Small RNA Sequencing across Diverse Biofluids Identifies Optimal Methods for exRNA Isolation.

Poor reproducibility within and across studies arising from lack of knowledge regarding the performance of extracellular RNA (exRNA) isolation methods has hindered progress in the exRNA field. A systematic comparison of 10 exRNA isolation methods across 5 biofluids revealed marked differences in the complexity and reproducibility of the resulting small RNA-seq profiles. The relative efficiency with which each method accessed different exRNA carrier subclasses was determined by estimating the proportions of extracellular vesicle (EV)-, ribonucleoprotein (RNP)-, and high-density lipoprotein (HDL)-specific miRNA signatures in each profile. An interactive web-based application (miRDaR) was developed to help investigators select the optimal exRNA isolation method for their studies. miRDar provides comparative statistics for all expressed miRNAs or a selected subset of miRNAs in the desired biofluid for each exRNA isolation method and returns a ranked list of exRNA isolation methods prioritized by complexity, expression level, and reproducibility. These results will improve reproducibility and stimulate further progress in exRNA biomarker development.

Histopathologic and Transcriptomic Profiling Identifies Novel Trophoblast Defects in Patients With Preeclampsia and Maternal Vascular Malperfusion.

Preeclampsia (PE) is a heterogeneous disease for which the current clinical classification system is based on the presence or absence of specific clinical features. PE-associated placentas also show heterogeneous findings on pathologic examination, suggesting that further subclassification is possible. We combined clinical, pathologic, immunohistochemical, and transcriptomic profiling of placentas to develop integrated signatures for multiple subclasses of PE. In total, 303 PE and 1388 nonhypertensive control placentas were included. We found that maternal vascular malperfusion (MVM) in the placenta was associated with preterm PE with severe features and with small-for-gestational-age neonates. Interestingly, PE placentas with either MVM or no histologic pattern of injury showed a linear decrease in proliferative (p63+) cytotrophoblast per villous area with increasing gestational age, similar to placentas obtained from the nonhypertensive patient cohort; however, PE placentas with fetal vascular malperfusion or villitis of unknown etiology lost this phenotype. This is mainly because of cases of fetal vascular malperfusion in placentas of patients with preterm PE and villitis of unknown etiology in placentas of patients with term PE, which are associated with a decrease or increase, respectively, in the cytotrophoblast per villous area. Finally, a transcriptomic analysis identified pathways associated with hypoxia, inflammation, and reduced cell proliferation in PE-MVM placentas and further subclassified this group into extravillous trophoblast-high and extravillous trophoblast-low PE, confirmed using an immunohistochemical analysis of trophoblast lineage-specific markers. Our findings suggest that within specific histopathologic patterns of placental injury, PE can be subclassified based on specific cellular and molecular defects, allowing the identification of pathways that may be targeted for diagnostic and therapeutic purposes.

Comparative analysis of mouse and human placentae across gestation reveals species-specific regulators of placental development.

An increasing body of evidence points to significant spatio-temporal differences in early placental development between mouse and human, but a detailed comparison of placentae in these two species is missing. We set out to compare placentae from both species across gestation, with a focus on trophoblast progenitor markers. We found that CDX2 and ELF5, but not EOMES, are expressed in early post-implantation trophoblast subpopulations in both species. Genome-wide expression profiling of mouse and human placentae revealed clusters of genes with distinct co-expression patterns across gestation. Overall, there was a closer fit between patterns observed in the placentae when the inter-species comparison was restricted to human placentae through gestational week 16 (thus, excluding full-term samples), suggesting that the developmental timeline in mouse runs parallel to the first half of human placental development. In addition, we identified VGLL1 as a human-specific marker of proliferative cytotrophoblast, where it is co-expressed with the transcription factor TEAD4. As TEAD4 is involved in trophectoderm specification in the mouse, we posit a regulatory role for VGLL1 in early events during human placental development.

Calixarene-mediated liquid membrane transport of choline conjugates 3: The effect of handle variation on neurotransmitter transport.

Upper rim phosphonic acid functionalized calix[4]arene affects selective transport of multiple molecular payloads through a liquid membrane. The secret is in the attachment of a receptor-complementary handle to the payload. We find that the trimethylammonium ethylene group present in choline is one of several general handles for the transport of drug and drug-like species. Herein we compare the effect of handle variation against the transport of serotonin and dopamine. We find that several ionizable amine termini handles are sufficient for transport and identify two ideal candidates. Their performance is significantly enhanced in HEPES buffered solutions. This inquiry completes a series of 3 studies aimed at optimization of this strategy. In completion a new approach towards synthetic receptor mediated selective small molecule transport has emerged; future work in vesicular and cellular systems will follow.

Stage-specific regulation of the WNT/ß-catenin pathway enhances differentiation of hESCs into hepatocytes.

BACKGROUND & AIMS: Hepatocytes differentiated from human embryonic stem cells (hESCs) have the potential to overcome the shortage of primary hepatocytes for clinical use and drug development. Many strategies for this process have been reported, but the functionality of the resulting cells is incomplete. We hypothesize that the functionality of hPSC-derived hepatocytes might be improved by making the differentiation method more similar to normal in vivo hepatic development. METHODS: We tested combinations of growth factors and small molecules targeting candidate signaling pathways culled from the literature to identify optimal conditions for differentiation of hESCs to hepatocytes, using qRT-PCR for stage-specific markers to identify the best conditions. Immunocytochemistry was then used to validate the selected conditions. Finally, induction of expression of metabolic enzymes in terminally differentiated cells was used to assess the functionality of the hESC-derived hepatocytes. RESULTS: Optimal differentiation of hESCs was attained using a 5-stage protocol. After initial induction of definitive endoderm (stage 1), we showed that inhibition of the WNT/ß-catenin pathway during the 2nd and 3rd stages of differentiation was required to specify first posterior foregut, and then hepatic gut cells. In contrast, during the 4th stage of differentiation, we found that activation of the WNT/ß-catenin pathway allowed generation of proliferative bipotent hepatoblasts, which then were efficiently differentiated into hepatocytes in the 5th stage by dual inhibition of TGF-ß and NOTCH signaling. CONCLUSION: Here, we show that stage-specific regulation of the WNT/ß-catenin pathway results in improved differentiation of hESCs to functional hepatocytes.

Pb, Sr and Ba calix[6]arene hexacarboxylic acid octahedral complexation: a dramatic effect of dealkylation.

Calix[6]arene hexacarboxylic acid binds instantly and with low symmetry to Pb, Sr and Ba. Later a highly symmetric up-down alternating conformation emerges. The solution structures are identical to their p-tert-butylcalix[6]arene hexacarboxylic acid counterparts. With either receptor an octahedral cage is formed around the metal. The transformation from low to high symmetry however proceeds at significantly faster rates for the de-t-butylated host.

Molecular profiling of human blastocysts reveals primitive endoderm defects among embryos of decreased implantation potential.

Human embryo implantation is remarkably inefficient, and implantation failure remains among the greatest obstacles in treating infertility. Gene expression data from human embryos have accumulated rapidly in recent years; however, identification of the subset of genes that determine successful implantation remains a challenge. We leverage clinical morphologic grading-known for decades to correlate with implantation potential-and transcriptome analyses of matched embryonic and abembryonic samples to identify factors and pathways enriched and depleted in human blastocysts of good and poor morphology. Unexpectedly, we discovered that the greatest difference was in the state of extraembryonic primitive endoderm (PrE) development, with relative deficiencies in poor morphology blastocysts. Our results suggest that implantation success is most strongly influenced by the embryonic compartment and that deficient PrE development is common among embryos with decreased implantation potential. Our study provides a valuable resource for those investigating the markers and mechanisms of human embryo implantation.

Discovery and verification of extracellular microRNA biomarkers for diagnostic and prognostic assessment of preeclampsia at triage.

We report on the identification of extracellular miRNA (ex-miRNA) biomarkers for early diagnosis and prognosis of preeclampsia (PE). Small RNA sequencing of maternal serum prospectively collected from participants undergoing evaluation for suspected PE revealed distinct patterns of ex-miRNA expression among different categories of hypertensive disorders in pregnancy. Applying an iterative machine learning method identified three bivariate miRNA biomarkers (miR-522-3p/miR-4732-5p, miR-516a-5p/miR-144-3p, and miR-27b-3p/let-7b-5p) that, when applied serially, distinguished between PE cases of different severity and differentiated cases from controls with a sensitivity of 93%, specificity of 79%, positive predictive value (PPV) of 55%, and negative predictive value (NPV) of 89%. In a small independent validation cohort, these ex-miRNA biomarkers had a sensitivity of 91% and specificity of 57%. Combining these ex-miRNA biomarkers with the established sFlt1:PlGF protein biomarker ratio performed better than either set of biomarkers alone (sensitivity of 89.4%, specificity of 91.3%, PPV of 95.5%, and NPV of 80.8%).

Measurement variation determines the gene network topology reconstructed from experimental data: a case study of the yeast cyclin network.

Inference of the topology of gene regulatory networks from experimental data is one of the primary challenges of systems biology. In an example of a genetic network of cyclins in the yeast cell cycle, we analyzed static genome-wide location data together with microarray kinetic measurements using a recurrent neural network-based model of gene expression and a newly developed, unbiased algorithm based on evolutionary programming principles. The modeling and simulation of gene expression dynamics identified cyclin genetic networks that were active during the cell cycle. We document that because there is inherent experimental variation, it is not possible to identify a single genetic network, only a set of equivalent networks with the same probability of occurrence. Analysis of these networks showed that each target gene was controlled by only a few regulators and that the control was robust. These results led to the reformulation of the cyclin genetic network in the yeast cell cycle as previously published. The analysis shows that with the methodologies that are currently available, it is not possible to predict only one genetic network; rather, we must work with the hypothesis of multiple, equivalent networks. Chromatin immunoprecipitation (ChIP)-on-chip experiments are not sufficient to predict the functional networks that are active during an investigated process. Such predictions must be considered as only potential, and their actual realization during particular cellular processes must be identified by incorporating both kinetic and other types of data.

Discovery and Verification of Extracellular miRNA Biomarkers for Non-invasive Prediction of Pre-eclampsia in Asymptomatic Women.

Development of effective prevention and treatment strategies for pre-eclampsia is limited by the lack of accurate methods for identification of at-risk pregnancies. We performed small RNA sequencing (RNA-seq) of maternal serum extracellular RNAs (exRNAs) to discover and verify microRNAs (miRNAs) differentially expressed in patients who later developed pre-eclampsia. Sera collected from 73 pre-eclampsia cases and 139 controls between 17 and 28 weeks gestational age (GA), divided into separate discovery and verification cohorts, are analyzed by small RNA-seq. Discovery and verification of univariate and bivariate miRNA biomarkers reveal that bivariate biomarkers verify at a markedly higher rate than univariate biomarkers. The majority of verified biomarkers contain miR-155-5p, which has been reported to mediate the pre-eclampsia-associated repression of endothelial nitric oxide synthase (eNOS) by tumor necrosis factor alpha (TNF-α). Deconvolution analysis reveals that several verified miRNA biomarkers come from the placenta and are likely carried by placenta-specific extracellular vesicles.

Supervised inference of gene-regulatory networks.

BACKGROUND: Inference of protein interaction networks from various sources of data has become an important topic of both systems and computational biology. Here we present a supervised approach to identification of gene expression regulatory networks. RESULTS: The method is based on a kernel approach accompanied with genetic programming. As a data source, the method utilizes gene expression time series for prediction of interactions among regulatory proteins and their target genes. The performance of the method was verified using Saccharomyces cerevisiae cell cycle and DNA/RNA/protein biosynthesis gene expression data. The results were compared with independent data sources. Finally, a prediction of novel interactions within yeast gene expression circuits has been performed. CONCLUSION: Results show that our algorithm gives, in most cases, results identical with the independent experiments, when compared with the YEASTRACT database. In several cases our algorithm gives predictions of novel interactions which have not been reported.

A parallel genetic algorithm for single class pattern classification and its application for gene expression profiling in Streptomyces coelicolor.

BACKGROUND: Identification of coordinately regulated genes according to the level of their expression during the time course of a process allows for discovering functional relationships among genes involved in the process. RESULTS: We present a single class classification method for the identification of genes of similar function from a gene expression time series. It is based on a parallel genetic algorithm which is a supervised computer learning method exploiting prior knowledge of gene function to identify unknown genes of similar function from expression data. The algorithm was tested with a set of randomly generated patterns; the results were compared with seven other classification algorithms including support vector machines. The algorithm avoids several problems associated with unsupervised clustering methods, and it shows better performance then the other algorithms. The algorithm was applied to the identification of secondary metabolite gene clusters of the antibiotic-producing eubacterium Streptomyces coelicolor. The algorithm also identified pathways associated with transport of the secondary metabolites out of the cell. We used the method for the prediction of the functional role of particular ORFs based on the expression data. CONCLUSION: Through analysis of a time series of gene expression, the algorithm identifies pathways which are directly or indirectly associated with genes of interest, and which are active during the time course of the experiment.

Miniaturization Technologies for Efficient Single-Cell Library Preparation for Next-Generation Sequencing.

As the cost of next-generation sequencing has decreased, library preparation costs have become a more significant proportion of the total cost, especially for high-throughput applications such as single-cell RNA profiling. Here, we have applied novel technologies to scale down reaction volumes for library preparation. Our system consisted of in vitro differentiated human embryonic stem cells representing two stages of pancreatic differentiation, for which we prepared multiple biological and technical replicates. We used the Fluidigm (San Francisco, CA) C1 single-cell Autoprep System for single-cell complementary DNA (cDNA) generation and an enzyme-based tagmentation system (Nextera XT; Illumina, San Diego, CA) with a nanoliter liquid handler (mosquito HTS; TTP Labtech, Royston, UK) for library preparation, reducing the reaction volume down to 2 µL and using as little as 20 pg of input cDNA. The resulting sequencing data were bioinformatically analyzed and correlated among the different library reaction volumes. Our results showed that decreasing the reaction volume did not interfere with the quality or the reproducibility of the sequencing data, and the transcriptional data from the scaled-down libraries allowed us to distinguish between single cells. Thus, we have developed a process to enable efficient and cost-effective high-throughput single-cell transcriptome sequencing.

Neural Stem Cells Derived from Human Parthenogenetic Stem Cells Engraft and Promote Recovery in a Nonhuman Primate Model of Parkinson's Disease.

Cell therapy has attracted considerable interest as a promising therapeutic alternative for patients with Parkinson's disease (PD). Clinical studies have shown that grafted fetal neural tissue can achieve considerable biochemical and clinical improvements in PD. However, the source of fetal tissue grafts is limited and ethically controversial. Human parthenogenetic stem cells offer a good alternative because they are derived from unfertilized oocytes without destroying potentially viable human embryos and can be used to generate an unlimited supply of neural cells for transplantation. We have previously reported that human parthenogenetic stem cell-derived neural stem cells (hpNSCs) successfully engraft, survive long term, and increase brain dopamine (DA) levels in rodent and nonhuman primate models of PD. Here we report the results of a 12-month transplantation study of hpNSCs in 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-lesioned African green monkeys with moderate to severe clinical parkinsonian symptoms. The hpNSCs manufactured under current good manufacturing practice (cGMP) conditions were injected bilaterally into the striatum and substantia nigra of immunosuppressed monkeys. Transplantation of hpNSCs was safe and well tolerated by the animals with no dyskinesia, tumors, ectopic tissue formation, or other test article-related serious adverse events. We observed that hpNSCs promoted behavioral recovery; increased striatal DA concentration, fiber innervation, and number of dopaminergic neurons; and induced the expression of genes and pathways downregulated in PD compared to vehicle control animals. These results provide further evidence for the clinical translation of hpNSCs and support the approval of the world's first pluripotent stem cell-based phase I/IIa study for the treatment of PD (Clinical Trial Identifier NCT02452723).