scholarly journals Computational Biology and Machine Learning Approaches to Understand Mechanistic Microbiome-Host Interactions

2021 ◽  
Vol 12 ◽  
Author(s):  
Padhmanand Sudhakar ◽  
Kathleen Machiels ◽  
Bram Verstockt ◽  
Tamas Korcsmaros ◽  
Séverine Vermeire
Keyword(s):  
Machine Learning ◽  
Computational Biology ◽  
Molecular Mechanisms ◽  
Community Level ◽  
Therapeutic Interventions ◽  
Host Responses ◽  
Learning Approaches ◽  
Host Interactions ◽  
Bowel Diseases ◽  
Biological Interpretation

The microbiome, by virtue of its interactions with the host, is implicated in various host functions including its influence on nutrition and homeostasis. Many chronic diseases such as diabetes, cancer, inflammatory bowel diseases are characterized by a disruption of microbial communities in at least one biological niche/organ system. Various molecular mechanisms between microbial and host components such as proteins, RNAs, metabolites have recently been identified, thus filling many gaps in our understanding of how the microbiome modulates host processes. Concurrently, high-throughput technologies have enabled the profiling of heterogeneous datasets capturing community level changes in the microbiome as well as the host responses. However, due to limitations in parallel sampling and analytical procedures, big gaps still exist in terms of how the microbiome mechanistically influences host functions at a system and community level. In the past decade, computational biology and machine learning methodologies have been developed with the aim of filling the existing gaps. Due to the agnostic nature of the tools, they have been applied in diverse disease contexts to analyze and infer the interactions between the microbiome and host molecular components. Some of these approaches allow the identification and analysis of affected downstream host processes. Most of the tools statistically or mechanistically integrate different types of -omic and meta -omic datasets followed by functional/biological interpretation. In this review, we provide an overview of the landscape of computational approaches for investigating mechanistic interactions between individual microbes/microbiome and the host and the opportunities for basic and clinical research. These could include but are not limited to the development of activity- and mechanism-based biomarkers, uncovering mechanisms for therapeutic interventions and generating integrated signatures to stratify patients.

scholarly journals Machine learning approaches to identify sleep genes

2021 ◽  
Author(s):  
Yin Yeng Lee ◽  
Mehari Endale ◽  
Gang Wu ◽  
Marc D Ruben ◽  
Lauren J Francey ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prior Knowledge ◽  
Molecular Mechanisms ◽  
Genetic Regulation ◽  
Learning Approaches ◽  
Sleep Regulation ◽  
Genome Wide ◽  
Genome Wide Data ◽  
Key Genes

Genetics impacts sleep, yet, the molecular mechanisms underlying sleep regulation remain elusive. We built machine learning (ML) models to predict genes based on their similarity to known sleep genes. Our predictions fit with prior knowledge of sleep regulation and also identify several key genes/pathways to pursue in follow-up studies. We tested one of our findings, the NF-κB pathway, and showed that its genetic alteration affects sleep duration in mice. Our study highlights the power of ML to integrate prior knowledge and genome-wide data to study genetic regulation of sleep and other complex behaviors.

scholarly journals Cellular and molecular mediators of lymphangiogenesis in inflammatory bowel disease

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Dickson Kofi Wiredu Ocansey ◽  
Bing Pei ◽  
Xinwei Xu ◽  
Lu Zhang ◽  
Chinasa Valerie Olovo ◽  
...  
Keyword(s):  
Immune Cells ◽  
Molecular Mechanisms ◽  
Lymphatic System ◽  
Wide Spectrum ◽  
Adaptive Immune System ◽  
Therapeutic Interventions ◽  
Complex Processes ◽  
Inflammatory Conditions ◽  
Bowel Diseases ◽  
Inflammatory Bowel

Abstract Background Recent studies reporting the intricate crosstalk between cellular and molecular mediators and the lymphatic endothelium in the development of inflammatory bowel diseases (IBD) suggest altered inflammatory cell drainage and lymphatic vasculature, implicating the lymphatic system as a player in the occurrence, development, and recurrence of intestinal diseases. This article aims to review recent data on the modulatory functions of cellular and molecular components of the IBD microenvironment on the lymphatic system, particularly lymphangiogenesis. It serves as a promising therapeutic target for IBD management and treatment. The interaction with gut microbiota is also explored. Main text Evidence shows that cells of the innate and adaptive immune system and certain non-immune cells participate in the complex processes of inflammatory-induced lymphangiogenesis through the secretion of a wide spectrum of molecular factors, which vary greatly among the various cells. Lymphangiogenesis enhances lymphatic fluid drainage, hence reduced infiltration of immunomodulatory cells and associated-inflammatory cytokines. Interestingly, some of the cellular mediators, including mast cells, neutrophils, basophils, monocytes, and lymphatic endothelial cells (LECs), are a source of lymphangiogenic molecules, and a target as they express specific receptors for lymphangiogenic factors. Conclusion The effective target of lymphangiogenesis is expected to provide novel therapeutic interventions for intestinal inflammatory conditions, including IBD, through both immune and non-immune cells and based on cellular and molecular mechanisms of lymphangiogenesis that facilitate inflammation resolution.

scholarly journals Machine learning reveals missing edges and putative interaction mechanisms in microbial ecosystem networks

2018 ◽  
Author(s):  
Demetrius DiMucci ◽  
Mark Kon ◽  
Daniel Segrè
Keyword(s):  
Machine Learning ◽  
Microbial Community ◽  
Rational Design ◽  
Molecular Mechanisms ◽  
Community Dynamics ◽  
Combinatorial Complexity ◽  
Therapeutic Interventions ◽  
Formal Representation ◽  
Form Complex ◽  
Pairwise Interactions

AbstractMicrobes affect each other’s growth in multiple, often elusive ways. The ensuing interdependencies form complex networks, believed to influence taxonomic composition, as well as community-level functional properties and dynamics. Elucidation of these networks is often pursued by measuring pairwise interaction in co-culture experiments. However, combinatorial complexity precludes the exhaustive experimental analysis of pairwise interactions even for moderately sized microbial communities. Here, we use a machine-learning random forest approach to address this challenge. In particular, we show how partial knowledge of a microbial interaction network, combined with trait-level representations of individual microbial species, can provide accurate inference of missing edges in the network and putative mechanisms underlying interactions. We applied our algorithm to two case studies: an experimentally mapped network of interactions between auxotrophic E. coli strains, and a large in silico network of metabolic interdependencies between 100 human gut-associated bacteria. For this last case, 5% of the network is enough to predict the remaining 95% with 80% accuracy, and mechanistic hypotheses produced by the algorithm accurately reflect known metabolic exchanges. Our approach, broadly applicable to any microbial or other ecological network, can drive the discovery of new interactions and new molecular mechanisms, both for therapeutic interventions involving natural communities and for the rational design of synthetic consortia.ImportanceDifferent organisms in a microbial community may drastically affect each other’s growth phenotype, significantly affecting the community dynamics, with important implications for human and environmental health. Novel culturing methods and decreasing costs of sequencing will gradually enable high-throughput measurements of pairwise interactions in systematic co-culturing studies. However, a thorough characterization of all interactions that occur within a microbial community is greatly limited both by the combinatorial complexity of possible assortments, and by the limited biological insight that interaction measurements typically provide without laborious specific follow-ups. Here we show how a simple and flexible formal representation of microbial pairs can be used for classification of interactions with machine learning. The approach we propose predicts with high accuracy the outcome of yet to be performed experiments, and generates testable hypotheses about the mechanisms of specific interactions.

scholarly journals Involvement of Machine Learning Tools in Healthcare Decision Making

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Senerath Mudalige Don Alexis Chinthaka Jayatilake ◽  
Gamage Upeksha Ganegoda
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Computational Biology ◽  
Learning Algorithms ◽  
Medical Data ◽  
Machine Learning Algorithms ◽  
Healthcare Sector ◽  
Accurate Information ◽  
Learning Approaches ◽  
Healthcare Applications

In the present day, there are many diseases which need to be identified at their early stages to start relevant treatments. If not, they could be uncurable and deadly. Due to this reason, there is a need of analysing complex medical data, medical reports, and medical images at a lesser time but with greater accuracy. There are even some instances where certain abnormalities cannot be directly recognized by humans. In healthcare for computational decision making, machine learning approaches are being used in these types of situations where a crucial data analysis needs to be performed on medical data to reveal hidden relationships or abnormalities which are not visible to humans. Implementing algorithms to perform such tasks itself is difficult, but what makes it even more challenging is to increase the accuracy of the algorithm while decreasing the required time for the algorithm to execute. In the early days, processing of large amount of medical data was an important task which resulted in machine learning being adapted in the biological domain. Since this happened, the biology and biomedical fields have been reaching higher levels by exploring more knowledge and identifying relationships which were never observed before. Reaching to its peak now the concern is being diverted towards treating patients not only based on the type of disease but also their genetics, which is known as precision medicine. Modifications in machine learning algorithms are being performed and tested daily to improve the performance of the algorithms in analysing and presenting more accurate information. In the healthcare field, starting from information extraction from medical documents until the prediction or diagnosis of a disease, machine learning has been involved. Medical imaging is a section that was greatly improved with the integration of machine learning algorithms to the field of computational biology. Nowadays, many disease diagnoses are being performed by medical image processing using machine learning algorithms. In addition, patient care, resource allocation, and research on treatments for various diseases are also being performed using machine learning-based computational decision making. Throughout this paper, various machine learning algorithms and approaches that are being used for decision making in the healthcare sector will be discussed along with the involvement of machine learning in healthcare applications in the current context. With the explored knowledge, it was evident that neural network-based deep learning methods have performed extremely well in the field of computational biology with the support of the high processing power of modern sophisticated computers and are being extensively applied because of their high predicting accuracy and reliability. When giving concern towards the big picture by combining the observations, it is noticeable that computational biology and biomedicine-based decision making in healthcare have now become dependent on machine learning algorithms, and thus they cannot be separated from the field of artificial intelligence.

scholarly journals An integrative machine learning approach to discovering multi-level molecular mechanisms of obesity using data from monozygotic twin pairs

2019 ◽  
Author(s):  
Milla Kibble ◽  
Suleiman A. Khan ◽  
Muhammad Ammad-ud-din ◽  
Sailalitha Bollepalli ◽  
Teemu Palviainen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Mechanisms ◽  
Monozygotic Twin ◽  
R Package ◽  
Data Sets ◽  
Learning Approaches ◽  
Machine Learning Approach ◽  
Using Data ◽  
Genomic Methylation

AbstractWe combined clinical, cytokine, genomic, methylation and dietary data from 43 young adult monozygotic twin pairs (aged 22 – 36, 53% female), where 25 of the twin pairs were substantially weight discordant (delta BMI > 3kg/ m2). These measurements were originally taken as part of the TwinFat study, a substudy of The Finnish Twin Cohort study. These five large multivariate data sets (comprising 42, 71, 1587, 1605 and 63 variables, respectively) were jointly analysed using an integrative machine learning method called Group Factor Analysis (GFA) to offer new hypotheses into the multi-molecular-level interactions associated with the development of obesity. New potential links between cytokines and weight gain are identified, as well as associations between dietary, inflammatory and epigenetic factors. This encouraging case study aims to enthuse the research community to boldly attempt new machine learning approaches which have the potential to yield novel and unintuitive hypotheses. The source code of the GFA method is publically available as the R package GFA.

scholarly journals An integrative machine learning approach to discovering multi-level molecular mechanisms of obesity using data from monozygotic twin pairs

2020 ◽  
Vol 7 (10) ◽  
pp. 200872
Author(s):  
Milla Kibble ◽  
Suleiman A. Khan ◽  
Muhammad Ammad-ud-din ◽  
Sailalitha Bollepalli ◽  
Teemu Palviainen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Mechanisms ◽  
Monozygotic Twin ◽  
R Package ◽  
Learning Approaches ◽  
Machine Learning Approach ◽  
Multi Level ◽  
Using Data ◽  
Genomic Methylation

We combined clinical, cytokine, genomic, methylation and dietary data from 43 young adult monozygotic twin pairs (aged 22–36 years, 53% female), where 25 of the twin pairs were substantially weight discordant (delta body mass index > 3 kg m −2 ). These measurements were originally taken as part of the TwinFat study, a substudy of The Finnish Twin Cohort study. These five large multivariate datasets (comprising 42, 71, 1587, 1605 and 63 variables, respectively) were jointly analysed using an integrative machine learning method called group factor analysis (GFA) to offer new hypotheses into the multi-molecular-level interactions associated with the development of obesity. New potential links between cytokines and weight gain are identified, as well as associations between dietary, inflammatory and epigenetic factors. This encouraging case study aims to enthuse the research community to boldly attempt new machine learning approaches which have the potential to yield novel and unintuitive hypotheses. The source code of the GFA method is publically available as the R package GFA.

scholarly journals Machine Learning Reveals Missing Edges and Putative Interaction Mechanisms in Microbial Ecosystem Networks

mSystems ◽  
2018 ◽  
Vol 3 (5) ◽  
Author(s):  
Demetrius DiMucci ◽  
Mark Kon ◽  
Daniel Segrè
Keyword(s):  
Machine Learning ◽  
Microbial Community ◽  
Rational Design ◽  
Molecular Mechanisms ◽  
Community Dynamics ◽  
Combinatorial Complexity ◽  
Therapeutic Interventions ◽  
Formal Representation ◽  
Form Complex ◽  
Pairwise Interactions

ABSTRACTMicrobes affect each other’s growth in multiple, often elusive, ways. The ensuing interdependencies form complex networks, believed to reflect taxonomic composition as well as community-level functional properties and dynamics. The elucidation of these networks is often pursued by measuring pairwise interactions in coculture experiments. However, the combinatorial complexity precludes an exhaustive experimental analysis of pairwise interactions, even for moderately sized microbial communities. Here, we used a machine learning random forest approach to address this challenge. In particular, we show how partial knowledge of a microbial interaction network, combined with trait-level representations of individual microbial species, can provide accurate inference of missing edges in the network and putative mechanisms underlying the interactions. We applied our algorithm to three case studies: an experimentally mapped network of interactions between auxotrophicEscherichia colistrains, a community of soil microbes, and a largein siliconetwork of metabolic interdependencies between 100 human gut-associated bacteria. For this last case, 5% of the network was sufficient to predict the remaining 95% with 80% accuracy, and the mechanistic hypotheses produced by the algorithm accurately reflected known metabolic exchanges. Our approach, broadly applicable to any microbial or other ecological network, may drive the discovery of new interactions and new molecular mechanisms, both for therapeutic interventions involving natural communities and for the rational design of synthetic consortia.IMPORTANCEDifferent organisms in a microbial community may drastically affect each other’s growth phenotypes, significantly affecting the community dynamics, with important implications for human and environmental health. Novel culturing methods and the decreasing costs of sequencing will gradually enable high-throughput measurements of pairwise interactions in systematic coculturing studies. However, a thorough characterization of all interactions that occur within a microbial community is greatly limited both by the combinatorial complexity of possible assortments and by the limited biological insight that interaction measurements typically provide without laborious specific follow-ups. Here, we show how a simple and flexible formal representation of microbial pairs can be used for the classification of interactions via machine learning. The approach we propose predicts with high accuracy the outcome of yet-to-be performed experiments and generates testable hypotheses about the mechanisms of specific interactions.

Inflammageing in the cardiovascular system: mechanisms, emerging targets, and novel therapeutic strategies

2020 ◽  
Vol 134 (17) ◽  
pp. 2243-2262
Author(s):  
Danlin Liu ◽  
Gavin Richardson ◽  
Fehmi M. Benli ◽  
Catherine Park ◽  
João V. de Souza ◽  
...  
Keyword(s):  
Cardiovascular Diseases ◽  
Cardiovascular System ◽  
Nlrp3 Inflammasome ◽  
Molecular Mechanisms ◽  
Molecular Targets ◽  
Therapeutic Interventions ◽  
Low Grade ◽  
Cardiovascular Research ◽  
Inflammatory Processes ◽  
Ach Receptors

Abstract In the elderly population, pathological inflammation has been associated with ageing-associated diseases. The term ‘inflammageing’, which was used for the first time by Franceschi and co-workers in 2000, is associated with the chronic, low-grade, subclinical inflammatory processes coupled to biological ageing. The source of these inflammatory processes is debated. The senescence-associated secretory phenotype (SASP) has been proposed as the main origin of inflammageing. The SASP is characterised by the release of inflammatory cytokines, elevated activation of the NLRP3 inflammasome, altered regulation of acetylcholine (ACh) nicotinic receptors, and abnormal NAD+ metabolism. Therefore, SASP may be ‘druggable’ by small molecule therapeutics targeting those emerging molecular targets. It has been shown that inflammageing is a hallmark of various cardiovascular diseases, including atherosclerosis, hypertension, and adverse cardiac remodelling. Therefore, the pathomechanism involving SASP activation via the NLRP3 inflammasome; modulation of NLRP3 via α7 nicotinic ACh receptors; and modulation by senolytics targeting other proteins have gained a lot of interest within cardiovascular research and drug development communities. In this review, which offers a unique view from both clinical and preclinical target-based drug discovery perspectives, we have focused on cardiovascular inflammageing and its molecular mechanisms. We have outlined the mechanistic links between inflammageing, SASP, interleukin (IL)-1β, NLRP3 inflammasome, nicotinic ACh receptors, and molecular targets of senolytic drugs in the context of cardiovascular diseases. We have addressed the ‘druggability’ of NLRP3 and nicotinic α7 receptors by small molecules, as these proteins represent novel and exciting targets for therapeutic interventions targeting inflammageing in the cardiovascular system and beyond.

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