scholarly journals Estimating Microbial Interaction Network:Zero-inflated Latent Ising Model Based Approach

2020 ◽  
Author(s):  
Jie Zhou ◽  
Weston D. Viles ◽  
Boran Lu ◽  
Zhigang Li ◽  
Juliette C. Madan ◽  
...  
Keyword(s):  
Ising Model ◽  
Gut Microbiome ◽  
Interaction Network ◽  
Interaction Networks ◽  
Supplementary Information ◽  
Accurate Estimation ◽  
Endocrine Regulation ◽  
Microbial Interaction ◽  
Infant Gut Microbiome ◽  
Step Algorithm

AbstractMotivationThroughout their lifespans, humans continually interact with the microbial world, including those organisms which live in and on the human body. Research in this domain has revealed the extensive links between the human-associated microbiota and health. In particular, the microbiota of the human gut plays essential roles in digestion, nutrient metabolism, immune maturation and homeostasis, neurological signaling, and endocrine regulation. Microbial interaction networks are frequently estimated from data and are an indispensable tool for representing and understanding the relationships among the microbes of a microbiota. In this high-dimensional setting, the zero-inflated and compositional data structure (subject to unit-sum constraint) pose challenges to the accurate estimation of microbial interaction networks.MethodWe propose the zero-inflated latent Ising (ZILI) model for microbial interaction network which assumes that the distribution of relative abundance of microbiota is determined by finite latent states. This assumption is partly supported by the existing findings in literature [20]. The ZILI model can circumvents the unit-sum constraint and alleviates the zero-inflation problem under given assumptions. As for the model selection of ZILI, a two-step algorithm is proposed. ZILI and two-step algorithm are evaluated through simulated data and subsequently applied in our investigation of an infant gut microbiome dataset from New Hampshire Birth Cohort Study. The results are compared with results from traditional Gaussian graphical model (GGM) and dichotomous Ising model (DIS).ResultsThrough the simulation studies, provided that the ZILI model is the true generative model for the data, it is shown that the two-step algorithm can estimate the graphical structure effectively and is robust to a range of alternative settings of the related factors. Both GGM and DIS can not achieve a satisfying performance in these settings. For the infant gut microbiome dataset, we use both ZILI and GGM to estimate microbial interaction network. The final estimated networks turn out to share a statistically significant overlap in which the ZILI and two-step algorithm tend to select the sparser network than those modeled by GGM. From the shared subnetwork, a hub taxon Lachnospiraceae is identified whose involvement in human disease development has been discovered recently in literature.AvailabilityThe data and programs involved in Section 4 and 5 are available on request from the correspondence [email protected] informationSupplementary materials are available at Bioinformatics

scholarly journals Identification of microbial interaction network: zero-inflated latent Ising model based approach

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Jie Zhou ◽  
Weston D. Viles ◽  
Boran Lu ◽  
Zhigang Li ◽  
Juliette C. Madan ◽  
...  
Keyword(s):  
Ising Model ◽  
Gut Microbiota ◽  
Relative Abundance ◽  
Interaction Network ◽  
Interaction Networks ◽  
Endocrine Regulation ◽  
Microbial Interaction ◽  
Conditional Correlation ◽  
Zero Inflation ◽  
Step Algorithm

Abstract Background Throughout their lifespans, humans continually interact with the microbial world, including those organisms which live in and on the human body. Research in this domain has revealed the extensive links between the human-associated microbiota and health. In particular, the microbiota of the human gut plays essential roles in digestion, nutrient metabolism, immune maturation and homeostasis, neurological signaling, and endocrine regulation. Microbial interaction networks are frequently estimated from data and are an indispensable tool for representing and understanding the conditional correlation between the microbes. In this high-dimensional setting, zero-inflation and unit-sum constraint for relative abundance data pose challenges to the reliable estimation of microbial interaction networks. Methods and Results To identify the microbial interaction network, the zero-inflated latent Ising (ZILI) model is proposed which assumes the distribution of relative abundance relies only on finite latent states and provides a novel way to solve issues induced by the unit-sum and zero-inflation constrains. A two-step algorithm is proposed for the model selection of ZILI. ZILI is evaluated through simulated data and subsequently applied to an infant gut microbiota dataset from New Hampshire Birth Cohort Study. The results are compared with results from Gaussian graphical model (GGM) and dichotomous Ising model (DIS). Providing ZILI is the true data-generating model, the simulation studies show that the two-step algorithm can identify the graphical structure effectively and is robust to a range of parameter settings. For the infant gut microbiota dataset, the final estimated networks from GGM and ZILI turn out to have significant overlap in which the ZILI tends to select the sparser network than those from GGM. From the shared subnetwork, a hub taxon Lachnospiraceae is identified whose involvement in human disease development has been discovered recently in literature. Conclusions Constrains induced by relative abundance of microbiota such as zero inflation and unit sum render the conditional correlation analysis unreliable for conventional methods such as GGM. The proposed optimal categoricalization based ZILI model provides an alternative yet elegant way to deal with these difficulties. The results from ZILI have reasonable biological interpretation. This model can also be used to study the microbial interaction in other body parts.

scholarly journals DTI-Voodoo: machine learning over interaction networks and ontology-based background knowledge predicts drug–target interactions

2021 ◽  
Author(s):  
Tilman Hinnerichs ◽  
Robert Hoehndorf
Keyword(s):  
Drug Target ◽  
Drug Targets ◽  
Interaction Network ◽  
Drug Repurposing ◽  
Computational Method ◽  
Interaction Networks ◽  
Supplementary Information ◽  
Prediction Methods ◽  
Link Type ◽  
Molecular Features

AbstractMotivationIn silico drug–target interaction (DTI) prediction is important for drug discovery and drug repurposing. Approaches to predict DTIs can proceed indirectly, top-down, using phenotypic effects of drugs to identify potential drug targets, or they can be direct, bottom-up and use molecular information to directly predict binding potentials. Both approaches can be combined with information about interaction networks.ResultsWe developed DTI-Voodoo as a computational method that combines molecular features and ontology-encoded phenotypic effects of drugs with protein–protein interaction networks, and uses a graph convolutional neural network to predict DTIs. We demonstrate that drug effect features can exploit information in the interaction network whereas molecular features do not. DTI-Voodoo is designed to predict candidate drugs for a given protein; we use this formulation to show that common DTI datasets contain intrinsic biases with major affects on performance evaluation and comparison of DTI prediction methods. Using a modified evaluation scheme, we demonstrate that DTI-Voodoo improves significantly over state of the art DTI prediction methods.AvailabilityDTI-Voodoo source code and data necessary to reproduce results are freely available at https://github.com/THinnerichs/DTI-VOODOO.Supplementary informationSupplementary data are available at https://github.com/ THinnerichs/DTI-VOODOO.

scholarly journals The effect of early probiotic exposure on the preterm infant gut microbiome development

Gut Microbes ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 1951113
Author(s):  
Yan Hui ◽  
Birgitte Smith ◽  
Martin Steen Mortensen ◽  
Lukasz Krych ◽  
Søren J. Sørensen ◽  
...  
Keyword(s):  
Preterm Infant ◽  
Gut Microbiome ◽  
Infant Gut Microbiome ◽  
Microbiome Development

scholarly journals A synbiotic intervention modulates meta-omics signatures of gut redox potential and acidity in elective caesarean born infants

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Christophe Lay ◽  
Collins Wenhan Chu ◽  
Rikky Wenang Purbojati ◽  
Enzo Acerbi ◽  
Daniela I. Drautz-Moses ◽  
...  
Keyword(s):  
Risk Factor ◽  
Gut Microbiome ◽  
Reference Group ◽  
Mode Of Delivery ◽  
Controlled Study ◽  
Double Blind ◽  
Lipopolysaccharide Biosynthesis ◽  
Infant Gut Microbiome ◽  
Stool Samples ◽  
The Impact

Abstract Background The compromised gut microbiome that results from C-section birth has been hypothesized as a risk factor for the development of non-communicable diseases (NCD). In a double-blind randomized controlled study, 153 infants born by elective C-section received an infant formula supplemented with either synbiotic, prebiotics, or unsupplemented from birth until 4 months old. Vaginally born infants were included as a reference group. Stool samples were collected from day 3 till week 22. Multi-omics were deployed to investigate the impact of mode of delivery and nutrition on the development of the infant gut microbiome, and uncover putative biological mechanisms underlying the role of a compromised microbiome as a risk factor for NCD. Results As early as day 3, infants born vaginally presented a hypoxic and acidic gut environment characterized by an enrichment of strict anaerobes (Bifidobacteriaceae). Infants born by C-section presented the hallmark of a compromised microbiome driven by an enrichment of Enterobacteriaceae. This was associated with meta-omics signatures characteristic of a microbiome adapted to a more oxygen-rich gut environment, enriched with genes associated with reactive oxygen species metabolism and lipopolysaccharide biosynthesis, and depleted in genes involved in the metabolism of milk carbohydrates. The synbiotic formula modulated expression of microbial genes involved in (oligo)saccharide metabolism, which emulates the eco-physiological gut environment observed in vaginally born infants. The resulting hypoxic and acidic milieu prevented the establishment of a compromised microbiome. Conclusions This study deciphers the putative functional hallmarks of a compromised microbiome acquired during C-section birth, and the impact of nutrition that may counteract disturbed microbiome development. Trial registration The study was registered in the Dutch Trial Register (Number: 2838) on 4th April 2011.

scholarly journals Infant gut microbiome composition is associated with non-social fear behavior in a pilot study

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Alexander L. Carlson ◽  
Kai Xia ◽  
M. Andrea Azcarate-Peril ◽  
Samuel P. Rosin ◽  
Jason P. Fine ◽  
...  
Keyword(s):  
Pilot Study ◽  
Gut Microbiome ◽  
Brain Structures ◽  
Experimental Manipulation ◽  
Human Infant ◽  
Microbiome Composition ◽  
Social Fear ◽  
Infant Gut Microbiome ◽  
First Year Of Life ◽  
Fear Behavior

AbstractExperimental manipulation of gut microbes in animal models alters fear behavior and relevant neurocircuitry. In humans, the first year of life is a key period for brain development, the emergence of fearfulness, and the establishment of the gut microbiome. Variation in the infant gut microbiome has previously been linked to cognitive development, but its relationship with fear behavior and neurocircuitry is unknown. In this pilot study of 34 infants, we find that 1-year gut microbiome composition (Weighted Unifrac; lower abundance of Bacteroides, increased abundance of Veillonella, Dialister, and Clostridiales) is significantly associated with increased fear behavior during a non-social fear paradigm. Infants with increased richness and reduced evenness of the 1-month microbiome also display increased non-social fear. This study indicates associations of the human infant gut microbiome with fear behavior and possible relationships with fear-related brain structures on the basis of a small cohort. As such, it represents an important step in understanding the role of the gut microbiome in the development of human fear behaviors, but requires further validation with a larger number of participants.

scholarly journals Oral–Gut Microbiome Axis in Gastrointestinal Disease and Cancer

Cancers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 2124
Author(s):  
Se-Young Park ◽  
Byeong-Oh Hwang ◽  
Mihwa Lim ◽  
Seung-Ho Ok ◽  
Sun-Kyoung Lee ◽  
...  
Keyword(s):  
Oral Cavity ◽  
Gut Microbiome ◽  
Gastrointestinal Disease ◽  
Oral Microbiota ◽  
Microbial Interaction ◽  
Barrier Dysfunction ◽  
Gut Barrier ◽  
Microbial Ecosystem ◽  
Precise Diagnosis

It is well-known that microbiota dysbiosis is closely associated with numerous diseases in the human body. The oral cavity and gut are the two largest microbial habitats, playing a major role in microbiome-associated diseases. Even though the oral cavity and gut are continuous regions connected through the gastrointestinal tract, the oral and gut microbiome profiles are well-segregated due to the oral–gut barrier. However, the oral microbiota can translocate to the intestinal mucosa in conditions of the oral–gut barrier dysfunction. Inversely, the gut-to-oral microbial transmission occurs as well in inter- and intrapersonal manners. Recently, it has been reported that oral and gut microbiomes interdependently regulate physiological functions and pathological processes. Oral-to-gut and gut-to-oral microbial transmissions can shape and/or reshape the microbial ecosystem in both habitats, eventually modulating pathogenesis of disease. However, the oral–gut microbial interaction in pathogenesis has been underappreciated to date. Here, we will highlight the oral–gut microbiome crosstalk and its implications in the pathogenesis of the gastrointestinal disease and cancer. Better understanding the role of the oral–gut microbiome axis in pathogenesis will be advantageous for precise diagnosis/prognosis and effective treatment.

scholarly journals Accurate estimation of isoelectric point of protein and peptide based on amino acid sequences

2015 ◽  
Vol 32 (6) ◽  
pp. 821-827 ◽  
Author(s):  
Enrique Audain ◽  
Yassel Ramos ◽  
Henning Hermjakob ◽  
Darren R. Flower ◽  
Yasset Perez-Riverol
Keyword(s):  
Machine Learning ◽  
Isoelectric Point ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Basis Set ◽  
Superior Performance ◽  
Supplementary Information ◽  
Training Dataset ◽  
Accurate Estimation ◽  
Prediction Methods

Abstract Motivation: In any macromolecular polyprotic system—for example protein, DNA or RNA—the isoelectric point—commonly referred to as the pI—can be defined as the point of singularity in a titration curve, corresponding to the solution pH value at which the net overall surface charge—and thus the electrophoretic mobility—of the ampholyte sums to zero. Different modern analytical biochemistry and proteomics methods depend on the isoelectric point as a principal feature for protein and peptide characterization. Protein separation by isoelectric point is a critical part of 2-D gel electrophoresis, a key precursor of proteomics, where discrete spots can be digested in-gel, and proteins subsequently identified by analytical mass spectrometry. Peptide fractionation according to their pI is also widely used in current proteomics sample preparation procedures previous to the LC-MS/MS analysis. Therefore accurate theoretical prediction of pI would expedite such analysis. While such pI calculation is widely used, it remains largely untested, motivating our efforts to benchmark pI prediction methods. Results: Using data from the database PIP-DB and one publically available dataset as our reference gold standard, we have undertaken the benchmarking of pI calculation methods. We find that methods vary in their accuracy and are highly sensitive to the choice of basis set. The machine-learning algorithms, especially the SVM-based algorithm, showed a superior performance when studying peptide mixtures. In general, learning-based pI prediction methods (such as Cofactor, SVM and Branca) require a large training dataset and their resulting performance will strongly depend of the quality of that data. In contrast with Iterative methods, machine-learning algorithms have the advantage of being able to add new features to improve the accuracy of prediction. Contact: [email protected] Availability and Implementation: The software and data are freely available at https://github.com/ypriverol/pIR. Supplementary information: Supplementary data are available at Bioinformatics online.

scholarly journals Natural history of the infant gut microbiome and impact of antibiotic treatment on bacterial strain diversity and stability

2016 ◽  
Vol 8 (343) ◽  
pp. 343ra81-343ra81 ◽  
Author(s):  
Moran Yassour ◽  
Tommi Vatanen ◽  
Heli Siljander ◽  
Anu-Maaria Hämäläinen ◽  
Taina Härkönen ◽  
...  
Keyword(s):  
Natural History ◽  
Antibiotic Treatment ◽  
Gut Microbiome ◽  
Bacterial Strain ◽  
Strain Diversity ◽  
Infant Gut Microbiome ◽  
History Of
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