A roadmap towards personalized immunology

Microvascular immunity is organ-specific and remodeled after kidney injury in mice

Many studies analyze tissue-resident or blood-borne leukocytes to monitor disease progression. We hypothesized that the microvasculature serves as a distinct site for immune cell activity. Here, we investigate microvascular leukocyte phenotypes before, during and after acute kidney injury (AKI) in mice, uncovering unique characteristics in the kidney, liver, and lung. Using single-cell sequencing, we identify several immune cells that were up to 100-fold expanded in the kidney vasculature, including macrophages, dendritic cells (DC), and B cells. Regeneration after AKI is characterized by sustained remodeling of the renal microvascular interface. Homeostatic microvascular C1q+ macrophages withdraw from the vascular barrier which is subsequently repopulated by new subsets, including CD11c+F480+ and CD11c+F480- cells. These newly arrived macrophages exhibit enhanced phagocytic activity toward circulating bacteria and secretion of tumor necrosis factor, pointing to maladaptive repair mechanisms after AKI. These data suggest organ- and disease-specific microvascular immune dynamics which are not detectable through conventional blood and tissue analysis.

Comparative assessment of phenotypic markers in patients with chronic inflammation: Differences on Bifidobacterium concerning liver status

BACKGROUND

The relationship between systemic lupus erythematosus (SLE) and low-grade metabolic inflammation (MI) with the microbiota is crucial for understanding the pathogenesis of these diseases and developing effective therapeutic interventions. In this context, it has been observed that the gut microbiota plays a key role in the immune regulation and inflammation contributing to the exacerbation through inflammatory mediators. This research aimed to describe similarities/differences in anthropometric, biochemical, inflammatory, and hepatic markers as well as to examine the putative role of gut microbiota concerning two inflammatory conditions: SLE and MI.

METHODS

Data were obtained from a cohort comprising adults with SLE and MI. Faecal samples were determined by 16S technique. Statistical analyses compared anthropometric and clinical variables, and LEfSe and MetagenomeSeq were used for metagenomic data. An interaction analysis was fitted to investigate associations of microbiota with fatty liver index (FLI) depending on the inflammatory condition.

RESULTS

Participants with low-grade MI showed worse values in anthropometry and biochemicals compared with patients with SLE. The liver profile of patients with MI was unhealthier, while no relevant differences were found in most of the inflammatory markers between groups. LEfSe analysis revealed an overrepresentation of Bifidobacteriaceae family in SLE group. An interactive association between gut Bifidobacterium abundance and type of disease was identified for FLI values, suggesting an effect modification of the gut microbiota concerning liver markers depending on the inflammatory condition.

CONCLUSION

This study found phenotypical and microbial similarities and disparities between these two inflammatory conditions, evidenced in clinical and hepatic markers, and showed the interactive interplay between gut Bifidobacterium and liver health (measured by FLI) that occur in a different manner depending on the type of inflammatory disease. These results underscore the importance of personalized approaches and individual microbiota in the screening of different inflammatory situations, considering unique hepatic and microbiota profiles.

A data management system for precision medicine

Precision, or personalised medicine has advanced requirements for medical data management systems (MedDMSs). MedDMS for precision medicine should be able to process hundreds of parameters from multiple sites, be adaptable while remaining in sync at multiple locations, real-time syncing to analytics and be compliant with international privacy legislation. This paper describes the LogiqSuite software solution, aimed to support a precision medicine solution at the patient care (LogiqCare), research (LogiqScience) and data science (LogiqAnalytics) level. LogiqSuite is certified and compliant with international medical data and privacy legislations. This paper evaluates a MedDMS in five types of use cases for precision medicine, ranging from data collection to algorithm development and from implementation to integration with real-world data. The MedDMS is evaluated in seven precision medicine data science projects in prehospital triage, cardiovascular disease, pulmonology, and oncology. The P4O2 consortium uses the MedDMS as an electronic case report form (eCRF) that allows real-time data management and analytics in long covid and pulmonary diseases. In an acute myeloid leukaemia, study data from different sources were integrated to facilitate easy descriptive analytics for various research questions. In the AIDPATH project, LogiqCare is used to process patient data, while LogiqScience is used for pseudonymous CAR-T cell production for cancer treatment. In both these oncological projects the data in LogiqAnalytics is also used to facilitate machine learning to develop new prediction models for clinical-decision support (CDS). The MedDMS is also evaluated for real-time recording of CDS data from U-Prevent for cardiovascular risk management and from the Stroke Triage App for prehospital triage. The MedDMS is discussed in relation to other solutions for privacy-by-design, integrated data stewardship and real-time data analytics in precision medicine. LogiqSuite is used for multi-centre research study data registrations and monitoring, data analytics in interdisciplinary consortia, design of new machine learning / artificial intelligence (AI) algorithms, development of new or updated prediction models, integration of care with advanced therapy production, and real-world data monitoring in using CDS tools. The integrated MedDMS application supports data management for care and research in precision medicine.

Multiomics approaches disclose very-early molecular and cellular switches during insect-venom allergen-specific immunotherapy: an observational study

Allergen-specific immunotherapy (AIT) induces immune tolerance, showing the highest success rate (>95%) for insect venom while a much lower chance for pollen allergy. However, the molecular switches leading to successful durable tolerance restoration remain elusive. The primary outcome of this observational study is the comprehensive immunological cellular characterization during the AIT initiation phase, whereas the secondary outcomes are the serological and Th2-cell-type-specific transcriptomic analyses. Here we apply a multilayer-omics approach to reveal dynamic peripheral immune landscapes during the AIT-initiation phase in venom allergy patients (VAP) versus pollen-allergic and healthy controls. Already at baseline, VAP exhibit altered abundances of several cell types, including classical monocytes (cMono), CD4+ hybrid type 1-type 17 cells (Th1-Th17 or Th1/17) and CD8+ counterparts (Tc1-Tc17 or Tc1/17). At 8-24 h following AIT launch in VAP, we identify a uniform AIT-elicited pulse of late-transitional/IL-10-producing B cells, IL-6 signaling within Th2 cells and non-inflammatory serum-IL-6 levels. Sequential induction of activation and survival protein markers also immediately occur. A disequilibrium between serum IL-6 and cMono in VAP baseline is restored at day seven following AIT launch. Our longitudinal analysis discovers molecular switches during initiation-phase insect-venom AIT that secure long-term outcomes. Trial number: NCT02931955.

The evolution of immune profiling: will there be a role for nanoparticles?

Immune profiling provides insights into the functioning of the immune system, including the distribution, abundance, and activity of immune cells. This understanding is essential for deciphering how the immune system responds to pathogens, vaccines, tumors, and other stimuli. Analyzing diverse immune cell types facilitates the development of personalized medicine approaches by characterizing individual variations in immune responses. With detailed immune profiles, clinicians can tailor treatment strategies to the specific immune status and needs of each patient, maximizing therapeutic efficacy while minimizing adverse effects. In this review, we discuss the evolution of immune profiling, from interrogating bulk cell samples in solution to evaluating the spatially-rich molecular profiles across intact preserved tissue sections. We also review various multiplexed imaging platforms recently developed, based on immunofluorescence and imaging mass spectrometry, and their impact on the field of immune profiling. Identifying and localizing various immune cell types across a patient's sample has already provided important insights into understanding disease progression, the development of novel targeted therapies, and predicting treatment response. We also offer a new perspective by highlighting the unprecedented potential of nanoparticles (NPs) that can open new horizons in immune profiling. NPs are known to provide enhanced detection sensitivity, targeting specificity, biocompatibility, stability, multimodal imaging features, and multiplexing capabilities. Therefore, we summarize the recent developments and advantages of NPs, which can contribute to advancing our understanding of immune function to facilitate precision medicine. Overall, NPs have the potential to offer a versatile and robust approach to profile the immune system with improved efficiency and multiplexed imaging power.

Combinatorial Genomic Biomarkers Associated with High Response in IgE-Dependent Degranulation in Human Mast Cells

Mast cells are the major effector cells that mediate IgE-dependent allergic reactions. We sought to use integrated network analysis to identify genomic biomarkers associated with high response in IgE-mediated activation of primary human mast cells. Primary human mast cell cultures derived from 262 normal donors were categorized into High, Average and Low responder groups according to their activation response profiles. Transcriptome analysis was used to identify genes that were differentially expressed in different responder cultures in their baseline conditions, and the data were analyzed by constructing a personalized perturbed profile (PEEP). For upregulated genes, the construction of PEEP for each individual sample of all three responder groups revealed that High responders exhibited a higher percentage of "perturbed" samples whose PEEP values lay outside the normal range of expression. Moreover, the integration of PEEP of four selected upregulated genes into distinct sets of combinatorial profiles demonstrated that the specific pattern of upregulated expression of these four genes, in a tandem combination, was observed exclusively among the High responders. In conclusion, this combinatorial approach was useful in identifying a set of genomic biomarkers that are associated with high degranulation response in human mast cell cultures derived from the blood of a cohort of normal donors.

Organoid-guided precision hepatology for metabolic liver disease

Metabolic dysfunction-associated steatotic liver disease affects millions of people worldwide. Progress towards a definitive cure has been incremental and treatment is currently limited to lifestyle modification. Hepatocyte-specific lipid accumulation is the main trigger of lipotoxic events, driving inflammation and fibrosis. The underlying pathology is extraordinarily heterogenous, and the manifestations of steatohepatitis are markedly influenced by metabolic communications across non-hepatic organs. Synthetic human tissue models have emerged as powerful platforms to better capture the mechanistic diversity in disease progression, while preserving person-specific genetic traits. In this review, we will outline current research efforts focused on integrating multiple synthetic tissue models of key metabolic organs, with an emphasis on organoid-based systems. By combining functional genomics and population-scale en masse profiling methodologies, human tissues derived from patients can provide insights into personalised genetic, transcriptional, biochemical, and metabolic states. These collective efforts will advance our understanding of steatohepatitis and guide the development of rational solutions for mechanism-directed diagnostic and therapeutic investigation.

Multi-omics subgroups associated with glycaemic deterioration in type 2 diabetes: an IMI-RHAPSODY Study

Introduction

Type 2 diabetes (T2D) onset, progression and outcomes differ substantially between individuals. Multi-omics analyses may allow a deeper understanding of these differences and ultimately facilitate personalised treatments. Here, in an unsupervised "bottom-up" approach, we attempt to group T2D patients based solely on -omics data generated from plasma.

Methods

Circulating plasma lipidomic and proteomic data from two independent clinical cohorts, Hoorn Diabetes Care System (DCS) and Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS), were analysed using Similarity Network Fusion. The resulting patient network was analysed with Logistic and Cox regression modelling to explore relationships between plasma -omic profiles and clinical characteristics.

Results

From a total of 1,134 subjects in the two cohorts, levels of 180 circulating plasma lipids and 1195 proteins were used to separate patients into two subgroups. These differed in terms of glycaemic deterioration (Hazard Ratio=0.56;0.73), insulin sensitivity and secretion (C-peptide, p=3.7e-11;2.5e-06, DCS and GoDARTS, respectively; Homeostatic model assessment 2 (HOMA2)-B; -IR; -S, p=0.0008;4.2e-11;1.1e-09, only in DCS). The main molecular signatures separating the two groups included triacylglycerols, sphingomyelin, testican-1 and interleukin 18 receptor.

Conclusions

Using an unsupervised network-based fusion method on plasma lipidomics and proteomics data from two independent cohorts, we were able to identify two subgroups of T2D patients differing in terms of disease severity. The molecular signatures identified within these subgroups provide insights into disease mechanisms and possibly new prognostic markers for T2D.

Early-to-mid stage idiopathic Parkinson's disease shows enhanced cytotoxicity and differentiation in CD8 T-cells in females

Neuroinflammation in the brain contributes to the pathogenesis of Parkinson's disease (PD), but the potential dysregulation of peripheral immunity has not been systematically investigated for idiopathic PD (iPD). Here we showed an elevated peripheral cytotoxic immune milieu, with more terminally-differentiated effector memory (TEMRA) CD8 T, CD8+ NKT cells and circulating cytotoxic molecules in fresh blood of patients with early-to-mid iPD, especially females, after analyzing > 700 innate and adaptive immune features. This profile, also reflected by fewer CD8+FOXP3+ T cells, was confirmed in another subcohort. Co-expression between cytotoxic molecules was selectively enhanced in CD8 TEMRA and effector memory (TEM) cells. Single-cell RNA-sequencing analysis demonstrated the accelerated differentiation within CD8 compartments, enhanced cytotoxic pathways in CD8 TEMRA and TEM cells, while CD8 central memory (TCM) and naïve cells were already more-active and transcriptionally-reprogrammed. Our work provides a comprehensive map of dysregulated peripheral immunity in iPD, proposing candidates for early diagnosis and treatments.

The ethical aspects of exposome research: a systematic review

In recent years, exposome research has been put forward as the next frontier for the study of human health and disease. Exposome research entails the analysis of the totality of environmental exposures and their corresponding biological responses within the human body. Increasingly, this is operationalized by big-data approaches to map the effects of internal as well as external exposures using smart sensors and multiomics technologies. However, the ethical implications of exposome research are still only rarely discussed in the literature. Therefore, we conducted a systematic review of the academic literature regarding both the exposome and underlying research fields and approaches, to map the ethical aspects that are relevant to exposome research. We identify five ethical themes that are prominent in ethics discussions: the goals of exposome research, its standards, its tools, how it relates to study participants, and the consequences of its products. Furthermore, we provide a number of general principles for how future ethics research can best make use of our comprehensive overview of the ethical aspects of exposome research. Lastly, we highlight three aspects of exposome research that are most in need of ethical reflection: the actionability of its findings, the epidemiological or clinical norms applicable to exposome research, and the meaning and action-implications of bias.

Advancing personalized medicine for tuberculosis through the application of immune profiling

While early and precise diagnosis is the key to eliminating tuberculosis (TB), conventional methods using culture conversion or sputum smear microscopy have failed to meet demand. This is especially true in high-epidemic developing countries and during pandemic-associated social restrictions. Suboptimal biomarkers have restricted the improvement of TB management and eradication strategies. Therefore, the research and development of new affordable and accessible methods are required. Following the emergence of many high-throughput quantification TB studies, immunomics has the advantages of directly targeting responsive immune molecules and significantly simplifying workloads. In particular, immune profiling has been demonstrated to be a versatile tool that potentially unlocks many options for application in TB management. Herein, we review the current approaches for TB control with regard to the potentials and limitations of immunomics. Multiple directions are also proposed to hopefully unleash immunomics' potential in TB research, not least in revealing representative immune biomarkers to correctly diagnose TB. The immune profiles of patients can be valuable covariates for model-informed precision dosing-based treatment monitoring, prediction of outcome, and the optimal dose prediction of anti-TB drugs.

Starring Role of Biomarkers and Anticancer Agents as a Major Driver in Precision Medicine of Cancer Therapy

Precision medicine is the most modern contemporary medicine approach today, based on great amount of data on people's health, individual characteristics, and life circumstances, and employs the most effective ways to prevent and cure diseases. Precision medicine in cancer is the most precise and viable treatment for every cancer patient based on the disease's genetic profile. Precision medicine changes the standard one size fits all medication model, which focuses on average responses to care. Consolidating modern methodologies for streamlining and checking anticancer drugs can have long-term effects on understanding the results. Precision medicine can help explicit anticancer treatments using various drugs and even in discovery, thus becoming the paradigm of future cancer medicine. Cancer biomarkers are significant in precision medicine, and findings of different biomarkers make this field more promising and challenging. Naturally, genetic instability and the collection of extra changes in malignant growth cells are ways cancer cells adapt and survive in a hostile environment, for example, one made by these treatment modalities. Precision medicine centers on recognizing the best treatment for individual patients, dependent on their malignant growth and genetic characterization. This new era of genomics progressively referred to as precision medicine, has ignited a new episode in the relationship between genomics and anticancer drug development.

Quantifying Spatial Heterogeneity of Tumor-Infiltrating Lymphocytes to Predict Survival of Individual Cancer Patients

Tumor-infiltrating lymphocytes (TILs), identified on HE-stained histopathological images in the cancer area, are indicators of the adaptive immune response against cancers and play a major role in personalized cancer immunotherapy. Recent works indicate that the spatial organization of TILs may be prognostic of disease-specific survival and recurrence. However, there are a limited number of methods that were proposed and tested in analyses of the spatial structure of TILs. In this work, we evaluated 14 different spatial measures, including the one developed for other omics data, on 10,532 TIL maps from 23 cancer types in terms of reproducibility, uniqueness, and impact on patient survival. For each spatial measure, 16 different scenarios for the definition of prognostic factor were tested. We found no difference in survival prediction when TIL maps were stored as binary images or continuous TIL probability scores. When spatial measures were discretized into a low and high category, a higher correlation with survival was observed. Three measures with the highest cancer prognosis capability were spatial autocorrelation, GLCM M1, and closeness centrality. Most of the tested measures could be further tuned to increase prediction performance.

Combinatorial analysis reveals highly coordinated early-stage immune reactions that predict later antiviral immunity in mild COVID-19 patients

While immunopathology has been widely studied in patients with severe COVID-19, immune responses in non-hospitalized patients have remained largely elusive. We systematically analyze 484 peripheral cellular or soluble immune features in a longitudinal cohort of 63 mild and 15 hospitalized patients versus 14 asymptomatic and 26 household controls. We observe a transient increase of IP10/CXCL10 and interferon-β levels, coordinated responses of dominant SARS-CoV-2-specific CD4 and fewer CD8 T cells, and various antigen-presenting and antibody-secreting cells in mild patients within 3 days of PCR diagnosis. The frequency of key innate immune cells and their functional marker expression are impaired in hospitalized patients at day 1 of inclusion. T cell and dendritic cell responses at day 1 are highly predictive for SARS-CoV-2-specific antibody responses after 3 weeks in mild but not hospitalized patients. Our systematic analysis reveals a combinatorial picture and trajectory of various arms of the highly coordinated early-stage immune responses in mild COVID-19 patients.

Extracellular Vesicles and Interleukins: Novel Frontiers in Diagnostic and Therapeutic for Cancer

Tumor cells present many strategies for survival and dissemination in the tumor environment. Extracellular vesicles are a vital pathway used in crosstalk between tumor and non-malignant cells. They carry different types of molecules that, when internalized by target cells, can activate signaling pathways and molecular processes that will promote and disseminate neoplastic cells. Proteins, nucleic acids, and different cytokines, such as interleukins, are the main classes of molecules carried by extracellular vesicles and are being studied to understand the molecular mechanisms present in the tumor microenvironment. In particular, although poorly understood, the association between EVs and interleukins has revealed potential approaches to the diagnosis and therapeutics of several neoplasms.

Single-Domain Antibodies for Targeting, Detection, and In Vivo Imaging of Human CD4+ Cells

The advancement of new immunotherapies necessitates appropriate probes to monitor the presence and distribution of distinct immune cell populations. Considering the key role of CD4⁺ cells in regulating immunological processes, we generated novel single-domain antibodies [nanobodies (Nbs)] that specifically recognize human CD4. After in-depth analysis of their binding properties, recognized epitopes, and effects on T-cell proliferation, activation, and cytokine release, we selected CD4-specific Nbs that did not interfere with crucial T-cell processes in vitro and converted them into immune tracers for noninvasive molecular imaging. By optical imaging, we demonstrated the ability of a high-affinity CD4-Nb to specifically visualize CD4⁺ cells in vivo using a xenograft model. Furthermore, quantitative high-resolution immune positron emission tomography (immunoPET)/MR of a human CD4 knock-in mouse model showed rapid accumulation of ⁶⁴Cu-radiolabeled CD4-Nb1 in CD4⁺ T cell-rich tissues. We propose that the CD4-Nbs presented here could serve as versatile probes for stratifying patients and monitoring individual immune responses during personalized immunotherapy in both cancer and inflammatory diseases.

Multi-Omics Approaches in Immunological Research

The immune system plays a vital role in health and disease, and is regulated through a complex interactive network of many different immune cells and mediators. To understand the complexity of the immune system, we propose to apply a multi-omics approach in immunological research. This review provides a complete overview of available methodological approaches for the different omics data layers relevant for immunological research, including genetics, epigenetics, transcriptomics, proteomics, metabolomics, and cellomics. Thereafter, we describe the various methods for data analysis as well as how to integrate different layers of omics data. Finally, we discuss the possible applications of multi-omics studies and opportunities they provide for understanding the complex regulatory networks as well as immune variation in various immune-related diseases.

A Roadmap towards Precision Periodontics

Periodontitis is among the most common health conditions and represents a major public health issue related to increasing prevalence and seriously negative socioeconomic impacts. Periodontitis-associated low-grade systemic inflammation and its pathological interplay with systemic conditions additionally raises awareness on the necessity for highly performant strategies for the prevention and management of periodontitis. Periodontal diagnosis is the backbone of a successful periodontal strategy, since prevention and treatment plans depend on the accuracy and precision of the respective diagnostics. Periodontal diagnostics is still founded on clinical and radiological parameters that provide limited therapeutic guidance due to the multifactorial complexity of periodontal pathology, which is why biomarkers have been introduced for the first time in the new classification of periodontal and peri-implant conditions as a first step towards precision periodontics. Since the driving forces of precision medicine are represented by biomarkers and machine learning algorithms, with the lack of periodontal markers validated for diagnostic use, the implementation of a precision medicine approach in periodontology remains in the very initial stage. This narrative review elaborates the unmet diagnostic needs in periodontal diagnostics, the concept of precision periodontics, periodontal biomarkers, and a roadmap toward the implementation of a precision medicine approach in periodontal practice.

IgE-Mediated Peanut Allergy: Current and Novel Predictive Biomarkers for Clinical Phenotypes Using Multi-Omics Approaches

Food allergy is a collective term for several immune-mediated responses to food. IgE-mediated food allergy is the best-known subtype. The patients present with a marked diversity of clinical profiles including symptomatic manifestations, threshold reactivity and reaction kinetics. In-vitro predictors of these clinical phenotypes are evasive and considered as knowledge gaps in food allergy diagnosis and risk management. Peanut allergy is a relevant disease model where pioneer discoveries were made in diagnosis, immunotherapy and prevention. This review provides an overview on the immune basis for phenotype variations in peanut-allergic individuals, in the light of future patient stratification along emerging omic-areas. Beyond specific IgE-signatures and basophil reactivity profiles with established correlation to clinical outcome, allergenomics, mass spectrometric resolution of peripheral allergen tracing, might be a fundamental approach to understand disease pathophysiology underlying biomarker discovery. Deep immune phenotyping is thought to reveal differential cell responses but also, gene expression and gene methylation profiles (eg, peanut severity genes) are promising areas for biomarker research. Finally, the study of microbiome-host interactions with a focus on the immune system modulation might hold the key to understand tissue-specific responses and symptoms. The immune mechanism underlying acute food-allergic events remains elusive until today. Deciphering this immunological response shall enable to identify novel biomarker for stratification of patients into reaction endotypes. The availability of powerful multi-omics technologies, together with integrated data analysis, network-based approaches and unbiased machine learning holds out the prospect of providing clinically useful biomarkers or biomarker signatures being predictive for reaction phenotypes.

Towards a Systems Immunology Approach to Unravel Responses to Cancer Immunotherapy

Immunotherapy, particularly immune checkpoint blockade and chimeric antigen receptor (CAR)-T cells, holds a great promise against cancer. These treatments have markedly improved survival in solid as well as in hematologic tumors previously considered incurable. However, durable responses occur in a fraction of patients, and existing biomarkers (e.g. PD-L1) have shown limited prediction power. This scenario highlights the need to dissect the complex interplay between immune and tumor cells to identify reliable biomarkers of response to be used for patients’ selection. In this context, systems immunology represents indeed the new frontier to address important clinical challenges in biomarker discovery. Through the integration of multiple layers of data obtained with several high-throughput approaches, systems immunology may give insights on the vast range of inter-individual differences and on the influences of genes and factors that cooperatively shape the individual immune response to a given treatment. In this Mini Review, we give an overview of the current high-throughput methodologies, including genomics, epigenomics, transcriptomics, metabolomics, proteomics, and multi-parametric phenotyping suitable for systems immunology as well as on the key steps of data integration and biological interpretation. Additionally, we review recent studies in which multi-omics technologies have been used to characterize mechanisms of response and to identify powerful biomarkers of response to checkpoint inhibitors, CAR-T cell therapy, dendritic cell-based and peptide-based cancer vaccines. We also highlight the need of favoring the collaboration of researchers with complementary expertise and of integrating multi-omics data into biological networks with the final goal of developing accurate markers of therapeutic response.

The Atlas of Inflammation Resolution (AIR)

Acute inflammation is a protective reaction by the immune system in response to invading pathogens or tissue damage. Ideally, the response should be localized, self-limited, and returning to homeostasis. If not resolved, acute inflammation can result in organ pathologies leading to chronic inflammatory phenotypes. Acute inflammation and inflammation resolution are complex coordinated processes, involving a number of cell types, interacting in space and time. The biomolecular complexity and the fact that several biomedical fields are involved, make a multi- and interdisciplinary approach necessary. The Atlas of Inflammation Resolution (AIR) is a web-based resource capturing an essential part of the state-of-the-art in acute inflammation and inflammation resolution research. The AIR provides an interface for users to search thousands of interactions, arranged in inter-connected multi-layers of process diagrams, covering a wide range of clinically relevant phenotypes. By mapping experimental data onto the AIR, it can be used to elucidate drug action as well as molecular mechanisms underlying different disease phenotypes. For the visualization and exploration of information, the AIR uses the Minerva platform, which is a well-established tool for the presentation of disease maps. The molecular details of the AIR are encoded using international standards. The AIR was created as a freely accessible resource, supporting research and education in the fields of acute inflammation and inflammation resolution. The AIR connects research communities, facilitates clinical decision making, and supports research scientists in the formulation and validation of hypotheses. The AIR is accessible through https://air.bio.informatik.uni-rostock.de.

WITHDRAWN: The Atlas of Inflammation Resolution (AIR)

The Publisher regrets that this article is an accidental duplication of an article that has already been published, https://doi.org/10.1016/j.mam.2020.100894. The duplicate article has therefore been withdrawn. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal.

The role of component-resolved diagnosis in Hymenoptera venom allergy

PURPOSE OF REVIEW

Component-resolved diagnostics (CRD) is a new tool aiming at detecting IgE-mediated sensitizations against individual, relevant allergens. Here, we discuss recent literature on molecular diagnosis in the field of Hymenoptera venom allergy (HVA) as well as CRD strengths and weaknesses.

RECENT FINDINGS

CRD, using single molecules or panels of allergens, may discriminate between primary sensitization and cross-reactivity in patients with double/multiple positivity in diagnostic tests with whole extracts, allowing the specialist to choose the most suitable venom for specific immunotherapy (VIT), avoiding unnecessary VIT and reducing the risk of side effects. Future availability of the cross-reactive recombinant pairs of allergens of different species may further increase the diagnostic performance. CRD may be useful in patients with negative allergy tests and a proven history of a previous systemic reaction, including those with mast cell disorders, who could benefit from VIT. In honeybee venom allergy, different sensitization profiles have been identified, which could be associated with a greater risk of VIT failure or treatment side effects.

SUMMARY

CRD is undoubtedly an innovative diagnostic method that leads to a more precise definition of the sensitization profile of the HVA patient. Together with a better knowledge of the molecular composition of different venom extracts, CRD may contribute to optimize patient-tailored therapy.

Network and Systems Medicine: Position Paper of the European Collaboration on Science and Technology Action on Open Multiscale Systems Medicine

Introduction: Network and systems medicine has rapidly evolved over the past decade, thanks to computational and integrative tools, which stem in part from systems biology. However, major challenges and hurdles are still present regarding validation and translation into clinical application and decision making for precision medicine. Methods: In this context, the Collaboration on Science and Technology Action on Open Multiscale Systems Medicine (OpenMultiMed) reviewed the available advanced technologies for multidimensional data generation and integration in an open-science approach as well as key clinical applications of network and systems medicine and the main issues and opportunities for the future. Results: The development of multi-omic approaches as well as new digital tools provides a unique opportunity to explore complex biological systems and networks at different scales. Moreover, the application of findable, applicable, interoperable, and reusable principles and the adoption of standards increases data availability and sharing for multiscale integration and interpretation. These innovations have led to the first clinical applications of network and systems medicine, particularly in the field of personalized therapy and drug dosing. Enlarging network and systems medicine application would now imply to increase patient engagement and health care providers as well as to educate the novel generations of medical doctors and biomedical researchers to shift the current organ- and symptom-based medical concepts toward network- and systems-based ones for more precise diagnoses, interventions, and ideally prevention. Conclusion: In this dynamic setting, the health care system will also have to evolve, if not revolutionize, in terms of organization and management.

Vaccines to Prevent Infectious Diseases in the Older Population: Immunological Challenges and Future Perspectives

Infectious diseases are a major cause for morbidity and mortality in the older population. Demographic changes will lead to increasing numbers of older persons over the next decades. Prevention of infections becomes increasingly important to ensure healthy aging for the individual, and to alleviate the socio-economic burden for societies. Undoubtedly, vaccines are the most efficient health care measure to prevent infections. Age-associated changes of the immune system are responsible for decreased immunogenicity and clinical efficacy of most currently used vaccines in older age. Efficacy of standard influenza vaccines is only 30-50% in the older population. Several approaches, such as higher antigen dose, use of MF59 as adjuvant and intradermal administration have been implemented in order to specifically target the aged immune system. The use of a 23-valent polysaccharide vaccine against Streptococcus pneumoniae has been amended by a 13-valent conjugated pneumococcal vaccine originally developed for young children several years ago to overcome at least some of the limitations of the T cell-independent polysaccharide antigens, but still is only approximately 50% protective against pneumonia. A live-attenuated vaccine against herpes zoster, which has been available for several years, demonstrated efficacy of 51% against herpes zoster and 67% against post-herpetic neuralgia. Protection was lower in the very old and decreased several years after vaccination. Recently, a recombinant vaccine containing the viral glycoprotein gE and the novel adjuvant AS01B has been licensed. Phase III studies demonstrated efficacy against herpes zoster of approx. 90% even in the oldest age groups after administration of two doses and many countries now recommend the preferential use of this vaccine. There are still many infectious diseases causing substantial morbidity in the older population, for which no vaccines are available so far. Extensive research is ongoing to develop vaccines against novel targets with several vaccine candidates already being clinically tested, which have the potential to substantially reduce health care costs and to save many lives. In addition to the development of novel and improved vaccines, which specifically target the aged immune system, it is also important to improve uptake of the existing vaccines in order to protect the vulnerable, older population.

ImmuCellAI: A Unique Method for Comprehensive T-Cell Subsets Abundance Prediction and its Application in Cancer Immunotherapy

The distribution and abundance of immune cells, particularly T-cell subsets, play pivotal roles in cancer immunology and therapy. T cells have many subsets with specific function and current methods are limited in estimating them, thus, a method for predicting comprehensive T-cell subsets is urgently needed in cancer immunology research. Here, Immune Cell Abundance Identifier (ImmuCellAI), a gene set signature-based method, is introduced for precisely estimating the abundance of 24 immune cell types including 18 T-cell subsets, from gene expression data. Performance evaluation on both the sequencing data with flow cytometry results and public expression data indicate that ImmuCellAI can estimate the abundance of immune cells with superior accuracy to other methods especially on many T-cell subsets. Application of ImmuCellAI to immunotherapy datasets reveals that the abundance of dendritic cells, cytotoxic T, and gamma delta T cells is significantly higher both in comparisons of on-treatment versus pre-treatment and responders versus non-responders. Meanwhile, an ImmuCellAI result-based model is built for predicting the immunotherapy response with high accuracy (area under curve 0.80-0.91). These results demonstrate the powerful and unique function of ImmuCellAI in tumor immune infiltration estimation and immunotherapy response prediction.

A Path Toward Precision Medicine for Neuroinflammatory Mechanisms in Alzheimer's Disease

Neuroinflammation commences decades before Alzheimer's disease (AD) clinical onset and represents one of the earliest pathomechanistic alterations throughout the AD continuum. Large-scale genome-wide association studies point out several genetic variants—TREM2, CD33, PILRA, CR1, MS4A, CLU, ABCA7, EPHA1, and HLA-DRB5-HLA-DRB1—potentially linked to neuroinflammation. Most of these genes are involved in proinflammatory intracellular signaling, cytokines/interleukins/cell turnover, synaptic activity, lipid metabolism, and vesicle trafficking. Proteomic studies indicate that a plethora of interconnected aberrant molecular pathways, set off and perpetuated by TNF-α, TGF-β, IL-1β, and the receptor protein TREM2, are involved in neuroinflammation. Microglia and astrocytes are key cellular drivers and regulators of neuroinflammation. Under physiological conditions, they are important for neurotransmission and synaptic homeostasis. In AD, there is a turning point throughout its pathophysiological evolution where glial cells sustain an overexpressed inflammatory response that synergizes with amyloid-β and tau accumulation, and drives synaptotoxicity and neurodegeneration in a self-reinforcing manner. Despite a strong therapeutic rationale, previous clinical trials investigating compounds with anti-inflammatory properties, including non-steroidal anti-inflammatory drugs (NSAIDs), did not achieve primary efficacy endpoints. It is conceivable that study design issues, including the lack of diagnostic accuracy and biomarkers for target population identification and proof of mechanism, may partially explain the negative outcomes. However, a recent meta-analysis indicates a potential biological effect of NSAIDs. In this regard, candidate fluid biomarkers of neuroinflammation are under analytical/clinical validation, i.e., TREM2, IL-1β, MCP-1, IL-6, TNF-α receptor complexes, TGF-β, and YKL-40. PET radio-ligands are investigated to accomplish in vivo and longitudinal regional exploration of neuroinflammation. Biomarkers tracking different molecular pathways (body fluid matrixes) along with brain neuroinflammatory endophenotypes (neuroimaging markers), can untangle temporal–spatial dynamics between neuroinflammation and other AD pathophysiological mechanisms. Robust biomarker–drug codevelopment pipelines are expected to enrich large-scale clinical trials testing new-generation compounds active, directly or indirectly, on neuroinflammatory targets and displaying putative disease-modifying effects: novel NSAIDs, AL002 (anti-TREM2 antibody), anti-Aβ protofibrils (BAN2401), and AL003 (anti-CD33 antibody). As a next step, taking advantage of breakthrough and multimodal techniques coupled with a systems biology approach is the path to pursue for developing individualized therapeutic strategies targeting neuroinflammation under the framework of precision medicine.

Noncoding RNAs Databases: Current Status and Trends

One of the most important resources for researchers of noncoding RNAs is the information available in public databases spread over the internet. However, the effective exploration of this data can represent a daunting task, given the large amount of databases available and the variety of stored data. This chapter describes a classification of databases based on information source, type of RNA, source organisms, data formats, and the mechanisms for information retrieval, detailing the relevance of each of these classifications and its usability by researchers. This classification is used to update a 2012 review, indexing now more than 229 public databases. This review will include an assessment of the new trends for ncRNA research based on the information that is being offered by the databases. Additionally, we will expand the previous analysis focusing on the usability and application of these databases in pathogen and disease research. Finally, this chapter will analyze how currently available database schemas can help the development of new and improved web resources.

Systems vaccinology and big data in the vaccine development chain

Systems vaccinology has proven a fascinating development in the last decade. Where traditionally vaccine development has been dominated by trial and error, systems vaccinology is a tool that provides novel and comprehensive understanding if properly used. Data sets retrieved from systems‐based studies endorse rational design and effective development of safe and efficacious vaccines. In this review we first describe different omics‐techniques that form the pillars of systems vaccinology. In the second part, the application of systems vaccinology in the different stages of vaccine development is described. Overall, this review shows that systems vaccinology has become an important tool anywhere in the vaccine development chain.

Multi-Omics Profiling of the Tumor Microenvironment: Paving the Way to Precision Immuno-Oncology

The tumor microenvironment (TME) is a multifaceted ecosystem characterized by profound cellular heterogeneity, dynamicity, and complex intercellular cross-talk. The striking responses obtained with immune checkpoint blockers, i.e., antibodies targeting immune-cell regulators to boost antitumor immunity, have demonstrated the enormous potential of anticancer treatments that target TME components other than tumor cells. However, as checkpoint blockade is currently beneficial only to a limited fraction of patients, there is an urgent need to understand the mechanisms orchestrating the immune response in the TME to guide the rational design of more effective anticancer therapies. In this Mini Review, we give an overview of the methodologies that allow studying the heterogeneity of the TME from multi-omics data generated from bulk samples, single cells, or images of tumor-tissue slides. These include approaches for the characterization of the different cell phenotypes and for the reconstruction of their spatial organization and inter-cellular cross-talk. We discuss how this broader vision of the cellular heterogeneity and plasticity of tumors, which is emerging thanks to these methodologies, offers the opportunity to rationally design precision immuno-oncology treatments. These developments are fundamental to overcome the current limitations of targeted agents and checkpoint blockers and to bring long-term clinical benefits to a larger fraction of cancer patients.