Transcriptomic and Proteomic Profiling of Human Mesenchymal Stem Cell Derived from Umbilical Cord in the Study of Preterm Birth

Use of Computation Ecosystems to Analyze the Kidney-Heart Crosstalk

The identification of mediators for physiologic processes, correlation of molecular processes, or even pathophysiological processes within a single organ such as the kidney or heart has been extensively studied to answer specific research questions using organ-centered approaches in the past 50 years. However, it has become evident that these approaches do not adequately complement each other and display a distorted single-disease progression, lacking holistic multilevel/multidimensional correlations. Holistic approaches have become increasingly significant in understanding and uncovering high dimensional interactions and molecular overlaps between different organ systems in the pathophysiology of multimorbid and systemic diseases like cardiorenal syndrome because of pathological heart-kidney crosstalk. Holistic approaches to unraveling multimorbid diseases are based on the integration, merging, and correlation of extensive, heterogeneous, and multidimensional data from different data sources, both -omics and nonomics databases. These approaches aimed at generating viable and translatable disease models using mathematical, statistical, and computational tools, thereby creating first computational ecosystems. As part of these computational ecosystems, systems medicine solutions focus on the analysis of -omics data in single-organ diseases. However, the data-scientific requirements to address the complexity of multimodality and multimorbidity reach far beyond what is currently available and require multiphased and cross-sectional approaches. These approaches break down complexity into small and comprehensible challenges. Such holistic computational ecosystems encompass data, methods, processes, and interdisciplinary knowledge to manage the complexity of multiorgan crosstalk. Therefore, this review summarizes the current knowledge of kidney-heart crosstalk, along with methods and opportunities that arise from the novel application of computational ecosystems providing a holistic analysis on the example of kidney-heart crosstalk.

Gaining a deeper understanding of social determinants of preterm birth by integrating multi-omics data

In the US, high rates of preterm birth (PTB) and profound Black-White disparities in PTB have persisted for decades. This review focuses on the role of social determinants of health (SDH), with an emphasis on maternal stress, in PTB disparity and biological embedding. It covers: (1) PTB disparity in US Black women and possible contributors; (2) the role of SDH, highlighting maternal stress, in the persistent racial disparity of PTB; (3) epigenetics at the interface between genes and environment; (4) the role of the genome in modifying maternal stress-PTB associations; (5) recent advances in multi-omics studies of PTB; and (6) future perspectives on integrating multi-omics with SDH to elucidate the Black-White disparity in PTB. Available studies have indicated that neither environmental exposures nor genetics alone can adequately explain the Black-White PTB disparity. Preliminary yet promising findings of epigenetic and gene-environment interaction studies underscore the value of integrating SDH with multi-omics in prospective birth cohort studies, especially among high-risk Black women. In an era of rapid advancements in biomedical sciences and technologies and a growing number of prospective birth cohort studies, we have unprecedented opportunities to advance this field and finally address the long history of health disparities in PTB. IMPACT: This review provides an overview of social determinants of health (SDH) with a focus on maternal stress and its role on Black-White disparity in preterm birth (PTB). It summarizes the available literature on the interplay of maternal stress with key biological layers (e.g., individual genome and epigenome in response to environmental stressors) and significant knowledge gaps. It offers perspectives that such knowledge may provide deeper insight into how SDH affects PTB and why some women are more vulnerable than others and underscores the critical need for integrating SDH with multi-omics in prospective birth cohort studies, especially among high-risk Black women.

Biomarker discovery for predicting spontaneous preterm birth from gene expression data by regularized logistic regression

In this work, we provide a computational method of regularized logistic regression for discovering biomarkers of spontaneous preterm birth (SPTB) from gene expression data. The successful identification of SPTB biomarkers will greatly benefit the interference of infant gestational age for reducing the risks of pregnant women and preemies. In recent years, various approaches have been proposed for the feature selection of identifying the subset of meaningful genes that can achieve accurate classification for disease samples from controls. Here, we comprehensively summarize the regularized logistic regression with seven effective penalties developed for the selection of strongly indicative genes of SPTB from microarray data. We compare their properties and assess their classification performances in multiple datasets. It shows that elastic net, lasso,L 1 / 2 and SCAD penalties get the better performance than others and can be successfully used to identify biomarkers of SPTB. Particularly, we make a functional enrichment analysis on these biomarkers and construct a logistic regression classifier based on them. The classifier generates an indicator of preterm risk score (PRS) for predicting SPTB. Based on the trained predictor, we verify the identified biomarkers on an independent dataset. The biomarkers achieve the AUC value of 0.933 in the SPTB classification. The results demonstrate the effectiveness and efficiency of the built-up strategy of biomarker discovery with regularized logistic regression. Obviously, the proposed method of discovering biomarkers for SPTB can be easily extended for other complex diseases.

Peptidome analysis of umbilical cord mesenchymal stem cell (hUC-MSC) conditioned medium from preterm and term infants

Background

The therapeutic role of mesenchymal stem cells (MSCs) has been widely confirmed in several animal models of premature infant diseases. Micromolecule peptides have shown promise for the treatment of premature infant diseases. However, the potential role of peptides secreted from MSCs has not been studied. The purpose of this study is to help to broaden the knowledge of the hUC-MSC secretome at the peptide level through peptidomic profile analysis.

Methods

We used tandem mass tag (TMT) labeling technology followed by tandem mass spectrometry to compare the peptidomic profile of preterm and term umbilical cord MSC (hUC-MSC) conditioned medium (CM). Gene Ontology (GO) enrichment analysis and ingenuity pathway analysis (IPA) were conducted to explore the differentially expressed peptides by predicting the functions of their precursor proteins. To evaluate the effect of candidate peptides on human lung epithelial cells stimulated by hydrogen peroxide (H₂O₂), quantitative real-time PCR (qRT-PCR), western blot analysis, and enzyme-linked immunosorbent assay (ELISA) were, respectively, adopted to detect inflammatory cytokines (TNF-α, IL-1β, and IL-6) expression levels at the mRNA and protein levels.

Results

A total of 131 peptides derived from 106 precursor proteins were differentially expressed in the preterm hUC-MSC CM compared with the term group, comprising 37 upregulated peptides and 94 downregulated peptides. Bioinformatics analysis showed that these differentially expressed peptides may be associated with developmental disorders, inflammatory response, and organismal injury. We also found that peptides ⁷¹¹⁸TGAKIKLVGT⁷¹²⁷ derived from MUC19 and ⁵⁰⁸AAAAGPANVH⁵¹⁷ derived from SIX5 reduced the expression levels of TNF-α, IL-1β, and IL-6 in H₂O₂-treated human lung epithelial cells.

Conclusions

In summary, this study provides further secretomics information on hUC-MSCs and provides a series of peptides that might have antiinflammatory effects on pulmonary epithelial cells and contribute to the prevention and treatment of respiratory diseases in premature infants.