Identification of biological processes and genes for gestational diabetes mellitus

Longitudinal proteomics of leptin treatment in humans with acute and chronic energy deficiency-induced hypoleptinemia reveal novel, mainly immune-related, pleiotropic effects

BACKGROUND

Leptin is known for its metabolic, immunomodulatory and neuroendocrine properties, but the full spectrum of molecules downstream of leptin and relevant underlying mechanisms remain to be fully clarified. Our objective was to identify proteins and pathways influenced by leptin through untargeted proteomics in two clinical trials involving leptin administration in lean individuals.

METHODS

We performed untargeted liquid chromatography-tandem mass spectrometry serum proteomics across two studies a) Short-term randomized controlled crossover study of lean male and female humans undergoing a 72-h fast with concurrent administration of either placebo or high-dose leptin; b) Long-term (36-week) randomized controlled trial of leptin replacement therapy in human females with acquired relative energy deficiency and hypoleptinemia. We explored longitudinal proteomic changes and run adjusted mixed models followed by post-hoc tests. We further attempted to identify ontological pathways modulated during each experimental condition and/or comparison, through integrated qualitative pathway and enrichment analyses. We also explored dynamic longitudinal relationships between the circulating proteome with clinical and hormonal outcomes.

RESULTS

289 and 357 unique proteins were identified per each respective study. Short-term leptin administration during fasting markedly upregulated several proinflammatory molecules, notably C-reactive protein (CRP) and cluster of differentiation (CD) 14, and downregulated lecithin cholesterol acyltransferase and several immunoglobulin variable chains, in contrast with placebo, which produced minimal changes. Quantitative pathway enrichment further indicated an upregulation of the acute phase response and downregulation of immunoglobulin- and B cell-mediated immunity by leptin. These changes were independent of participants' biological sex. In the long term study, leptin likewise robustly and persistently upregulated proteins of the acute phase response, and downregulated immunoglobulin-mediated immunity. Leptin also significantly and differentially affected a wide array of proteins related to immune function, defense response, coagulation, and inflammation compared with placebo. These changes were more notable at the 24-week visit, coinciding with the highest measured levels of serum leptin. We further identified distinct co-regulated clusters of proteins and clinical features during leptin administration indicating robust longitudinal correlations between the regulation of immunoglobulins, immune-related molecules, serpins (including cortisol and thyroxine-binding globulins), lipid transport molecules and growth factors, in contrast with placebo, which did not produce similar associations.

CONCLUSIONS

These high-throughput longitudinal results provide unique functional insights into leptin physiology, and pave the way for affinity-based proteomic analyses measuring several thousands of molecules, that will confirm these data and may fully delineate underlying mechanisms.

Contributions of artificial intelligence reported in Obstetrics and Gynecology journals: a systematic review

Background

The applications of artificial intelligence (AI) processes have grown significantly in all medical disciplines during the last decades. Two main types of AI have been applied in medicine: symbolic AI (eg, knowledge base and ontologies) and nonsymbolic AI (eg, machine learning and artificial neural networks). Consequently, AI has also been applied across most obstetrics and gynecology (OB/GYN) domains, including general obstetrics, gynecology surgery, fetal ultrasound, and assisted reproductive medicine, among others.

Objective

The aim of this study was to provide a systematic review to establish the actual contributions of AI reported in OB/GYN discipline journals.

Methods

The PubMed database was searched for citations indexed with “artificial intelligence” and at least one of the following medical subject heading (MeSH) terms between January 1, 2000, and April 30, 2020: “obstetrics”; “gynecology”; “reproductive techniques, assisted”; or “pregnancy.” All publications in OB/GYN core disciplines journals were considered. The selection of journals was based on disciplines defined in Web of Science. The publications were excluded if no AI process was used in the study. Review, editorial, and commentary articles were also excluded. The study analysis comprised (1) classification of publications into OB/GYN domains, (2) description of AI methods, (3) description of AI algorithms, (4) description of data sets, (5) description of AI contributions, and (6) description of the validation of the AI process.

Results

The PubMed search retrieved 579 citations and 66 publications met the selection criteria. All OB/GYN subdomains were covered: obstetrics (41%, 27/66), gynecology (3%, 2/66), assisted reproductive medicine (33%, 22/66), early pregnancy (2%, 1/66), and fetal medicine (21%, 14/66). Both machine learning methods (39/66) and knowledge base methods (25/66) were represented. Machine learning used imaging, numerical, and clinical data sets. Knowledge base methods used mostly omics data sets. The actual contributions of AI were method/algorithm development (53%, 35/66), hypothesis generation (42%, 28/66), or software development (3%, 2/66). Validation was performed on one data set (86%, 57/66) and no external validation was reported. We observed a general rising trend in publications related to AI in OB/GYN over the last two decades. Most of these publications (82%, 54/66) remain out of the scope of the usual OB/GYN journals.

Conclusions

In OB/GYN discipline journals, mostly preliminary work (eg, proof-of-concept algorithm or method) in AI applied to this discipline is reported and clinical validation remains an unmet prerequisite. Improvement driven by new AI research guidelines is expected. However, these guidelines are covering only a part of AI approaches (nonsymbolic) reported in this review; hence, updates need to be considered.

A functional polymorphism rs10830963 in melatonin receptor 1B associated with the risk of gestational diabetes mellitus

The melatonin receptor 1B (MTNR1B) polymorphism rs10830963 C>G has been reported to be associated with the risk of gestational diabetes mellitus (GDM) with inconsistent results. To clarify the effect of the polymorphism on the risk of GDM, a meta-analysis therefore was performed. Pooled OR with its corresponding 95%CI was used to estimate the strength of the association. Totally 14 eligible studies with a number of 5033 GDM patients and 5614 controls were included in this meta-analysis. Results indicated that the variant G allele was significantly associated with an increased GDM risk (CG vs. CC: OR = 1.25, 95% CI = 1.11−1.40, P < 0.001; GG vs. CC: OR = 1.78, 95% CI = 1.45−2.19, P < 0.001; G vs. C: OR = 1.33, 95% CI = 1.21−1.47, P < 0.001). In the stratified analysis by ethnicity, similar results were found in Asians (CG vs. CC: OR = 1.15, 95%CI = 1.02−1.28, P = 0.020; GG vs. CC: OR = 1.52, 95% CI = 1.23−1.89, P < 0.001; G vs. C: OR = 1.23, 95% CI = 1.10−1.37, P < 0.001) and in Caucasians (CG vs. CC: OR = 1.40, 95% CI = 1.16−1.70, P < 0.001; GG vs. CC: OR = 2.21, 95% CI = 1.54−3.17, P < 0.001; G vs. C: OR = 1.47, 95% CI = 1.24−1.73, P < 0.001). FPRP and TSA analyses confirmed findings support that the rs10830963 G allele increases the risk of GDM, and further functional experimental studies are warranted to explore and clarify the potential mechanism.

Analysis of key genes and their functions in placental tissue of patients with gestational diabetes mellitus

BACKGROUND

This study was aimed at screening out the potential key genes and pathways associated with gestational diabetes mellitus (GDM).

METHODS

The GSE70493 dataset used for this study was obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) in the placental tissue of women with GDM in relation to the control tissue samples were identified and submitted to protein-protein interaction (PPI) network analysis and subnetwork module mining. Functional enrichment analyses of the PPI network and subnetworks were subsequently carried out. Finally, the integrated miRNA-transcription factor (TF)-DEG regulatory network was analyzed.

RESULTS

In total, 238 DEGs were identified, of which 162 were upregulated and 76 were downregulated. Through PPI network construction, 108 nodes and 278 gene pairs were obtained, from which chemokine (C-X-C motif) ligand 9 (CXCL9), CXCL10, protein tyrosine phosphatase, receptor type C (PTPRC), and human leukocyte antigen (HLA) were screened out as hub genes. Moreover, genes associated with the immune-related pathway and immune responses were found to be significantly enriched in the process of GDM. Finally, miRNAs and TFs that target the DEGs were predicted.

CONCLUSIONS

Four candidate genes (viz., CXCL9, CXCL10, PTPRC, and HLA) are closely related to GDM. miR-223-3p, miR-520, and thioredoxin-binding protein may play important roles in the pathogenesis of this disease.

Circular RNA-Associated Competing Endogenous RNA Network and Prognostic Nomogram for Patients With Colorectal Cancer

Background: Genetic characteristics remain underutilized for establishing prognostic models for colorectal cancer (CRC). We explored the underlying regulatory mechanisms of circular RNAs (circRNAs) that act as competing endogenous RNAs (ceRNAs) and constructed a gene-based nomogram to predict overall survival (OS) in patients with CRC. Methods: We obtained circRNA expression profiling data from the Gene Expression Omnibus (GEO) database. MicroRNA (miRNA) and mRNA expression profiles, with associated clinical data, were obtained from The Cancer Genome Atlas (TCGA). A ceRNA network was established using Cytoscape. Interactions between differential genes were analyzed, and hub genes were identified using the cytoHubba application. The R package "clusterProfiler" was used to evaluate the Gene Ontology (GO) annotations of the differentially expressed mRNAs and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Database-extracted patients were randomized into a training and validation cohorts. A prognostic model was developed using the training set based on multivariate Cox analyses and was then assessed in the validation set. The accuracy of the model was evaluated using discrimination and calibration plots. Results: Thirteen circRNAs, 62 miRNAs, and 301 mRNAs were used to construct the ceRNA network; 10 hub genes were identified via the PPI network. Next, a circRNA- miRNA hub of gene-regulatory modules was established based on four differentially expressed circRNAs, eight differentially expressed miRNAs, and nine differentially expressed mRNAs (DEmRNAs). GO and KEGG pathway analyses indicated the possible association of DEmRNAs with CRC onset and progression. Multivariate analyses revealed that age, tumor stage, and CXCR5 expression were independent risk factors for OS. A CXCR5-based model was developed to predict the OS of patients with CRC in our training set. Our nomogram showed relatively good accuracy, with C-indices of 0.757 and 0.702 in the training and validation sets, respectively. The areas under the curve of the nomograms predicting 3- and 5-years OS were 0.749 and 0.805 in the training set and 0.706 and 0.779 in the validation set, respectively. Conclusions: Our data suggested that the hsa_circ_00001666/has-mir-1229/CXCR5 axis plays an important role in the pathogenesis of CRC, thereby identifying a potential therapeutic target. The proposed CXCR5-based nomogram may also assist surgeons in devising personalized treatments for patients with this disease.

Molecular and disease association of gestational diabetes mellitus affected mother and placental datasets reveal a strong link between insulin growth factor (IGF) genes in amino acid transport pathway: A network biology approach

Discerning the relationship between molecules involved in diseases based on their underlying biological mechanisms is one of the greatest challenges in therapeutic development today. Gestational diabetes mellitus (GDM) is one of the most common complications during pregnancy, which adversely affects both mothers and offspring during and after pregnancy. We have constructed two datasets of (GDM associated genes from affected mother and placenta to systematically analyze and evaluate their interactions like gene-gene, gene-protein, gene-microRNA (miRNA), gene-transcription factors, and gene-associated diseases to enhance our current knowledge, which may lead to further advancements in disease diagnosis, prognosis, and treatment. The results identify the key genes with respect to maternal dataset as insulin receptor, insulin (INS), leptin (LEP), glucokinase, and hepatocyte nuclear factor 1 alpha, whereas from placenta include insulin-like growth factor 1, growth hormone receptor, and breast cancer anti-estrogen resistance protein 1, which are found to be highly enriched in pancreas, ovary, adipocyte, heart, and placental tissues. The key transcription factors include Sp1 transcription factor, pancreatic and duodenal homeobox 1, and hepatocyte nuclear factor 4 alpha, whereas miRNA includes has-miR-5699-5p and has-miR-3158-3p. The study also reveals that GDM has associations with diseases like type I and II diabetes mellitus, obesity, and preeclampsia. More significantly, we could trace out a significant connection between the key molecules like LEP and placental growth hormone from mother and placental dataset, which plays a critical role in INS secretion, INS signaling, and β-cell dysfunction pathways.

CREB1 functional polymorphisms modulating promoter transcriptional activity are associated with type 2 diabetes mellitus risk in Chinese population

The cAMP responsive element binding protein 1 (CREB1) is a ubiquitous transcription factor that contributes to the regulation of gluconeogenesis. The mechanisms of the CREB1 function remain largely unknown. In this study, we aimed to explore genetic variations in CREB1 promoter region and determine whether these loci affect transcriptional activity and risk on type 2 diabetes (T2D). Three polymorphisms were identified and designated as MU₁, MU₂ and MU₃, respectively. Genotypic distribution analysis revealed that MU₁ genotypes presented similar distribution between T2D and healthy controls (P > 0.05), while the MU₂ and MU₃ showed significant differences (P < 0.05). Haplotypic blocks of the three loci were constructed, and H1-TGA, H2-TTT and H3-ATT had higher frequencies in T2D patients than those in controls. Association studies revealed that the three loci significantly affected plasma glucose, glycated hemoglobin and insulin secretion. Disequilibrium analysis identified that the MU₂ and MU₃ variants were strongly linked in T2D (r² = 0.348, D' = 1.0). Further analysis indicated that MU₂ (TT vs GG, OR = 2.38, 95%CI = 1.19-4.77, P = 0.01) and MU₃ (AA vs TT, OR = 1.16, 95%CI = 1.19-4.77, P = 0.04) were significantly associated with T2D in dominant genotypes. Luciferase assay showed that T-A haplotype from the highly linked MU₂ and MU₃ exhibited maximal promoter activity, which was consistent with the correlation results. We concluded that the TT genotype of MU₂ and the AA genotype of MU₃ could be used as molecular markers for evaluating the risk on T2D.

Identification of the functional pathways altered by placental cell exposure to high glucose: lessons from the transcript and metabolite interactome

The specific consequences of hyperglycaemia on placental metabolism and function are incompletely understood but likely contribute to poor pregnancy outcomes associated with diabetes mellitus (DM). This study aimed to identify the functional biochemical pathways perturbed by placental exposure to high glucose levels through integrative analysis of the trophoblast transcriptome and metabolome. The human trophoblast cell line, BeWo, was cultured in 5 or 25 mM glucose, as a model of the placenta in DM. Transcriptomic analysis using microarrays, demonstrated 5632 differentially expressed gene transcripts (≥± 1.3 fold change (FC)) following exposure to high glucose. These genes were used to generate interactome models of transcript response using BioGRID (non-inferred network: 2500 nodes (genes) and 10541 protein-protein interactions). Ultra performance-liquid chromatography-mass spectrometry (MS) and gas chromatography-MS analysis of intracellular extracts and culture medium were used to assess the response of metabolite profiles to high glucose concentration. The interactions of altered genes and metabolites were assessed using the MetScape interactome database, resulting in an integrated model of systemic transcriptome (2969 genes) and metabolome (41 metabolites) response within placental cells exposed to high glucose. The functional pathways which demonstrated significant change in response to high glucose included fatty acid β-oxidation, phospholipid metabolism and phosphatidylinositol phosphate signalling.

Autologous chondrocyte implantation-derived synovial fluids display distinct responder and non-responder proteomic profiles

Background

Autologous chondrocyte implantation (ACI) can be used in the treatment of focal cartilage injuries to prevent the onset of osteoarthritis (OA). However, we are yet to understand fully why some individuals do not respond well to this intervention. Identification of a reliable and accurate biomarker panel that can predict which patients are likely to respond well to ACI is needed in order to assign the patient to the most appropriate therapy. This study aimed to compare the baseline and mid-treatment proteomic profiles of synovial fluids (SFs) obtained from responders and non-responders to ACI.

Methods

SFs were derived from 14 ACI responders (mean Lysholm improvement of 33 (17–54)) and 13 non-responders (mean Lysholm decrease of 14 (4–46)) at the two stages of surgery (cartilage harvest and chondrocyte implantation). Label-free proteome profiling of dynamically compressed SFs was used to identify predictive markers of ACI success or failure and to investigate the biological pathways involved in the clinical response to ACI.

Results

Only 1 protein displayed a ≥2.0-fold differential abundance in the preclinical SF of ACI responders versus non-responders. However, there is a marked difference between these two groups with regard to their proteome shift in response to cartilage harvest, with 24 and 92 proteins showing ≥2.0-fold differential abundance between Stages I and II in responders and non-responders, respectively. Proteomic data has been uploaded to ProteomeXchange (identifier: PXD005220). We have validated two biologically relevant protein changes associated with this response, demonstrating that matrix metalloproteinase 1 was prominently elevated and S100 calcium binding protein A13 was reduced in response to cartilage harvest in non-responders.

Conclusions

The differential proteomic response to cartilage harvest noted in responders versus non-responders is completely novel. Our analyses suggest several pathways which appear to be altered in non-responders that are worthy of further investigation to elucidate the mechanisms of ACI failure. These protein changes highlight many putative biomarkers that may have potential for prediction of ACI treatment success.