Malaria-specific Type 1 regulatory T cells are more abundant in first pregnancies and associated with placental malaria.

BACKGROUND: Malaria in pregnancy (MIP) causes higher morbidity in primigravid compared to multigravid women; however, the correlates and mechanisms underlying this gravidity-dependent protection remain incompletely understood. We aimed to compare the cellular immune response between primigravid and multigravid women living in a malaria-endemic region and assess for correlates of protection against MIP. METHODS: We characterised the second trimester cellular immune response among 203 primigravid and multigravid pregnant women enrolled in two clinical trials of chemoprevention in eastern Uganda, utilizing RNA sequencing, flow cytometry, and functional assays. We compared responses across gravidity and determined associations with parasitaemia during pregnancy and placental malaria. FINDINGS: Using whole blood RNA sequencing, no significant differentially expressed genes were identified between primigravid (n = 12) and multigravid (n = 11) women overall (log 2(FC) > 2, FDR < 0.1). However, primigravid (n = 49) women had higher percentages of malaria-specific, non-naïve CD4+ T cells that co-expressed IL-10 and IFNγ compared with multigravid (n = 85) women (p = 0.000023), and higher percentages of these CD4+ T cells were associated with greater risks of parasitaemia in pregnancy (Rs = 0.49, p = 0.001) and placental malaria (p = 0.0073). These IL-10 and IFNγ co-producing CD4+ T cells had a genomic signature of Tr1 cells, including expression of transcription factors cMAF and BATF and cell surface makers CTLA4 and LAG-3. INTERPRETATION: Malaria-specific Tr1 cells were highly prevalent in primigravid Ugandan women, and their presence correlated with a higher risk of malaria in pregnancy. Understanding whether suppression of Tr1 cells plays a role in naturally acquired gravidity-dependent immunity may aid the development of new vaccines or treatments for MIP. FUNDING: This work was funded by NIH (PO1 HD059454, U01 AI141308, U19 AI089674, U01 AI155325, U01 AI150741), the March of Dimes (Basil O'Connor award), and the Bill and Melinda Gates Foundation (OPP 1113682).

Nucleic acid biomarkers of immune response and cell and tissue damage in children with COVID-19 and MIS-C.

Differential host responses in coronavirus disease 2019 (COVID-19) and multisystem inflammatory syndrome in children (MIS-C) remain poorly characterized. Here, we use next-generation sequencing to longitudinally analyze blood samples from pediatric patients with COVID-19 or MIS-C across three hospitals. Profiling of plasma cell-free nucleic acids uncovers distinct signatures of cell injury and death between COVID-19 and MIS-C, with increased multiorgan involvement in MIS-C encompassing diverse cell types, including endothelial and neuronal cells, and an enrichment of pyroptosis-related genes. Whole-blood RNA profiling reveals upregulation of similar pro-inflammatory pathways in COVID-19 and MIS-C but also MIS-C-specific downregulation of T cell-associated pathways. Profiling of plasma cell-free RNA and whole-blood RNA in paired samples yields different but complementary signatures for each disease state. Our work provides a systems-level view of immune responses and tissue damage in COVID-19 and MIS-C and informs future development of new disease biomarkers.

Big Data in Nephrology.

A huge array of data in nephrology is collected through patient registries, large epidemiological studies, electronic health records, administrative claims, clinical trial repositories, mobile health devices and molecular databases. Application of these big data, particularly using machine-learning algorithms, provides a unique opportunity to obtain novel insights into kidney diseases, facilitate personalized medicine and improve patient care. Efforts to make large volumes of data freely accessible to the scientific community, increased awareness of the importance of data sharing and the availability of advanced computing algorithms will facilitate the use of big data in nephrology. However, challenges exist in accessing, harmonizing and integrating datasets in different formats from disparate sources, improving data quality and ensuring that data are secure and the rights and privacy of patients and research participants are protected. In addition, the optimism for data-driven breakthroughs in medicine is tempered by scepticism about the accuracy of calibration and prediction from in silico techniques. Machine-learning algorithms designed to study kidney health and diseases must be able to handle the nuances of this specialty, must adapt as medical practice continually evolves, and must have global and prospective applicability for external and future datasets.

Opportunities and Challenges in Democratizing Immunology Datasets.

The field of immunology is rapidly progressing toward a systems-level understanding of immunity to tackle complex infectious diseases, autoimmune conditions, cancer, and beyond. In the last couple of decades, advancements in data acquisition techniques have presented opportunities to explore untapped areas of immunological research. Broad initiatives are launched to disseminate the datasets siloed in the global, federated, or private repositories, facilitating interoperability across various research domains. Concurrently, the application of computational methods, such as network analysis, meta-analysis, and machine learning have propelled the field forward by providing insight into salient features that influence the immunological response, which was otherwise left unexplored. Here, we review the opportunities and challenges in democratizing datasets, repositories, and community-wide knowledge sharing tools. We present use cases for repurposing open-access immunology datasets with advanced machine learning applications and more.

Application of Machine Learning for Cytometry Data.

Modern cytometry technologies present opportunities to profile the immune system at a single-cell resolution with more than 50 protein markers, and have been widely used in both research and clinical settings. The number of publicly available cytometry datasets is growing. However, the analysis of cytometry data remains a bottleneck due to its high dimensionality, large cell numbers, and heterogeneity between datasets. Machine learning techniques are well suited to analyze complex cytometry data and have been used in multiple facets of cytometry data analysis, including dimensionality reduction, cell population identification, and sample classification. Here, we review the existing machine learning applications for analyzing cytometry data and highlight the importance of publicly available cytometry data that enable researchers to develop and validate machine learning methods.

A robust and interpretable end-to-end deep learning model for cytometry data.

Cytometry technologies are essential tools for immunology research, providing high-throughput measurements of the immune cells at the single-cell level. Existing approaches in interpreting and using cytometry measurements include manual or automated gating to identify cell subsets from the cytometry data, providing highly intuitive results but may lead to significant information loss, in that additional details in measured or correlated cell signals might be missed. In this study, we propose and test a deep convolutional neural network for analyzing cytometry data in an end-to-end fashion, allowing a direct association between raw cytometry data and the clinical outcome of interest. Using nine large cytometry by time-of-flight mass spectrometry or mass cytometry (CyTOF) studies from the open-access ImmPort database, we demonstrated that the deep convolutional neural network model can accurately diagnose the latent cytomegalovirus (CMV) in healthy individuals, even when using highly heterogeneous data from different studies. In addition, we developed a permutation-based method for interpreting the deep convolutional neural network model. We were able to identify a CD27- CD94+ CD8+ T cell population significantly associated with latent CMV infection, confirming the findings in previous studies. Finally, we provide a tutorial for creating, training, and interpreting the tailored deep learning model for cytometry data using Keras and TensorFlow (https://github.com/hzc363/DeepLearningCyTOF).

Open Data Revolution in Clinical Research: Opportunities and Challenges.

Efforts for sharing individual clinical data are gaining momentum due to a heightened recognition that integrated data sets can catalyze biomedical discoveries and drug development. Among the benefits are the fact that data sharing can help generate and investigate new research hypothesis beyond those explored in the original study. Despite several accomplishments establishing public systems and guidance for data sharing in clinical trials, this practice is not the norm. Among the reasons are ethical challenges, such as privacy of individuals, data ownership, and control. This paper creates awareness of the potential benefits and challenges of sharing individual clinical data, how to overcome these challenges, and how as a clinical pharmacology community we can shape future directions in this field.

Assessment of Postdonation Outcomes in US Living Kidney Donors Using Publicly Available Data Sets.

Importance: There are limited resources providing postdonation conditions that can occur in living donors (LDs) of solid-organ transplant. Consequently, it is difficult to visualize and understand possible postdonation outcomes in LDs. Objective: To assemble an open access resource that is representative of the demographic characteristics in the US national registry, maintained by the Organ Procurement and Transplantation Network and administered by the United Network for Organ Sharing, but contains more follow-up information to help to examine postdonation outcomes in LDs. Design, Setting, and Participants: Cohort study in which the data for the resource and analyses stemmed from the transplant data set derived from 27 clinical studies from the ImmPort database, which is an open access repository for clinical studies. The studies included data collected from 1963 to 2016. Data from the United Network for Organ Sharing Organ Procurement and Transplantation Network national registry collected from October 1987 to March 2016 were used to determine representativeness. Data analysis took place from June 2016 to May 2018. Data from 20 ImmPort clinical studies (including clinical trials and observational studies) were curated, and a cohort of 11 263 LDs was studied, excluding deceased donors, LDs with 95% or more missing data, and studies without a complete data dictionary. The harmonization process involved the extraction of common features from each clinical study based on categories that included demographic characteristics as well as predonation and postdonation data. Main Outcomes and Measures: Thirty-six postdonation events were identified, represented, and analyzed via a trajectory network analysis. Results: The curated data contained 10 869 living kidney donors (median [interquartile range] age, 39 [31-48] years; 6175 [56.8%] women; and 9133 [86.6%] of European descent). A total of 9558 living kidney donors with postdonation data were analyzed. Overall, 1406 LDs (14.7%) had postdonation events. The 4 most common events were hypertension (806 [8.4%]), diabetes (190 [2.0%]), proteinuria (171 [1.8%]), and postoperative ileus (147 [1.5%]). Relatively few events (n = 269) occurred before the 2-year postdonation mark. Of the 1746 events that took place 2 years or more after donation, 1575 (90.2%) were nonsurgical; nonsurgical conditions tended to occur in the wide range of 2 to 40 years after donation (odds ratio, 38.3; 95% CI, 4.12-1956.9). Conclusions and Relevance: Most events that occurred more than 2 years after donation were nonsurgical and could occur up to 40 years after donation. Findings support the construction of a national registry for long-term monitoring of LDs and confirm the value of secondary reanalysis of clinical studies.

A pilot study showing a stronger H1N1 influenza vaccination response during pregnancy in women who subsequently deliver preterm.

PROBLEM: Preterm birth (PTB), or the delivery of an infant prior to 37 weeks of gestation, is a major health concern. Although a variety of social, environmental, and maternal factors have been implicated in PTB, causes of preterm labor have remained largely unknown. There is evidence of effectiveness and safety of influenza vaccination during pregnancy, however fewer studies have looked at vaccination response as an indicator of an innate host response that may be associated with adverse pregnancy outcomes. We carried out a pilot study to analyze the flu vaccine response during pregnancy of women who later deliver preterm or term. METHOD OF STUDY: We performed a secondary analysis of the individual-level data from an influenza vaccination response study (openly available from ImmPort) measured by hemagglutination inhibition assay of 91 pregnant women with term deliveries and 11 women who went on to deliver preterm. Flu vaccination responses for H1N1 and H3N2 influenza strains were compared between term and preterm deliveries. RESULTS: Women who went on to deliver preterm showed a significantly (P < 0.001) greater flu vaccine response for the H1N1 strain than women who delivered at term. The vaccine response for H3N2 was not significantly different between these two groups (P = 0.97). CONCLUSIONS: Although the sample size is limited and additional validation is required, our findings suggest an increased activation of the maternal immune system as shown by the stronger vaccination response to H1N1 in women who subsequently delivered preterm, in comparison to women who delivered at term.

Prototype of running clinical trials in an untrustworthy environment using blockchain.

Monitoring and ensuring the integrity of data within the clinical trial process is currently not always feasible with the current research system. We propose a blockchain-based system to make data collected in the clinical trial process immutable, traceable, and potentially more trustworthy. We use raw data from a real completed clinical trial, simulate the trial onto a proof of concept web portal service, and test its resilience to data tampering. We also assess its prospects to provide a traceable and useful audit trail of trial data for regulators, and a flexible service for all members within the clinical trials network. We also improve the way adverse events are currently reported. In conclusion, we advocate that this service could offer an improvement in clinical trial data management, and could bolster trust in the clinical research process and the ease at which regulators can oversee trials.

The 10,000 Immunomes Project: Building a Resource for Human Immunology.

There is increasing appreciation that the immune system plays critical roles not only in the traditional domains of infection and inflammation but also in many areas of biology, including tumorigenesis, metabolism, and even neurobiology. However, one of the major barriers for understanding human immunological mechanisms is that immune assays have not been reproducibly characterized for a sufficiently large and diverse healthy human cohort. Here, we present the 10,000 Immunomes Project (10KIP), a framework for growing a diverse human immunology reference, from ImmPort, a publicly available resource of subject-level immunology data. Although some measurement types are sparse in the presently deposited ImmPort database, the extant data allow for a diversity of robust comparisons. Using 10KIP, we describe variations in serum cytokines and leukocytes by age, race, and sex; define a baseline cell-cytokine network; and describe immunologic changes in pregnancy. All data in the resource are available for visualization and download at http://10kimmunomes.org/.

MetaCyto: A Tool for Automated Meta-analysis of Mass and Flow Cytometry Data.

While meta-analysis has demonstrated increased statistical power and more robust estimations in studies, the application of this commonly accepted methodology to cytometry data has been challenging. Different cytometry studies often involve diverse sets of markers. Moreover, the detected values of the same marker are inconsistent between studies due to different experimental designs and cytometer configurations. As a result, the cell subsets identified by existing auto-gating methods cannot be directly compared across studies. We developed MetaCyto for automated meta-analysis of both flow and mass cytometry (CyTOF) data. By combining clustering methods with a silhouette scanning method, MetaCyto is able to identify commonly labeled cell subsets across studies, thus enabling meta-analysis. Applying MetaCyto across a set of ten heterogeneous cytometry studies totaling 2,926 samples enabled us to identify multiple cell populations exhibiting differences in abundance between demographic groups. Software is released to the public through Bioconductor (http://bioconductor.org/packages/release/bioc/html/MetaCyto.html).

Whole-genome sequencing of Atacama skeleton shows novel mutations linked with dysplasia.

Over a decade ago, the Atacama humanoid skeleton (Ata) was discovered in the Atacama region of Chile. The Ata specimen carried a strange phenotype-6-in stature, fewer than expected ribs, elongated cranium, and accelerated bone age-leading to speculation that this was a preserved nonhuman primate, human fetus harboring genetic mutations, or even an extraterrestrial. We previously reported that it was human by DNA analysis with an estimated bone age of about 6-8 yr at the time of demise. To determine the possible genetic drivers of the observed morphology, DNA from the specimen was subjected to whole-genome sequencing using the Illumina HiSeq platform with an average 11.5× coverage of 101-bp, paired-end reads. In total, 3,356,569 single nucleotide variations (SNVs) were found as compared to the human reference genome, 518,365 insertions and deletions (indels), and 1047 structural variations (SVs) were detected. Here, we present the detailed whole-genome analysis showing that Ata is a female of human origin, likely of Chilean descent, and its genome harbors mutations in genes (COL1A1, COL2A1, KMT2D, FLNB, ATR, TRIP11, PCNT) previously linked with diseases of small stature, rib anomalies, cranial malformations, premature joint fusion, and osteochondrodysplasia (also known as skeletal dysplasia). Together, these findings provide a molecular characterization of Ata's peculiar phenotype, which likely results from multiple known and novel putative gene mutations affecting bone development and ossification.

ImmPort, toward repurposing of open access immunological assay data for translational and clinical research.

Immunology researchers are beginning to explore the possibilities of reproducibility, reuse and secondary analyses of immunology data. Open-access datasets are being applied in the validation of the methods used in the original studies, leveraging studies for meta-analysis, or generating new hypotheses. To promote these goals, the ImmPort data repository was created for the broader research community to explore the wide spectrum of clinical and basic research data and associated findings. The ImmPort ecosystem consists of four components-Private Data, Shared Data, Data Analysis, and Resources-for data archiving, dissemination, analyses, and reuse. To date, more than 300 studies have been made freely available through the Shared Data portal (www.immport.org/immport-open), which allows research data to be repurposed to accelerate the translation of new insights into discoveries.

Precision annotation of digital samples in NCBI's gene expression omnibus.

The Gene Expression Omnibus (GEO) contains more than two million digital samples from functional genomics experiments amassed over almost two decades. However, individual sample meta-data remains poorly described by unstructured free text attributes preventing its largescale reanalysis. We introduce the Search Tag Analyze Resource for GEO as a web application (http://STARGEO.org) to curate better annotations of sample phenotypes uniformly across different studies, and to use these sample annotations to define robust genomic signatures of disease pathology by meta-analysis. In this paper, we target a small group of biomedical graduate students to show rapid crowd-curation of precise sample annotations across all phenotypes, and we demonstrate the biological validity of these crowd-curated annotations for breast cancer. STARGEO.org makes GEO data findable, accessible, interoperable and reusable (i.e., FAIR) to ultimately facilitate knowledge discovery. Our work demonstrates the utility of crowd-curation and interpretation of open 'big data' under FAIR principles as a first step towards realizing an ideal paradigm of precision medicine.

Murine glomerular transcriptome links endothelial cell-specific molecule-1 deficiency with susceptibility to diabetic nephropathy.

Diabetic nephropathy (DN) is the leading cause of kidney disease; however, there are no early biomarkers and no cure. Thus, there is a large unmet need to predict which individuals will develop nephropathy and to understand the molecular mechanisms that govern this susceptibility. We compared the glomerular transcriptome from mice with distinct susceptibilities to DN at four weeks after induction of diabetes, but before histologic injury, and identified differential regulation of genes that modulate inflammation. From these genes, we identified endothelial cell specific molecule-1 (Esm-1), as a glomerular-enriched determinant of resistance to DN. Glomerular Esm-1 mRNA and protein were lower in DN-susceptible, DBA/2, compared to DN-resistant, C57BL/6, mice. We demonstrated higher Esm-1 secretion from primary glomerular cultures of diabetic mice, and high glucose was sufficient to increase Esm-1 mRNA and protein secretion in both strains of mice. However, induction was significantly attenuated in DN-susceptible mice. Urine Esm-1 was also significantly higher only in DN-resistant mice. Moreover, using intravital microscopy and a biomimetic microfluidic assay, we showed that Esm-1 inhibited rolling and transmigration in a dose-dependent manner. For the first time we have uncovered glomerular-derived Esm-1 as a potential non-invasive biomarker of DN. Esm-1 inversely correlates with disease susceptibility and inhibits leukocyte infiltration, a critical factor in protecting the kidney from DN.

RImmPort: an R/Bioconductor package that enables ready-for-analysis immunology research data.

Summary: : Open access to raw clinical and molecular data related to immunological studies has created a tremendous opportunity for data-driven science. We have developed RImmPort that prepares NIAID-funded research study datasets in ImmPort (immport.org) for analysis in R. RImmPort comprises of three main components: (i) a specification of R classes that encapsulate study data, (ii) foundational methods to load data of a specific study and (iii) generic methods to slice and dice data across different dimensions in one or more studies. Furthermore, RImmPort supports open formalisms, such as CDISC standards on the open source bioinformatics platform Bioconductor, to ensure that ImmPort curated study datasets are seamlessly accessible and ready for analysis, thus enabling innovative bioinformatics research in immunology. Availability and Implementation: RImmPort is available as part of Bioconductor (bioconductor.org/packages/RImmPort). Contact: rshankar@stanford.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

VDJML: a file format with tools for capturing the results of inferring immune receptor rearrangements.

BACKGROUND: The genes that produce antibodies and the immune receptors expressed on lymphocytes are not germline encoded; rather, they are somatically generated in each developing lymphocyte by a process called V(D)J recombination, which assembles specific, independent gene segments into mature composite genes. The full set of composite genes in an individual at a single point in time is referred to as the immune repertoire. V(D)J recombination is the distinguishing feature of adaptive immunity and enables effective immune responses against an essentially infinite array of antigens. Characterization of immune repertoires is critical in both basic research and clinical contexts. Recent technological advances in repertoire profiling via high-throughput sequencing have resulted in an explosion of research activity in the field. This has been accompanied by a proliferation of software tools for analysis of repertoire sequencing data. Despite the widespread use of immune repertoire profiling and analysis software, there is currently no standardized format for output files from V(D)J analysis. Researchers utilize software such as IgBLAST and IMGT/High V-QUEST to perform V(D)J analysis and infer the structure of germline rearrangements. However, each of these software tools produces results in a different file format, and can annotate the same result using different labels. These differences make it challenging for users to perform additional downstream analyses. RESULTS: To help address this problem, we propose a standardized file format for representing V(D)J analysis results. The proposed format, VDJML, provides a common standardized format for different V(D)J analysis applications to facilitate downstream processing of the results in an application-agnostic manner. The VDJML file format specification is accompanied by a support library, written in C++ and Python, for reading and writing the VDJML file format. CONCLUSIONS: The VDJML suite will allow users to streamline their V(D)J analysis and facilitate the sharing of scientific knowledge within the community. The VDJML suite and documentation are available from https://vdjserver.org/vdjml/ . We welcome participation from the community in developing the file format standard, as well as code contributions.