Reduced Maternal Circulating Cell‐Free Mitochondrial DNA Is Associated With the Development of Preeclampsia

Background Circulating cell‐free mitochondrial DNA (ccf‐mtDNA) is a damage‐associated molecular pattern that reflects cell stress responses and tissue damage, but little is known about ccf‐mtDNA in preeclampsia. The main objectives of this study were to determine (1) absolute concentrations of ccf‐mtDNA in plasma and mitochondrial DNA content in peripheral blood mononuclear cells and (2) forms of ccf‐mtDNA transport in blood from women with preeclampsia and healthy controls. In addition, we sought to establish the association between aberrance in circulating DNA‐related metrics, including ccf‐mtDNA and DNA clearance mechanisms, and the clinical diagnosis of preeclampsia using bootstrapped penalized logistic regression. Methods and Results Absolute concentrations of ccf‐mtDNA were reduced in plasma from women with preeclampsia compared with healthy controls ( P ≤0.02), while mtDNA copy number in peripheral blood mononuclear cells did not differ between groups ( P >0.05). While the pattern of reduced ccf‐mtDNA in patients with preeclampsia remained, DNA isolation from plasma using membrane lysis buffer resulted in 1000‐fold higher ccf‐mtDNA concentrations in the preeclampsia group ( P =0.0014) and 430‐fold higher ccf‐mtDNA concentrations in the control group ( P <0.0001). Plasma from women with preeclampsia did not induce greater Toll‐like receptor‐9–induced nuclear factor kappa‐light‐chain enhancer of activated B cells‐dependent responses in human embryonic kidney 293 cells overexpressing the human TLR‐9 gene ( P >0.05). Penalized regression analysis showed that women with preeclampsia were more likely to have lower concentrations of ccf‐mtDNA as well as higher concentrations of nuclear DNA and DNase I compared with their matched controls. Conclusions Women with preeclampsia have aberrant circulating DNA dynamics, including reduced ccf‐mtDNA concentrations and DNA clearance mechanisms, compared with gestational age–matched healthy pregnant women.

Space-log: a novel approach to inferring gene-gene net-works using SPACE model with log penalty

Gene expression data have been used to infer gene-gene networks (GGN) where an edge between two genes implies the conditional dependence of these two genes given all the other genes. Such gene-gene networks are of-ten referred to as gene regulatory networks since it may reveal expression regulation. Most of existing methods for identifying GGN employ penalized regression with L1 (lasso), L2 (ridge), or elastic net penalty, which spans the range of L1 to L2 penalty. However, for high dimensional gene expression data, a penalty that spans the range of L0 and L1 penalty, such as the log penalty, is often needed for variable selection consistency. Thus, we develop a novel method that em-ploys log penalty within the framework of an earlier network identification method space (Sparse PArtial Correlation Estimation), and implement it into a R package space-log. We show that the space-log is computationally efficient (source code implemented in C), and has good performance comparing with other methods, particularly for networks with hubs.Space-log is open source and available at GitHub, https://github.com/wuqian77/SpaceLog

Elements for Building Supervised Statistical Machine Learning Models

This chapter gives details of the linear multiple regression model including assumptions and some pros and cons, the maximum likelihood. Gradient descendent methods are described for learning the parameters under this model. Penalized linear multiple regression is derived under Ridge and Lasso penalties, which also emphasizes the estimation of the regularization parameter of importance for its successful implementation. Examples are given for both penalties (Ridge and Lasso) and but not for penalized regression multiple regression framework for illustrating the circumstances when the penalized versions should be preferred. Finally, the fundamentals of penalized and non-penalized logistic regression are provided under a gradient descendent framework. We give examples of logistic regression. Each example comes with the corresponding R codes to facilitate their quick understanding and use.

A systems genomics approach uncovers molecular associates of RSV severity

Respiratory syncytial virus (RSV) infection results in millions of hospitalizations and thousands of deaths each year. Variations in the adaptive and innate immune response appear to be associated with RSV severity. To investigate the host response to RSV infection in infants, we performed a systems-level study of RSV pathophysiology, incorporating high-throughput measurements of the peripheral innate and adaptive immune systems and the airway epithelium and microbiota. We implemented a novel multi-omic data integration method based on multilayered principal component analysis, penalized regression, and feature weight back-propagation, which enabled us to identify cellular pathways associated with RSV severity. In both airway and immune cells, we found an association between RSV severity and activation of pathways controlling Th17 and acute phase response signaling, as well as inhibition of B cell receptor signaling. Dysregulation of both the humoral and mucosal response to RSV may play a critical role in determining illness severity.

An Efficient Algorithm for the Detection of Outliers in Mislabeled Omics Data

High dimensionality and noise have made it difficult to detect related biomarkers in omics data. Through previous study, penalized maximum trimmed likelihood estimation is effective in identifying mislabeled samples in high-dimensional data with mislabeled error. However, the algorithm commonly used in these studies is the concentration step (C-step), and the C-step algorithm that is applied to robust penalized regression does not ensure that the criterion function is gradually optimized iteratively, because the regularized parameters change during the iteration. This makes the C-step algorithm runs very slowly, especially when dealing with high-dimensional omics data. The AR-Cstep (C-step combined with an acceptance-rejection scheme) algorithm is proposed. In simulation experiments, the AR-Cstep algorithm converged faster (the average computation time was only 2% of that of the C-step algorithm) and was more accurate in terms of variable selection and outlier identification than the C-step algorithm. The two algorithms were further compared on triple negative breast cancer (TNBC) RNA-seq data. AR-Cstep can solve the problem of the C-step not converging and ensures that the iterative process is in the direction that improves criterion function. As an improvement of the C-step algorithm, the AR-Cstep algorithm can be extended to other robust models with regularized parameters.

Polygenic Risk Scores for Prediction of Breast Cancer Risk in Women of African Ancestry: a Cross-Ancestry Approach

Polygenic risk scores (PRSs) are useful to predict breast cancer risk, but the prediction accuracy of existing PRSs in women of African ancestry (AA) remain relatively low. We aim to develop optimal PRSs for prediction of overall and estrogen receptor (ER) subtype-specific breast cancer risk in women of African ancestry. The AA dataset comprised 9,235 cases and 10,184 controls from four genome-wide association study (GWAS) consortia and a GWAS study in Ghana. We randomly divided samples into training and validation sets. Genetic variants were selected by forward stepwise logistic regression or lasso penalized regression in the training set and the corresponding PRSs were evaluated in the validation set. To improve accuracy, we also developed joint PRSs that combined 1) the best PRSs built in the AA training dataset, 2) a previously-developed 313-variant PRS in women of European ancestry, and 3) PRSs using variants that were discovered in previous GWASs in women of European and African ancestry and were nominally significant the training set. For overall breast cancer, the odd ratio (OR) per standard deviation of the joint PRS in the validation set was 1.39 (95%CI: 1.31-1.46) with area under receiver operating characteristic curve (AUC) of 0.590. Compared to women with average risk (40th-60th PRS percentile), women in the top decile of the PRS had a 2.03-fold increased risk (95%CI: 1.68-2.44). For PRSs of ER-positive and ER-negative breast cancer, the AUCs were 0.609 and 0.597, respectively. The proposed PRS can improve prediction of breast cancer risk in women of African ancestry.

Pre-pandemic Predictors of Loneliness in Adult Men During COVID-19

Loneliness is a major public health issue, with its prevalence rising during COVID-19 pandemic lockdowns and mandated “social distancing” practices. A 2020 global study (n = 46,054) found that, in comparison to women, men experienced the greatest levels of loneliness. Although research on predictors of loneliness during COVID-19 is increasing, little is known about the characteristics of men who may be particularly vulnerable. Studies using prospective data are needed to inform preventative measures to support men at risk of loneliness. The current study draws on rare longitudinal data from an Australian cohort of men in young to mid-adulthood (n = 283; aged M = 34.6, SD = 1.38 years) to examine 25 pre-pandemic psychosocial predictors of loneliness during COVID-19 social restrictions (March–September 2020). Adjusted linear regressions identified 22 pre-pandemic predictors of loneliness across a range of trait-based, relational, career/home and mental health variables. Given the extensive set of predictors, we then conducted penalized regression models (LASSO), a machine learning approach, allowing us to identify the best fitting multivariable set of predictors of loneliness during the pandemic. In these models, men's sense of pre-pandemic environmental mastery emerged as the strongest predictor of loneliness. Depression, neuroticism and social support also remained key predictors of pandemic loneliness (R2 = 26, including covariates). Our findings suggest that men's loneliness can be detected prospectively and under varying levels of social restriction, presenting possible targets for prevention efforts for those most vulnerable.

Multi-Omics Analysis of Novel Signature for Immunotherapy Response and Tumor Microenvironment Regulation Patterns in Urothelial Cancer

The tumor microenvironment (TME) is mainly composed of tumor cells, tumor-infiltrating immune cells, and stromal components. It plays an essential role in the prognosis and therapeutic response of patients. Nonetheless, the TME landscape of urothelial cancer (UC) has not been fully elucidated. In this study, we systematically analyzed several UC cohorts, and three types of TME patterns (stromal-activation subtype, immune-enriched subtype and immune-suppressive subtype) were defined. The tumor microenvironment signature (TMSig) was constructed by modified Lasso penalized regression. Patients were stratified into high- and low-TMSig score groups. The low-score group had a better prognosis (p &lt; 0.0001), higher M1 macrophage infiltration (p &lt; 0.01), better response to immunotherapy (p &lt; 0.05), and more similar molecular characteristics to the luminal (differentiated) subtype. The accuracy of the TMSig for predicting the immunotherapy response was also verified in three independent cohorts. We highlighted that the TMSig is an effective predictor of patient prognosis and immunotherapy response. Quantitative evaluation of a single sample is valuable for us to combine histopathological and molecular characteristics to comprehensively evaluate the status of the patient. Targeted macrophage treatment has great potential for the individualized precision therapy of UC patients.