Plasma adenosine deaminase-1 and -2 activities are lower at birth in Papua New Guinea than in The Gambia but converge over the first weeks of life.

Introduction: Dynamic cellular and molecular adaptations in early life significantly impact health and disease. Upon birth, newborns are immediately challenged by their environment, placing urgent demands on the infant immune system. Adenosine deaminases (ADAs) are enzymatic immune modulators present in two isoforms - ADA-1 and ADA-2. Infants exhibit low ADA activity, resulting in high plasma adenosine concentrations and a consequent anti-inflammatory/anti-Th1 bias. While longitudinal studies of plasma ADA have been conducted in infants in The Gambia (GAM), little is known regarding ADA trajectories in other parts of the world. Methods: Herein, we characterized plasma ADA activity in an infant cohort in Papua New Guinea (PNG; n=83) and compared to ontogeny of ADA activity in a larger cohort in GAM (n=646). Heparinized peripheral blood samples were collected at day of life (DOL) 0, DOL7, DOL30, and DOL128. Plasma ADA-1, ADA-2, and total ADA activities were measured by chromogenic assay. Results: Compared to GAM infants, PNG infants had significantly lower ADA-1 (0.9-fold), ADA-2 (0.42-fold), and total ADA (0.84-fold) activities at birth which converged by DOL30. Discussion: Overall, discovery of a distinct baseline and a consistent pattern of increasing plasma ADA activity in early life in two genetically and geographically distinct populations validates and extends previous findings on the robustness of early life immune ontogeny.

Predictive gene expression signature diagnoses neonatal sepsis before clinical presentation.

BACKGROUND: Neonatal sepsis is a deadly disease with non-specific clinical signs, delaying diagnosis and treatment. There remains a need for early biomarkers to facilitate timely intervention. Our objective was to identify neonatal sepsis gene expression biomarkers that could predict sepsis at birth, prior to clinical presentation. METHODS: Among 720 initially healthy full-term neonates in two hospitals (The Gambia, West Africa), we identified 21 newborns who were later hospitalized for sepsis in the first 28 days of life, split into early-onset sepsis (EOS, onset ≤7 days of life) and late-onset sepsis (LOS, onset 8-28 days of life), 12 neonates later hospitalized for localized infection without evidence of systemic involvement, and 33 matched control neonates who remained healthy. RNA-seq was performed on peripheral blood collected at birth when all neonates were healthy and also within the first week of life to identify differentially expressed genes (DEGs). Machine learning methods (sPLS-DA, LASSO) identified genes expressed at birth that predicted onset of neonatal sepsis at a later time. FINDINGS: Neonates who later developed EOS already had ∼1000 DEGs at birth when compared to control neonates or those who later developed a localized infection or LOS. Based on these DEGs, a 4-gene signature (HSPH1, BORA, NCAPG2, PRIM1) for predicting EOS at birth was developed (training AUC = 0.94, sensitivity = 0.93, specificity = 0.92) and validated in an external cohort (validation AUC = 0.72, sensitivity = 0.83, and specificity = 0.83). Additionally, during the first week of life, EOS disrupted expression of >1800 genes including those influencing immune and metabolic transitions observed in healthy controls. INTERPRETATION: Despite appearing healthy at birth, neonates who later developed EOS already had distinct whole blood gene expression changes at birth, which enabled the development of a 4-gene predictive signature for EOS. This could facilitate early recognition and treatment of neonatal sepsis, potentially mitigating its long-term sequelae. FUNDING: CIHR and NIH/NIAID.

PEPerMINT: peptide abundance imputation in mass spectrometry-based proteomics using graph neural networks.

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.

Longitudinal plasma proteomic analysis of 1117 hospitalized patients with COVID-19 identifies features associated with severity and outcomes.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is characterized by highly heterogeneous manifestations ranging from asymptomatic cases to death for still incompletely understood reasons. As part of the IMmunoPhenotyping Assessment in a COVID-19 Cohort study, we mapped the plasma proteomes of 1117 hospitalized patients with COVID-19 from 15 hospitals across the United States. Up to six samples were collected within ~28 days of hospitalization resulting in one of the largest COVID-19 plasma proteomics cohorts with 2934 samples. Using perchloric acid to deplete the most abundant plasma proteins allowed for detecting 2910 proteins. Our findings show that increased levels of neutrophil extracellular trap and heart damage markers are associated with fatal outcomes. Our analysis also identified prognostic biomarkers for worsening severity and death. Our comprehensive longitudinal plasma proteomics study, involving 1117 participants and 2934 samples, allowed for testing the generalizability of the findings of many previous COVID-19 plasma proteomics studies using much smaller cohorts.

Integrated longitudinal multiomics study identifies immune programs associated with acute COVID-19 severity and mortality.

BACKGROUNDPatients hospitalized for COVID-19 exhibit diverse clinical outcomes, with outcomes for some individuals diverging over time even though their initial disease severity appears similar to that of other patients. A systematic evaluation of molecular and cellular profiles over the full disease course can link immune programs and their coordination with progression heterogeneity.METHODSWe performed deep immunophenotyping and conducted longitudinal multiomics modeling, integrating 10 assays for 1,152 Immunophenotyping Assessment in a COVID-19 Cohort (IMPACC) study participants and identifying several immune cascades that were significant drivers of differential clinical outcomes.RESULTSIncreasing disease severity was driven by a temporal pattern that began with the early upregulation of immunosuppressive metabolites and then elevated levels of inflammatory cytokines, signatures of coagulation, formation of neutrophil extracellular traps, and T cell functional dysregulation. A second immune cascade, predictive of 28-day mortality among critically ill patients, was characterized by reduced total plasma Igs and B cells and dysregulated IFN responsiveness. We demonstrated that the balance disruption between IFN-stimulated genes and IFN inhibitors is a crucial biomarker of COVID-19 mortality, potentially contributing to failure of viral clearance in patients with fatal illness.CONCLUSIONOur longitudinal multiomics profiling study revealed temporal coordination across diverse omics that potentially explain the disease progression, providing insights that can inform the targeted development of therapies for patients hospitalized with COVID-19, especially those who are critically ill.TRIAL REGISTRATIONClinicalTrials.gov NCT04378777.FUNDINGNIH (5R01AI135803-03, 5U19AI118608-04, 5U19AI128910-04, 4U19AI090023-11, 4U19AI118610-06, R01AI145835-01A1S1, 5U19AI062629-17, 5U19AI057229-17, 5U19AI125357-05, 5U19AI128913-03, 3U19AI077439-13, 5U54AI142766-03, 5R01AI104870-07, 3U19AI089992-09, 3U19AI128913-03, and 5T32DA018926-18); NIAID, NIH (3U19AI1289130, U19AI128913-04S1, and R01AI122220); and National Science Foundation (DMS2310836).

Host-microbe multiomic profiling reveals age-dependent immune dysregulation associated with COVID-19 immunopathology.

Age is a major risk factor for severe coronavirus disease 2019 (COVID-19), yet the mechanisms behind this relationship have remained incompletely understood. To address this, we evaluated the impact of aging on host immune response in the blood and the upper airway, as well as the nasal microbiome in a prospective, multicenter cohort of 1031 vaccine-naïve patients hospitalized for COVID-19 between 18 and 96 years old. We performed mass cytometry, serum protein profiling, anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody assays, and blood and nasal transcriptomics. We found that older age correlated with increased SARS-CoV-2 viral abundance upon hospital admission, delayed viral clearance, and increased type I interferon gene expression in both the blood and upper airway. We also observed age-dependent up-regulation of innate immune signaling pathways and down-regulation of adaptive immune signaling pathways. Older adults had lower naïve T and B cell populations and higher monocyte populations. Over time, older adults demonstrated a sustained induction of pro-inflammatory genes and serum chemokines compared with younger individuals, suggesting an age-dependent impairment in inflammation resolution. Transcriptional and protein biomarkers of disease severity differed with age, with the oldest adults exhibiting greater expression of pro-inflammatory genes and proteins in severe disease. Together, our study finds that aging is associated with impaired viral clearance, dysregulated immune signaling, and persistent and potentially pathologic activation of pro-inflammatory genes and proteins.

Host-Microbe Multiomic Profiling Reveals Age-Dependent COVID-19 Immunopathology.

Age is a major risk factor for severe coronavirus disease-2019 (COVID-19), yet the mechanisms responsible for this relationship have remained incompletely understood. To address this, we evaluated the impact of aging on host and viral dynamics in a prospective, multicenter cohort of 1,031 patients hospitalized for COVID-19, ranging from 18 to 96 years of age. We performed blood transcriptomics and nasal metatranscriptomics, and measured peripheral blood immune cell populations, inflammatory protein expression, anti-SARS-CoV-2 antibodies, and anti-interferon (IFN) autoantibodies. We found that older age correlated with an increased SARS-CoV-2 viral load at the time of admission, and with delayed viral clearance over 28 days. This contributed to an age-dependent increase in type I IFN gene expression in both the respiratory tract and blood. We also observed age-dependent transcriptional increases in peripheral blood IFN-γ, neutrophil degranulation, and Toll like receptor (TLR) signaling pathways, and decreases in T cell receptor (TCR) and B cell receptor signaling pathways. Over time, older adults exhibited a remarkably sustained induction of proinflammatory genes (e.g., CXCL6) and serum chemokines (e.g., CXCL9) compared to younger individuals, highlighting a striking age-dependent impairment in inflammation resolution. Augmented inflammatory signaling also involved the upper airway, where aging was associated with upregulation of TLR, IL17, type I IFN and IL1 pathways, and downregulation TCR and PD-1 signaling pathways. Metatranscriptomics revealed that the oldest adults exhibited disproportionate reactivation of herpes simplex virus and cytomegalovirus in the upper airway following hospitalization. Mass cytometry demonstrated that aging correlated with reduced naïve T and B cell populations, and increased monocytes and exhausted natural killer cells. Transcriptional and protein biomarkers of disease severity markedly differed with age, with the oldest adults exhibiting greater expression of TLR and inflammasome signaling genes, as well as proinflammatory proteins (e.g., IL6, CXCL8), in severe COVID-19 compared to mild/moderate disease. Anti-IFN autoantibody prevalence correlated with both age and disease severity. Taken together, this work profiles both host and microbe in the blood and airway to provide fresh insights into aging-related immune changes in a large cohort of vaccine-naïve COVID-19 patients. We observed age-dependent immune dysregulation at the transcriptional, protein and cellular levels, manifesting in an imbalance of inflammatory responses over the course of hospitalization, and suggesting potential new therapeutic targets.

Quantitative profiling of posttranslational modifications of pathological tau via sarkosyl fractionation and mass spectrometry.

Tau protein aggregation is associated with posttranslational modifications (PTMs) in 75% of all dementia cases. The distribution of tau pathology and the presence of specific tau phosphorylation sites of interest are typically visualized and measured using antibodies. However, previous knowledge of the target epitopes is required. Additionally, antibodies can be used in a semi-quantitative manner but cannot be used to determine the absolute amount of tau or the extent of the modifications at specific sites or domains. Here we present a discovery assay that characterizes the global qualitative and quantitative tau modification landscape of a sample without a priori knowledge. Our workflow uses sarkosyl fractionation to extract the pathological tau species from sample-limited brain specimens, followed by mass spectrometry (MS) to characterize and quantify tau PTMs. The two-step MS-based proteomics approach includes an exploratory tau PTM analysis and a targeted full-length expressed stable isotope-labeled tau assay, which monitors specific unmodified tau peptides using a heavy isotope-labeled internal standard as a reference. This enables the absolute quantification of the respective tau peptides and the total tau amount in the sample, thus providing the modification extent of tau PTMs. This approach provides precise, comprehensive, qualitative and quantitative tau PTM profiling of the sample. It also enables the detailed molecular comparison of tau across multiple experiments, including a comparison between neurodegenerative diseases, stages of the disease, human patient heterogeneity and characterization of animal models. The approach is useful for studying the molecular features of pathological tau in neurodegeneration. The procedure requires 7-8 d and is suitable for users with expertise in targeted and untargeted MS-based protein analysis.

Analytical challenges in omics research on asthma and allergy: A National Institute of Allergy and Infectious Diseases workshop.

Studies of asthma and allergy are generating increasing volumes of omics data for analysis and interpretation. The National Institute of Allergy and Infectious Diseases (NIAID) assembled a workshop comprising investigators studying asthma and allergic diseases using omics approaches, omics investigators from outside the field, and NIAID medical and scientific officers to discuss the following areas in asthma and allergy research: genomics, epigenomics, transcriptomics, microbiomics, metabolomics, proteomics, lipidomics, integrative omics, systems biology, and causal inference. Current states of the art, present challenges, novel and emerging strategies, and priorities for progress were presented and discussed for each area. This workshop report summarizes the major points and conclusions from this NIAID workshop. As a group, the investigators underscored the imperatives for rigorous analytic frameworks, integration of different omics data types, cross-disciplinary interaction, strategies for overcoming current limitations, and the overarching goal to improve scientific understanding and care of asthma and allergic diseases.

Features of acute COVID-19 associated with post-acute sequelae of SARS-CoV-2 phenotypes: results from the IMPACC study.

Post-acute sequelae of SARS-CoV-2 (PASC) is a significant public health concern. We describe Patient Reported Outcomes (PROs) on 590 participants prospectively assessed from hospital admission for COVID-19 through one year after discharge. Modeling identified 4 PRO clusters based on reported deficits (minimal, physical, mental/cognitive, and multidomain), supporting heterogenous clinical presentations in PASC, with sub-phenotypes associated with female sex and distinctive comorbidities. During the acute phase of disease, a higher respiratory SARS-CoV-2 viral burden and lower Receptor Binding Domain and Spike antibody titers were associated with both the physical predominant and the multidomain deficit clusters. A lower frequency of circulating B lymphocytes by mass cytometry (CyTOF) was observed in the multidomain deficit cluster. Circulating fibroblast growth factor 21 (FGF21) was significantly elevated in the mental/cognitive predominant and the multidomain clusters. Future efforts to link PASC to acute anti-viral host responses may help to better target treatment and prevention of PASC.

Early cellular and molecular signatures correlate with severity of West Nile virus infection.

Infection with West Nile virus (WNV) drives a wide range of responses, from asymptomatic to flu-like symptoms/fever or severe cases of encephalitis and death. To identify cellular and molecular signatures distinguishing WNV severity, we employed systems profiling of peripheral blood from asymptomatic and severely ill individuals infected with WNV. We interrogated immune responses longitudinally from acute infection through convalescence employing single-cell protein and transcriptional profiling complemented with matched serum proteomics and metabolomics as well as multi-omics analysis. At the acute time point, we detected both elevation of pro-inflammatory markers in innate immune cell types and reduction of regulatory T cell activity in participants with severe infection, whereas asymptomatic donors had higher expression of genes associated with anti-inflammatory CD16+ monocytes. Therefore, we demonstrated the potential of systems immunology using multiple cell-type and cell-state-specific analyses to identify correlates of infection severity and host cellular activity contributing to an effective anti-viral response.

Integrated longitudinal multi-omics study identifies immune programs associated with COVID-19 severity and mortality in 1152 hospitalized participants.

Hospitalized COVID-19 patients exhibit diverse clinical outcomes, with some individuals diverging over time even though their initial disease severity appears similar. A systematic evaluation of molecular and cellular profiles over the full disease course can link immune programs and their coordination with progression heterogeneity. In this study, we carried out deep immunophenotyping and conducted longitudinal multi-omics modeling integrating ten distinct assays on a total of 1,152 IMPACC participants and identified several immune cascades that were significant drivers of differential clinical outcomes. Increasing disease severity was driven by a temporal pattern that began with the early upregulation of immunosuppressive metabolites and then elevated levels of inflammatory cytokines, signatures of coagulation, NETosis, and T-cell functional dysregulation. A second immune cascade, predictive of 28-day mortality among critically ill patients, was characterized by reduced total plasma immunoglobulins and B cells, as well as dysregulated IFN responsiveness. We demonstrated that the balance disruption between IFN-stimulated genes and IFN inhibitors is a crucial biomarker of COVID-19 mortality, potentially contributing to the failure of viral clearance in patients with fatal illness. Our longitudinal multi-omics profiling study revealed novel temporal coordination across diverse omics that potentially explain disease progression, providing insights that inform the targeted development of therapies for hospitalized COVID-19 patients, especially those critically ill.

Proteomics-Based Mapping of Bronchopulmonary Dysplasia-Associated Changes in Noninvasively Accessible Oral Secretions.

OBJECTIVE: To determine if oral secretions (OS) can be used as a noninvasively collected body fluid, in lieu of tracheal aspirates (TA), to track respiratory status and predict bronchopulmonary dysplasia (BPD) development in infants born <32 weeks. STUDY DESIGN: This was a retrospective, single center cohort study that included data and convenience samples from week-of-life (WoL) 3 from 2 independent preterm infant cohorts. Using previously banked samples, we applied our sample-sparing, high-throughput proteomics technology to compare OS and TA proteomes in infants born <32 weeks admitted to the Neonatal Intensive Care Unit (NICU) (Cohort 1; n = 23 infants). In a separate similar cohort, we mapped the BPD-associated changes in the OS proteome (Cohort 2; n = 17 infants including 8 with BPD). RESULTS: In samples collected during the first month of life, we identified 607 proteins unique to OS, 327 proteins unique to TA, and 687 overlapping proteins belonging to pathways involved in immune effector processes, neutrophil degranulation, leukocyte mediated immunity, and metabolic processes. Furthermore, we identified 37 OS proteins that showed significantly differential abundance between BPD cases and controls: 13 were associated with metabolic and immune dysregulation, 10 of which (eg, SERPINC1, CSTA, BPI) have been linked to BPD or other prematurity-related lung disease based on blood or TA investigations, but not OS. CONCLUSIONS: OS are a noninvasive, easily accessible alternative to TA and amenable to high-throughput proteomic analysis in preterm newborns. OS samples hold promise to yield actionable biomarkers of BPD development, particularly for prospective categorization and timely tailored treatment of at-risk infants with novel therapies.

Integrative systems biology characterizes immune-mediated neurodevelopmental changes in murine Zika virus microcephaly.

Characterizing perturbation of molecular pathways in congenital Zika virus (ZIKV) infection is critical for improved therapeutic approaches. Leveraging integrative systems biology, proteomics, and RNA-seq, we analyzed embryonic brain tissues from an immunocompetent, wild-type congenital ZIKV infection mouse model. ZIKV induced a robust immune response accompanied by the downregulation of critical neurodevelopmental gene programs. We identified a negative correlation between ZIKV polyprotein abundance and host cell cycle-inducing proteins. We further captured the downregulation of genes/proteins, many of which are known to be causative for human microcephaly, including Eomesodermin/T-box Brain Protein 2 (EOMES/TBR2) and Neuronal Differentiation 2 (NEUROD2). Disturbances of distinct molecular pathways in neural progenitors and post-mitotic neurons may contribute to complex brain phenotype of congenital ZIKV infection. Overall, this report on protein- and transcript-level dynamics enhances understanding of the ZIKV immunopathological landscape through characterization of fetal immune response in the developing brain.

Multi-omic longitudinal study reveals immune correlates of clinical course among hospitalized COVID-19 patients.

The IMPACC cohort, composed of >1,000 hospitalized COVID-19 participants, contains five illness trajectory groups (TGs) during acute infection (first 28 days), ranging from milder (TG1-3) to more severe disease course (TG4) and death (TG5). Here, we report deep immunophenotyping, profiling of >15,000 longitudinal blood and nasal samples from 540 participants of the IMPACC cohort, using 14 distinct assays. These unbiased analyses identify cellular and molecular signatures present within 72 h of hospital admission that distinguish moderate from severe and fatal COVID-19 disease. Importantly, cellular and molecular states also distinguish participants with more severe disease that recover or stabilize within 28 days from those that progress to fatal outcomes (TG4 vs. TG5). Furthermore, our longitudinal design reveals that these biologic states display distinct temporal patterns associated with clinical outcomes. Characterizing host immune responses in relation to heterogeneity in disease course may inform clinical prognosis and opportunities for intervention.

Plasma proteome perturbation for CMV DNAemia in kidney transplantation.

BACKGROUND: Cytomegalovirus (CMV) infection, either de novo or as reactivation after allotransplantation and chronic immunosuppression, is recognized to cause detrimental alloimmune effects, inclusive of higher susceptibility to graft rejection and substantive impact on chronic graft injury and reduced transplant survival. To obtain further insights into the evolution and pathogenesis of CMV infection in an immunocompromised host we evaluated changes in the circulating host proteome serially, before and after transplantation, and during and after CMV DNA replication (DNAemia), as measured by quantitative polymerase chain reaction (QPCR). METHODS: LC-MS-based proteomics was conducted on 168 serially banked plasma samples, from 62 propensity score-matched kidney transplant recipients. Patients were stratified by CMV replication status into 31 with CMV DNAemia and 31 without CMV DNAemia. Patients had blood samples drawn at protocol times of 3- and 12-months post-transplant. Additionally, blood samples were also drawn before and 1 week and 1 month after detection of CMV DNAemia. Plasma proteins were analyzed using an LCMS 8060 triple quadrupole mass spectrometer. Further, public transcriptomic data on time matched PBMCs samples from the same patients was utilized to evaluate integrative pathways. Data analysis was conducted using R and Limma. RESULTS: Samples were segregated based on their proteomic profiles with respect to their CMV Dnaemia status. A subset of 17 plasma proteins was observed to predict the onset of CMV at 3 months post-transplant enriching platelet degranulation (FDR, 4.83E-06), acute inflammatory response (FDR, 0.0018), blood coagulation (FDR, 0.0018) pathways. An increase in many immune complex proteins were observed at CMV infection. Prior to DNAemia the plasma proteome showed changes in the anti-inflammatory adipokine vaspin (SERPINA12), copper binding protein ceruloplasmin (CP), complement activation (FDR = 0.03), and proteins enriched in the humoral (FDR = 0.01) and innate immune responses (FDR = 0.01). CONCLUSION: Plasma proteomic and transcriptional perturbations impacting humoral and innate immune pathways are observed during CMV infection and provide biomarkers for CMV disease prediction and resolution. Further studies to understand the clinical impact of these pathways can help in the formulation of different types and duration of anti-viral therapies for the management of CMV infection in the immunocompromised host.

Meta-analysis of published cerebrospinal fluid proteomics data identifies and validates metabolic enzyme panel as Alzheimer's disease biomarkers.

To develop therapies for Alzheimer's disease, we need accurate in vivo diagnostics. Multiple proteomic studies mapping biomarker candidates in cerebrospinal fluid (CSF) resulted in little overlap. To overcome this shortcoming, we apply the rarely used concept of proteomics meta-analysis to identify an effective biomarker panel. We combine ten independent datasets for biomarker identification: seven datasets from 150 patients/controls for discovery, one dataset with 20 patients/controls for down-selection, and two datasets with 494 patients/controls for validation. The discovery results in 21 biomarker candidates and down-selection in three, to be validated in the two additional large-scale proteomics datasets with 228 diseased and 266 control samples. This resulting 3-protein biomarker panel differentiates Alzheimer's disease (AD) from controls in the two validation cohorts with areas under the receiver operating characteristic curve (AUROCs) of 0.83 and 0.87, respectively. This study highlights the value of systematically re-analyzing previously published proteomics data and the need for more stringent data deposition.

Choroid plexus-targeted NKCC1 overexpression to treat post-hemorrhagic hydrocephalus.

Post-hemorrhagic hydrocephalus (PHH) refers to a life-threatening accumulation of cerebrospinal fluid (CSF) that occurs following intraventricular hemorrhage (IVH). An incomplete understanding of this variably progressive condition has hampered the development of new therapies beyond serial neurosurgical interventions. Here, we show a key role for the bidirectional Na-K-Cl cotransporter, NKCC1, in the choroid plexus (ChP) to mitigate PHH. Mimicking IVH with intraventricular blood led to increased CSF [K+] and triggered cytosolic calcium activity in ChP epithelial cells, which was followed by NKCC1 activation. ChP-targeted adeno-associated viral (AAV)-NKCC1 prevented blood-induced ventriculomegaly and led to persistently increased CSF clearance capacity. These data demonstrate that intraventricular blood triggered a trans-choroidal, NKCC1-dependent CSF clearance mechanism. Inactive, phosphodeficient AAV-NKCC1-NT51 failed to mitigate ventriculomegaly. Excessive CSF [K+] fluctuations correlated with permanent shunting outcome in humans following hemorrhagic stroke, suggesting targeted gene therapy as a potential treatment to mitigate intracranial fluid accumulation following hemorrhage.

A simple, time- and cost-effective, high-throughput depletion strategy for deep plasma proteomics.

We introduce a cost-effective, robust high-throughput-compatible plasma depletion method enabling in-depth profiling of plasma that detects >1300 proteins per run with a throughput of 60 samples per day. The method has been fully validated by processing >3000 samples with no apparent batch effect at a cost for the depletion step of ~$2.5 per sample.