Challenges and perspectives in computational deconvolution of genomics data.

Deciphering cell-type heterogeneity is crucial for systematically understanding tissue homeostasis and its dysregulation in diseases. Computational deconvolution is an efficient approach for estimating cell-type abundances from a variety of omics data. Despite substantial methodological progress in computational deconvolution in recent years, challenges are still outstanding. Here we enlist four important challenges related to computational deconvolution: the quality of the reference data, generation of ground truth data, limitations of computational methodologies, and benchmarking design and implementation. Finally, we make recommendations on reference data generation, new directions of computational methodologies, and strategies to promote rigorous benchmarking.

Regulated accumulation of desmosterol integrates macrophage lipid metabolism and inflammatory responses.

Inflammation and macrophage foam cells are characteristic features of atherosclerotic lesions, but the mechanisms linking cholesterol accumulation to inflammation and LXR-dependent response pathways are poorly understood. To investigate this relationship, we utilized lipidomic and transcriptomic methods to evaluate the effect of diet and LDL receptor genotype on macrophage foam cell formation within the peritoneal cavities of mice. Foam cell formation was associated with significant changes in hundreds of lipid species and unexpected suppression, rather than activation, of inflammatory gene expression. We provide evidence that regulated accumulation of desmosterol underlies many of the homeostatic responses, including activation of LXR target genes, inhibition of SREBP target genes, selective reprogramming of fatty acid metabolism, and suppression of inflammatory-response genes, observed in macrophage foam cells. These observations suggest that macrophage activation in atherosclerotic lesions results from extrinsic, proinflammatory signals generated within the artery wall that suppress homeostatic and anti-inflammatory functions of desmosterol.

BML: a versatile web server for bipartite motif discovery.

Motif discovery and characterization are important for gene regulation analysis. The lack of intuitive and integrative web servers impedes the effective use of motifs. Most motif discovery web tools are either not designed for non-expert users or lacking optimization steps when using default settings. Here we describe bipartite motifs learning (BML), a parameter-free web server that provides a user-friendly portal for online discovery and analysis of sequence motifs, using high-throughput sequencing data as the input. BML utilizes both position weight matrix and dinucleotide weight matrix, the latter of which enables the expression of the interdependencies of neighboring bases. With input parameters concerning the motifs are given, the BML achieves significantly higher accuracy than other available tools for motif finding. When no parameters are given by non-expert users, unlike other tools, BML employs a learning method to identify motifs automatically and achieve accuracy comparable to the scenario where the parameters are set. The BML web server is freely available at http://motif.t-ridership.com/ (https://github.com/Mohammad-Vahed/BML).

Prediction of spontaneous preterm birth using supervised machine learning on metabolomic data: A case-cohort study.

OBJECTIVES: To identify and internally validate metabolites predictive of spontaneous preterm birth (sPTB) using multiple machine learning methods and sequential maternal serum samples, and to predict spontaneous early term birth (sETB) using these metabolites. DESIGN: Case-cohort design within a prospective cohort study. SETTING: Cambridge, UK. POPULATION OR SAMPLE: A total of 399 Pregnancy Outcome Prediction study participants, including 98 cases of sPTB. METHODS: An untargeted metabolomic analysis of maternal serum samples at 12, 20, 28 and 36 weeks of gestation was performed. We applied six supervised machine learning methods and a weighted Cox model to measurements at 28 weeks of gestation and sPTB, followed by feature selection. We used logistic regression with elastic net penalty, followed by best subset selection, to reduce the number of predictive metabolites further. We applied coefficients from the chosen models to measurements from different gestational ages to predict sPTB and sETB. MAIN OUTCOME MEASURES: sPTB and sETB. RESULTS: We identified 47 metabolites, mostly lipids, as important predictors of sPTB by two or more methods and 22 were identified by three or more methods. The best 4-predictor model had an optimism-corrected area under the receiver operating characteristics curve (AUC) of 0.703 at 28 weeks of gestation. The model also predicted sPTB in 12-week samples (0.606, 95% CI 0.544-0.667) and 20-week samples (0.657, 95% CI 0.597-0.717) and it predicted sETB in 36-week samples (0.727, 95% CI 0.606-0.849). A lysolipid, 1-palmitoleoyl-GPE (16:1)*, was the strongest predictor of sPTB at 12 weeks of gestation (0.609, 95% CI 0.548-0.670), 20 weeks (0.630, 95% CI 0.569-0.690) and 28 weeks (0.660, 95% CI 0.599-0.722), and of sETB at 36 weeks (0.739, 95% CI 0.618-0.860). CONCLUSIONS: We identified and internally validated maternal serum metabolites predictive of sPTB. A lysolipid, 1-palmitoleoyl-GPE (16:1)*, is a novel predictor of sPTB and sETB. Further validation in external populations is required.

Cox-nnet v2.0: improved neural-network-based survival prediction extended to large-scale EMR data.

SUMMARY: Cox-nnet is a neural-network-based prognosis prediction method, originally applied to genomics data. Here, we propose the version 2 of Cox-nnet, with significant improvement on efficiency and interpretability, making it suitable to predict prognosis based on large-scale population data, including those electronic medical records (EMR) datasets. We also add permutation-based feature importance scores and the direction of feature coefficients. When applied on a kidney transplantation dataset, Cox-nnet v2.0 reduces the training time of Cox-nnet up to 32-folds (n =10 000) and achieves better prediction accuracy than Cox-PH (P<0.05). It also achieves similarly superior performance on a publicly available SUPPORT data (n=8000). The high efficiency and accuracy make Cox-nnet v2.0 a desirable method for survival prediction in large-scale EMR data. AVAILABILITY AND IMPLEMENTATION: Cox-nnet v2.0 is freely available to the public at https://github.com/lanagarmire/Cox-nnet-v2.0. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Placentas delivered by pre-pregnant obese women have reduced abundance and diversity in the microbiome.

Maternal pre-pregnancy obesity may have an impact on both maternal and fetal health. We examined the microbiome recovered from placentas in a multi-ethnic maternal pre-pregnant obesity cohort, through an optimized microbiome protocol to enrich low bacterial biomass samples. We found that the microbiomes recovered from the placentas of obese pre-pregnant mothers are less abundant and less diverse when compared to those from mothers of normal pre-pregnancy weight. Microbiome richness also decreases from the maternal side to the fetal side, demonstrating heterogeneity by geolocation within the placenta. In summary, our study shows that the microbiomes recovered from the placentas are associated with pre-pregnancy obesity. IMPORTANCE: Maternal pre-pregnancy obesity may have an impact on both maternal and fetal health. The placenta is an important organ at the interface of the mother and fetus, and supplies nutrients to the fetus. We report that the microbiomes enriched from the placentas of obese pre-pregnant mothers are less abundant and less diverse when compared to those from mothers of normal pre-pregnancy weight. More over, the microbiomes also vary by geolocation within the placenta.

The maternal blood lipidome is indicative of the pathogenesis of severe preeclampsia.

Preeclampsia is a pregnancy-specific syndrome characterized by hypertension and proteinuria after 20 weeks of gestation. However, it is not well understood what lipids are involved in the development of this condition, and even less is known how these lipids mediate its formation. To reveal the relationship between lipids and preeclampsia, we conducted lipidomic profiling of maternal sera of 44 severe preeclamptic and 20 healthy pregnant women from a multiethnic cohort in Hawaii. Correlation network analysis showed that oxidized phospholipids have increased intercorrelations and connections in preeclampsia, whereas other lipids, including triacylglycerols, have reduced network correlations and connections. A total of 10 lipid species demonstrate significant changes uniquely associated with preeclampsia but not any other clinical confounders. These species are from the lipid classes of lysophosphatidylcholines, phosphatidylcholines (PCs), cholesteryl esters, phosphatidylethanolamines, lysophosphatidylethanolamines, and ceramides. A random forest classifier built on these lipids shows highly accurate and specific prediction (F1 statistic = 0.94; balanced accuracy = 0.88) of severe preeclampsia, demonstrating their potential as biomarkers for this condition. These lipid species are enriched in dysregulated biological pathways, including insulin signaling, immune response, and phospholipid metabolism. Moreover, causality inference shows that various PCs and lysophosphatidylcholines mediate severe preeclampsia through PC 35:1e. Our results suggest that the lipidome may play a role in the pathogenesis and serve as biomarkers of severe preeclampsia.

Enter the Matrix: Factorization Uncovers Knowledge from Omics.

Omics data contain signals from the molecular, physical, and kinetic inter- and intracellular interactions that control biological systems. Matrix factorization (MF) techniques can reveal low-dimensional structure from high-dimensional data that reflect these interactions. These techniques can uncover new biological knowledge from diverse high-throughput omics data in applications ranging from pathway discovery to timecourse analysis. We review exemplary applications of MF for systems-level analyses. We discuss appropriate applications of these methods, their limitations, and focus on the analysis of results to facilitate optimal biological interpretation. The inference of biologically relevant features with MF enables discovery from high-throughput data beyond the limits of current biological knowledge - answering questions from high-dimensional data that we have not yet thought to ask.

Th17 cells transdifferentiate into regulatory T cells during resolution of inflammation.

Inflammation is a beneficial host response to infection but can contribute to inflammatory disease if unregulated. The Th17 lineage of T helper (Th) cells can cause severe human inflammatory diseases. These cells exhibit both instability (they can cease to express their signature cytokine, IL-17A) and plasticity (they can start expressing cytokines typical of other lineages) upon in vitro re-stimulation. However, technical limitations have prevented the transcriptional profiling of pre- and post-conversion Th17 cells ex vivo during immune responses. Thus, it is unknown whether Th17 cell plasticity merely reflects change in expression of a few cytokines, or if Th17 cells physiologically undergo global genetic reprogramming driving their conversion from one T helper cell type to another, a process known as transdifferentiation. Furthermore, although Th17 cell instability/plasticity has been associated with pathogenicity, it is unknown whether this could present a therapeutic opportunity, whereby formerly pathogenic Th17 cells could adopt an anti-inflammatory fate. Here we used two new fate-mapping mouse models to track Th17 cells during immune responses to show that CD4(+) T cells that formerly expressed IL-17A go on to acquire an anti-inflammatory phenotype. The transdifferentiation of Th17 into regulatory T cells was illustrated by a change in their signature transcriptional profile and the acquisition of potent regulatory capacity. Comparisons of the transcriptional profiles of pre- and post-conversion Th17 cells also revealed a role for canonical TGF-ß signalling and consequently for the aryl hydrocarbon receptor (AhR) in conversion. Thus, Th17 cells transdifferentiate into regulatory cells, and contribute to the resolution of inflammation. Our data suggest that Th17 cell instability and plasticity is a therapeutic opportunity for inflammatory diseases.

Prepregnant Obesity of Mothers in a Multiethnic Cohort Is Associated with Cord Blood Metabolomic Changes in Offspring.

Maternal obesity has become a growing global health concern that may predispose the offspring to medical conditions later in life. However, the metabolic link between maternal prepregnant obesity and healthy offspring has not yet been fully elucidated. In this study, we conducted a case-control study using a coupled untargeted and targeted metabolomic approach from the newborn cord blood metabolomes associated with a matched maternal prepregnant obesity cohort of 28 cases and 29 controls. The subjects were recruited from multiethnic populations in Hawaii, including rarely reported Native Hawaiian and other Pacific Islanders (NHPI). We found that maternal obesity was the most important factor contributing to differences in cord blood metabolomics. Using an elastic net regularization-based logistic regression model, we identified 29 metabolites as potential early-life biomarkers manifesting intrauterine effect of maternal obesity, with accuracy as high as 0.947 after adjusting for clinical confounding (maternal and paternal age, ethnicity, parity, and gravidity). We validated the model results in a subsequent set of samples (N = 30) with an accuracy of 0.822. Among the metabolites, six metabolites (galactonic acid, butenylcarnitine, 2-hydroxy-3-methylbutyric acid, phosphatidylcholine diacyl C40:3, 1,5-anhydrosorbitol, and phosphatidylcholine acyl-alkyl 40:3) were individually and significantly different between the maternal obese and normal-weight groups. Interestingly, hydroxy-3-methylbutyric acid showed significantly higher levels in cord blood from the NHPI group compared to that from Asian and Caucasian groups. In summary, significant associations were observed between maternal prepregnant obesity and offspring metabolomic alternation at birth, revealing the intergenerational impact of maternal obesity.

Single cell transcriptome research in human placenta.

Human placenta is a complex and heterogeneous organ interfacing between the mother and the fetus that supports fetal development. Alterations to placental structural components are associated with various pregnancy complications. To reveal the heterogeneity among various placenta cell types in normal and diseased placentas, as well as elucidate molecular interactions within a population of placental cells, a new genomics technology called single cell RNA-seq (or scRNA-seq) has been employed in the last couple of years. Here we review the principles of scRNA-seq technology, and summarize the recent human placenta studies at scRNA-seq level across gestational ages as well as in pregnancy complications, such as preterm birth and preeclampsia. We list the computational analysis platforms and resources available for the public use. Lastly, we discuss the future areas of interest for placenta single cell studies, as well as the data analytics needed to accomplish them.

Multifractal structure of the monthly rainfall regime in Catalonia (NE Spain): Evaluation of the non-linear structural complexity.

The complex non-linear regime of the monthly rainfall in Catalonia (NE Spain) is analyzed by means of the reconstruction fractal theorem and the multifractal detrended fluctuation analysis algorithm. Areas with a notable degree of complex physical mechanisms are detected by using the concepts of persistence (Hurst exponent), complexity (embedding dimension), predictive uncertainty (Lyapunov exponents), loss of memory of the mechanism (Kolmogorov exponent), and the set of multifractal parameters (Hölder exponents, spectral asymmetry, spectral width, and complexity index). Besides these analyses permitting a detailed description of monthly rainfall pattern characteristics, the obtained results should also be relevant for new research studies concerning monthly amounts forecasting at a monthly scale. On one hand, the number of necessary monthly data for autoregressive processes could change with the complexity of the multifractal structure of the monthly rainfall regime. On the other hand, the discrepancies between real monthly amounts and those generated by some autoregressive algorithms could be related to some parameters of the reconstruction fractal theorem, such as the Lyapunov and Kolmogorov exponents.

Whole-genome microRNA screening identifies let-7 and mir-18 as regulators of germ layer formation during early embryogenesis.

Tight control over the segregation of endoderm, mesoderm, and ectoderm is essential for normal embryonic development of all species, yet how neighboring embryonic blastomeres can contribute to different germ layers has never been fully explained. We postulated that microRNAs, which fine-tune many biological processes, might modulate the response of embryonic blastomeres to growth factors and other signals that govern germ layer fate. A systematic screen of a whole-genome microRNA library revealed that the let-7 and miR-18 families increase mesoderm at the expense of endoderm in mouse embryonic stem cells. Both families are expressed in ectoderm and mesoderm, but not endoderm, as these tissues become distinct during mouse and frog embryogenesis. Blocking let-7 function in vivo dramatically affected cell fate, diverting presumptive mesoderm and ectoderm into endoderm. siRNA knockdown of computationally predicted targets followed by mutational analyses revealed that let-7 and miR-18 down-regulate Acvr1b and Smad2, respectively, to attenuate Nodal responsiveness and bias blastomeres to ectoderm and mesoderm fates. These findings suggest a crucial role for the let-7 and miR-18 families in germ layer specification and reveal a remarkable conservation of function from amphibians to mammals.

Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data.

Artificial neural networks (ANN) are computing architectures with many interconnections of simple neural-inspired computing elements, and have been applied to biomedical fields such as imaging analysis and diagnosis. We have developed a new ANN framework called Cox-nnet to predict patient prognosis from high throughput transcriptomics data. In 10 TCGA RNA-Seq data sets, Cox-nnet achieves the same or better predictive accuracy compared to other methods, including Cox-proportional hazards regression (with LASSO, ridge, and mimimax concave penalty), Random Forests Survival and CoxBoost. Cox-nnet also reveals richer biological information, at both the pathway and gene levels. The outputs from the hidden layer node provide an alternative approach for survival-sensitive dimension reduction. In summary, we have developed a new method for accurate and efficient prognosis prediction on high throughput data, with functional biological insights. The source code is freely available at https://github.com/lanagarmire/cox-nnet.

Thread-Lifts: A Double-Edged Suture? A Comprehensive Review of the Literature.

BACKGROUND: Thread-lifting has experienced a recent surge in popularity as interest in minimally invasive techniques for facial rejuvenation continues to grow. By placing barbed sutures along specific trajectories to lift the superficial musculoaponeurotic system to address facial ptosis, thread-lifting carries several advantages over the traditional facelift. Over the past few decades, there have been ongoing advances in the implementation of thread-lifting using barbed sutures. OBJECTIVE: To review the current evidence regarding the safety and efficacy of thread-lifting for facial rejuvenation. MATERIALS AND METHODS: An analysis was conducted of PubMed indexed English language literature regarding thread-lifting using barbed sutures. RESULTS: Early studies using nonabsorbable barbed sutures described high revision and complication rates. Newer techniques implementing absorbable sutures have fewer adverse events, although the long-term efficacy of these procedures remains unclear. CONCLUSION: Thread-lifts play a substantial role in minimally invasive techniques for facial rejuvenation. Additional studies are needed to better evaluate the safety and long-term efficacy of this quick, in-office procedure.

Deep Learning Accurately Predicts Estrogen Receptor Status in Breast Cancer Metabolomics Data.

Metabolomics holds the promise as a new technology to diagnose highly heterogeneous diseases. Conventionally, metabolomics data analysis for diagnosis is done using various statistical and machine learning based classification methods. However, it remains unknown if deep neural network, a class of increasingly popular machine learning methods, is suitable to classify metabolomics data. Here we use a cohort of 271 breast cancer tissues, 204 positive estrogen receptor (ER+), and 67 negative estrogen receptor (ER-) to test the accuracies of feed-forward networks, a deep learning (DL) framework, as well as six widely used machine learning models, namely random forest (RF), support vector machines (SVM), recursive partitioning and regression trees (RPART), linear discriminant analysis (LDA), prediction analysis for microarrays (PAM), and generalized boosted models (GBM). DL framework has the highest area under the curve (AUC) of 0.93 in classifying ER+/ER- patients, compared to the other six machine learning algorithms. Furthermore, the biological interpretation of the first hidden layer reveals eight commonly enriched significant metabolomics pathways (adjusted P-value <0.05) that cannot be discovered by other machine learning methods. Among them, protein digestion and absorption and ATP-binding cassette (ABC) transporters pathways are also confirmed in integrated analysis between metabolomics and gene expression data in these samples. In summary, deep learning method shows advantages for metabolomics based breast cancer ER status classification, with both the highest prediction accuracy (AUC = 0.93) and better revelation of disease biology. We encourage the adoption of feed-forward networks based deep learning method in the metabolomics research community for classification.

Necrobiosis lipoidica.

We present a case of necrobiosis lipoidica (NL) of the right abdomen in a 75-year-old man. A skin biopsy performed showed a layered infiltrate of mono and multinucleated histiocytes palisaded around degenerated collagen bundles. Laboratory workup was unremarkable. The patient was treated with topical corticosteroids with cessation of progression of his disease, although the eruption did not resolve. There are a number of treatments for NL reported in the literature, all with varying efficacy. Although NL lesions are usually asymptomatic, patients with NL must be monitored closely for signs of ulceration or malignant transformation, in which case more aggressive treatment options may be warranted.

Lasers, Light, and the Treatment of Acne: A Comprehensive Review of the Literature.

INTRODUCTION: Acne vulgaris is common dermatologic condition with an estimated prevalence of 70 to 87%. Acne has been shown to have a significant impact on patient quality of life and mental health, especially as inflammatory lesions typically occur on cosmetically sensitive areas with the potential for permanent scarring. There have been numerous advances in the treatment of inflammatory acne with light-based and laser devices. OBJECTIVE: To review the current evidence for light-based and laser treatments in the management of inflammatory acne. METHODS: An analysis was conducted of PubMed indexed English language literature regarding management of inflammatory acne using light-based and laser treatments. RESULTS: Evidence for the utilization of laser and light-based therapy for acne was summarized in a comprehensive review. Laser and light-based treatment holds the advantages of improved patient compliance and safety profiles in comparison to pharmacologic therapy. Efficacy of device based treatment varied in comparison to standard topical treatment regimens, often more effective when used in combination therapy. Adverse effects reported were generally self-limited. DISCUSSION: These treatments do and will continue to play an important and enlarging role in the management of acne. Larger scale studies with standardization of treatment protocols are warranted.

J Drugs Dermatol. 2017;16(11):1095-1102.

Primary anetoderma with undifferentiated connective tissue disease.

Anetoderma is a rare benign elastolytic disorder that is characterized by focal loss of elastin fibers on histopathology and is often recalcitrant to treatment. We present a case of a patient with a 20-year history of pruritic and painful hyperpigmented atrophic papules clustered on the neck, axillae, inframammary folds, and right medial thigh. Although the histopathologyof her axillary lesions was consistent with anetoderma, her clinical presentation is unusual given the extent of involvement, reported pain and pruritus, and sharp demarcation of the distribution. The diagnosticuncertainty of this case led to added difficulty in management of a disease that is already notoriously difficult to treat and may significantly impact patient's quality of life.