Transcriptomic Differences in Peripheral Monocyte Populations in Septic Patients Based on Outcome.

ABSTRACT: Post-sepsis early mortality is being replaced by survivors who experience either a rapid recovery and favorable hospital discharge or the development of chronic critical illness (CCI) with suboptimal outcomes. The underlying immunological response that determines these clinical trajectories remains poorly defined at the transcriptomic level. As classical and non-classical monocytes are key leukocytes in both the innate and adaptive immune systems, we sought to delineate the transcriptomic response of these cell types. Using single-cell RNA sequencing and pathway analyses, we identified gene expression patterns between these two groups that are consistent with differences in TNFα production based on clinical outcome. This may provide therapeutic targets for those at risk for CCI in order to improve their phenotype/endotype, morbidity, and long-term mortality.

A genomic data archive from the Network for Pancreatic Organ donors with Diabetes.

The Network for Pancreatic Organ donors with Diabetes (nPOD) is the largest biorepository of human pancreata and associated immune organs from donors with type 1 diabetes (T1D), maturity-onset diabetes of the young (MODY), cystic fibrosis-related diabetes (CFRD), type 2 diabetes (T2D), gestational diabetes, islet autoantibody positivity (AAb+), and without diabetes. nPOD recovers, processes, analyzes, and distributes high-quality biospecimens, collected using optimized standard operating procedures, and associated de-identified data/metadata to researchers around the world. Herein describes the release of high-parameter genotyping data from this collection. 372 donors were genotyped using a custom precision medicine single nucleotide polymorphism (SNP) microarray. Data were technically validated using published algorithms to evaluate donor relatedness, ancestry, imputed HLA, and T1D genetic risk score. Additionally, 207 donors were assessed for rare known and novel coding region variants via whole exome sequencing (WES). These data are publicly-available to enable genotype-specific sample requests and the study of novel genotype:phenotype associations, aiding in the mission of nPOD to enhance understanding of diabetes pathogenesis to promote the development of novel therapies.

PaintOmics 4: new tools for the integrative analysis of multi-omics datasets supported by multiple pathway databases.

PaintOmics is a web server for the integrative analysis and visualisation of multi-omics datasets using biological pathway maps. PaintOmics 4 has several notable updates that improve and extend analyses. Three pathway databases are now supported: KEGG, Reactome and MapMan, providing more comprehensive pathway knowledge for animals and plants. New metabolite analysis methods fill gaps in traditional pathway-based enrichment methods. The metabolite hub analysis selects compounds with a high number of significant genes in their neighbouring network, suggesting regulation by gene expression changes. The metabolite class activity analysis tests the hypothesis that a metabolic class has a higher-than-expected proportion of significant elements, indicating that these compounds are regulated in the experiment. Finally, PaintOmics 4 includes a regulatory omics module to analyse the contribution of trans-regulatory layers (microRNA and transcription factors, RNA-binding proteins) to regulate pathways. We show the performance of PaintOmics 4 on both mouse and plant data to highlight how these new analysis features provide novel insights into regulatory biology. PaintOmics 4 is available at https://paintomics.org/.

Integrative analyses of TEDDY Omics data reveal lipid metabolism abnormalities, increased intracellular ROS and heightened inflammation prior to autoimmunity for type 1 diabetes.

BACKGROUND: The Environmental Determinants of Diabetes in the Young (TEDDY) is a prospective birth cohort designed to study type 1 diabetes (T1D) by following children with high genetic risk. An integrative multi-omics approach was used to evaluate islet autoimmunity etiology, identify disease biomarkers, and understand progression over time. RESULTS: We identify a multi-omics signature that was predictive of islet autoimmunity (IA) as early as 1 year before seroconversion. At this time, abnormalities in lipid metabolism, decreased capacity for nutrient absorption, and intracellular ROS accumulation are detected in children progressing towards IA. Additionally, extracellular matrix remodeling, inflammation, cytotoxicity, angiogenesis, and increased activity of antigen-presenting cells are observed, which may contribute to beta cell destruction. Our results indicate that altered molecular homeostasis is present in IA-developing children months before the actual detection of islet autoantibodies, which opens an interesting window of opportunity for therapeutic intervention. CONCLUSIONS: The approach employed herein for assessment of the TEDDY cohort showcases the utilization of multi-omics data for the modeling of complex, multifactorial diseases, like T1D.

Transcriptional Differences for COVID-19 Disease Map Genes between Males and Females Indicate a Different Basal Immunophenotype Relevant to the Disease.

Worldwide COVID-19 epidemiology data indicate differences in disease incidence amongst sex and gender demographic groups. Specifically, male patients are at a higher death risk than female patients, and the older population is significantly more affected than young individuals. Whether this difference is a consequence of a pre-existing differential response to the virus, has not been studied in detail. We created DeCovid, an R shiny app that combines gene expression (GE) data of different human tissue from the Genotype-Tissue Expression (GTEx) project along with the COVID-19 Disease Map and COVID-19 related pathways gene collections to explore basal GE differences across healthy demographic groups. We used this app to study differential gene expression of COVID-19 associated genes in different age and sex groups. We identified that healthy women show higher expression-levels of interferon genes. Conversely, healthy men exhibit higher levels of proinflammatory cytokines. Additionally, young people present a stronger complement system and maintain a high level of matrix metalloproteases than older adults. Our data suggest the existence of different basal immunophenotypes amongst different demographic groups, which are relevant to COVID-19 progression and may contribute to explaining sex and age biases in disease severity. The DeCovid app is an effective and easy to use tool for exploring the GE levels relevant to COVID-19 across demographic groups and tissues.

Harmonization of quality metrics and power calculation in multi-omic studies.

Multi-omic studies combine measurements at different molecular levels to build comprehensive models of cellular systems. The success of a multi-omic data analysis strategy depends largely on the adoption of adequate experimental designs, and on the quality of the measurements provided by the different omic platforms. However, the field lacks a comparative description of performance parameters across omic technologies and a formulation for experimental design in multi-omic data scenarios. Here, we propose a set of harmonized Figures of Merit (FoM) as quality descriptors applicable to different omic data types. Employing this information, we formulate the MultiPower method to estimate and assess the optimal sample size in a multi-omics experiment. MultiPower supports different experimental settings, data types and sample sizes, and includes graphical for experimental design decision-making. MultiPower is complemented with MultiML, an algorithm to estimate sample size for machine learning classification problems based on multi-omic data.

STATegra, a comprehensive multi-omics dataset of B-cell differentiation in mouse.

Multi-omics approaches use a diversity of high-throughput technologies to profile the different molecular layers of living cells. Ideally, the integration of this information should result in comprehensive systems models of cellular physiology and regulation. However, most multi-omics projects still include a limited number of molecular assays and there have been very few multi-omic studies that evaluate dynamic processes such as cellular growth, development and adaptation. Hence, we lack formal analysis methods and comprehensive multi-omics datasets that can be leveraged to develop true multi-layered models for dynamic cellular systems. Here we present the STATegra multi-omics dataset that combines measurements from up to 10 different omics technologies applied to the same biological system, namely the well-studied mouse pre-B-cell differentiation. STATegra includes high-throughput measurements of chromatin structure, gene expression, proteomics and metabolomics, and it is complemented with single-cell data. To our knowledge, the STATegra collection is the most diverse multi-omics dataset describing a dynamic biological system.

Changes in the uterine metabolome of the cow during the first 7 days after estrus.

The uterine microenvironment during the first 7 days after ovulation accommodates and facilitates sperm transit to the oviduct and constitutes the sole source of nutrients required for the development of preimplantation embryos. Knowledge of the composition of uterine fluid is largely incomplete. Using untargeted mass spectrometry, we characterized the uterine metabolome during the first 7 days of the estrous cycle. Bovine uteri were collected on Days 0 (N = 4), 3 ( N = 4), 5 ( N = 3), and 7 ( N = 4) relative to ovulation and flushed with Dulbecco's phosphate-buffered saline. A total of 1,993 molecular features were detected of which 184 peaks with putative identification represent 147 unique metabolites, including amino acids, benzoic acids, lipid molecules, carbohydrates, purines, pyrimidines, vitamins, and other intermediate and secondary metabolites. Results revealed changes in the uterine metabolome as the cow transitions from ovulation to Day 7 of the estrous cycle. The majority of metabolites that changed with day reached maximum intensity on either Day 5 or 7 relative to ovulation. Moreover, several metabolites found in the uterine fluid have signaling capabilities and some have been shown to affect preimplantation embryonic development. In conclusion, the metabolome of the bovine uterus changes during early stages of the estrous cycle and is likely to participate in the regulation of preimplantation embryonic development. Data reported here will serve as the basis for future studies aiming to evaluate maternal regulation of preimplantation embryonic development and optimal conditions for the culture of embryos.

Tumor microenvironment-targeted poly-L-glutamic acid-based combination conjugate for enhanced triple negative breast cancer treatment.

The intrinsic characteristics of the tumor microenvironment (TME), including acidic pH and overexpression of hydrolytic enzymes, offer an exciting opportunity for the rational design of TME-drug delivery systems (DDS). We developed and characterized a pH-responsive biodegradable poly-L-glutamic acid (PGA)-based combination conjugate family with the aim of optimizing anticancer effects. We obtained combination conjugates bearing Doxorubicin (Dox) and aminoglutethimide (AGM) with two Dox loadings and two different hydrazone pH-sensitive linkers that promote the specific release of Dox from the polymeric backbone within the TME. Low Dox loading coupled with a short hydrazone linker yielded optimal effects on primary tumor growth, lung metastasis (∼90% reduction), and toxicological profile in a preclinical metastatic triple-negative breast cancer (TNBC) murine model. The use of transcriptomic analysis helped us to identify the molecular mechanisms responsible for such results including a differential immunomodulation and cell death pathways among the conjugates. This data highlights the advantages of targeting the TME, the therapeutic value of polymer-based combination approaches, and the utility of -omics-based analysis to accelerate anticancer DDS.

PaintOmics 3: a web resource for the pathway analysis and visualization of multi-omics data.

The increasing availability of multi-omic platforms poses new challenges to data analysis. Joint visualization of multi-omics data is instrumental in better understanding interconnections across molecular layers and in fully utilizing the multi-omic resources available to make biological discoveries. We present here PaintOmics 3, a web-based resource for the integrated visualization of multiple omic data types onto KEGG pathway diagrams. PaintOmics 3 combines server-end capabilities for data analysis with the potential of modern web resources for data visualization, providing researchers with a powerful framework for interactive exploration of their multi-omics information. Unlike other visualization tools, PaintOmics 3 covers a comprehensive pathway analysis workflow, including automatic feature name/identifier conversion, multi-layered feature matching, pathway enrichment, network analysis, interactive heatmaps, trend charts, and more. It accepts a wide variety of omic types, including transcriptomics, proteomics and metabolomics, as well as region-based approaches such as ATAC-seq or ChIP-seq data. The tool is freely available at www.paintomics.org.

Infectivity and virulence of Trypanosoma evansi and Trypanosoma equiperdum Venezuelan strains from three different host species.

The infectivity and virulence of seven Trypanosoma evansi and Trypanosoma equiperdum Venezuelan strains isolated from horses, donkeys and capybaras were compared in a mouse model up to 41 days, for parasitemia, animal weight, survival rates, packed cell volume, haemoglobin and erythrocyte count. Two T. equiperdum strains and three of the T. evansi strains resulted in 100% mice mortality, while the two T. evansi donkey strains exhibited lower infectivity and mortality. T. equiperdum strains had shorter pre-patent periods (4 days) than the T. evansi strains (4-12 days). In terms of pathogenicity, only the T. evansi horse strain and the two capybara strains produced a significant decrease of the packed cell volume, in haemoglobin concentration and in red blood cell count. In contrast, the T. evansi donkey strains did not show any changes in the hematological parameters. From the seven variables studied, only pre-patent period, day of maximum parasitemia, day of first parasitemia peak and number of parasitemia peaks were statistically significant. Weight decrease was only observed in mice infected with the T. evansi horse strain. T. equiperdum strains showed the highest mice lethality (7% survival by day 8 post-infection) with no change in the hematological parameters. The three T. evansi horse and capybara strains showed 80%, 87% and 97% survival rates, respectively by day 12 post-infection. However, by day 20 post-inoculation all the mice infected with the T. evansi horse strain died, while 53% and 27% capybara strains infected survived. Whereas by day 40 post-infection, 53 and 73% of the mice infected with the T. evansi donkey strains had survived. These results demonstrate striking infectivity and virulence differences between Venezuelan T. evansi and T. equiperdum strains in NMRI mice and open new possibilities to characterize inter and intra-species variations that may contribute to the pathogenicity of these two species.

Evidence of reversible bradycardia and arrhythmias caused by immunogenic proteins secreted by T. cruzi in isolated rat hearts.

RATIONALE: Chagas cardiomyopathy, caused by the protozoan Trypanosoma cruzi, is characterized by alterations in intracellular ion, heart failure and arrhythmias. Arrhythmias have been related to sudden death, even in asymptomatic patients, and their molecular mechanisms have not been fully elucidated. OBJECTIVE: The aim of this study is to demonstrate the effect of proteins secreted by T. cruzi on healthy, isolated beating rat heart model under a non-damage-inducing protocol. METHODS AND RESULTS: We established a non-damage-inducing recirculation-reoxygenation model where ultrafiltrate fractions of conditioned medium control or conditioned infected medium were perfused at a standard flow rate and under partial oxygenation. Western blotting with chagasic patient serum was performed to determine the antigenicity of the conditioned infected medium fractions. We observed bradycardia, ventricular fibrillation and complete atrioventricular block in hearts during perfusion with >50 kDa conditioned infected culture medium. The preincubation of conditioned infected medium with chagasic serum abolished the bradycardia and arrhythmias. The proteins present in the conditioned infected culture medium of >50 kDa fractions were recognized by the chagasic patient sera associated with arrhythmias. CONCLUSIONS: These results suggest that proteins secreted by T. cruzi are involved in Chagas disease arrhythmias and may be a potential biomarker in chagasic patients.