biological level. METHODS: The differentially abundant proteins of plasma samples from patients with pmSAH (n = 30) and aSAH (n = 30) were analyzed by data-independent acquisition proteomics, and candidate biomarkers were screened. RESULTS: 291 candidate biomarkers were obtained that could be used to distinguish pmSAH patients from aSAH patients, among which 76 were upregulated and 215 were downregulated in pmSAH. Subsequently, the 10 candidate biomarkers were validated by enzyme-linked immunosorbent assay in a validation cohort of 72 subjects. ORM1, ORM2, HP and NMNAT1 were specifically down-regulated in the pmSAH group, while ANP32A was specifically up-regulated in the pmSAH group. FGL2 was specifically up-regulated in the aSAH group. The combined model of ORM2, HP and ANP32A had the best discriminative power (AUC = 0.880). CONCLUSIONS: This study identified ORM2, HP, and ANP32A as candidate biomarkers reflecting biological differences between pmSAH and aSAH. SIGNIFICANCE: Although some proteomic studies have analyzed aneurysmal subarachnoid hemorrhage, to date, there have been no reports on the circulating proteomic analysis of pmSAH. Comparative analysis of the circulating proteomic differences between pmSAH and aSAH may not only help understand the causes of pmSAH, but also contribute to a deeper understanding of mechanisms showing how pmSAH differs from the formation and rupture mechanisms of intracranial aneurysms.

Humans

MAFin: motif detection in multiple alignment files.

MOTIVATION: Whole Genome and Proteome Alignments, represented by the multiple alignment file format, have become a standard approach in comparative genomics and proteomics. These often require identifying conserved motifs, which is crucial for understanding functional and evolutionary relationships. However, current approaches lack a direct method for motif detection within MAF files. We present MAFin, a novel tool that enables efficient motif detection and conservation analysis in MAF files to address this gap, streamlining genomic and proteomic research. RESULTS: We developed MAFin, the first motif detection tool for Multiple Alignment Format files. MAFin enables the multithreaded search of conserved motifs using three approaches: (i) using user-specified k-mers to search the sequences. (ii) with regular expressions, in which case one or more patterns are searched, and (iii) with predefined Position Weight Matrices. Once the motif has been found, MAFin detects the motif instances and calculates the conservation across the aligned sequences. MAFin also calculates a conservation percentage, which provides information about the conservation levels of each motif across the aligned sequences, based on the number of matches relative to the length of the motif. A set of statistics enables the interpretation of each motif's conservation level, and the detected motifs are exported in JSON and CSV files for downstream analyses. AVAILABILITY AND IMPLEMENTATION: MAFin is offered as a Python package under the GPL license as a multi-platform application and is available at: https://github.com/Georgakopoulos-Soares-lab/MAFin.

Software

Comparison of Proteomic Analysis of Cerebrospinal Fluid From Neurological Patients With and Without Amyotrophic Lateral Sclerosis.

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder characterised by progressive muscle weakness in both bulbar and extremity muscles, leading to a diverse clinical phenotype with motor and non-motor symptoms. Approximately 85% of ALS cases are sporadic (sALS), while the remaining 10%-15% are familial (fALS). Biological biomarkers of sporadic ALS remain poorly understood, hindering precise patient screening, delaying diagnosis and negatively affecting prognosis. This study aims to identify potential proteomic biomarkers by comparing the cerebrospinal fluid (CSF) of sALS patients with that of patients suffering from other neurological diseases. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used for proteomic profiling of CSF samples from 24 sALS patients and 26 patients with other neurological diseases. The complete protein expression profiles were compared using a two-tailed Student's t-test, with a p <&#x2009;0.05 considered statistically significant with additional FDR correction at the 0.1 level. Proteomic analysis of CSF samples identified significant quantitative changes in 96 proteins with threshold p&#x2009;<&#x2009;0.05 and 74 proteins with FDR <&#x2009;0.1 between sALS and non-ALS patients, including alterations in proteins associated with neurodegenerative processes, such as amyloid precursor proteins and inflammatory markers. CSF proteomic analysis reveals altered inflammatory and neurodegenerative metabolic pathways, providing valuable insights into the proteomic landscape of sALS. Several dysregulated proteins were consistent with the disease mechanisms highlighted in previous studies. These findings represent a step forward in developing personalised approaches for diagnosing and managing the disease.

Humans

Time-Dependent Effects of Rapid-Acting Antidepressants in iPSC-Derived Neurons from Treatment-Resistant Depression and Healthy Volunteers.

UNLABELLED: Rapid-acting antidepressants like ketamine and serotonergic psychedelics show promise for treatment-resistant depression (TRD), but the molecular mechanisms that contribute to their therapeutic effects remain unclear. Induced pluripotent stem cells (iPSCs) offer a platform to model human cortical neurons and investigate drug effects in a human-relevant system. Here, iPSCs from individuals with TRD and healthy volunteers (HVs) were differentiated into mature cortical-like neurons and treated for six and 24 hours with agents being investigated as rapid-acting antidepressants, including (2R,6R)-hydroxynorketamine (HNK), psilocybin, lysergic acid diethylamide (LSD), and 2,5-Dimethoxy-4-iodoamphetamine (DOI). Bulk and single-cell RNA sequencing assessed global and cell-type-specific transcriptomic responses. Synaptic proteins were evaluated via Western blotting and immunocytochemistry. To validate translational relevance, transcriptomic results were compared to CSF proteomics from ketamine-treated HVs. Despite differing initial pharmacological targets, overall gene expression across all compounds was highly correlated at matched timepoints compared to vehicle control, suggesting shared downstream effects. Both glutamatergic and serotonergic drugs converged on pathways involving inflammation, mTORC1 signaling, and cellular growth. At the single-cell level, HNK showed distinct cell-type specific alterations: upregulation in excitatory neurons and concomitant downregulation of inhibitory neuron populations. Differentially expressed genes from HNK-treated neurons also overlapped with CSF proteomic signatures from ketamine-treated individuals, supporting the model's translational relevance. This study is the first to assess multiple putative rapid-acting antidepressants in parallel using an iPSC-derived neuron model. Both convergent and drug-specific changes in gene expression and pathway enrichment were observed across diverse compounds, supporting the use of human iPSC-derived neurons in antidepressant drug discovery. CLINICAL TRIAL REGISTRY: www.clinicaltrials.gov, NCT02484456.

Journal Article