Application of multi-omics data integration and machine learning approaches to identify epigenetic and transcriptomic differences between in vitro and in vivo produced bovine embryos

Pregnancy rates for in vitro produced (IVP) embryos are usually lower than for embryos produced in vivo after ovarian superovulation (MOET). This is potentially due to alterations in their trophectoderm (TE), the outermost layer in physical contact with the maternal endometrium. The main objective was to apply a multi-omics data integration approach to identify both temporally differentially expressed and differentially methylated genes (DEG and DMG), between IVP and MOET embryos, that could impact TE function. To start, four and five published transcriptomic and epigenomic datasets, respectively, were processed for data integration. Second, DEG from day 7 to days 13 and 16 and DMG from day 7 to day 17 were determined in the TE from IVP vs. MOET embryos. Third, genes that were both DE and DM were subjected to hierarchical clustering and functional enrichment analysis. Finally, findings were validated through a machine learning approach with two additional datasets from day 15 embryos. There were 1535 DEG and 6360 DMG, with 490 overlapped genes, whose expression profiles at days 13 and 16 resulted in three main clusters. Cluster 1 (188) and Cluster 2 (191) genes were down-regulated at day 13 or day 16, respectively, while Cluster 3 genes (111) were up-regulated at both days, in IVP embryos compared to MOET embryos. The top enriched terms were the KEGG pathway "focal adhesion" in Cluster 1 (FDR = 0.003), and the cellular component: "extracellular exosome" in Cluster 2 (FDR<0.0001), also enriched in Cluster 1 (FDR = 0.04). According to the machine learning approach, genes in Cluster 1 showed a similar expression pattern between IVP and less developed (short) MOET conceptuses; and between MOET and DKK1-treated (advanced) IVP conceptuses. In conclusion, these results suggest that early conceptuses derived from IVP embryos exhibit epigenomic and transcriptomic changes that later affect its elongation and focal adhesion, impairing post-transfer survival.

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Weight loss trajectories and related factors in a 16-week mobile obesity intervention: A retrospective observational study (Preprint)

10.2196/preprints.29380 ◽

2021 ◽

Author(s):

Ho Heon Kim ◽

Young In Kim ◽

Andreas Michaelides ◽

Yu Rang Park

Keyword(s):

Machine Learning ◽

Weight Loss ◽

Effect Size ◽

Weight Change ◽

Average Weight ◽

Learning Approach ◽

Obesity Management ◽

Significant Difference ◽

Machine Learning Approach ◽

Cluster 2

BACKGROUND In obesity management, whether patients lose 5% or more of their initial weight is a critical factor in their clinical outcome. However, evaluations that only take this approach cannot identify and distinguish between individuals whose weight change varies and those who steadily lose weight. Evaluation of weight loss considering the volatility of weight change through a mobile-based intervention for obesity can facilitate the understanding of individuals’ behavior and weight changes from a longitudinal perspective. OBJECTIVE With machine learning approach, we examined weight loss trajectories and explored the factors related to behavioral and app usage characteristics that induce weight loss. METHODS We used the lifelog data of 19,784 individuals who enrolled in a 16-week obesity management program on the healthcare app Noom in the US during August 8, 2013 to August 8, 2019. We performed K-means clustering with dynamic time warping to cluster the weight loss time series and inspected the quality of clusters with the total sum of distance within the clusters. To identify the usage factors to determine clustering assignment, we longitudinally compared weekly usage statistics with effect size on a weekly basis. RESULTS Initial Body Mass Index (BMI) of participants was 33.9±5.9 kg/m2, and ultimately reached an average BMI of 32.0±5.7 kg/m2. In their weight log, 5 Clusters were identified: Cluster 1 (sharp decrease) showed a high proportion of weight reduction class between 10% and 15%—the highest among the five clusters (n=2,364/12,796, 18.9%)—followed by Cluster 2 (moderate decrease), Cluster 3 (increase), Cluster 4 (yoyo), Cluster 5 (other). In comparison between cluster 2 and cluster 4, although the effect size of difference in the average meal input adherence and average weight input adherence did not show a significant difference in the first week, it increased continuously for 7 weeks (Cohen’s d=0.408; 0.38). CONCLUSIONS With machine learning approach clustering shape-based timeseries similarity, this study identified 5 weight loss trajectories in mobile weight management app. Overall adherence and early adherence related to self-monitoring emerged as a potential predictor of these trajectories.

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Comparing the utility of in vivo transposon mutagenesis approaches in yeast species to infer gene essentiality

10.1101/732552 ◽

2019 ◽

Cited By ~ 1

Author(s):

Anton Levitan ◽

Andrew N. Gale ◽

Emma K. Dallon ◽

Darby W. Kozan ◽

Kyle W. Cunningham ◽

...

Keyword(s):

Machine Learning ◽

Large Scale ◽

Yeast Species ◽

Transposon Mutagenesis ◽

Learning Approach ◽

Long Distance ◽

Gene Essentiality ◽

Genome Wide ◽

Machine Learning Approach

ABSTRACTIn vivo transposon mutagenesis, coupled with deep sequencing, enables large-scale genome-wide mutant screens for genes essential in different growth conditions. We analyzed six large-scale studies performed on haploid strains of three yeast species (Saccharomyces cerevisiae, Schizosaccaromyces pombe, and Candida albicans), each mutagenized with two of three different heterologous transposons (AcDs, Hermes, and PiggyBac). Using a machine-learning approach, we evaluated the ability of the data to predict gene essentiality. Important data features included sufficient numbers and distribution of independent insertion events. All transposons showed some bias in insertion site preference because of jackpot events, and preferences for specific insertion sequences and short-distance vs long-distance insertions. For PiggyBac, a stringent target sequence limited the ability to predict essentiality in genes with few or no target sequences. The machine learning approach also robustly predicted gene function in less well-studied species by leveraging cross-species orthologs. Finally, comparisons of isogenic diploid versus haploid S. cerevisiae isolates identified several genes that are haplo-insufficient, while most essential genes, as expected, were recessive. We provide recommendations for the choice of transposons and the inference of gene essentiality in genome-wide studies of eukaryotic haploid microbes such as yeasts, including species that have been less amenable to classical genetic studies.

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A Machine Learning Approach for Efficient Selection of Enzyme Concentrations and Its Application for Flux Optimization

Catalysts ◽

10.3390/catal10030291 ◽

2020 ◽

Vol 10 (3) ◽

pp. 291 ◽

Cited By ~ 1

Author(s):

Anamya Ajjolli Nagaraja ◽

Philippe Charton ◽

Xavier F. Cadet ◽

Nicolas Fontaine ◽

Mathieu Delsaut ◽

...

Keyword(s):

Machine Learning ◽

Glass Ceiling ◽

Principal Component ◽

Enzyme Concentration ◽

Learning Approach ◽

Neural Network Approach ◽

Free System ◽

Machine Learning Approach ◽

Selection Of

The metabolic engineering of pathways has been used extensively to produce molecules of interest on an industrial scale. Methods like gene regulation or substrate channeling helped to improve the desired product yield. Cell-free systems are used to overcome the weaknesses of engineered strains. One of the challenges in a cell-free system is selecting the optimized enzyme concentration for optimal yield. Here, a machine learning approach is used to select the enzyme concentration for the upper part of glycolysis. The artificial neural network approach (ANN) is known to be inefficient in extrapolating predictions outside the box: high predicted values will bump into a sort of “glass ceiling”. In order to explore this “glass ceiling” space, we developed a new methodology named glass ceiling ANN (GC-ANN). Principal component analysis (PCA) and data classification methods are used to derive a rule for a high flux, and ANN to predict the flux through the pathway using the input data of 121 balances of four enzymes in the upper part of glycolysis. The outcomes of this study are i. in silico selection of optimum enzyme concentrations for a maximum flux through the pathway and ii. experimental in vitro validation of the “out-of-the-box” fluxes predicted using this new approach. Surprisingly, flux improvements of up to 63% were obtained. Gratifyingly, these improvements are coupled with a cost decrease of up to 25% for the assay.

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A Machine Learning Approach to Delineating Carotid Atherosclerotic Plaque Structure and Composition by ARFI Ultrasound, In Vivo

2018 IEEE International Ultrasonics Symposium (IUS) ◽

10.1109/ultsym.2018.8579957 ◽

2018 ◽

Cited By ~ 4

Author(s):

Gabriela Torres ◽

Tomasz J. Czemuszewicz ◽

Jonathon W. Homeister ◽

Mark A. Farber ◽

Caterina M. Gallippi

Keyword(s):

Machine Learning ◽

Atherosclerotic Plaque ◽

Learning Approach ◽

Carotid Atherosclerotic Plaque ◽

Machine Learning Approach

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Low Amplitude Burst Detection of Catecholamines

10.21203/rs.3.rs-828112/v1 ◽

2021 ◽

Author(s):

Amnah Eltahir ◽

Jason White ◽

Terry Lohrenz ◽

P. Read Montague

Keyword(s):

Machine Learning ◽

Electrochemical Detection ◽

Perception And Action ◽

Learning Approach ◽

Voltage Waveform ◽

Machine Learning Approach ◽

Dependent Signal ◽

Low Amplitude ◽

New Machine

Abstract Machine learning advances in electrochemical detection have recently produced subsecond and concurrent detection of dopamine and serotonin during perception and action tasks in conscious humans. Here, we present a new machine learning approach to subsecond, concurrent separation of dopamine, norepinephrine, and serotonin. The method exploits a low amplitude burst protocol for the controlled voltage waveform and we demonstrate its efficacy by showing how it separates dopamine-induced signals from norepinephrine induced signals. Previous efforts to deploy electrochemical detection of dopamine in vivo have not separated the dopamine-dependent signal from a norepinephrine-dependent signal. Consequently, this new method can provide new insights into concurrent signaling by these two important neuromodulators.

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Integrating –omics data into genome-scale metabolic network models: principles and challenges

Essays in Biochemistry ◽

10.1042/ebc20180011 ◽

2018 ◽

Vol 62 (4) ◽

pp. 563-574 ◽

Cited By ~ 10

Author(s):

Charlotte Ramon ◽

Mattia G. Gollub ◽

Jörg Stelling

Keyword(s):

Data Integration ◽

Large Scale ◽

Network Models ◽

Omics Data ◽

Scale Models ◽

Common Framework ◽

Genome Scale ◽

Constraint Based Models ◽

Omics Data Integration

At genome scale, it is not yet possible to devise detailed kinetic models for metabolism because data on the in vivo biochemistry are too sparse. Predictive large-scale models for metabolism most commonly use the constraint-based framework, in which network structures constrain possible metabolic phenotypes at steady state. However, these models commonly leave many possibilities open, making them less predictive than desired. With increasingly available –omics data, it is appealing to increase the predictive power of constraint-based models (CBMs) through data integration. Many corresponding methods have been developed, but data integration is still a challenge and existing methods perform less well than expected. Here, we review main approaches for the integration of different types of –omics data into CBMs focussing on the methods’ assumptions and limitations. We argue that key assumptions – often derived from single-enzyme kinetics – do not generally apply in the context of networks, thereby explaining current limitations. Emerging methods bridging CBMs and biochemical kinetics may allow for –omics data integration in a common framework to provide more accurate predictions.

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