Identification of maternal continuous glucose monitoring metrics related to newborn birth weight in pregnant women with gestational diabetes.

PURPOSE: To identify the specific glucose metrics derived from maternal continuous glucose monitoring (CGM) data, which were associated with a higher percentile of offspring birth weight. METHODS: In this cohort study, we recruited singleton pregnant women with GDM who underwent CGM for 5-14 days at a mean of 28.8 gestational weeks between Jan 2017 and Nov 2018. Commonly used single summary glucose metrics of glucose exposure (including mean 24-h, daytime, and nighttime glucose level) and variability (including J-index and mean amplitude of glycaemic excursions) were derived from CGM data. A novel comprehensive glucose metric-hours per-day spent in a severe variability glucose mode (HSSV)-was identified using the spectral clustering method, which reflects both glucose level and variability. Multiple linear regression models were used to estimate the associations of sex- and gestational age-adjusted birth weight percentile with CGM parameters. RESULTS: Ninety-seven women comprising 127,279 glucose measurements were included. Each 1-SD increase in maternal nighttime mean glucose level and HSSV was associated with 6.0 (95% CI 0.4, 11.5) and 6.3 (95% CI 0.4, 12.2) percentage points increase in birth weight percentile, respectively. No associations were found between other glucose metrics and birth weight percentile. CONCLUSION: Nighttime mean glucose level has a comparable effect size to HSSV in association with fetal growth, suggesting that endogenous hyperglycemia might drive the association between maternal hyperglycemia and birth weight. Further studies need to examine the effect of lowering nighttime glucose level and/or HSSV on preventing fetal overgrowth in GDM women.

Identification of Circulating Diagnostic Biomarkers for Coronary Microvascular Disease in Postmenopausal Women Using Machine-Learning Techniques.

Coronary microvascular disease (CMD) is a common form of heart disease in postmenopausal women. It is not due to plaque formation but dysfunction of microvessels that feed the heart muscle. The majority of the patients do not receive a proper diagnosis, are discharged prematurely and must go back to the hospital with persistent symptoms. Because of the lack of diagnostic biomarkers, in the current study, we focused on identifying novel circulating biomarkers of CMV that could potentially be used for developing a diagnostic test. We hypothesized that plasma metabolite composition is different for postmenopausal women with no heart disease, CAD, or CMD. A total of 70 postmenopausal women, 26 healthy individuals, 23 individuals with CMD and 21 individuals with CAD were recruited. Their full health screening and tests were completed. Basic cardiac examination, including detailed clinical history, additional disease and prescribed drugs, were noted. Electrocardiograph, transthoracic echocardiography and laboratory analysis were also obtained. Additionally, we performed full metabolite profiling of plasma samples from these individuals using gas chromatography-mass spectrometry (GC-MS) analysis, identified and classified circulating biomarkers using machine learning approaches. Stearic acid and ornithine levels were significantly higher in postmenopausal women with CMD. In contrast, valine levels were higher for women with CAD. Our research identified potential circulating plasma biomarkers of this debilitating heart disease in postmenopausal women, which will have a clinical impact on diagnostic test design in the future.

The transcriptomic profile of endometrial receptivity in recurrent miscarriage.

OBJECTIVE: To characterise the endometrial transcriptomic profiles of women who suffered recurrent miscarriage and to set the foundation for the development of an endometrial receptivity test that could predict the fate of subsequent pregnancies. STUDY DESIGN: This was a prospective multicentre cohort study performed at the Tommy's National Centre for Miscarriage Research in Birmingham, Saint Mary's Hospital in Manchester and Royal Devon & Exeter Hospital, United Kingdom. The study was conducted between December 2017 and December 2019. Endometrial biopsies were obtained during the window of implantation from 24 women aged 18-35 years, who were not pregnant and regularly menstruating, diagnosed with unexplained recurrent miscarriage by standard investigations as per the ESHRE guidelines. Exclusion criteria included risk factors such as smoking, obesity or hyperprolactinemia. The RNA transcripts abundances were quantified using Kallisto. R packages tximport and DESeq2 were used to summarize count estimates at the gene level and to analyse the differential gene expression. RESULTS: Women who suffered four or more miscarriages had 19 differently expressed genes after adjustment for multiple comparisons. They were related to biological processes such as immunity (HLA-DMA, CCR8, ALOX5), energy production (ATP12A), hormone secretion (CGA), adhesion (CHAD, ADGRF2, AQP5, TBCD, CTNND1, NKD2) and cell proliferation (NCCRP1). Based on 421 differently expressed genes, women who achieved a subsequent live birth displayed an enrichment of processes related to the regulation of cell structure and proliferation, and a depletion of processes related to immunity, trans-membrane transport and coagulation. CONCLUSIONS: Women in the extreme miscarriage cohort had a distinctive endometrial transcriptomic signature compared to women with low order miscarriages. There was a partial overlap with the transcriptome of asynchronous endometrium suggesting the endometrial factor to be a different entity in the context of recurrent miscarriage. Women who achieved a live birth in their subsequent pregnancy displayed an enrichment of genes related to the regulation of cell structure and proliferation, while women who suffered a subsequent miscarriage displayed an enrichment of genes related to immunity, trans-membrane transport and coagulation.

Target Product Profile for an endometrial receptivity test: women's perspective.

OBJECTIVE: To assess the women's views in relation to the characteristics of an endometrial receptivity test in the context of recurrent miscarriage with an overarching aim to guide the development of a Target Product Profile (TPP) based on minimum acceptable ("worst-case") and ideal ("best-case") features. STUDY DESIGN: This was a descriptive cross-sectional study involving a total of 131 women who answered questions related to the development of an endometrial receptivity test between December 2017 and May 2018. Women attending the recurrent miscarriage clinic at the Tommy's National Centre for Miscarriage Research in Birmingham, United Kingdom, were invited to participate. Referral criteria included two or more miscarriages irrespective of the timing in relation to successful pregnancies. The 'best-case' (ideal) and 'worst-case' (minimum acceptable) thresholds were arbitrary set to satisfy at least 80% and 40% of responders, respectively. RESULTS: The ideal endometrial receptivity test should be indicated after two miscarriages to comply with the wish of 80.9% (106 women) of responders. It should be performed in a window of three to four days within the menstrual cycle (93.2%; 122 women) and results should be available within one to two days (87.7%; 115 women). Invasiveness of testing should not extend beyond a vaginal examination (85.4%; 112 women). Repeating the test should not be required more than twice (96.1%; 125 women) and the results should remain useful for at least six menstrual cycles (89.3%; 117 women). The importance score given for the endometrium was weakly associated with the willingness to pay for testing; however, there was no evidence to suggest this correlation was different from 0 (Kendall's tau = 0.1101765, z = 1.4327, p-value = 0.1519; Spearman's rho = 0.1268444, S = 327136, p-value = 0.1488). CONCLUSIONS: Women understand the important role the endometrium plays for a successful pregnancy and they have specific views in relation to the indication, timing and invasiveness of testing, need for test repetition, validity of results and costs of testing.

pcaReduce: hierarchical clustering of single cell transcriptional profiles.

BACKGROUND: Advances in single cell genomics provide a way of routinely generating transcriptomics data at the single cell level. A frequent requirement of single cell expression analysis is the identification of novel patterns of heterogeneity across single cells that might explain complex cellular states or tissue composition. To date, classical statistical analysis tools have being routinely applied, but there is considerable scope for the development of novel statistical approaches that are better adapted to the challenges of inferring cellular hierarchies. RESULTS: We have developed a novel agglomerative clustering method that we call pcaReduce to generate a cell state hierarchy where each cluster branch is associated with a principal component of variation that can be used to differentiate two cell states. Using two real single cell datasets, we compared our approach to other commonly used statistical techniques, such as K-means and hierarchical clustering. We found that pcaReduce was able to give more consistent clustering structures when compared to broad and detailed cell type labels. CONCLUSIONS: Our novel integration of principal components analysis and hierarchical clustering establishes a connection between the representation of the expression data and the number of cell types that can be discovered. In doing so we found that pcaReduce performs better than either technique in isolation in terms of characterising putative cell states. Our methodology is complimentary to other single cell clustering techniques and adds to a growing palette of single cell bioinformatics tools for profiling heterogeneous cell populations.

A graph theoretical approach to data fusion.

The rapid development of high throughput experimental techniques has resulted in a growing diversity of genomic datasets being produced and requiring analysis. Therefore, it is increasingly being recognized that we can gain deeper understanding about underlying biology by combining the insights obtained from multiple, diverse datasets. Thus we propose a novel scalable computational approach to unsupervised data fusion. Our technique exploits network representations of the data to identify similarities among the datasets. We may work within the Bayesian formalism, using Bayesian nonparametric approaches to model each dataset; or (for fast, approximate, and massive scale data fusion) can naturally switch to more heuristic modeling techniques. An advantage of the proposed approach is that each dataset can initially be modeled independently (in parallel), before applying a fast post-processing step to perform data integration. This allows us to incorporate new experimental data in an online fashion, without having to rerun all of the analysis. We first demonstrate the applicability of our tool on artificial data, and then on examples from the literature, which include yeast cell cycle, breast cancer and sporadic inclusion body myositis datasets.

Derivative processes for modelling metabolic fluxes.

MOTIVATION: One of the challenging questions in modelling biological systems is to characterize the functional forms of the processes that control and orchestrate molecular and cellular phenotypes. Recently proposed methods for the analysis of metabolic pathways, for example, dynamic flux estimation, can only provide estimates of the underlying fluxes at discrete time points but fail to capture the complete temporal behaviour. To describe the dynamic variation of the fluxes, we additionally require the assumption of specific functional forms that can capture the temporal behaviour. However, it also remains unclear how to address the noise which might be present in experimentally measured metabolite concentrations. RESULTS: Here we propose a novel approach to modelling metabolic fluxes: derivative processes that are based on multiple-output Gaussian processes (MGPs), which are a flexible non-parametric Bayesian modelling technique. The main advantages that follow from MGPs approach include the natural non-parametric representation of the fluxes and ability to impute the missing data in between the measurements. Our derivative process approach allows us to model changes in metabolite derivative concentrations and to characterize the temporal behaviour of metabolic fluxes from time course data. Because the derivative of a Gaussian process is itself a Gaussian process, we can readily link metabolite concentrations to metabolic fluxes and vice versa. Here we discuss how this can be implemented in an MGP framework and illustrate its application to simple models, including nitrogen metabolism in Escherichia coli. AVAILABILITY AND IMPLEMENTATION: R code is available from the authors upon request.

StochSens--Matlab package for sensitivity analysis of stochastic chemical systems.

MOTIVATION: The growing interest in the role of stochasticity in biochemical systems drives the demand for tools to analyse stochastic dynamical models of chemical reactions. One powerful tool to elucidate performance of dynamical systems is sensitivity analysis. Traditionally, however, the concept of sensitivity has mainly been applied to deterministic systems, and the difficulty to generalize these concepts for stochastic systems results from necessity of extensive Monte Carlo simulations. RESULTS: Here we present a Matlab package, StochSens, that implements sensitivity analysis for stochastic chemical systems using the concept of the Fisher Information Matrix (FIM). It uses the linear noise approximation to represent the FIM in terms of solutions of ordinary differential equations. This is the first computational tool that allows for quick computation of the Information Matrix for stochastic systems without the need for Monte Carlo simulations. AVAILABILITY: http://www.theosysbio.bio.ic.ac.uk/resources/stns SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.