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

Jehan, Fyezah; Sazawal, Sunil; Baqui, Abdullah H; Dhingra, Usha; Khanam, Rasheda; Ilyas, Muhammad; Dutta, Arup; Mitra, Dipak Kumar; Mehmood, Usma; Deb, Saikat; Mahmud, Arif; Hotwani, Aneeta; Ali, Said M.; Rahman, Sayedur; Nizar, Ambreen; Makame, Shaali; Moin, Mamun Ibne; Sajid, Muhammad; Chauhan, A. S.; Begum, Nazma; Khan, Waqasuddin; Das, Sayan; Ahmed, Salahuddin; Hasan, Tarik; Khalid, Javairia; Rizvi, Syed Jafar Raza; Juma, Mohammed Hamad; Chowdhury, Nabidul Haque; Kabir, Furqan; Aftab, Fahad; Quaiyum, Abdul; Manu, Alexander; Yoshida, Sachiyo; Bahl, Rajiv; Rahman, Anisur; Pervin, Jesmin; Winston, Jennifer; Musonda, Patrick; Stringer, Jeffrey S. A.; Litch, James A.; Ghaemi, Mohammad Sajjad; Moufarrej, Mira N.; Contrepois, Kévin; Chen, Songjie; Stelzer, Ina A.; Stanley, Natalie; Chang, Alan L.; Hammad, Ghaith Bany; Wong, Ronald J.; Liu, Candace; Quaintance, Cecele C.; Culos, Anthony; Espinosa, Camilo; Xenochristou, Maria; Becker, Martin; Fallahzadeh, Ramin; Ganio, Edward A.; Tsai, Amy S.; Gaudilliere, Dyani; Tsai, Eileen S.; Han, Xiaoyuan; Ando, Kazuo; Tingle, Martha; Marić, Ivana; Wise, Paul H.; Winn, Virginia D.; Druzin, Maurice L.; Gibbs, Ronald S.; Darmstadt, Gary L.; Murray, Jeffrey C.; Shaw, Gary M.; Stevenson, David K.; Snyder, M; Quake, Stephen R.; Angst, Martin S.; Gaudillière, Brice; Aghaeepour, Nima; Maternal, Alliance for; Stillbirth,

doi:10.1001/jamanetworkopen.2020.29655

Cited by 67 publications

(50 citation statements)

References 64 publications

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“…This is in line with a recent multi-omics analysis in preterm birth using cell-free RNA and plasma proteomics in preterm birth. 70 After we and other investigators reported the value of aptamer-based SomaLogic assays to predict early 52 and late preeclampsia, 45 in this study, we evaluated this platform to predict preterm birth and found the SomaLogic assay to be of superior value when compared to whole-blood transcriptomics in predicting spontaneous preterm delivery. The plasma proteomics signatures of spontaneous preterm birth identified here have direct implications for the development of future SomaSignal Tests that were demonstrated in other health applications by combining reproducible proteomic signals with machine learning.…”

Section: Discussionmentioning

confidence: 84%

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Crowdsourcing assessment of maternal blood multi-omics for predicting gestational age and preterm birth

Tarca

Pataki

Romero

et al. 2021

Cell Reports Medicine

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Section: Discussionmentioning

confidence: 84%

“… 69 IL-6 and ANGPT4, a member of the angiopoietins family, were highlighted as predictors of preterm birth based on proteomics analysis of maternal plasma at 8–20 weeks of gestation in a population of women from low- and middle-income countries. 70 …”

Section: Discussionmentioning

confidence: 99%

Crowdsourcing assessment of maternal blood multi-omics for predicting gestational age and preterm birth

Tarca

Pataki

Romero

et al. 2021

Cell Reports Medicine

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“…These genes were up-regulated at delivery, and functional analysis identified biological processes related to an immune/inflammatory response to a stimulus. Biomarkers targeting these processes were identified in predictive models based on a whole-blood transcriptome 42 , 89 , 90 . The pro-inflammatory cytokines, IL-1β , CXCL8 , CCL3 , CCL4 , and CCL20 , were among the strongest predictors selected in all iterations of leave-one-out-cross-validation.…”

Section: Discussionmentioning

confidence: 99%

The amniotic fluid cell-free transcriptome in spontaneous preterm labor

Bhatti

Romero

Gomez‐Lopez

et al. 2021

Sci Rep

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The amniotic fluid (AF) cell-free RNA was shown to reflect physiological and pathological processes in pregnancy, but its value in the prediction of spontaneous preterm delivery is unknown. Herein we profiled cell-free RNA in AF samples collected from women who underwent transabdominal amniocentesis after an episode of spontaneous preterm labor and subsequently delivered within 24 h (n = 10) or later (n = 28) in gestation. Expression of known placental single-cell RNA-Seq signatures was quantified in AF cell-free RNA and compared between the groups. Random forest models were applied to predict time-to-delivery after amniocentesis. There were 2385 genes differentially expressed in AF samples of women who delivered within 24 h of amniocentesis compared to gestational age-matched samples from women who delivered after 24 h of amniocentesis. Genes with cell-free RNA changes were associated with immune and inflammatory processes related to the onset of labor, and the expression of placental single-cell RNA-Seq signatures of immune cells was increased with imminent delivery. AF transcriptomic prediction models captured these effects and predicted delivery within 24 h of amniocentesis (AUROC = 0.81). These results may inform the development of biomarkers for spontaneous preterm birth.

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“…Metabolomics/metabonomics [ 35 ], exosome metabolomics (exometabolomics) [ 35 ], and multi-OMICS approaches [ 36 , 37 ] represent further innovative strategies for (sub-)typing gynecological patients. Finally, phenomics [ 38 ] aims at systematically and comprehensively measuring and quantitatively assessing the human phenome, which is the array of physical, chemical, biochemical, and biophysical traits expressed and/or generated by an individual during its development, adaptation to genetic influences, and environmental stimuli.…”

Section: Gynecology and Omics Datamentioning

confidence: 99%

Gynecology Meets Big Data in the Disruptive Innovation Medical Era: State-of-Art and Future Prospects

Khamisy‐Farah

Furstenau

Kong

et al. 2021

IJERPH

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Tremendous scientific and technological achievements have been revolutionizing the current medical era, changing the way in which physicians practice their profession and deliver healthcare provisions. This is due to the convergence of various advancements related to digitalization and the use of information and communication technologies (ICTs)—ranging from the internet of things (IoT) and the internet of medical things (IoMT) to the fields of robotics, virtual and augmented reality, and massively parallel and cloud computing. Further progress has been made in the fields of addictive manufacturing and three-dimensional (3D) printing, sophisticated statistical tools such as big data visualization and analytics (BDVA) and artificial intelligence (AI), the use of mobile and smartphone applications (apps), remote monitoring and wearable sensors, and e-learning, among others. Within this new conceptual framework, big data represents a massive set of data characterized by different properties and features. These can be categorized both from a quantitative and qualitative standpoint, and include data generated from wet-lab and microarrays (molecular big data), databases and registries (clinical/computational big data), imaging techniques (such as radiomics, imaging big data) and web searches (the so-called infodemiology, digital big data). The present review aims to show how big and smart data can revolutionize gynecology by shedding light on female reproductive health, both in terms of physiology and pathophysiology. More specifically, they appear to have potential uses in the field of gynecology to increase its accuracy and precision, stratify patients, provide opportunities for personalized treatment options rather than delivering a package of “one-size-fits-it-all” healthcare management provisions, and enhance its effectiveness at each stage (health promotion, prevention, diagnosis, prognosis, and therapeutics).

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

Abstract: This diagnostic/prognostic study describes the use of cell-free transcriptomics, urine metabolomics, and plasma proteomics for identifying the biological measurements associated with preterm birth.

Cited by 67 publications

References 64 publications

Crowdsourcing assessment of maternal blood multi-omics for predicting gestational age and preterm birth

Crowdsourcing assessment of maternal blood multi-omics for predicting gestational age and preterm birth

The amniotic fluid cell-free transcriptome in spontaneous preterm labor

Gynecology Meets Big Data in the Disruptive Innovation Medical Era: State-of-Art and Future Prospects

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