Multiomic immune clockworks of pregnancy

Preterm birth is the leading cause of mortality in children under the age of five worldwide. Despite major efforts, we still lack the ability to accurately predict and effectively prevent preterm birth. While multiple factors contribute to preterm labor, dysregulations of immunological adaptations required for the maintenance of a healthy pregnancy is at its pathophysiological core. Consequently, a precise understanding of these chronologically paced immune adaptations and of the biological pacemakers that synchronize the pregnancy “immune clock” is a critical first step towards identifying deviations that are hallmarks of peterm birth. Here, we will review key elements of the fetal, placental, and maternal pacemakers that program the immune clock of pregnancy. We will then emphasize multiomic studies that enable a more integrated view of pregnancy-related immune adaptations. Such multiomic assessments can strengthen the biological plausibility of immunological findings and increase the power of biological signatures predictive of preterm birth

Multiomics Longitudinal Modeling of Preeclamptic Pregnancies

Preeclampsia is a complex disease of pregnancy whose physiopathology remains unclear and that poses a threat to both mothers and infants. Specific complex changes in women\u27s physiology precede a diagnosis of preeclampsia. Understanding multiple aspects of such a complex changes at different levels of biology, can be enabled by simultaneous application of multiple assays. We developed prediction models for preeclampsia risk by analyzing six omics datasets from a longitudinal cohort of pregnant women. A machine learning-based multiomics model had high accuracy (area under the receiver operating characteristics curve (AUC) of 0.94, 95% confidence intervals (CI):[0.90, 0.99]). A prediction model using only ten urine metabolites provided an accuracy of the whole metabolomic dataset and was validated using an independent cohort of 16 women (AUC= 0.87, 95% CI:[0.76, 0.99]). Integration with clinical variables further improved prediction accuracy of the urine metabolome model (AUC= 0.90, 95% CI:[0.80, 0.99], urine metabolome, validated). We identified several biological pathways to be associated with preeclampsia. The findings derived from models were integrated with immune system cytometry data, confirming known physiological alterations associated with preeclampsia and suggesting novel associations between the immune and proteomic dynamics. While further validation in larger populations is necessary, these encouraging results will serve as a basis for a simple, early diagnostic test for preeclampsia

Multiomics Characterization of Preterm Birth in Low- and Middle-Income Countries.

Importance: Worldwide, preterm birth (PTB) is the single largest cause of deaths in the perinatal and neonatal period and is associated with increased morbidity in young children. The cause of PTB is multifactorial, and the development of generalizable biological models may enable early detection and guide therapeutic studies. Objective: To investigate the ability of transcriptomics and proteomics profiling of plasma and metabolomics analysis of urine to identify early biological measurements associated with PTB. Design, Setting, and Participants: This diagnostic/prognostic study analyzed plasma and urine samples collected from May 2014 to June 2017 from pregnant women in 5 biorepository cohorts in low- and middle-income countries (LMICs; ie, Matlab, Bangladesh; Lusaka, Zambia; Sylhet, Bangladesh; Karachi, Pakistan; and Pemba, Tanzania). These cohorts were established to study maternal and fetal outcomes and were supported by the Alliance for Maternal and Newborn Health Improvement and the Global Alliance to Prevent Prematurity and Stillbirth biorepositories. Data were analyzed from December 2018 to July 2019. Exposures: Blood and urine specimens that were collected early during pregnancy (median sampling time of 13.6 weeks of gestation, according to ultrasonography) were processed, stored, and shipped to the laboratories under uniform protocols. Plasma samples were assayed for targeted measurement of proteins and untargeted cell-free ribonucleic acid profiling; urine samples were assayed for metabolites. Main Outcomes and Measures: The PTB phenotype was defined as the delivery of a live infant before completing 37 weeks of gestation. Results: Of the 81 pregnant women included in this study, 39 had PTBs (48.1%) and 42 had term pregnancies (51.9%) (mean [SD] age of 24.8 [5.3] years). Univariate analysis demonstrated functional biological differences across the 5 cohorts. A cohort-adjusted machine learning algorithm was applied to each biological data set, and then a higher-level machine learning modeling combined the results into a final integrative model. The integrated model was more accurate, with an area under the receiver operating characteristic curve (AUROC) of 0.83 (95% CI, 0.72-0.91) compared with the models derived for each independent biological modality (transcriptomics AUROC, 0.73 [95% CI, 0.61-0.83]; metabolomics AUROC, 0.59 [95% CI, 0.47-0.72]; and proteomics AUROC, 0.75 [95% CI, 0.64-0.85]). Primary features associated with PTB included an inflammatory module as well as a metabolomic module measured in urine associated with the glutamine and glutamate metabolism and valine, leucine, and isoleucine biosynthesis pathways. Conclusions and Relevance: This study found that, in LMICs and high PTB settings, major biological adaptations during term pregnancy follow a generalizable model and the predictive accuracy for PTB was augmented by combining various omics data sets, suggesting that PTB is a condition that manifests within multiple biological systems. These data sets, with machine learning partnerships, may be a key step in developing valuable predictive tests and intervention candidates for preventing PTB

Barriers to achieving controlled rheumatoid arthritis in the United Arab Emirates: a cross-sectional study

To better understand the factors that affect low disease activity (DAS28 ≤ 3.2, LDA) in rheumatoid arthritis (RA) and barriers within the UAE, demographic/treatment data and DAS28 scores were collected through chart reviews of 182 consecutive RA patients seen at a private clinic in Dubai over a 2-month period. Patients were separated into a LDA group and a group comprised of moderate (3.2 < DAS28 < 5.1) or high disease activity (DAS28 ≥ 5.1) (MHDA). We then examined variables that may be associated with LDA and re-examined the MHDA group for barriers. While 97 (53 %) of the 182 patients had achieved the treatment target of DAS28 ≤ 3.2, 85 (47 %) had MHDA. A significantly larger portion of LDA patients had been previously treated with sulfasalazine (36 in LDA vs. 14 in MHDA, P = 0.002) or was presently on biological treatments (24 vs. 9, P = 0.013). For the 85 MHDA patients, 40 (22 % of 182) exhibited resistant disease with 25 (13.7 % of 182) failing their current first tier disease-modifying anti-rheumatic drug (DMARD) treatment or combinations and 15 (8.2 % of 182) failing current anti-TNF or biologic treatment. Reasons listed were primarily socioeconomic with 40 % of the resistant disease group unable to afford biologicals and 52 % of the patient-driven preference group discontinuing DMARDs against professional advice. Going forward, emphasis on the agreement between patient and rheumatologist on treatment, specifically regarding how DMARDs help relieve symptoms and their proper use, could help reduce the percentage of MHDA patients in the UAE

Cell types of origin of the cell-free transcriptome.

Cell-free RNA from liquid biopsies can be analyzed to determine disease tissue of origin. We extend this concept to identify cell types of origin using the Tabula Sapiens transcriptomic cell atlas as well as individual tissue transcriptomic cell atlases in combination with the Human Protein Atlas RNA consensus dataset. We define cell type signature scores, which allow the inference of cell types that contribute to cell-free RNA for a variety of diseases

Characterization of the expression of the pro-metastatic MenaINV isoform during breast tumor progression

Several functionally distinct isoforms of the actin regulatory Mena are produced by alternative splicing during tumor progression. Forced expression of the Mena[superscript INV] isoform drives invasion, intravasation and metastasis. However, the abundance and distribution of endogenously expressed Mena[superscript INV] within primary tumors during progression remain unknown, as most studies to date have only assessed relative mRNA levels from dissociated tumor samples. We have developed a Mena[superscript INV] isoform-specific monoclonal antibody and used it to examine Mena[superscript INV]expression patterns in mouse mammary and human breast tumors. Mena[superscript INV] expression increases during tumor progression and to examine the relationship between Mena[superscript INV] expression and markers for epithelial or mesenchymal status, stemness, stromal cell types and hypoxic regions. Further, while Mena[superscript INV] robustly expressed in vascularized areas of the tumor, it is not confined to cells adjacent to blood vessels. Altogether, these data demonstrate the specificity and utility of the anti-Mena[superscript INV]-isoform specific antibody, and provide the first description of endogenous Mena[superscript INV]protein expression in mouse and human tumors.United States. Dept. of Defense. Breast Cancer Research Program (Grants W81XWH-10-1-0040 and W81XWH-13-1-0031)National Institutes of Health (U.S.) (Grants U54-CA112967 and GM58801)Massachusetts Institute of Technology. Ludwig Center for Molecular Oncolog

Early prediction and longitudinal modeling of preeclampsia from multiomics.

Preeclampsia is a complex disease of pregnancy whose physiopathology remains unclear. We developed machine-learning models for early prediction of preeclampsia (first 16&nbsp;weeks of pregnancy) and over gestation by analyzing six omics datasets from a longitudinal cohort of pregnant women. For early pregnancy, a prediction model using nine urine metabolites had the highest accuracy and was validated on an independent cohort (area under the receiver-operating characteristic curve [AUC]&nbsp;= 0.88, 95% confidence interval [CI] [0.76, 0.99] cross-validated; AUC&nbsp;= 0.83, 95% CI [0.62,1] validated). Univariate analysis demonstrated statistical significance of identified metabolites. An integrated multiomics model further improved accuracy (AUC&nbsp;= 0.94). Several biological pathways were identified including tryptophan, caffeine, and arachidonic acid metabolisms. Integration with immune cytometry data suggested novel associations between immune and proteomic dynamics. While further validation in a larger population is necessary, these encouraging results can serve as a basis for a simple, early diagnostic test for preeclampsia

Characterization of the expression of the pro-metastatic MenaINV isoform during breast tumor progression

Several functionally distinct isoforms of the actin regulatory Mena are produced by alternative splicing during tumor progression. Forced expression of the Mena[superscript INV] isoform drives invasion, intravasation and metastasis. However, the abundance and distribution of endogenously expressed Mena[superscript INV] within primary tumors during progression remain unknown, as most studies to date have only assessed relative mRNA levels from dissociated tumor samples. We have developed a Mena[superscript INV] isoform-specific monoclonal antibody and used it to examine Mena[superscript INV]expression patterns in mouse mammary and human breast tumors. Mena[superscript INV] expression increases during tumor progression and to examine the relationship between Mena[superscript INV] expression and markers for epithelial or mesenchymal status, stemness, stromal cell types and hypoxic regions. Further, while Mena[superscript INV] robustly expressed in vascularized areas of the tumor, it is not confined to cells adjacent to blood vessels. Altogether, these data demonstrate the specificity and utility of the anti-Mena[superscript INV]-isoform specific antibody, and provide the first description of endogenous Mena[superscript INV]protein expression in mouse and human tumors.United States. Dept. of Defense. Breast Cancer Research Program (Grants W81XWH-10-1-0040 and W81XWH-13-1-0031)National Institutes of Health (U.S.) (Grants U54-CA112967 and GM58801)Massachusetts Institute of Technology. Ludwig Center for Molecular Oncolog