Introduction: Current techniques to diagnose and/or monitor critically ill neonates with bronchopulmonary dysplasia (BPD) require invasive sampling of body fluids, which is suboptimal in these frail neonates. We tested our hypothesis that it is feasible to use noninvasively collected urine samples for proteomics from extremely low gestational age newborns (ELGANs) at risk for BPD to confirm previously identified proteins and biomarkers associated with BPD. Methods: We developed a robust high-throughput urine proteomics methodology that requires only 50 μL of urine. We utilized the methodology with a proof-of-concept study validating proteins previously identified in invasively collected sample types such as blood and/or tracheal aspirates on urine collected within 72 h of birth from ELGANs (gestational age [26 ± 1.2] weeks) who were admitted to a single Neonatal Intensive Care Unit (NICU), half of whom eventually developed BPD (n = 21), while the other half served as controls (n = 21). Results: Our high-throughput urine proteomics approach clearly identified several BPD-associated changes in the urine proteome recapitulating expected blood proteome changes, and several urinary proteins predicted BPD risk. Interestingly, 16 of the identified urinary proteins are known targets of drugs approved by the Food and Drug Administration. Conclusion: In addition to validating numerous proteins, previously found in invasively collected blood, tracheal aspirate, and bronchoalveolar lavage, that have been implicated in BPD pathophysiology, urine proteomics also suggested novel potential therapeutic targets. Ease of access to urine could allow for sequential proteomic evaluations for longitudinal monitoring of disease progression and impact of therapeutic intervention in future studies.

Wilms’ tumor is the most common type of childhood kidney cancer. To improve risk stratification and identify novel therapeutic targets for patients with Wilms’ tumor, we used high-resolution mass spectrometry proteomics to identify urine tumor markers associated with Wilms’ tumor relapse. We determined the urine proteomes at diagnosis of 49 patients with Wilms’ tumor, non–Wilms’ tumor renal tumors, and age-matched controls, leading to the quantitation of 6520 urine proteins. Supervised analysis revealed specific urine markers of renal rhabdoid tumors, kidney clear cell sarcomas, renal cell carcinomas as well as those detected in patients with cured and relapsed Wilms’ tumor. In particular, urine prohibitin was significantly elevated at diagnosis in patients with relapsed as compared with cured Wilms’ tumor. In a validation cohort of 139 patients, a specific urine prohibitin ELISA demonstrated that prohibitin concentrations greater than 998 ng/mL at diagnosis were significantly associated with ultimate Wilms’ tumor relapse. Immunohistochemical analysis revealed that prohibitin was highly expressed in primary Wilms’ tumor specimens and associated with disease stage. Using functional genetic experiments, we found that prohibitin was required for the growth and survival of Wilms’ tumor cells. Overexpression of prohibitin was sufficient to block intrinsic mitochondrial apoptosis and to cause resistance to diverse chemotherapy drugs, at least in part by dysregulating factors that control apoptotic cytochrome c release from mitochondrial cristae. Thus, urine prohibitin may improve therapy stratification, noninvasive monitoring of treatment response, and early disease detection. In addition, therapeutic targeting of chemotherapy resistance induced by prohibitin dysregulation may offer improved therapies for patients with Wilms’ and other relapsed or refractory tumors.

Herpesviruses have complex mechanisms enabling infection of the human CNS and evasion of the immune system, allowing for indefinite latency in the host. Herpesvirus infections can cause severe complications of the central nervous system (CNS). Here, we provide a novel characterization of cerebrospinal fluid (CSF) proteomes from patients with meningitis or encephalitis caused by human herpes simplex virus 1 (HSV-1), which is the most prevalent human herpesvirus associated with the most severe morbidity. The CSF proteome was compared with those from patients with meningitis or encephalitis due to human herpes simplex virus 2 (HSV-2) or varicella-zoster virus (VZV, also known as human herpesvirus 3) infections. Virus-specific differences in CSF proteomes, most notably elevated 14-3-3 family proteins and calprotectin (i.e., S100-A8 and S100-A9), were observed in HSV-1 compared to HSV-2 and VZV samples, while metabolic pathways related to cellular and small molecule metabolism were downregulated in HSV-1 infection. Our analyses show the feasibility of developing CNS proteomic signatures of the host response in alpha herpes infections, which is paramount for targeted studies investigating the pathophysiology driving virus-associated neurological disorders, developing biomarkers of morbidity, and generating personalized therapeutic strategies.

Motivation: Accurate quantitative information about protein abundance is crucial for understanding a biological system and its dynamics. Protein abundance is commonly estimated using label-free, bottom-up mass spectrometry (MS) protocols. Here, proteins are digested into peptides before quantification via MS. However, missing peptide abundance values, which can make up more than 50% of all abundance values, are a common issue. They result in missing protein abundance values, which then hinder accurate and reliable downstream analyses. Results: To impute missing abundance values, we propose PEPerMINT, a graph neural network model working directly on the peptide level that flexibly takes both peptide-to-protein relationships in a graph format as well as amino acid sequence information into account. We benchmark our method against 11 common imputation methods on 6 diverse datasets, including cell lines, tissue, and plasma samples. We observe that PEPerMINT consistently outperforms other imputation methods. Its prediction performance remains high for varying degrees of missingness, different evaluation approaches, and differential expression prediction. As an additional novel feature, PEPerMINT provides meaningful uncertainty estimates and allows for tailoring imputation to the user’s needs based on the reliability of imputed values. Availability and implementation: The code is available at https://github.com/DILiS-lab/pepermint.

Motivation Accurate quantitative information about the protein abundance is crucial for understanding a biological system and its dynamics. Protein abundance is commonly estimated using label-free, bottom-up mass spectrometry protocols. Here, proteins are digested into peptides before quantification via mass spectrometry. However, missing peptide abundance values, which can make up more than 50% of all abundance values, are a common issue. They result in missing protein abundance values, which then hinder accurate and reliable downstream analyses. Results To impute missing abundance values, we propose PEPerMINT, a graph neural network model working directly on the peptide level that flexibly takes both peptide-to-protein relationships in a graph format as well as amino acid sequence information into account. We benchmark our method against eleven common imputation methods on six diverse datasets, including cell lines, tissue, and plasma samples. We observe that PEPerMINT consistently outperforms other imputation methods. Its prediction performance remains high for varying degrees of missingness, different evaluation approaches and differential expression prediction. As an additional novel feature, PEPerMINT provides meaningful uncertainty estimates and allows for tailoring imputation to the user’s needs based on the reliability of imputed values. Availability and implementation The code is available at https://github.com/DILiS-lab/pepermint.