Computational analysis of large-scale metagenomics sequencing datasets provides valuable isolate-level taxonomic and functional insights from complex microbial communities. However, the ever-expanding ecosystem of metagenomics-specific methods and file formats makes designing scalable workflows and seamlessly exploring output data increasingly challenging. Although one-click bioinformatics pipelines can help organize these tools into workflows, they face compatibility and maintainability challenges that can prevent replication. To address the gap in easily extensible yet robustly distributable metagenomics workflows, we have developed the Core Analysis Modular Pipeline (CAMP), a module-based metagenomics analysis system written in Snakemake, with a standardized module and directory architecture. Each module can run independently or in sequence to produce target data formats (e.g. short-read preprocessing alone or followed by de novo assembly), and provides output summary statistics reports and Jupyter notebook-based visualizations. We applied CAMP to a set of 10 metagenomics samples, demonstrating how a modular analysis system with built-in data visualization facilitates rich seamless communication between outputs from different analytical purposes. The CAMP ecosystem (module template and analysis modules) can be found at https://github.com/Meta-CAMP.

RNA viruses represent an integral component of human-associated environments and human health. However, the ecology of environmental RNA viruses remains largely unexplored. Here, we analyzed 2922 metatranscriptomic samples collected from urban and surrounding environments-including human-dense settings (e.g., transit hubs, hospitals, banks), alongside peri-urban settings - across 102 cities in 31 countries and constructed the Urban & Peri-urban RNA Virus Atlas (UPVAtlas), comprising 54,945 RNA viruses, 77% of which had not been previously observed. Phylogenetic reconstruction based on RNA-dependent RNA polymerases from UPVAtlas greatly expanded the evolutionary diversity of RNA viruses, leading to the identification of two potential candidate phyla, one candidate class, and several unclassified clades. Host association analyses further revealed the ecological complexity of environmental RNA viruses, with the diversity of vertebrate-related and ESKAPE pathogen-related viruses underscoring the importance of continued monitoring of urban environments for tracking RNA viral prevalence and dynamics, with direct relevance to future public health.

SUMMARY:Whole-exome (WES) and whole-genome (WGS) sequencing are rapidly becoming preferred methods for population-scale analysis of the human genetic landscape. However, there are currently no standardized quality control (QC) pipelines for human WES and WGS datasets. In this paper, we present WxS-QC, a powerful, scalable, and convenient pipeline for the QC of human germline short-variant WGS and WES cohorts for population-scale analyses. Our pipeline is suitable for both rare-variant discovery and common-variant association studies. It is based on deeply refactored gnomAD v3 and v4 quality control pipelines, contains several methods we have developed de novo, and is aligned with current best practices in WGS/WES germline cohort QC. We provide all methods in a single codebase, aligned to work together and controlled via a single YAML config, with automatic export of resulting graphs and summary tables, excellent performance and scalability, and comprehensive documentation. The pipeline can run in any UNIX-like environment and can efficiently process cohorts of up to 200 000 whole-exome samples, with the potential to handle bigger datasets. AVAILABILITY AND IMPLEMENTATION:The pipeline code is written in Python using the Hail library and is freely available under the BSD-3 license here: https://github.com/wtsi-hgi/wxs-qc. The detailed description of the pipeline is available in the pipeline documentation: https://github.com/wtsi-hgi/wxs-qc/blob/main/README.md. We also provide an open dataset with all required metadata, which is available at https://wxs-qc-data.cog.sanger.ac.uk/wxs-qc_public_dataset_v3.tar. An example of test dataset analysis is available in the supplementary materials.

Metagenomics has revolutionized our understanding of microbial communities, offering unprecedented insights into their genetic and functional diversity across Earth’s diverse ecosystems. Beyond their roles as environmental constituents, microbiomes act as symbionts, profoundly influencing the health and function of their host organisms. Given the inherent complexity of these communities and the diverse environments where they reside, the components of a metagenomics study must be carefully tailored to yield accurate results that are representative of the populations of interest. This Primer examines the methodological advancements and current practices that have shaped the field, from initial stages of sample collection and DNA extraction to the advanced bioinformatics tools employed for data analysis, with a particular focus on the profound impact of next-generation sequencing on the scale and accuracy of metagenomics studies. We critically assess the challenges and limitations inherent in metagenomics experimentation, available technologies and computational analysis methods. Beyond technical methodologies, we explore the application of metagenomics across various domains, including human health, agriculture and environmental monitoring. Looking ahead, we advocate for the development of more robust computational frameworks and enhanced interdisciplinary collaborations. This Primer serves as a comprehensive guide for advancing the precision and applicability of metagenomic studies, positioning them to address the complexities of microbial ecology and their broader implications for human health and environmental sustainability. Metagenomics describes the analysis of genetic information in a microbial community to provide taxonomic or functional information on the constituent microorganisms. This Primer describes suitable sample types, sampling handling and processing workflows for metagenomics, and gives a detailed discussion of the various analysis techniques to generate meaningful information from metagenomic data.

BACKGROUND:Microbiome-based disease prediction has significant potential as an early, noninvasive marker of multiple health conditions linked to dysbiosis of the human gut microbiota, thanks in part to decreasing sequencing and analysis costs. Microbiome health indices and other computational tools currently proposed in the field often are based on a microbiome's species richness and are completely reliant on taxonomic classification. A resurgent interest in a metabolism-centric, ecological approach has led to an increased understanding of microbiome metabolic and phenotypic complexity, revealing substantial restrictions of taxonomy-reliant approaches. FINDINGS:In this study, we introduce a new metagenomic health index developed as an answer to recent developments in microbiome definitions, in an effort to distinguish between healthy and unhealthy microbiomes, here in focus, inflammatory bowel disease (IBD). The novelty of our approach is a shift from a traditional Linnean phylogenetic classification toward a more holistic consideration of the metabolic functional potential underlining ecological interactions between species. Based on well-explored data cohorts, we compare our method and its performance with the most comprehensive indices to date, the taxonomy-based Gut Microbiome Health Index (GMHI), and the high-dimensional principal component analysis (hiPCA) methods, as well as to the standard taxon- and function-based Shannon entropy scoring. After demonstrating better performance on the initially targeted IBD cohorts, in comparison with other methods, we retrain our index on an additional 27 datasets obtained from different clinical conditions and validate our index's ability to distinguish between healthy and disease states using a variety of complementary benchmarking approaches. Finally, we demonstrate its superiority over the GMHI and the hiPCA on a longitudinal COVID-19 cohort and highlight the distinct robustness of our method to sequencing depth. CONCLUSIONS:Overall, we emphasize the potential of this metagenomic approach and advocate a shift toward functional approaches to better understand and assess microbiome health as well as provide directions for future index enhancements. Our method, q2-predict-dysbiosis (Q2PD), is freely available (https://github.com/Kizielins/q2-predict-dysbiosis).

Motivation Computational analysis of large-scale metagenomics sequencing datasets has proved to be both incredibly valuable for extracting isolate-level taxonomic and functional insights from complex microbial communities. However, thanks to an ever-expanding ecosystem of metagenomics-specific algorithms and file formats, designing studies, implementing seamless and scalable end-to-end workflows, and exploring the massive amounts of output data have become studies unto themselves. Furthermore, there is little inter-communication between output data of different analytic purposes, such as short-read classification and metagenome assembled genomes (MAG) reconstruction. One-click pipelines have helped to organize these tools into targeted workflows, but they suffer from general compatibility and maintainability issues. Results To address the gap in easily extensible yet robustly distributable metagenomics workflows, we have developed a module-based metagenomics analysis system written in Snakemake, a popular workflow management system, along with a standardized module and working directory architecture. Each module can be run independently or conjointly with a series of others to produce the target data format (ex. short-read preprocessing alone, or short-read preprocessing followed by de novo assembly), and outputs aggregated summary statistics reports and semi-guided Jupyter notebook-based visualizations, The module system is a bioinformatics-optimzied scaffold designed to be rapidly iterated upon by the research community at large. Availability The module template as well as the modules described below can be found at https://github.com/MetaSUB-CAMP . Contact lam4003@med.cornell.edu , btt4001@med.cornell.edu , chm2042@med.cornell.edu , or imh2003@med.cornell.edu Supplementary information Supplementary data are available at Bioinformatics online.

Whole-exome (WES) and whole-genome (WGS) sequencing are rapidly becoming preferred methods for population-scale analysis of the human genetic landscape. However, there are currently no standardized quality control (QC) pipelines for human WES and WGS datasets. Moveover, there are no open datasets that can be used to test QC pipelines, because most projects (like 1000 genomes and gnomAD) publish only post-QC results. We present WxS-QC, a powerful, scalable, and convenient pipeline for the QC of human WGS and WES cohorts, developed at the Wellcome Sanger Institute (WSI). Our pipeline is based on a deep refactoring of the gnomAD quality control pipeline code and is aligned with current best practices in WGS/WES cohort data QC. It offers a set of novel QC techniques, automatic export of resulting graphs and summary tables, excellent performance and scalability, and incorporates comprehensive documentation. To test our pipeline and similar solutions, we also assembled an open dataset with all required metadata. The pipeline code is written in Python using the Hail library and is freely available under the BSD-3 license here: https://github.com/wtsi-hgi/wxs-qc . It can run in any UNIX-like environment and has been able to efficiently process cohorts of up to 200,000 whole-exome samples with the potential to handle bigger datasets, depending on the available hardware. The detailed description of the pipeline, resources and test data is available in the pipeline documentation: https://github.com/wtsi-hgi/wxs-qc/blob/main/README.md The open test dataset is available to download from https://wxs-qc-data.cog.sanger.ac.uk/wxs-qc_public_dataset_v3.tar . An example of test dataset analysis is available in the supplementary materials.

Forensic microbiology is a subject of interest and research development across the forensic community. Studies of pathogen outbreaks, biocrime or bioterrorism attacks, and analyses of crime scenes fall within this field. Significant progress has been made in evolving DNA sequencing technologies in recent decades. These newly emerged transformative tools have become available to both biomedicine and forensics. Based on the published literature, this review provides an overview of the current trends and developments of NGS (next-generation sequencing) technologies applied to forensic microbiology. These new methods present numerous advantages over traditional sequencing technology and are useful in several practical applications within this forensic field. This article then examines the main challenges and limitations of this technology in forensics, providing a comprehensive review of NGS technology capabilities in expanding the precision and effectiveness of microbial forensic investigations—with the aim of inspiring scientists, forensic experts, lawyers, public health professionals, and policymakers alike to approach this newly powerful sequencing tool appropriately.

Objective: Pre-eclampsia (PE) is a syndrome that affects multiple organ systems and is the most severe hypertensive disorder in pregnancy. It frequently leads to preterm delivery, maternal and fetal morbidity and mortality and life-long complications. We currently lack efficient screening tools and early therapies to address PE. Design and method: To identify candidate biomarkers and operative pathways in early onset PE (eoPE, severe PE with onset before week 34), we performed spatio-temporal multi-omics profiling of human eoPE placentae and healthy controls, and validated targets in early gestation in a longitudinal clinical cohort. We used a single-nuclei RNA-sequencing combined with spatial proteo- and transcriptomics and mechanistic in vitro signalling analyses to bridge the gap from late pregnancy disease to early pregnancy pathomechanisms. Results: We discovered a key disruption in villous trophoblast differentiation, which is driven by the increase of transcriptional coactivator p300, that ultimately ends with a senescence-associated secretory phenotype (SASP) of trophoblasts in eoPE. We found a significant increase in the senescence markers in preeclamptic maternal serum in early gestation and late gestation, before the development of clinical symptoms, indicating that the placental syndrome drives systemic maternal syndrome, even before clinical manifestation of eoPE. Conclusions: Our work describes a new disease progression model, starting with dysregulated transition in villous trophoblast differentiation. Our study identifies potential pathophysiology-relevant biomarkers for the early diagnosis of the disease as well as possible targets for interventions, which would be crucial steps toward protecting the mother and child from gestational mortality and morbidity and an increased risk of cardiovascular disease later in life.

Objective: Preeclampsia (PE), the most common pregnancy disease, is the main cause for maternal and fetal mortality and morbidity worldwide. It is characterized by a sudden increase in blood pressure in combination with signs of end organ damage, e.g. proteinuria. The mechanisms how pathological pregnancy leads to future cardiovascular morbidity are poorly understood. There are number of risk factors shared between PE and cardiovascular disease such as endothelial dysfunction, obesity, hypertension, thus, leading to a hypothesis that metabolic disturbance may be a possible underlying common mechanism. More nuanced changes in biological processes postpartum in heart after PE are often overlooked and require comprehensive animal models to detect dysregulated metabolic pathways. Design and method: We used well-established transgenic rat model for PE and RNA-sequencing to create expression profiles at different time points (non-pregnant, end of pregnancy and postpartum) of preeclamptic rats and healthy controls. We performed bioinformatical analysis to identify differentially expressed genes for different groups of comparisons (physiological and pathological changes) in 4 heart regions. We used gene set enrichment analysis to identify affected biological processes with comparison to existing heart failure models. To support gene expression data and identify the most affected heart regions we also used speckle tracking echocardiography and immunostaining. Results: We found that all heart regions of PE animals are still affected post-partum (PEpp), while healthy controls recover after pregnancy (WTpp). Data from echocardiography and histological staining support the hypothesis of persistent cardiovascular changes after PE. Using RNAseq data, 39 genes were found to be differentially expressed in all heart regions of the heart of preeclamptic rats compared to the controls (adjusted p-value < 0.05, |logFC| > 2). Among the most disrupted pathways are fatty acid metabolism, oxidative phosphorilation and interferon-alpha/gamma responses. Conclusions: Our expression data supports the hypothesis that preterm metabolic aging of the heart could be the reason for increased cardiovascular risk after a preeclamptic pregnancy.

BACKGROUND: Preeclampsia is a severe hypertensive disorder in pregnancy that causes preterm delivery, maternal and fetal morbidity, mortality, and life-long sequelae. Understanding the pathogenesis of preeclampsia is a critical first step toward protecting mother and child from this syndrome and increased risk of cardiovascular disease later in life. However, effective early predictive tests and therapies for preeclampsia are scarce. METHODS: To identify novel markers and signaling pathways for early onset preeclampsia, we profiled human maternal-fetal interface units (fetal villi and maternal decidua) from early onset preeclampsia and healthy controls using single-nucleus RNA sequencing combined with spatial transcriptomics. The placental syncytiotrophoblast is in direct contact with maternal blood and forms the barrier between fetal and maternal circulation. RESULTS: We identified different transcriptomic states of the endocrine syncytiotrophoblast nuclei with patterns of dysregulation associated with a senescence-associated secretory phenotype and a spatial dysregulation of senescence in the placental trophoblast layer. Elevated senescence markers were validated in placental tissues of clinical multicenter cohorts. Importantly, several secreted senescence-associated secretory phenotype factors were elevated in maternal blood already in the first trimester. We verified the secreted senescence markers, PAI-1 (plasminogen activator inhibitor 1) and activin A, as identified in our single-nucleus RNA sequencing model as predictive markers before clinical preeclampsia diagnosis. CONCLUSIONS: This indicates that increased syncytiotrophoblast senescence appears weeks before clinical manifestation of early onset preeclampsia, suggesting that the dysregulated preeclamptic placenta starts with higher cell maturation resulting in premature and increased senescence-associated secretory phenotype release. These senescence-associated secretory phenotype markers may serve as an additional early diagnostic tool for this syndrome.

Conflicts and natural disasters affect entire populations of the countries involved and, in addition to the thousands of lives destroyed, have a substantial negative impact on the scientific advances these countries provide. The unprovoked invasion of Ukraine by Russia, the devastating earthquake in Turkey and Syria, and the ongoing conflicts in the Middle East are just a few examples. Millions of people have been killed or displaced, their futures uncertain. These events have resulted in extensive infrastructure collapse, with loss of electricity, transportation, and access to services. Schools, universities, and research centers have been destroyed along with decades' worth of data, samples, and findings. Scholars in disaster areas face short- and long-term problems in terms of what they can accomplish now for obtaining grants and for employment in the long run. In our interconnected world, conflicts and disasters are no longer a local problem but have wide-ranging impacts on the entire world, both now and in the future. Here, we focus on the current and ongoing impact of war on the scientific community within Ukraine and from this draw lessons that can be applied to all affected countries where scientists at risk are facing hardship. We present and classify examples of effective and feasible mechanisms used to support researchers in countries facing hardship and discuss how these can be implemented with help from the international scientific community and what more is desperately needed. Reaching out, providing accessible training opportunities, and developing collaborations should increase inclusion and connectivity, support scientific advancements within affected communities, and expedite postwar and disaster recovery.

Preeclampsia (PE), the most common pregnancy disease, is the main cause for maternal and fetal mortality and morbidity worldwide. The disease is characterized by a sudden increase in blood pressure in combination with signs of end organ damage, e.g. proteinuria and the underlying mechanisms are poorly understood. Immune dysregulation and endothelial dysfunction are important pathomechanisms leading to PE, we hypothesize that anti-angiogenesis and dysregulation of microvascular perfusion postpartum lead to the increased cardiovascular risk in later life. In addition, we will test if the examination of retinal vessels can be used as a diagnostic window for early risk prediction of cardiovascular alterations. In a well-established transgenic rat model for PE, we performed speckle tracking echocardiography, immunostaining, RNA sequencing and small vessel imaging by micro-computed tomography of the heart and the eyes in former preeclamptic rats (PEpp) and healthy controls (WTpp). A reduction of cardiac vessels (AUC 0-200 μm: WTpp 77.19±4.64 vs. PEpp 62.59±2.72) in combination with an increase of small vessels in the retina (AUC 0-50 μm: WTpp 52.89±1.47 vs. PEpp 59.54±1.09) is shown postpartum. Data from echocardiography (Global longitudinal strain: WTpp -22.03±2.47 % vs. PEpp -14.50±1.80 %) and histological staining (CD31: WTpp 292.7±30.9 per fov vs. PEpp 219.0±10.5 per fov) support the hypothesis of persistent cardiovascular alterations after PE. Performing RNAseq, 65 genes were found to be permanently altered in the left ventricle of the heart of preeclamptic rats compared to 4 genes in controls. In the retinal tissue, 39 genes in PEpp and no genes in WTpp were identified postpartum compared to never pregnant animals (adjusted p-value < 0.05,

Tissue insults in response to inflammation, hypoxia and ischemia are accompanied by the release of ATP into the extracellular space. There, ATP modulates several pathological processes, including chemotaxis, inflammasome induction and platelet activation. ATP hydrolysis is significantly enhanced in human pregnancy, suggesting that increased conversion of extracellular ATP is an important anti-inflammatory process in preventing exaggerated inflammation, platelet activation and hemostasis in gestation. Extracellular ATP is converted into AMP, and subsequently into adenosine by the two major nucleotide-metabolizing enzymes CD39 and CD73. Here, we aimed to elucidate developmental changes of placental CD39 and CD73 over gestation, compared their expression in placental tissue from patients with preeclampsia and healthy controls, and analyzed their regulation in response to platelet-derived factors and different oxygen conditions in placental explants as well as the trophoblast cell line BeWo. Linear regression analysis showed a significant increase in placental CD39 expression, while at the same time CD73 levels declined at term of pregnancy. Neither maternal smoking during first trimester, fetal sex, maternal age, nor maternal BMI revealed any effects on placental CD39 and CD73 expression. Immunohistochemistry detected both, CD39 and CD73, predominantly in the syncytiotrophoblast layer. Placental CD39 and CD73 expression were significantly increased in pregnancies complicated with preeclampsia, when compared to controls. Cultivation of placental explants under different oxygen conditions had no effect on the ectonucleotidases, whereas presence of platelet releasate from pregnant women led to deregulated CD39 expression. Overexpression of recombinant human CD39 in BeWo cells decreased extracellular ATP levels after culture in presence of platelet-derived factors. Moreover, platelet-derived factors-induced upregulation of the pro-inflammatory cytokine, interleukin-1β, was abolished by CD39 overexpression. Our study shows that placental CD39 is upregulated in preeclampsia, suggesting an increasing demand for extracellular ATP hydrolysis at the utero-placental interface. Increased placental CD39 in response to platelet-derived factors may lead to enhanced conversion of extracellular ATP levels, which in turn could represent an important anti-coagulant defense mechanism of the placenta.

Preeclampsia (PE), the most common pregnancy disease, is the main cause for maternal and fetal mortality and morbidity worldwide. The disease is characterized by a sudden increase in blood pressure in combination with signs of end organ damage, e.g. proteinuria and the underlying mechanisms are poorly understood. Immune dysregulation and endothelial dysfunction are important pathomechanisms leading to PE, we hypothesize that anti-angiogenesis and dysregulation of microvascular perfusion postpartum lead to the increased cardiovascular risk in later life. In addition, we will test if the examination of retinal vessels can be used as a diagnostic window for early risk prediction of cardiovascular alterations. In a well-established transgenic rat model for PE, we performed speckle tracking echocardiography, immunostaining, RNA sequencing and small vessel imaging by micro-computed tomography of the heart and the eyes in former preeclamptic rats (PEpp) and healthy controls (WTpp). A reduction of cardiac vessels (AUC 0-200 μm: WTpp 77.19±4.64 vs. PEpp 62.59±2.72) in combination with an increase of small vessels in the retina (AUC 0-50 μm: WTpp 52.89±1.47 vs. PEpp 59.54±1.09) is shown postpartum. Data from echocardiography (Global longitudinal strain: WTpp -22.03±2.47 % vs. PEpp -14.50±1.80 %) and histological staining (CD31: WTpp 292.7±30.9 per fov vs. PEpp 219.0±10.5 per fov) support the hypothesis of persistent cardiovascular alterations after PE. Performing RNAseq, 65 genes were found to be permanently altered in the left ventricle of the heart of preeclamptic rats compared to 4 genes in controls. In the retinal tissue, 39 genes in PEpp and no genes in WTpp were identified postpartum compared to never pregnant animals (adjusted p-value < 0.05,

Preeclampsia (PE), the most common pregnancy disease, is the main cause for maternal and fetal mortality and morbidity worldwide. It is characterized by a sudden increase in blood pressure in combination with signs of end organ damage, e.g. proteinuria. The mechanisms how pathological pregnancy leads to future cardiovascular morbidity are poorly understood, while there is a lack of comprehensive animal models describing more nuanced biological processes in the heart connected to immune dysregulation and endothelial dysfunction, important pathomechanisms leading to PE. We used well-established transgenic rat model for PE and RNA-sequencing to create expression profiles at different time points (non-pregnant, after-delivery and few weeks later, i.e. post-partum) of preeclamptic rats and healthy controls. We performed bioinformatical analysis to identify differentially expressed genes for different groups of comparisons (physiological and pathological changes) in 4 heart regions. We used gene set enrichment analysis and more advanced techniques for regulatory networks inference (Bayesian networks learning) to identify affected biological processes with comparison to existing heart failure models. We found that all heart regions of PE animals are still affected postpartum, while healthy controls recover after pregnancy. The data give more insights into the long-term consequences of pathological pregnancy and potential biomarkers for prediction.

Introduction: Scientific articles serve as vital sources of biomedical information, but with the yearly growth in publication volume, processing such vast amounts of information has become increasingly challenging. This difficulty is particularly pronounced when it requires the expertise of highly qualified professionals. Our research focused on the domain-specific articles classification to determine whether they contain information about drug-induced liver injury (DILI). DILI is a clinically significant condition and one of the reasons for drug registration failures. The rapid and accurate identification of drugs that may cause such conditions can prevent side effects in millions of patients.Methods: Developing a text classification method can help regulators, such as the FDA, much faster at a massive scale identify facts of potential DILI of concrete drugs. In our study, we compared several text classification methodologies, including transformers, LSTMs, information theory, and statistics-based methods. We devised a simple and interpretable text classification method that is as fast as Naïve Bayes while delivering superior performance for topic-oriented text categorisation. Moreover, we revisited techniques and methodologies to handle the imbalance of the data.Results: Transformers achieve the best results in cases if the distribution of classes and semantics of test data matches the training set. But in cases of imbalanced data, simple statistical-information theory-based models can surpass complex transformers, bringing more interpretable results that are so important for the biomedical domain. As our results show, neural networks can achieve better results if they are pre-trained on domain-specific data, and the loss function was designed to reflect the class distribution.Discussion: Overall, transformers are powerful architecture, however, in certain cases, such as topic classification, its usage can be redundant and simple statistical approaches can achieve compatible results while being much faster and explainable. However, we see potential in combining results from both worlds. Development of new neural network architectures, loss functions and training procedures that bring stability to unbalanced data is a promising topic of development.

Summary Pre-eclampsia (PE) is a syndrome that affects multiple organ systems and is the most severe hypertensive disorder in pregnancy. It frequently leads to preterm delivery, maternal and fetal morbidity and mortality and life-long complications 1 . We currently lack efficient screening tools 2, 3 and early therapies 4, 5 to address PE. To investigate the early stages of early onset PE, and identify candidate markers and pathways, we performed spatio-temporal multi-omics profiling of human PE placentae and healthy controls and validated targets in early gestation in a longitudinal clinical cohort. We used a single-nuclei RNA-seq approach combined with spatial proteo- and transcriptomics and mechanistic in vitro signalling analyses to bridge the gap from late pregnancy disease to early pregnancy pathomechanisms. We discovered a key disruption in villous trophoblast differentiation, which is driven by the increase of transcriptional coactivator p300, that ultimately ends with a senescence-associated secretory phenotype (SASP) of trophoblasts. We found a significant increase in the senescence marker activin A in preeclamptic maternal serum in early gestation, before the development of clinical symptoms, indicating a translation of the placental syndrome to the maternal side. Our work describes a new disease progression, starting with a disturbed transition in villous trophoblast differentiation. Our study identifies potential pathophysiology-relevant biomarkers for the early diagnosis of the disease as well as possible targets for interventions, which would be crucial steps toward protecting the mother and child from gestational mortality and morbidity and an increased risk of cardiovascular disease later in life.

Since February 24, 2022, invasion of Ukraine by Russia has affected the entire population of Ukraine, including the scientific community. The attack by Russian forces has resulted in the destruction of infrastructure of Ukrainian cities, including universities, research centers, and educational institutions. Millions of people, including educators and scientists, have been displaced from their homes, their futures uncertain. This conflict is unfolding in a country with a large and diverse scientific community and a rich history of higher education and scientific research. The global research community has provided effective and timely response by offering research opportunities and fellowships for displaced Ukrainian academic faculty and students. However, many of these opportunities were primarily targeted to individuals capable of leaving the country. Many Ukrainian scholars remaining in the country need the help of the international community to help find short- or long-term employment. Moving forward, we hope to increase collaboration, provision of training provided by educational organizations worldwide, and spur rapid advancements in science and technology that will lead and expedite the postwar reconstruction of Ukraine.