Proteomic Investigation of the Role of Nucleostemin in Nucleophosmin-Mutated OCI-AML 3 Cell Line

GraphPI: Efficient Protein Inference with Graph Neural Networks

The integration of deep learning approaches in biomedical research has been transformative, enabling breakthroughs in various applications. Despite these strides, its application in protein inference is impeded by the scarcity of extensively labeled data sets, a challenge compounded by the high costs and complexities of accurate protein annotation. In this study, we introduce GraphPI, a novel framework that treats protein inference as a node classification problem. We treat proteins as interconnected nodes within a protein-peptide-PSM graph, utilizing a graph neural network-based architecture to elucidate their interrelations. To address label scarcity, we train the model on a set of unlabeled public protein data sets with pseudolabels derived from an existing protein inference algorithm, enhanced by self-training to iteratively refine labels based on confidence scores. Contrary to prevalent methodologies necessitating data set-specific training, our research illustrates that GraphPI, due to the well-normalized nature of Percolator features, exhibits universal applicability without data set-specific fine-tuning, a feature that not only mitigates the risk of overfitting but also enhances computational efficiency. Our empirical experiments reveal notable performance on various test data sets and deliver significantly reduced computation times compared to common protein inference algorithms.

High-throughput transcriptome profiling indicates ribosomal RNAs to be associated with resistance to immunotherapy in non-small cell lung cancer (NSCLC)

BACKGROUND

Despite the impressive outcomes with immune checkpoint inhibitor (ICI) in non-small cell lung cancer (NSCLC), only a minority of the patients show long-term benefits from ICI. In this study, we used retrospective cohorts of ICI treated patients with NSCLC to discover and validate spatially resolved protein markers associated with resistance to programmed cell death protein-1 (PD-1) axis inhibition.

METHODS

Pretreatment samples from 56 patients with NSCLC treated with ICI were collected and analyzed in a tissue microarray (TMA) format in including four different tumor regions per patient using the GeoMx platform for spatially informed transcriptomics. 34 patients had assessable tissue with tumor compartment in all 4 TMA spots, 22 with leukocyte compartment and 12 with CD68 compartment. The patients' tissue that was not assessable in fourfold redundancy in each compartment was designated as the validation cohort; cytokeratin (CK) (N=22), leukocytes CD45 (N=31), macrophages, CD68 (N=43). The human whole transcriptome, represented by~18,000 individual genes assessed by oligonucleotide-tagged in situ hybridization, was sequenced on the NovaSeq platform to quantify the RNAs present in each region of interest.

RESULTS

54,000 gene variables were generated per case, from them 25,740 were analyzed after removing targets with expression lower than a prespecified frequency. Cox proportional-hazards model analysis was performed for overall and progression-free survival (OS, PFS, respectively). After identifying genes significantly associated with limited survival benefit (HR>1)/progression per spot per patient, we used the intersection of them across the four TMA spots per patient. This resulted in a list of 12 genes in the tumor-cell compartment (RPL13A, GNL3, FAM83A, CYBA, ACSL4, SLC25A6, EPAS1, RPL5, APOL1, HSPD1, RPS4Y1, ADI1). RPL13A, GNL3 in tumor-cell compartment were also significantly associated with OS and PFS, respectively, in the validation cohort (CK: HR, 2.48; p=0.02 and HR, 5.33; p=0.04). In CD45 compartment, secreted frizzled-related protein 2, was associated with OS in the discovery cohort but not in the validation cohort. Similarly, in the CD68 compartment ARHGAP and PNN interacting serine and arginine rich protein were significantly associated with PFS and OS, respectively, in the majority but not all four spots per patient.

CONCLUSION

This work highlights RPL13A and GNL3 as potential indicative biomarkers of resistance to PD-1 axis blockade that might help to improve precision immunotherapy strategies for lung cancer.

Molecular Aspects of Hematological Malignancies and Benign Hematological Disorders

Hematology represents a dynamic specialty in clinical medicine that requires solid knowledge of normal and pathological hematopoiesis, cytomorphology, pathology, immunology, genetics and molecular biology [...].

Iron Dyshomeostasis in COVID-19: Biomarkers Reveal a Functional Link to 5-Lipoxygenase Activation

Coronavirus disease 2019 (COVID-19) is characterized by a broad spectrum of clinical symptoms. After acute infection, some subjects develop a post-COVID-19 syndrome known as long-COVID. This study aims to recognize the molecular and functional mechanisms that occur in COVID-19 and long-COVID patients and identify useful biomarkers for the management of patients with COVID-19 and long-COVID. Here, we profiled the response to COVID-19 by performing a proteomic analysis of lymphocytes isolated from patients. We identified significant changes in proteins involved in iron metabolism using different biochemical analyses, considering ceruloplasmin (Cp), transferrin (Tf), hemopexin (HPX), lipocalin 2 (LCN2), and superoxide dismutase 1 (SOD1). Moreover, our results show an activation of 5-lipoxygenase (5-LOX) in COVID-19 and in long-COVID possibly through an iron-dependent post-translational mechanism. Furthermore, this work defines leukotriene B4 (LTB4) and lipocalin 2 (LCN2) as possible markers of COVID-19 and long-COVID and suggests novel opportunities for prevention and treatment.

Extracellular Vesicles in Regenerative Processes Associated with Muscle Injury Recovery of Professional Athletes Undergoing Sub Maximal Strength Rehabilitation

Platelet-rich plasma (PRP) has great potential in regenerative medicine. In addition to the well-known regenerative potential of secreted growth factors, extracellular vesicles (EVs) are emerging as potential key players in the regulation of tissue repair. However, little is known about their therapeutic potential as regenerative agents. In this study, we have identified and subtyped circulating EVs (platelet-, endothelial-, and leukocyte-derived EVs) in the peripheral blood of athletes recovering from recent muscular injuries and undergoing a submaximal strength rehabilitation program. We found a significant increase in circulating platelet-derived EVs at the end of the rehabilitation program. Moreover, EVs from PRP samples were isolated by fluorescence-activated cell sorting and analyzed by label-free proteomics. The proteomic analysis of PRP-EVs revealed that 32% of the identified proteins were associated to "defense and immunity", and altogether these proteins were involved in vesicle-mediated transport (GO: 0016192; FDR = 3.132 × 10^-19), as well as in wound healing (GO: 0042060; FDR = 4.252 × 10^-13) and in the events regulating such a process (GO: 0061041; FDR = 2.812 × 10^-12). Altogether, these data suggest that platelet-derived EVs may significantly contribute to the regeneration potential of PRP preparations.