Longitudinal proteomic analysis of severe COVID-19 reveals survival-associated signatures, tissue-specific cell death, and cell-cell interactions

Identification of early prognostic biomarkers in Severe Fever with Thrombocytopenia Syndrome using machine learning algorithms

OBJECTIVE

We aimed at identifying acute phase biomarkers in Severe Fever with Thrombocytopenia Syndrome (SFTS), and to establish a model to predict mortality outcomes.

METHODS

A retrospective analysis was conducted on multicenter clinical data. Group-based trajectory modeling (GBTM) was utilized to demonstrate the overall trend of laboratory indicators and their correlation with mortality. Six different machine learning algorithms were employed to develop prognostic models based on the clinical features during the acute phase, which were reduced using Lasso regression.

RESULTS

Seven indicators (ALT, AST, BUN, LDH, a-HBDH, DD, and PLT) at 7-10 days post-onset and their change slopes were found to be crucial during disease progression. These, along with other clinical features, were reduced to 8 variables using Lasso regression for model construction. The random forest model demonstrated the best performance in both internal validation (AUC: 0.961) and external validation (AUC: 0.948). Decision Curve Analysis indicated a good balance between model benefits and risks.

CONCLUSIONS

a-HBDH and its change slope along with central nervous symptom manifestations within 7-10 days after onset accurately predicted mortality in SFTS. Various algorithms provided a comprehensive overview of disease progression and constructed more stable and efficient models.

Obesity-Induced Metabolic Priming Exacerbates SARS-CoV-2 Inflammation

Despite the early recognition that individuals living with obesity are more prone to develop adverse outcomes during COVID-19, the mechanisms underlying these conditions are still unclear. During obesity, an accumulation of free fatty acids (FFAs) in the circulation promotes low-grade inflammation. Here, we show that FFAs induce epigenetic reprogramming of monocytes, exacerbating their inflammatory profile after SARS-CoV-2 infection, a mechanism named metabolic-primed immunity. Monocytes from people with obesity or primed with palmitate, a central component of circulating FFAs, presented elevated viral load and higher gene expression of IL-6. Palmitate-primed monocytes upregulate fatty acid oxidation and FFAs entry into the mitochondria. FFA-derived acetyl-CoA is then converted into citrate, exiting the mitochondria and is used to support H3K18 histone acetylation, which regulates IL-6 accessibility. Ingestion of palm oil by lean and healthy individuals increased circulating FFAs levels and was sufficient to exacerbate the inflammatory profile of monocytes upon SARS-CoV-2 infection. Our findings demonstrate that obesity-derived FFAs induce the metabolic priming of monocytes, which exacerbates the inflammatory response observed in people with severe COVID-19.

LEOPARD: missing view completion for multi-timepoint omics data via representation disentanglement and temporal knowledge transfer

Longitudinal multi-view omics data offer unique insights into the temporal dynamics of individual-level physiology, which provides opportunities to advance personalized healthcare. However, the common occurrence of incomplete views makes extrapolation tasks difficult, and there is a lack of tailored methods for this critical issue. Here, we introduce LEOPARD, an innovative approach specifically designed to complete missing views in multi-timepoint omics data. By disentangling longitudinal omics data into content and temporal representations, LEOPARD transfers the temporal knowledge to the omics-specific content, thereby completing missing views. The effectiveness of LEOPARD is validated on four real-world omics datasets constructed with data from the MGH COVID study and the KORA cohort, spanning periods from 3 days to 14 years. Compared to conventional imputation methods, such as missForest, PMM, GLMM, and cGAN, LEOPARD yields the most robust results across the benchmark datasets. LEOPARD-imputed data also achieve the highest agreement with observed data in our analyses for age-associated metabolites detection, estimated glomerular filtration rate-associated proteins identification, and chronic kidney disease prediction. Our work takes the first step toward a generalized treatment of missing views in longitudinal omics data, enabling comprehensive exploration of temporal dynamics and providing valuable insights into personalized healthcare.

OpenPVSignal Knowledge Graph: Pharmacovigilance Signal Reports in a Computationally Exploitable FAIR Representation

INTRODUCTION

Pharmacovigilance signal report (PVSR) documents contain valuable condensed information published by drug monitoring organizations, typically in a free-text format. They provide initial insights into potential links between drugs and harmful effects. Still, their unstructured format prevents this valuable information from being integrated into data-processing pipelines (e.g., to support either the investigation of drug safety signals or decision-making in the clinical context).

OBJECTIVE

OpenPVSignal is a data model designed specifically to publish PVSRs via a computationally exploitable format, compliant with the FAIR (Findable, Accessible, Interoperable, Reusable) principles to promote ease of access and reusability of these valuable data.

METHODS

This paper outlines the procedure for converting pharmacovigilance signals published by the World Health Organization Uppsala Monitoring Centre (WHO-UMC) into the OpenPVSignal data model, resulting in a Knowledge Graph (KG). It details each step of the process, including the technical validation by KG engineers and the qualitative verification by medical and pharmacovigilance experts, leading to the finalized KG.

RESULTS

A total of 101 PVSRs from 2011 to 2019 were incorporated into the openly available KG.

CONCLUSION

The presented KG could be useful in various data-processing pipelines, including systems that support drug safety activities.

Plasma proteomic profiles correlate with organ dysfunction in COVID-19 ARDS

Severe COVID-19 is often complicated by hypoxemic respiratory failure and acute respiratory distress syndrome (ARDS). Mechanisms governing lung injury and repair in ARDS remain poorly understood. We hypothesized that plasma proteomics may uncover protein biomarkers correlated with COVID-19 ARDS severity. We analyzed the plasma proteome from 32 patients with ARDS and COVID-19 using an aptamer-based platform of 7289 proteins, and correlated protein measurements with sequential organ failure assessment (SOFA) scores at days 1 and 7 of ICU admission. We identified 184 differentially abundant proteins correlated with SOFA at day 1 and 46 proteins at day 7. In a longitudinal analysis, we correlated dynamic changes in protein abundance and SOFA between days 1 and 7 and identified 40 significant proteins. Pathway analysis of significant proteins identified increased ephrin signaling and acute phase response signaling correlated with increased SOFA scores between days 1 and 7, while pathways related to pulmonary fibrosis signaling and wound healing had a negative correlation. These findings suggest that persistent inflammation may drive disease severity, while repair processes correlate with improvements in organ dysfunction. This approach is generalizable to future ARDS cohorts for identification of biomarkers and disease mechanisms as we strive towards targeted therapies in ARDS.

APNet, an explainable sparse deep learning model to discover differentially active drivers of severe COVID-19

MOTIVATION

Computational analyses of bulk and single-cell omics provide translational insights into complex diseases, such as COVID-19, by revealing molecules, cellular phenotypes, and signalling patterns that contribute to unfavourable clinical outcomes. Current in silico approaches dovetail differential abundance, biostatistics, and machine learning, but often overlook nonlinear proteomic dynamics, like post-translational modifications, and provide limited biological interpretability beyond feature ranking.

RESULTS

We introduce APNet, a novel computational pipeline that combines differential activity analysis based on SJARACNe co-expression networks with PASNet, a biologically informed sparse deep learning model, to perform explainable predictions for COVID-19 severity. The APNet driver-pathway network ingests SJARACNe co-regulation and classification weights to aid result interpretation and hypothesis generation. APNet outperforms alternative models in patient classification across three COVID-19 proteomic datasets, identifying predictive drivers and pathways, including some confirmed in single-cell omics and highlighting under-explored biomarker circuitries in COVID-19.

AVAILABILITY AND IMPLEMENTATION

APNet's R, Python scripts, and Cytoscape methodologies are available at https://github.com/BiodataAnalysisGroup/APNet.

In Search of Candidate Protein Biomarkers Related to COVID-19 in Solid Tissues and Non-Blood Fluids: An Update

PURPOSE

During COVID-19, significant changes in protein abundance can be linked with disease-related processes. The mass spectrometry-based proteomics of COVID-19-related biomarkers can help with the prognosis and diagnosis of this severe disease.

DESIGN

Here, we surveyed scientific works in terms of proteomic analysis of solid tissues and non-blood fluids from COVID-19 patients. Works published since 2022 to date have been covered.

RESULTS

Brain, lymph nodes, heart, spleen, aorta walls, liver, adrenal gland and kidneys were investigated as solid organs/tissues. The non-blood fluids involved exhaled breath particles, airway mucus, saliva, swabs, colostrum/milk and urine. The provided table depicts studies/experimental platforms to analyse COVID-19-related candidate protein biomarkers.

CONCLUSION

Even eminent research input has been made in this field, continuation towards deeper findings should be made. Translation of proteomics into the clinics to help with diagnostics and therapeutical strategies, is a highly important task. The analysed candidate protein biomarkers are the perspective molecules for pending clinical decisions making and treatments.

Ultrasensitive detection of intact SARS-CoV-2 particles in complex biofluids using microfluidic affinity capture

Measuring virus in biofluids is complicated by confounding biomolecules coisolated with viral nucleic acids. To address this, we developed an affinity-based microfluidic device for specific capture of intact severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Our approach used an engineered angiotensin-converting enzyme 2 to capture intact virus from plasma and other complex biofluids. Our device leverages a staggered herringbone pattern, nanoparticle surface coating, and processing conditions to achieve detection of as few as 3 viral copies per milliliter. We further validated our microfluidic assay on 103 plasma, 36 saliva, and 29 stool samples collected from unique patients with COVID-19, showing SARS-CoV-2 detection in 72% of plasma samples. Longitudinal monitoring in the plasma revealed our device's capacity for ultrasensitive detection of active viral infections over time. Our technology can be adapted to target other viruses using relevant cell entry molecules for affinity capture. This versatility underscores the potential for widespread application in viral load monitoring and disease management.

The Circulating Proteome─Technological Developments, Current Challenges, and Future Trends

Recent improvements in proteomics technologies have fundamentally altered our capacities to characterize human biology. There is an ever-growing interest in using these novel methods for studying the circulating proteome, as blood offers an accessible window into human health. However, every methodological innovation and analytical progress calls for reassessing our existing approaches and routines to ensure that the new data will add value to the greater biomedical research community and avoid previous errors. As representatives of HUPO's Human Plasma Proteome Project (HPPP), we present our 2024 survey of the current progress in our community, including the latest build of the Human Plasma Proteome PeptideAtlas that now comprises 4608 proteins detected in 113 data sets. We then discuss the updates of established proteomics methods, emerging technologies, and investigations of proteoforms, protein networks, extracellualr vesicles, circulating antibodies and microsamples. Finally, we provide a prospective view of using the current and emerging proteomics tools in studies of circulating proteins.

Vascular fibrosis and extracellular matrix remodelling in post-COVID 19 conditions

Causal associations between viral infections and acute myocardial injury are not fully understood, with mechanisms potentially involving direct cardiovascular involvement or systemic inflammation. This review explores plausible mechanisms of vascular fibrosis in patients with post-COVID-19 syndrome, focusing on extracellular matrix remodelling. Despite global attention, significant mechanistic or translational breakthroughs in the management of post-viral syndromes remain limited. No effective pharmacological or non-pharmacological interventions are currently available for patients experiencing persistent symptoms following COVID-19 infection. The substantial expansion of scientific knowledge resulting from collaborative efforts by medical experts, scientists, and government organisations in undertaking COVID-19 research could inform treatment strategies for other post-viral syndromes and respiratory illnesses. There is a critical need for clinical trials to evaluate potential therapeutic candidates, providing evidence to guide treatment decisions for post-COVID-19 syndromes.

Longitudinal Assessment of Nasopharyngeal Biomarkers Post-COVID-19: Unveiling Persistent Markers and Severity Correlations

SARS-CoV-19 infection provokes a variety of symptoms; most patients present mild/moderate symptoms, whereas a small proportion of patients progress to severe illness with multiorgan failure accompanied by metabolic disturbances requiring ICU-level care. Given the importance of the disease, researchers focused on identifying severity-associated biomarkers in infected patients as well as markers associated with patients suffering long-COVID. However, little is known about the presence of biomarkers that remain a few years after SARS-CoV-2 infection once the patients fully recover of the symptoms. In this study, we evaluated the presence of persistent biomarkers in the nasopharyngeal tract two years after SARS-Cov-2 infection in fully asymptomatic patients, taking into account the severity of their infection (mild/moderate and severe infections). In addition to the direct identification of several components of the Coronavirus Infection Pathway in those individuals that suffered severe infections, we describe herein 371 proteins and their associated canonical pathways that define the different adverse effects of SARS-CoV-2 infections. The persistence of these biomarkers for up to two years after infection, along with their ability to distinguish the severity of the infection endured, highlights the surprising presence of persistent nasopharyngeal exudate changes in fully recovered patients.

Cerebral small vessel injury in mice with damage to ACE2-expressing cerebral vascular endothelial cells and post COVID-19 patients

INTRODUCTION

The angiotensin-converting enzyme 2 (ACE2), which is expressed in cerebral vascular endothelial cells (CVECs), has been currently identified as a functional receptor for SARS-CoV-2.

METHODS

We specifically induced injury to ACE2-expressing CVECs in mice and evaluated the effects of such targeted damage through magnetic resonance imaging (MRI) and cognitive behavioral tests. In parallel, we recruited a single-center cohort of COVID-19 survivors and further assessed their brain microvascular injury based on cognition and emotional scales, cranial MRI scans, and blood proteomic measurements.

RESULTS

Here, we show an array of pathological and behavioral alterations characteristic of cerebral small vessel disease (CSVD) in mice that targeted damage to ACE2-expressing CVECs, and COVID-19 survivors. These CSVD-like manifestations persist for at least 7 months post-recovery from COVID-19.

DISCUSSION

Our findings suggest that SARS-CoV-2 may induce cerebral small vessel damage with persistent sequelae, underscoring the imperative for heightened clinical vigilance in mitigating or treating SARS-CoV-2-mediated cerebral endothelial injury throughout infection and convalescence.

HIGHLIGHTS

Cerebral small vessel disease-associated changes were observed after targeted damage to angiotensin-converting enzyme 2-expressing cerebral vascular endothelial cells. SARS-CoV-2 may induce cerebral small vessel damage with persistent sequelae. Clinical vigilance is needed in preventing SARS-CoV-2-induced cerebral endothelial damage during infection and recovery.

Endocrine dysregulation in COVID-19: molecular mechanisms and insights

This review describes the impact of COVID-19 on the endocrine system, focusing on cortisol signaling and growth factor-induced endocrine resistance. As expected, SARS-CoV-2 infection induces systemic inflammation, resulting in stimulation of the adrenal glands leading to elevated cortisol levels with normal adrenocorticotropic hormone (ACTH) levels. The cytokine storm could also stimulate cortisol production. However, in some instances, cortisol levels rise independently of ACTH due to a phenomenon known as "pseudo-Cushing's syndrome," where adrenal glands become less responsive to ACTH. Plasma proteomic analyses showed that this pattern was variably observed among COVID-19 patients, potentially involving calcium dysregulation and GNAS-regulated activities, ultimately impacting the regulation of microvascular permeability. COVID-19 also exhibited a syndrome resembling endocrine resistance, governed by receptor tyrosine kinase signaling pathways. Mild cases displayed elevated activity of EGFR and MMP9, along with increased expression of survival factors like Bax and Bcl2. In contrast, more severe cases involved IGFR-I and enhanced NOTCH signaling, with altered expression of Bcl2, AKT1, and MAPK8. In summary, these findings describe the complex interplay between COVID-19 and endocrine pathology, particularly endocrine resistance. These insights suggest potential endocrine targets for therapeutic interventions to improve short- and long-term outcomes for COVID-19 patients.

Process-Specific Blood Biomarkers and Outcomes in COVID-19 Versus Non-COVID-19 ARDS (APEL-COVID Study): A Prospective, Observational Cohort Study

Background: Severe acute respiratory syndrome (SARS) and acute respiratory distress syndrome (ARDS) are often considered separate clinico-radiological entities. Whether these conditions also present a single process-specific systemic biomolecular phenotype and how this relates to patient outcomes remains unknown. A prospective cohort study was conducted, including adult patients admitted to the ICU and general floors for COVID-19-related (COVID+) or non-COVID-19-related (COVID-) acute respiratory failure during the main phase of the pandemic. The primary objective was to study blood biomarkers and outcomes among different groups and severity subsets. Results: A total of 132 patients were included, as follows: 67 COVID+, 54 COVID- (with 11 matched control subjects for biomarker reference), and 58 of these patients allowed for further pre- and post-analysis. The baseline apelin (APL) levels were higher in COVID+ patients (p < 0.0001 vs. COVID- patients) and in SARS COVID+ patients (p ≤ 0.02 vs. ARDS), while the IL-6 levels were higher in ARDS COVID- patients (p ≤ 0.0001 vs. SARS). Multivariable logistic regression analyses with cohort biomarkers and outcome parameters revealed the following: (i) log-transformed neprilysin (NEP) activity was significantly higher in COVID+ patients (1.11 [95% CI: 0.4-1.9] vs. 0.37 [95% CI: 0.1-0.8], fold change (FC): 1.43 [95% CI: 1.04-1.97], p = 0.029) and in SARS patients (FC: 1.65 [95% CI: 1.05-2.6], p = 0.032 vs. non-SARS COVID+ patients, and 1.73 [95% CI: 1.19-2.5], p = 0.005 vs. ARDS COVID- patients) and (ii) higher lysyl oxidase (LOX) activity and APL levels were respectively associated with death and a shorter length of hospital stay in SARS COVID+ patients (Odds Ratios (OR): 1.01 [1.00-1.02], p = 0.05, and OR: -0.007 [-0.013-0.0001], p = 0.048). Conclusion: Process-specific blood biomarkers exhibited distinct profiles between COVID+ and COVID- patients, and across stages of severity. NEP and LOX activities, as well as APL levels, are particularly linked to COVID+ patients and their outcomes (ClinicalTrials.gov Identifier: NCT04632732).

Potential immunomodulatory effects of CAS+IMD monoclonal antibody cocktail in hospitalized patients with COVID-19

BACKGROUND

Passive administration of SARS-CoV-2 neutralizing monoclonal antibodies (mAbs), such as CAS + IMD (Casirivimab + Imdevimab) antibody cocktail demonstrated beneficial effects on clinical outcomes in hospitalized patients with COVID-19 who were seronegative at baseline and outpatients. However, little is known about their impact on the host immunophenotypes.

METHODS

We conducted an immunoprofiling study in 46 patients from a single site of a multi-site trial of CAS + IMD in hospitalized patients. We collected longitudinal samples during October 2020 ∼ April 2021, prior to the emergence of the Delta and Omicron variants and the use of COVID-19 vaccines. All collected samples were analyzed without exclusion and post-hoc statistical analysis was performed. We examined the dynamic interplay of CAS + IMD with host immunity applying dimensional reduction approach on plasma proteomics and high dimensional flow cytometry data.

FINDINGS

Using an unbiased clustering method, we identified unique immunophenotypes associated with acute inflammation and disease resolution. Compared to placebo group, administration of CAS + IMD accelerated the transition from an acute inflammatory immunophenotype, to a less inflammatory or "resolving" immunophenotype, as characterized by reduced tissue injury, proinflammatory markers and restored lymphocyte/monocyte imbalance independent of baseline serostatus. Moreover, CAS + IMD did not impair the magnitude or the quality of host T cell immunity against SARS-CoV-2 spike protein.

INTERPRETATION

Our results identified immunophenotypic changes indicative of a possible SARS-CoV-2 neutralizing antibodies-induced anti-inflammatory effect, without an evident impairment of cellular antiviral immunity, suggesting that further studies of Mabs effects on SAS-CoV-2 or other viral mediated inflammation are warranted.

FUNDING

Regeneron Pharmaceuticals Inc and federal funds from the Department of Health and Human Services; Administration for Strategic Preparedness and Response; Biomedical Advanced Research and Development Authority, under OT number: HHSO100201700020C.

Discovery of sparse, reliable omic biomarkers with Stabl

Adoption of high-content omic technologies in clinical studies, coupled with computational methods, has yielded an abundance of candidate biomarkers. However, translating such findings into bona fide clinical biomarkers remains challenging. To facilitate this process, we introduce Stabl, a general machine learning method that identifies a sparse, reliable set of biomarkers by integrating noise injection and a data-driven signal-to-noise threshold into multivariable predictive modeling. Evaluation of Stabl on synthetic datasets and five independent clinical studies demonstrates improved biomarker sparsity and reliability compared to commonly used sparsity-promoting regularization methods while maintaining predictive performance; it distills datasets containing 1,400-35,000 features down to 4-34 candidate biomarkers. Stabl extends to multi-omic integration tasks, enabling biological interpretation of complex predictive models, as it hones in on a shortlist of proteomic, metabolomic and cytometric events predicting labor onset, microbial biomarkers of pre-term birth and a pre-operative immune signature of post-surgical infections. Stabl is available at https://github.com/gregbellan/Stabl .

Alterations in the plasma proteome persist ten months after recovery from mild to moderate SARS-CoV-2 infection

Background

Limited data are available describing the effects of SARS-CoV-2 breakthrough infections on the plasma proteome.

Methods

PCR-positive SARS-CoV-2 patients, enrolled in a natural history study, underwent analysis of the plasma proteome. A prospective cohort of 66 unvaccinated and 24 vaccinated persons with different degrees of infection severity were evaluated acutely (within 40 days of symptom onset), and at three and ten months. Comparisons based on vaccination status alone and unsupervised hierarchical clustering were performed. A second cohort of vaccinated Omicron patients were evaluated acutely and at ten months.

Results

Acutely, unvaccinated patients manifested overexpression of proteins involved in immune and inflammatory responses, while vaccinated patients exhibited adaptive immune responses without significant inflammation. At three and ten months, only unvaccinated patients had diminished but sustained inflammatory (C3b, CCL15, IL17RE) and immune responses (DEFA5,TREM1). Both groups had underexpression of pathways essential for cellular function, signaling, and angiogenesis (AKT1, MAPK14, HSPB1) across phases. Unsupervised clustering, based on protein expression, identified four groups of patients with variable vaccination rates demonstrating that additional clinical factors influence the plasma proteome. The proteome of vaccinated Omicron patients did not differ from vaccinated pre-Omicron patients.

Conclusions

Vaccination attenuates the inflammatory response to SARS-CoV-2 infection across phases. However, at ten months after symptom onset, changes in the plasma proteome persist in both vaccinated and unvaccinated individuals, which may be relevant to post-acute sequelae of SARS-CoV-2 and other viral infections associated with post-acute infection syndromes.

Plasma Inflammatory Proteome Profile in a Cohort of Patients with Recurrent Vulvovaginal Candidiasis in Kenya

Vulvovaginal candidiasis (VVC) affects up to 75% of women at least once during their lifetime, and up to 8% of women suffer from frequent recurrent episodes of VVC (RVVC). A lack of a protective host response underlies vaginal Candida infections, while a dysregulated hyperinflammatory response may drive RVVC. This study aimed to investigate the systemic inflammatory protein profile in women with RVVC in an African population, considering the potential influence of hormonal contraceptive use on systemic inflammation. Using multiplex Proximity Extension Assay technology, we measured 92 circulatory inflammatory proteins in plasma samples from 158 RVVC patients and 92 asymptomatic women (controls). Hormonal contraceptive use was not found to have a statistically significant correlation with a systemic inflammatory protein profile in either RVVC patients or the asymptomatic women. RVVC women had lower circulating Fibroblast Growth Factor 21 (FGF-21) concentrations compared with healthy controls (adjusted p value = 0.028). Reduced concentrations of FGF-21 may be linked to the immune pathology observed in RVVC cases through IL-1β. This study may help to identify new biomarkers for the diagnosis and future development of novel immunomodulatory treatments for RVVC.

Type I Interferon Autoantibodies Correlate With Cellular Immune Alterations in Severe COVID-19

BACKGROUND

Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can lead to severe disease with increased morbidity and mortality among certain risk groups. The presence of autoantibodies against type I interferons (aIFN-Abs) is one mechanism that contributes to severe coronavirus disease 2019 (COVID-19).

METHODS

This study aimed to investigate the presence of aIFN-Abs in relation to the soluble proteome, circulating immune cell numbers, and cellular phenotypes, as well as development of adaptive immunity.

RESULTS

aIFN-Abs were more prevalent in critical compared to severe COVID-19 but largely absent in the other viral and bacterial infections studied here. The antibody and T-cell response to SARS-CoV-2 remained largely unaffected by the presence aIFN-Abs. Similarly, the inflammatory response in COVID-19 was comparable in individuals with and without aIFN-Abs. Instead, presence of aIFN-Abs had an impact on cellular immune system composition and skewing of cellular immune pathways.

CONCLUSIONS

Our data suggest that aIFN-Abs do not significantly influence development of adaptive immunity but covary with alterations in immune cell numbers.

Cell-cell communication: new insights and clinical implications

Multicellular organisms are composed of diverse cell types that must coordinate their behaviors through communication. Cell-cell communication (CCC) is essential for growth, development, differentiation, tissue and organ formation, maintenance, and physiological regulation. Cells communicate through direct contact or at a distance using ligand-receptor interactions. So cellular communication encompasses two essential processes: cell signal conduction for generation and intercellular transmission of signals, and cell signal transduction for reception and procession of signals. Deciphering intercellular communication networks is critical for understanding cell differentiation, development, and metabolism. First, we comprehensively review the historical milestones in CCC studies, followed by a detailed description of the mechanisms of signal molecule transmission and the importance of the main signaling pathways they mediate in maintaining biological functions. Then we systematically introduce a series of human diseases caused by abnormalities in cell communication and their progress in clinical applications. Finally, we summarize various methods for monitoring cell interactions, including cell imaging, proximity-based chemical labeling, mechanical force analysis, downstream analysis strategies, and single-cell technologies. These methods aim to illustrate how biological functions depend on these interactions and the complexity of their regulatory signaling pathways to regulate crucial physiological processes, including tissue homeostasis, cell development, and immune responses in diseases. In addition, this review enhances our understanding of the biological processes that occur after cell-cell binding, highlighting its application in discovering new therapeutic targets and biomarkers related to precision medicine. This collective understanding provides a foundation for developing new targeted drugs and personalized treatments.

Interleukin-6 drives endothelial glycocalyx damage in COVID-19 and bacterial sepsis

Damage of the endothelial glycocalyx (eGC) plays a central role in the development of vascular hyperpermeability and organ damage during systemic inflammation. However, the specific signalling pathways for eGC damage remain poorly defined. Aim of this study was to combine sublingual video-microscopy, plasma proteomics and live cell imaging to uncover further pathways of eGC damage in patients with coronavirus disease 2019 (COVID-19) or bacterial sepsis. This secondary analysis of the prospective multicenter MICROCODE study included 22 patients with COVID-19 and 43 patients with bacterial sepsis admitted to intermediate or intensive care units and 10 healthy controls. Interleukin-6 (IL-6) was strongly associated with damaged eGC and correlated both with eGC dimensions (rs=0.36, p = 0.0015) and circulating eGC biomarkers. In vitro, IL-6 reduced eGC height and coverage, which was inhibited by blocking IL-6 signalling with the anti-IL-6 receptor antibody tocilizumab or the Janus kinase inhibitor tofacitinib. Exposure of endothelial cells to 5% serum from COVID-19 or sepsis patients resulted in a significant decrease in eGC height, which was attenuated by co-incubation with tocilizumab. In an external COVID-19 cohort of 219 patients from Massachusetts General Hospital, a previously identified proteomic eGC signature correlated with IL-6 (rs=-0.58, p < 0.0001) and predicted the combined endpoint of 28-day mortality and/or intubation (ROC-AUC: 0.86 [95% CI: 0.81-0.91], p < 0.001). The data suggest that IL-6 may significantly drive eGC damage in COVID-19 and bacterial sepsis. Our findings provide valuable insights into pathomechanisms of vascular dysfunction during systemic inflammation and highlight the need for further in vivo studies.

Longitudinal analysis of the lung proteome reveals persistent repair months after mild to moderate COVID-19

In order to assess homeostatic mechanisms in the lung after COVID-19, changes in the protein signature of bronchoalveolar lavage from 45 patients with mild to moderate disease at three phases (acute, recovery, and convalescent) are evaluated over a year. During the acute phase, inflamed and uninflamed phenotypes are characterized by the expression of tissue repair and host defense response molecules. With recovery, inflammatory and fibrogenic mediators decline and clinical symptoms abate. However, at 9 months, quantified radiographic abnormalities resolve in the majority of patients, and yet compared to healthy persons, all showed ongoing activation of cellular repair processes and depression of the renin-kallikrein-kinin, coagulation, and complement systems. This dissociation of prolonged reparative processes from symptom and radiographic resolution suggests that occult ongoing disruption of the lung proteome is underrecognized and may be relevant to recovery from other serious viral pneumonias.

Potential Immune-Inflammatory Proteome Biomarkers for Guiding the Treatment of Patients with Primary Acute Angle-Closure Glaucoma Caused by COVID-19

Primary acute angle-closure glaucoma (PAACG) is a sight-threatening condition that can lead to blindness. With the increasing incidence of COVID-19, a multitude of people are experiencing acute vision loss and severe swelling of the eyes and head. These patients were then diagnosed with acute angle closure, with or without a history of PACG. However, the mechanism by which viral infection causes PACG has not been clarified. This is the first study to explore the specific inflammatory proteomic landscape in SARS-CoV-2-induced PAACG. The expression of 92 inflammation-related proteins in 19 aqueous humor samples from PAACGs or cataract patients was detected using the Olink Target 96 Inflammation Panel based on a highly sensitive and specific proximity extension assay technology. The results showed that 76 proteins were significantly more abundant in the PAACG group than in the cataract group. Notably, the top eight differentially expressed proteins were IL-8, MCP-1, TNFRSF9, DNER, CCL4, Flt3L, CXCL10, and CD40. Generally, immune markers are related to inflammation, macrophage activation, and viral infection, revealing the crucial role of macrophages in the occurrence of PAACGs caused by SARS-CoV-2.

Reduction in ACE2 expression in peripheral blood mononuclear cells during COVID-19 - implications for post COVID-19 conditions

BACKGROUND

Severe COVID-19 is uncommon, restricted to 19% of the total population. In response to the first virus wave (alpha variant of SARS-CoV-2), we investigated whether a biomarker indicated severity of disease and, in particular, if variable expression of angiotensin converting enzyme 2 (ACE2) in blood might clarify this difference in risk and of post COVID -19 conditions (PCC).

METHODS

The IRB-approved study compared patients hospitalized with severe COVID-19 to healthy controls. Severe infection was defined requiring oxygen or increased oxygen need from baseline at admission with positive COVID-19 PCR. A single blood sample was obtained from patients within a day of admission. ACE2 RNA expression in blood cells was measured by an RT-PCR assay. Plasma ACE1 and ACE2 enzyme activities were quantified by fluorescent peptides. Plasma TIMP-1, PIIINP and MMP-9 antigens were quantified by ELISA. Data were entered into REDCap and analyzed using STATA v 14 and GraphPad Prism v 10.

RESULTS

Forty-eight patients and 72 healthy controls were recruited during the pandemic. ACE2 RNA expression in peripheral blood mononuclear cells (PBMC) was rarely detected acutely during severe COVID-19 but common in controls (OR for undetected ACE2: 12.4 [95% CI: 2.62-76.1]). ACE2 RNA expression in PBMC did not determine plasma ACE1 and ACE2 activity, suggesting alternative cell-signaling pathways. Markers of fibrosis (TIMP-1 and PIIINP) and vasculopathy (MMP-9) were additionally elevated. ACE2 RNA expression during severe COVID-19 often responded within hours to convalescent plasma. Analogous to oncogenesis, we speculate that potent, persistent, cryptic processes following COVID-19 (the renin-angiotensin system (RAS), fibrosis and vasculopathy) initiate or promote post-COVID-19 conditions (PCC) in susceptible individuals.

CONCLUSIONS

This work elucidates biological and temporal plausibility for ACE2, TIMP1, PIIINP and MMP-9 in the pathogenesis of PCC. Intersection of these independent systems is uncommon and may in part explain the rarity of PCC.

Longitudinal soluble marker profiles reveal strong association between cytokine storms resulting from macrophage activation and disease severity in COVID-19 disease

SARS-CoV2 infection results in a range of disease severities, but the underlying differential pathogenesis is still not completely understood. At presentation it remains difficult to estimate and predict severity, in particular, identify individuals at greatest risk of progression towards the most severe disease-states. Here we used advanced models with circulating serum analytes as variables in combination with daily assessment of disease severity using the SCODA-score, not only at single time points but also during the course of disease, to correlate analyte levels and disease severity. We identified a remarkably strong pro-inflammatory cytokine/chemokine profile with high levels for sCD163, CCL20, HGF, CHintinase3like1 and Pentraxin3 in serum which correlated with COVID-19 disease severity and overall outcome. Although precise analyte levels differed, resulting biomarker profiles were highly similar at early and late disease stages, and even during convalescence similar biomarkers were elevated and further included CXCL3, CXCL6 and Osteopontin. Taken together, strong pro-inflammatory marker profiles were identified in patients with COVID-19 disease which correlated with overall outcome and disease severity.

Low-grade inflammation in the post-COVID period as a strategic goal of treatment and rehabilitation

As of the beginning of 2023, there are more than 660 million convalescents of a new coronavirus infection in the world, however, even despite successful treatment of the acute period of the disease, such patients have a high risk of developing long-term complications in the post-COVID period, primarily cardiovascular events. One factor that seriously increases the risk of these complications is the state of lowgrade systemic inflammation (LGSI). LGSI is not a clinical diagnosis, it is characterized by a level of C-reactive protein in peripheral blood in the range of 3–10 mg/l and is most often detected during routine examination of patients, who in most cases have no clinical symptoms. In this regard, the condition of LGSI most often remains unnoticed and unreasonably ignored, despite quite extensive literature data on the effect of LGSI on the pathogenesis of many cardiovascular diseases. The development of drug therapy for LGSI is complicated by the multifactorial etiology of this condition. The causes of LGSI can be both genetic factors, which are practically impossible to correct, and conditions that are amenable to drug and non-drug treatment, such as, for example, increased intestinal permeability to pro-inflammatory agents, including lipopolysaccharide of gram-negative flora, the presence of a chronic untreated infection site and endocrine pathology (obesity and type 2 diabetes). This review presents the main information to date on the state of LGSI in patients who had a new coronavirus infection, including the results of our own observations of patients who have undergone a course of rehabilitation measures, as well as the most significant, in our opinion, factors predisposing to the development of LGSI in such patients.

ADAM-17 Activity and Its Relation to ACE2: Implications for Severe COVID-19

There is a lack of studies aiming to assess cellular a disintegrin and metalloproteinase-17 (ADAM-17) activity in COVID-19 patients and the eventual associations with the shedding of membrane-bound angiotensin-converting enzyme 2 (mACE2). In addition, studies that investigate the relationship between ACE2 and ADAM-17 gene expressions in organs infected by SARS-CoV-2 are lacking. We used data from the Massachusetts general hospital COVID-19 study (306 COVID-19 patients and 78 symptomatic controls) to investigate the association between plasma levels of 33 different ADAM-17 substrates and COVID-19 severity and mortality. As a surrogate of cellular ADAM-17 activity, an ADAM-17 substrate score was calculated. The associations between soluble ACE2 (sACE2) and the ADAM-17 substrate score, renin, key inflammatory markers, and lung injury markers were investigated. Furthermore, we used data from the Genotype-Tissue Expression (GTEx) database to evaluate ADAM-17 and ACE2 gene expressions by age and sex in ages between 20-80 years. We found that increased ADAM-17 activity, as estimated by the ADAM-17 substrates score, was associated with COVID-19 severity (p = 0.001). ADAM-17 activity was also associated with increased mortality but did not reach statistical significance (p = 0.06). Soluble ACE2 showed the strongest positive correlation with the ADAM-17 substrate score, follow by renin, interleukin-6, and lung injury biomarkers. The ratio of ADAM-17 to ACE2 gene expression was highest in the lung. This study indicates that increased ADAM-17 activity is associated with severe COVID-19. Our findings also indicate that there may a bidirectional relationship between membrane-bound ACE2 shedding via increased ADAM-17 activity, dysregulated renin-angiotensin system (RAS) and immune signaling. Additionally, differences in ACE2 and ADAM-17 gene expressions between different tissues may be of importance in explaining why the lung is the organ most severely affected by COVID-19, but this requires further evaluation in prospective studies.

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.

A reduced proteomic signature in critically ill Covid-19 patients determined with plasma antibody micro-array and machine learning

BACKGROUND

COVID-19 is a complex, multi-system disease with varying severity and symptoms. Identifying changes in critically ill COVID-19 patients' proteomes enables a better understanding of markers associated with susceptibility, symptoms, and treatment. We performed plasma antibody microarray and machine learning analyses to identify novel proteins of COVID-19.

METHODS

A case-control study comparing the concentration of 2000 plasma proteins in age- and sex-matched COVID-19 inpatients, non-COVID-19 sepsis controls, and healthy control subjects. Machine learning was used to identify a unique proteome signature in COVID-19 patients. Protein expression was correlated with clinically relevant variables and analyzed for temporal changes over hospitalization days 1, 3, 7, and 10. Expert-curated protein expression information was analyzed with Natural language processing (NLP) to determine organ- and cell-specific expression.

RESULTS

Machine learning identified a 28-protein model that accurately differentiated COVID-19 patients from ICU non-COVID-19 patients (accuracy = 0.89, AUC = 1.00, F1 = 0.89) and healthy controls (accuracy = 0.89, AUC = 1.00, F1 = 0.88). An optimal nine-protein model (PF4V1, NUCB1, CrkL, SerpinD1, Fen1, GATA-4, ProSAAS, PARK7, and NET1) maintained high classification ability. Specific proteins correlated with hemoglobin, coagulation factors, hypertension, and high-flow nasal cannula intervention (P < 0.01). Time-course analysis of the 28 leading proteins demonstrated no significant temporal changes within the COVID-19 cohort. NLP analysis identified multi-system expression of the key proteins, with the digestive and nervous systems being the leading systems.

CONCLUSIONS

The plasma proteome of critically ill COVID-19 patients was distinguishable from that of non-COVID-19 sepsis controls and healthy control subjects. The leading 28 proteins and their subset of 9 proteins yielded accurate classification models and are expressed in multiple organ systems. The identified COVID-19 proteomic signature helps elucidate COVID-19 pathophysiology and may guide future COVID-19 treatment development.

Temporal associations of plasma levels of the secreted phospholipase A2 family and mortality in severe COVID-19

Previous research suggests that group IIA-secreted phospholipase A2 (sPLA2-IIA) plays a role in and predicts lethal COVID-19 disease. The current study reanalyzed a longitudinal proteomic data set to determine the temporal relationship between levels of several members of a family of sPLA2 isoforms and the severity of COVID-19 in 214 ICU patients. The levels of six secreted PLA2 isoforms, sPLA2-IIA, sPLA2-V, sPLA2-X, sPLA2-IB, sPLA2-IIC, and sPLA2-XVI, increased over the first 7 ICU days in those who succumbed to the disease but attenuated over the same time period in survivors. In contrast, a reversed pattern in sPLA2-IID and sPLA2-XIIB levels over 7 days suggests a protective role of these two isoforms. Furthermore, decision tree models demonstrated that sPLA2-IIA outperformed top-ranked cytokines and chemokines as a predictor of patient outcome. Taken together, proteomic analysis revealed temporal sPLA2 patterns that reflect the critical roles of sPLA2 isoforms in severe COVID-19 disease.

HIV-2 mediated effects on target and bystander cells induce plasma proteome remodeling

Despite low or undetectable plasma viral load, people living with HIV-2 (PLWH2) typically progress toward AIDS. The driving forces behind HIV-2 disease progression and the role of viremia are still not known, but low-level replication in tissues is believed to play a role. To investigate the impact of viremic and aviremic HIV-2 infection on target and bystander cell pathology, we used data-independent acquisition mass spectrometry to determine plasma signatures of tissue and cell type engagement. Proteins derived from target and bystander cells in multiple tissues, such as the gastrointestinal tract and brain, were detected at elevated levels in plasma of PLWH2, compared with HIV negative controls. Moreover, viremic HIV-2 infection appeared to induce enhanced release of proteins from a broader range of tissues compared to aviremic HIV-2 infection. This study expands the knowledge on the link between plasma proteome remodeling and the pathological cell engagement in tissues during HIV-2 infection.

Plasma Proteomics Elucidated a Protein Signature in COVID-19 Patients with Comorbidities and Early-Diagnosis Biomarkers

Despite great scientific efforts, deep understanding of coronavirus-19 disease (COVID-19) immunopathology and clinical biomarkers remains a challenge. Pre-existing comorbidities increase the mortality rate and aggravate the exacerbated immune response against the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection, which can result in more severe symptoms as well as long-COVID and post-COVID complications. In this study, we applied proteomics analysis of plasma samples from 28 patients with SARS-CoV-2, with and without pre-existing comorbidities, as well as their corresponding controls to determine the systemic protein changes caused by the SARS-CoV-2 infection. As a result, the protein signature shared amongst COVID-19 patients with comorbidities was revealed to be characterized by alterations in the coagulation and complement pathways, acute-phase response proteins, tissue damage and remodeling, as well as cholesterol metabolism. These altered proteins may play a relevant role in COVID-19 pathophysiology. Moreover, several novel potential biomarkers for early diagnosis of the SARS-CoV-2 infection were detected, such as increased levels of keratin K22E, extracellular matrix protein-1 (ECM1), and acute-phase response protein α-2-antiplasmin (A2AP). Importantly, elevated A2AP may contribute to persistent clotting complications associated with the long-COVID syndrome in patients with comorbidities. This study provides new insights into COVID-19 pathogenesis and proposes novel potential biomarkers for early diagnosis that could be facilitated for clinical application by further validation studies.

Using genetics and proteomics data to identify proteins causally related to COVID-19, healthspan and lifespan: a Mendelian randomization study

BACKGROUND

COVID-19 pandemic poses a heavy burden on public health and accounts for substantial mortality and morbidity. Proteins are building blocks of life, but specific proteins causally related to COVID-19, healthspan and lifespan have not been systematically examined.

METHODS

We conducted a Mendelian randomization study to assess the effects of 1,361 plasma proteins on COVID-19, healthspan and lifespan, using large GWAS of severe COVID-19 (up to 13,769 cases and 1,072,442 controls), COVID-19 hospitalization (32,519 cases and 2,062,805 controls) and SARS-COV2 infection (122,616 cases and 2,475,240 controls), healthspan (n = 300,477) and parental lifespan (~0.8 million of European ancestry).

RESULTS

We identified 35, 43, and 63 proteins for severe COVID, COVID-19 hospitalization, and SARS-COV2 infection, and 4, 32, and 19 proteins for healthspan, father's attained age, and mother's attained age. In addition to some proteins reported previously, such as SFTPD related to severe COVID-19, we identified novel proteins involved in inflammation and immunity (such as ICAM-2 and ICAM-5 which affect COVID-19 risk, CXCL9, HLA-DRA and LILRB4 for healthspan and lifespan), apoptosis (such as FGFR2 and ERBB4 which affect COVID-19 risk and FOXO3 which affect lifespan) and metabolism (such as PCSK9 which lowers lifespan). We found 2, 2 and 3 proteins shared between COVID-19 and healthspan/lifespan, such as CXADR and LEFTY2, shared between severe COVID-19 and healthspan/lifespan. Three proteins affecting COVID-19 and seven proteins affecting healthspan/lifespan are targeted by existing drugs.

CONCLUSIONS

Our study provided novel insights into protein targets affecting COVID-19, healthspan and lifespan, with implications for developing new treatment and drug repurposing.

Proteome profiling of home-sampled dried blood spots reveals proteins of SARS-CoV-2 infections

BACKGROUND

Self-sampling of dried blood spots (DBS) offers new routes to gather valuable health-related information from the general population. Yet, the utility of using deep proteome profiling from home-sampled DBS to obtain clinically relevant insights about SARS-CoV-2 infections remains largely unexplored.

METHODS

Our study involved 228 individuals from the general Swedish population who used a volumetric DBS sampling device and completed questionnaires at home during spring 2020 and summer 2021. Using multi-analyte COVID-19 serology, we stratified the donors by their response phenotypes, divided them into three study sets, and analyzed 276 proteins by proximity extension assays (PEA). After normalizing the data to account for variances in layman-collected samples, we investigated the association of DBS proteomes with serology and self-reported information.

RESULTS

Our three studies display highly consistent variance of protein levels and share associations of proteins with sex (e.g., MMP3) and age (e.g., GDF-15). Studying seropositive (IgG+) and seronegative (IgG-) donors from the first pandemic wave reveals a network of proteins reflecting immunity, inflammation, coagulation, and stress response. A comparison of the early-infection phase (IgM+IgG-) with the post-infection phase (IgM-IgG+) indicates several proteins from the respiratory system. In DBS from the later pandemic wave, we find that levels of a virus receptor on B-cells differ between seropositive (IgG+) and seronegative (IgG-) donors.

CONCLUSIONS

Proteome analysis of volumetric self-sampled DBS facilitates precise analysis of clinically relevant proteins, including those secreted into the circulation or found on blood cells, augmenting previous COVID-19 reports with clinical blood collections. Our population surveys support the usefulness of DBS, underscoring the role of timing the sample collection to complement clinical and precision health monitoring initiatives.

Human lung cancer harbors spatially organized stem-immunity hubs associated with response to immunotherapy

The organization of immune cells in human tumors is not well understood. Immunogenic tumors harbor spatially localized multicellular 'immunity hubs' defined by expression of the T cell-attracting chemokines CXCL10/CXCL11 and abundant T cells. Here, we examined immunity hubs in human pre-immunotherapy lung cancer specimens and found an association with beneficial response to PD-1 blockade. Critically, we discovered the stem-immunity hub, a subtype of immunity hub strongly associated with favorable PD-1-blockade outcome. This hub is distinct from mature tertiary lymphoid structures and is enriched for stem-like TCF7+PD-1+CD8+ T cells, activated CCR7+LAMP3+ dendritic cells and CCL19+ fibroblasts as well as chemokines that organize these cells. Within the stem-immunity hub, we find preferential interactions between CXCL10+ macrophages and TCF7-CD8+ T cells as well as between mature regulatory dendritic cells and TCF7+CD4+ and regulatory T cells. These results provide a picture of the spatial organization of the human intratumoral immune response and its relevance to patient immunotherapy outcomes.

Multiple sclerosis endophenotypes identified by high-dimensional blood signatures are associated with distinct disease trajectories

One of the biggest challenges in managing multiple sclerosis is the heterogeneity of clinical manifestations and progression trajectories. It still remains to be elucidated whether this heterogeneity is reflected by discrete immune signatures in the blood as a surrogate of disease pathophysiology. Accordingly, individualized treatment selection based on immunobiological principles is still not feasible. Using two independent multicentric longitudinal cohorts of patients with early multiple sclerosis (n = 309 discovery and n = 232 validation), we were able to identify three distinct peripheral blood immunological endophenotypes by a combination of high-dimensional flow cytometry and serum proteomics, followed by unsupervised clustering. Longitudinal clinical and paraclinical follow-up data collected for the cohorts revealed that these endophenotypes were associated with disease trajectories of inflammation versus early structural damage. Investigating the capacity of immunotherapies to normalize endophenotype-specific immune signatures revealed discrete effect sizes as illustrated by the limited effect of interferon-β on endophenotype 3-related immune signatures. Accordingly, patients who fell into endophenotype 3 subsequently treated with interferon-β exhibited higher disease progression and MRI activity over a 4-year follow-up compared with treatment with other therapies. We therefore propose that ascertaining a patient's blood immune signature before immunomodulatory treatment initiation may facilitate prediction of clinical disease trajectories and enable personalized treatment decisions based on pathobiological principles.

Disrupting CD38-driven T cell dysfunction restores sensitivity to cancer immunotherapy

A central problem in cancer immunotherapy with immune checkpoint blockade (ICB) is the development of resistance, which affects 50% of patients with metastatic melanoma1,2. T cell exhaustion, resulting from chronic antigen exposure in the tumour microenvironment, is a major driver of ICB resistance3. Here, we show that CD38, an ecto-enzyme involved in nicotinamide adenine dinucleotide (NAD+) catabolism, is highly expressed in exhausted CD8+ T cells in melanoma and is associated with ICB resistance. Tumour-derived CD38hiCD8+ T cells are dysfunctional, characterised by impaired proliferative capacity, effector function, and dysregulated mitochondrial bioenergetics. Genetic and pharmacological blockade of CD38 in murine and patient-derived organotypic tumour models (MDOTS/PDOTS) enhanced tumour immunity and overcame ICB resistance. Mechanistically, disrupting CD38 activity in T cells restored cellular NAD+ pools, improved mitochondrial function, increased proliferation, augmented effector function, and restored ICB sensitivity. Taken together, these data demonstrate a role for the CD38-NAD+ axis in promoting T cell exhaustion and ICB resistance, and establish the efficacy of CD38 directed therapeutic strategies to overcome ICB resistance using clinically relevant, patient-derived 3D tumour models.

Intrinsic factors behind long COVID: IV. Hypothetical roles of the SARS-CoV-2 nucleocapsid protein and its liquid-liquid phase separation

When the SARS-CoV-2 virus infects humans, it leads to a condition called COVID-19 that has a wide spectrum of clinical manifestations, from no symptoms to acute respiratory distress syndrome. The virus initiates damage by attaching to the ACE-2 protein on the surface of endothelial cells that line the blood vessels and using these cells as hosts for replication. Reactive oxygen species levels are increased during viral replication, which leads to oxidative stress. About three-fifths (~60%) of the people who get infected with the virus eradicate it from their body after 28 days and recover their normal activity. However, a large fraction (~40%) of the people who are infected with the virus suffer from various symptoms (anosmia and/or ageusia, fatigue, cough, myalgia, cognitive impairment, insomnia, dyspnea, and tachycardia) beyond 12 weeks and are diagnosed with a syndrome called long COVID. Long-term clinical studies in a group of people who contracted SARS-CoV-2 have been contrasted with a noninfected matched group of people. A subset of infected people can be distinguished by a set of cytokine markers to have persistent, low-grade inflammation and often self-report two or more bothersome symptoms. No medication can alleviate their symptoms efficiently. Coronavirus nucleocapsid proteins have been investigated extensively as potential drug targets due to their key roles in virus replication, among which is their ability to bind their respective genomic RNAs for incorporation into emerging virions. This review highlights basic studies of the nucleocapsid protein and its ability to undergo liquid-liquid phase separation. We hypothesize that this ability of the nucleocapsid protein for phase separation may contribute to long COVID. This hypothesis unlocks new investigation angles and could potentially open novel avenues for a better understanding of long COVID and treating this condition.

Host Response Changes and Their Association with Mortality in COVID-19 Patients with Lymphopenia

Rationale: Lymphopenia in coronavirus disease (COVID-19) is associated with increased mortality. Objectives: To explore the association between lymphopenia, host response aberrations, and mortality in patients with lymphopenic COVID-19. Methods: We determined 43 plasma biomarkers reflective of four pathophysiological domains: endothelial cell and coagulation activation, inflammation and organ damage, cytokine release, and chemokine release. We explored if decreased concentrations of lymphocyte-derived proteins in patients with lymphopenia were associated with an increase in mortality. We sought to identify host response phenotypes in patients with lymphopenia by cluster analysis of plasma biomarkers. Measurements and Main Results: A total of 439 general ward patients with COVID-19 were stratified by baseline lymphocyte counts: normal (>1.0 × 109/L; n = 167), mild lymphopenia (>0.5 to ⩽1.0 × 109/L; n = 194), and severe lymphopenia (⩽0.5 × 109/L; n = 78). Lymphopenia was associated with alterations in each host response domain. Lymphopenia was associated with increased mortality. Moreover, in patients with lymphopenia (n = 272), decreased concentrations of several lymphocyte-derived proteins (e.g., CCL5, IL-4, IL-13, IL-17A) were associated with an increase in mortality (at P < 0.01 or stronger significance levels). A cluster analysis revealed three host response phenotypes in patients with lymphopenia: "hyporesponsive" (23.2%), "hypercytokinemic" (36.4%), and "inflammatory-injurious" (40.4%), with substantially differing mortality rates of 9.5%, 5.1%, and 26.4%, respectively. A 10-biomarker model accurately predicted these host response phenotypes in an external cohort with similar mortality distribution. The inflammatory-injurious phenotype showed a remarkable combination of relatively high inflammation and organ damage markers with high antiinflammatory cytokine levels yet low proinflammatory cytokine levels. Conclusions: Lymphopenia in COVID-19 signifies a heterogenous group of patients with distinct host response features. Specific host responses contribute to lymphopenia-associated mortality in COVID-19, including reduced CCL5 levels.

Exploring candidate biomarkers for rheumatoid arthritis through cardiovascular and cardiometabolic serum proteome profiling

Introduction

RA patients are at higher risk of cardiovascular disease, influenced by therapies. Studying their cardiovascular and cardiometabolic proteome can unveil biomarkers and insights into related biological pathways.

Methods

This study included two cohorts of RA patients: newly diagnosed individuals (n=25) and those with established RA (disease duration >25 years, n=25). Both cohorts were age and sex-matched with a control group (n=25). Additionally, a longitudinal investigation was conducted on a cohort of 25 RA patients treated with methotrexate and another cohort of 25 RA patients treated with tofacitinib for 6 months. Clinical and analytical variables were recorded, and serum profiling of 184 proteins was performed using the Olink technology platform.

Results

RA patients exhibited elevated levels of 75 proteins that might be associated with cardiovascular disease. In addition, 24 proteins were increased in RA patients with established disease. Twenty proteins were commonly altered in both cohorts of RA patients. Among these, elevated levels of CTSL1, SORT1, SAA4, TNFRSF10A, ST6GAL1 and CCL18 discriminated RA patients and HDs with high specificity and sensitivity. Methotrexate treatment significantly reduced the levels of 13 proteins, while tofacitinib therapy modulated the expression of 10 proteins. These reductions were associated with a decrease in DAS28. Baseline levels of SAA4 and high levels of BNP were associated to the non-response to methotrexate. Changes in IL6 levels were specifically linked to the response to methotrexate. Regarding tofacitinib, differences in baseline levels of LOX1 and CNDP1 were noted between non-responder and responder RA patients. In addition, response to tofacitinib correlated with changes in SAA4 and TIMD4 levels.

Conclusion

In summary, this study pinpoints molecular changes linked to cardiovascular disease in RA and proposes candidate protein biomarkers for distinguishing RA patients from healthy individuals. It also highlights how methotrexate and tofacitinib impact these proteins, with distinct alterations corresponding to each drug's response, identifying potential candidates, as SAA4, for the response to these therapies.

SARS-CoV-2 infection elucidates unique features of pregnancy-specific immunity

Pregnancy is a risk factor for increased severity of SARS-CoV-2 and other respiratory infections. The mechanisms underlying this risk have not been well-established, partly due to a limited understanding of how pregnancy shapes immune responses. To gain insight into the role of pregnancy in modulating immune responses at steady state and upon perturbation, we collected peripheral blood mononuclear cells (PBMC), plasma, and stool from 226 women, including 152 pregnant individuals (n = 96 with SARS-CoV-2 infection and n = 56 healthy controls) and 74 non-pregnant women (n = 55 with SARS-CoV-2 and n = 19 healthy controls). We found that SARS-CoV-2 infection was associated with altered T cell responses in pregnant compared to non-pregnant women. Differences included a lower percentage of memory T cells, a distinct clonal expansion of CD4-expressing CD8 + T cells, and the enhanced expression of T cell exhaustion markers, such as programmed cell death-1 (PD-1) and T cell immunoglobulin and mucin domain-3 (Tim-3), in pregnant women. We identified additional evidence of immune dysfunction in severely and critically ill pregnant women, including a lack of expected elevation in regulatory T cell (Treg) levels, diminished interferon responses, and profound suppression of monocyte function. Consistent with earlier data, we found maternal obesity was also associated with altered immune responses to SARS-CoV-2 infection, including enhanced production of inflammatory cytokines by T cells. Certain gut bacterial species were altered in pregnancy and upon SARS-CoV-2 infection in pregnant individuals compared to non-pregnant women. Shifts in cytokine and chemokine levels were also identified in the sera of pregnant individuals, most notably a robust increase of interleukin-27 (IL-27), a cytokine known to drive T cell exhaustion, in the pregnant uninfected control group compared to all non-pregnant groups. IL-27 levels were also significantly higher in uninfected pregnant controls compared to pregnant SARS-CoV-2-infected individuals. Using two different preclinical mouse models of inflammation-induced fetal demise and respiratory influenza viral infection, we found that enhanced IL-27 protects developing fetuses from maternal inflammation but renders adult female mice vulnerable to viral infection. These combined findings from human and murine studies reveal nuanced pregnancy-associated immune responses, suggesting mechanisms underlying the increased susceptibility of pregnant individuals to viral respiratory infections.

Longitudinal plasma proteomics reveals biomarkers of alveolar-capillary barrier disruption in critically ill COVID-19 patients

The pathobiology of respiratory failure in COVID-19 consists of a complex interplay between viral cytopathic effects and a dysregulated host immune response. In critically ill patients, imatinib treatment demonstrated potential for reducing invasive ventilation duration and mortality. Here, we perform longitudinal profiling of 6385 plasma proteins in 318 hospitalised patients to investigate the biological processes involved in critical COVID-19, and assess the effects of imatinib treatment. Nine proteins measured at hospital admission accurately predict critical illness development. Next to dysregulation of inflammation, critical illness is characterised by pathways involving cellular adhesion, extracellular matrix turnover and tissue remodelling. Imatinib treatment attenuates protein perturbations associated with inflammation and extracellular matrix turnover. These proteomic alterations are contextualised using external pulmonary RNA-sequencing data of deceased COVID-19 patients and imatinib-treated Syrian hamsters. Together, we show that alveolar capillary barrier disruption in critical COVID-19 is reflected in the plasma proteome, and is attenuated with imatinib treatment. This study comprises a secondary analysis of both clinical data and plasma samples derived from a clinical trial that was registered with the EU Clinical Trials Register (EudraCT 2020-001236-10, https://www.clinicaltrialsregister.eu/ctr-search/trial/2020-001236-10/NL ) and Netherlands Trial Register (NL8491, https://www.trialregister.nl/trial/8491 ).

Using the power of innate immunoprofiling to understand vaccine design, infection, and immunity

In the field of immunology, a systems biology approach is crucial to understanding the immune response to infection and vaccination considering the complex interplay between genetic, epigenetic, and environmental factors. Significant progress has been made in understanding the innate immune response, including cell players and critical signaling pathways, but many questions remain unanswered, including how the innate immune response dictates host/pathogen responses and responses to vaccines. To complicate things further, it is becoming increasingly clear that the innate immune response is not a linear pathway but is formed from complex networks and interactions. To further our understanding of the crosstalk and complexities, systems-level analyses and expanded experimental technologies are now needed. In this review, we discuss the most recent immunoprofiling techniques and discuss systems approaches to studying the global innate immune landscape which will inform on the development of personalized medicine and innovative vaccine strategies.

Development of a proteomic signature associated with severe disease for patients with COVID-19 using data from 5 multicenter, randomized, controlled, and prospective studies

Significant progress has been made in preventing severe COVID-19 disease through the development of vaccines. However, we still lack a validated baseline predictive biologic signature for the development of more severe disease in both outpatients and inpatients infected with SARS-CoV-2. The objective of this study was to develop and externally validate, via 5 international outpatient and inpatient trials and/or prospective cohort studies, a novel baseline proteomic signature, which predicts the development of moderate or severe (vs mild) disease in patients with COVID-19 from a proteomic analysis of 7000 + proteins. The secondary objective was exploratory, to identify (1) individual baseline protein levels and/or (2) protein level changes within the first 2 weeks of acute infection that are associated with the development of moderate/severe (vs mild) disease. For model development, samples collected from 2 randomized controlled trials were used. Plasma was isolated and the SomaLogic SomaScan platform was used to characterize protein levels for 7301 proteins of interest for all studies. We dichotomized 113 patients as having mild or moderate/severe COVID-19 disease. An elastic net approach was used to develop a predictive proteomic signature. For validation, we applied our signature to data from three independent prospective biomarker studies. We found 4110 proteins measured at baseline that significantly differed between patients with mild COVID-19 and those with moderate/severe COVID-19 after adjusting for multiple hypothesis testing. Baseline protein expression was associated with predicted disease severity with an error rate of 4.7% (AUC = 0.964). We also found that five proteins (Afamin, I-309, NKG2A, PRS57, LIPK) and patient age serve as a signature that separates patients with mild COVID-19 and patients with moderate/severe COVID-19 with an error rate of 1.77% (AUC = 0.9804). This panel was validated using data from 3 external studies with AUCs of 0.764 (Harvard University), 0.696 (University of Colorado), and 0.893 (Karolinska Institutet). In this study we developed and externally validated a baseline COVID-19 proteomic signature associated with disease severity for potential use in both outpatients and inpatients with COVID-19.

Crowdsourcing temporal transcriptomic coronavirus host infection data: Resources, guide, and novel insights

The emergence of severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) reawakened the need to rapidly understand the molecular etiologies, pandemic potential, and prospective treatments of infectious agents. The lack of existing data on SARS-CoV-2 hampered early attempts to treat severe forms of coronavirus disease-2019 (COVID-19) during the pandemic. This study coupled existing transcriptomic data from severe acute respiratory syndrome-related coronavirus 1 (SARS-CoV-1) lung infection animal studies with crowdsourcing statistical approaches to derive temporal meta-signatures of host responses during early viral accumulation and subsequent clearance stages. Unsupervised and supervised machine learning approaches identified top dysregulated genes and potential biomarkers (e.g. CXCL10, BEX2, and ADM). Temporal meta-signatures revealed distinct gene expression programs with biological implications to a series of host responses underlying sustained Cxcl10 expression and Stat signaling. Cell cycle switched from G1/G0 phase genes, early in infection, to a G2/M gene signature during late infection that correlated with the enrichment of DNA damage response and repair genes. The SARS-CoV-1 meta-signatures were shown to closely emulate human SARS-CoV-2 host responses from emerging RNAseq, single cell, and proteomics data with early monocyte-macrophage activation followed by lymphocyte proliferation. The circulatory hormone adrenomedullin was observed as maximally elevated in elderly patients who died from COVID-19. Stage-specific correlations to compounds with potential to treat COVID-19 and future coronavirus infections were in part validated by a subset of twenty-four that are in clinical trials to treat COVID-19. This study represents a roadmap to leverage existing data in the public domain to derive novel molecular and biological insights and potential treatments to emerging human pathogens.

Subtyping of COVID-19 samples based on cell-cell interaction in single cell transcriptomes

In single-cell transcriptome analysis, numerous biomarkers related to COVID-19 severity, including cell subtypes, genes, and pathways, have been identified. Nevertheless, most studies have focused on severity groups based on clinical features, neglecting immunological heterogeneity within the same severity level. In this study, we employed sample-level clustering using cell-cell interaction scores to investigate patient heterogeneity and uncover novel subtypes. The clustering results were validated using external datasets, demonstrating superior reproducibility and purity compared to gene expression- or gene set enrichment-based clustering. Furthermore, the cell-cell interaction score-based clusters exhibited a strong correlation with the WHO ordinal severity score based on clinical characteristics. By characterizing the identified subtypes through known COVID-19 severity-associated biomarkers, we discovered a "Severe-like moderate" subtype. This subtype displayed clinical features akin to moderate cases; however, molecular features, such as gene expression and cell-cell interactions, resembled those of severe cases. Notably, all patients who progressed from moderate to severe belonged to this subtype, underscoring the significance of cell-cell interactions in COVID-19 patient heterogeneity and severity.

Organotypic heterogeneity in microvascular endothelial cell responses in sepsis-a molecular treasure trove and pharmacological Gordian knot

In the last decades, it has become evident that endothelial cells (ECs) in the microvasculature play an important role in the pathophysiology of sepsis-associated multiple organ dysfunction syndrome (MODS). Studies on how ECs orchestrate leukocyte recruitment, control microvascular integrity and permeability, and regulate the haemostatic balance have provided a wealth of knowledge and potential molecular targets that could be considered for pharmacological intervention in sepsis. Yet, this information has not been translated into effective treatments. As MODS affects specific vascular beds, (organotypic) endothelial heterogeneity may be an important contributing factor to this lack of success. On the other hand, given the involvement of ECs in sepsis, this heterogeneity could also be leveraged for therapeutic gain to target specific sites of the vasculature given its full accessibility to drugs. In this review, we describe current knowledge that defines heterogeneity of organ-specific microvascular ECs at the molecular level and elaborate on studies that have reported EC responses across organ systems in sepsis patients and animal models of sepsis. We discuss hypothesis-driven, single-molecule studies that have formed the basis of our understanding of endothelial cell engagement in sepsis pathophysiology, and include recent studies employing high-throughput technologies. The latter deliver comprehensive data sets to describe molecular signatures for organotypic ECs that could lead to new hypotheses and form the foundation for rational pharmacological intervention and biomarker panel development. Particularly results from single cell RNA sequencing and spatial transcriptomics studies are eagerly awaited as they are expected to unveil the full spatiotemporal signature of EC responses to sepsis. With increasing awareness of the existence of distinct sepsis subphenotypes, and the need to develop new drug regimen and companion diagnostics, a better understanding of the molecular pathways exploited by ECs in sepsis pathophysiology will be a cornerstone to halt the detrimental processes that lead to MODS.

NULISA: a proteomic liquid biopsy platform with attomolar sensitivity and high multiplexing

The blood proteome holds great promise for precision medicine but poses substantial challenges due to the low abundance of most plasma proteins and the vast dynamic range of the plasma proteome. Here we address these challenges with NUcleic acid Linked Immuno-Sandwich Assay (NULISA™), which improves the sensitivity of traditional proximity ligation assays by ~10,000-fold to attomolar level, by suppressing assay background via a dual capture and release mechanism built into oligonucleotide-conjugated antibodies. Highly multiplexed quantification of both low- and high-abundance proteins spanning a wide dynamic range is achieved by attenuating signals from abundant targets with unconjugated antibodies and next-generation sequencing of barcoded reporter DNA. A 200-plex NULISA containing 124 cytokines and chemokines and other proteins demonstrates superior sensitivity to a proximity extension assay in detecting biologically important low-abundance biomarkers in patients with autoimmune diseases and COVID-19. Fully automated NULISA makes broad and in-depth proteomic analysis easily accessible for research and diagnostic applications.

Creation of a High-Throughput Microfluidic Platform for Single-Cell Transcriptome Sequencing of Cell-Cell Interactions

Cell-cell interaction is one of the major modalities for transmitting information between cells and activating the effects of functional cells. However, the construction of high-throughput analysis technologies from cell omics focusing on the impact of interactions of functional cells on targets has been relatively unexplored. Here, they propose a droplet-based microfluidic platform for cell-cell interaction sequencing (c-c-seq) and screening in vitro to address this challenge. A class of interacting cells is pre-labeled using cell molecular tags, and additional single-cell sequencing reagents are introduced to quickly form functional droplet mixes. Lastly, gene expression analysis is used to deduce the impact of the interaction, while molecular sequence tracing identifies the type of interaction. Research into the active effect between antigen-presenting cells and T cells, one of the most common cell-to-cell interactions, is crucial for the advancement of cancer therapy, particularly T cell receptor-engineered T cell therapy. As it allows for high throughput, this platform is superior to well plates as a research platform for cell-to-cell interactions. When combined with the next generation of sequencing, the platform may be able to more accurately evaluate interactions between epitopes and receptors and verify their functional relevance.