Performance of a validated spontaneous preterm delivery predictor in South Asian and Sub-Saharan African women: a nested case control study.

OBJECTIVES: To address the disproportionate burden of preterm birth (PTB) in low- and middle-income countries, this study aimed to (1) verify the performance of the United States-validated spontaneous PTB (sPTB) predictor, comprised of the IBP4/SHBG protein ratio, in subjects from Bangladesh, Pakistan and Tanzania enrolled in the Alliance for Maternal and Newborn Health Improvement (AMANHI) biorepository study, and (2) discover biomarkers that improve performance of IBP4/SHBG in the AMANHI cohort. STUDY DESIGN: The performance of the IBP4/SHBG biomarker was first evaluated in a nested case control validation study, then utilized in a follow-on discovery study performed on the same samples. Levels of serum proteins were measured by targeted mass spectrometry. Differences between the AMANHI and U.S. cohorts were adjusted using body mass index (BMI) and gestational age (GA) at blood draw as covariates. Prediction of sPTB < 37 weeks and < 34 weeks was assessed by area under the receiver operator curve (AUC). In the discovery phase, an artificial intelligence method selected additional protein biomarkers complementary to IBP4/SHBG in the AMANHI cohort. RESULTS: The IBP4/SHBG biomarker significantly predicted sPTB < 37 weeks (n = 88 vs. 171 terms ≥ 37 weeks) after adjusting for BMI and GA at blood draw (AUC= 0.64, 95% CI: 0.57-0.71, p < .001). Performance was similar for sPTB < 34 weeks (n = 17 vs. 184 ≥ 34 weeks): AUC = 0.66, 95% CI: 0.51-0.82, p = .012. The discovery phase of the study showed that the addition of endoglin, prolactin, and tetranectin to the above model resulted in the prediction of sPTB < 37 with an AUC= 0.72 (95% CI: 0.66-0.79, p-value < .001) and prediction of sPTB < 34 with an AUC of 0.78 (95% CI: 0.67-0.90, p < .001). CONCLUSION: A protein biomarker pair developed in the U.S. may have broader application in diverse non-U.S. populations.

MRPC: An R Package for Inference of Causal Graphs.

Understanding the causal relationships between variables is a central goal of many scientific inquiries. Causal relationships may be represented by directed edges in a graph (or equivalently, a network). In biology, for example, gene regulatory networks may be viewed as a type of causal networks, where X→Y represents gene X regulating (i.e., being causal to) gene Y. However, existing general-purpose graph inference methods often result in a high number of false edges, whereas current causal inference methods developed for observational data in genomics can handle only limited types of causal relationships. We present MRPC (a PC algorithm with the principle of Mendelian Randomization), an R package that learns causal graphs with improved accuracy over existing methods. Our algorithm builds on the powerful PC algorithm (named after its developers Peter Spirtes and Clark Glymour), a canonical algorithm in computer science for learning directed acyclic graphs. The improvements in MRPC result in increased accuracy in identifying v-structures (i.e., X→Y←Z), and robustness to how the nodes are arranged in the input data. In the special case of genomic data that contain genotypes and phenotypes (e.g., gene expression) at the individual level, MRPC incorporates the principle of Mendelian randomization as constraints on edge direction to help orient the edges. MRPC allows for inference of causal graphs not only for general purposes, but also for biomedical data where multiple types of data may be input to provide evidence for causality. The R package is available on CRAN and is a free open-source software package under a GPL (≥2) license.

Bacterial Populations in International Artisanal Kefirs.

Artisanal kefir is a traditional fermented dairy product made using kefir grains. Kefir has documented natural antimicrobial activity and health benefits. A typical kefir microbial community includes lactic acid bacteria (LAB), acetic acid bacteria, and yeast among other species in a symbiotic matrix. In the presented work, the 16S rRNA gene sequencing was used to reveal bacterial populations and elucidate the diversity and abundance of LAB species in international artisanal kefirs from Fusion Tea, Britain, the Caucuses region, Ireland, Lithuania, and South Korea. Bacterial species found in high abundance in most artisanal kefirs included Lactobacillus kefiranofaciens, Lentilactobacillus kefiri,Lactobacillus ultunensis, Lactobacillus apis, Lactobacillus gigeriorum, Gluconobacter morbifer, Acetobacter orleanensis, Acetobacter pasteurianus, Acidocella aluminiidurans, and Lactobacillus helveticus. Some of these bacterial species are LAB that have been reported for their bacteriocin production capabilities and/or health promoting properties.

Antimicrobial Activity of Six International Artisanal Kefirs Against Bacillus cereus, Listeria monocytogenes, Salmonella enterica serovar Enteritidis, and Staphylococcus aureus.

Kefir, a fermented dairy beverage, exhibits antimicrobial activity due to many metabolic products, including bacteriocins, generated by lactic acid bacteria. In this study, the antimicrobial activities of artisanal kefir products from Fusion Tea (A), Britain (B), Ireland (I), Lithuania (L), the Caucuses region (C), and South Korea (K) were investigated against select foodborne pathogens. Listeria monocytogenes CWD 1198, Salmonella enterica serovar Enteritidis ATCC 13076, Staphylococcus aureus ATCC 25923, and Bacillus cereus ATCC 14579 were inhibited by artisanal kefirs made with kefir grains from diverse origins. Kefirs A, B, and I inhibited all bacterial indicator strains examined at varying levels, except Escherichia coli ATCC 12435 (non-pathogenic, negative control). Kefirs K, L, and C inhibited all indicator strains, except S. aureus ATCC 25923 and E. coli ATCC 12435. Bacteriocins present in artisanal kefirs were determined to be the main antimicrobials in all kefirs examined. Kefir-based antimicrobials are being proposed as promising natural biopreservatives as per the results of the study.

Imputation of single-cell gene expression with an autoencoder neural network.

BACKGROUND: Single-cell RNA-sequencing (scRNA-seq) is a rapidly evolving technology that enables measurement of gene expression levels at an unprecedented resolution. Despite the explosive growth in the number of cells that can be assayed by a single experiment, scRNA-seq still has several limitations, including high rates of dropouts, which result in a large number of genes having zero read count in the scRNA-seq data, and complicate downstream analyses. METHODS: To overcome this problem, we treat zeros as missing values and develop nonparametric deep learning methods for imputation. Specifically, our LATE (Learning with AuToEncoder) method trains an autoencoder with random initial values of the parameters, whereas our TRANSLATE (TRANSfer learning with LATE) method further allows for the use of a reference gene expression data set to provide LATE with an initial set of parameter estimates. RESULTS: On both simulated and real data, LATE and TRANSLATE outperform existing scRNA-seq imputation methods, achieving lower mean squared error in most cases, recovering nonlinear gene-gene relationships, and better separating cell types. They are also highly scalable and can efficiently process over 1 million cells in just a few hours on a GPU. CONCLUSIONS: We demonstrate that our nonparametric approach to imputation based on autoencoders is powerful and highly efficient.

Learning Causal Biological Networks With the Principle of Mendelian Randomization.

Although large amounts of genomic data are available, it remains a challenge to reliably infer causal (i. e., regulatory) relationships among molecular phenotypes (such as gene expression), especially when multiple phenotypes are involved. We extend the interpretation of the Principle of Mendelian randomization (PMR) and present MRPC, a novel machine learning algorithm that incorporates the PMR in the PC algorithm, a classical algorithm for learning causal graphs in computer science. MRPC learns a causal biological network efficiently and robustly from integrating individual-level genotype and molecular phenotype data, in which directed edges indicate causal directions. We demonstrate through simulation that MRPC outperforms several popular general-purpose network inference methods and PMR-based methods. We apply MRPC to distinguish direct and indirect targets among multiple genes associated with expression quantitative trait loci. Our method is implemented in the R package MRPC, available on CRAN (https://cran.r-project.org/web/packages/MRPC/index.html).

Metabolic analysis of antibody producing Chinese hamster ovary cell culture under different stresses conditions.

Chinese hamster ovary (CHO) cells are commonly used as the host cell lines concerning their ability to produce therapeutic proteins with complex post-translational modifications. In this study, we have investigated the time course extra- and intracellular metabolome data of the CHO-K1 cell line, under a control and stress conditions. The addition of NaCl and trehalose greatly suppressed cell growth, where the maximum viable cell density of NaCl and trehalose cultures were 2.2-fold and 2.8-fold less than that of a control culture. Contrariwise, the antibody production of both the NaCl and trehalose cultures was sustained for a longer time to surpass that of the control culture. The NaCl and trehalose cultures showed relatively similar dynamics of cell growth, antibody production, and substrate/product concentrations, while they indicated different dynamics from the control culture. The principal component analysis of extra- and intracellular metabolome dynamics indicated that their dynamic behaviors were consistent with biological functions. The qualitative pattern matching classification and hierarchical clustering analyses for the intracellular metabolome identified the metabolite clusters whose dynamic behaviors depend on NaCl and trehalose. The volcano plot revealed several reporter metabolites whose dynamics greatly change between in the NaCl and trehalose cultures. The elastic net identified some critical, intracellular metabolites that are distinct between the NaCl and trehalose. While a relatively small number of intracellular metabolites related to the cell growth, glucose, glutamine, lactate and ammonium ion concentrations, the mechanism of antibody production was suggested to be very complicated or not to be explained by elastic net regression analysis.

Robust complementary hierarchical clustering for gene expression data analysis by ß-divergence.

A hierarchical clustering (HC) algorithm is one of the most widely used unsupervised statistical techniques for analyzing microarray gene expression data. When applying the HC algorithm to the gene expression data to cluster individuals, most of the HC algorithms generate clusters based on the highly differentially expressed (DE) genes that have very similar expression patterns. These highly DE genes may sometimes be irrelevant in biological processes. The serious problem is that those irrelevant genes with high expressions potentially drown out the low expressed genes that have important biological functions. To overcome the problem, Nowak and Tibshirani proposed the complementary hierarchical clustering (CHC) (Biostatistics, 9, 467-483, 2008). However, it is not robust against outlying expression and often produces misleading results if there exist some contaminations in the gene expression data. Thus, we propose the robust CHC (RCHC) method to robustify the CHC with respect to outliers by maximizing the ß-likelihood function for sequential extraction of a gene-set with proper groups of individuals. Note that the proposed method reduces to the CHC with the tuning parameter ߠ→ 0. A value of ß plays a key role in the performance of the RCHC method, which controls the tradeoff between the robustness and efficiency of the estimators. Using simulation and real gene expression analysis, the RCHC method shows robust properties to gene expression clustering with respect to data contaminations, overcomes the problem of the CHC, and predicts critically important genes from breast cancer data.