Contextualizing context for synthetic biology--identifying causes of failure of synthetic biological systems.

Despite the efforts that bioengineers have exerted in designing and constructing biological processes that function according to a predetermined set of rules, their operation remains fundamentally circumstantial. The contextual situation in which molecules and single-celled or multi-cellular organisms find themselves shapes the way they interact, respond to the environment and process external information. Since the birth of the field, synthetic biologists have had to grapple with contextual issues, particularly when the molecular and genetic devices inexplicably fail to function as designed when tested in vivo. In this review, we set out to identify and classify the sources of the unexpected divergences between design and actual function of synthetic systems and analyze possible methodologies aimed at controlling, if not preventing, unwanted contextual issues

The Computational Diet: A Review of Computational Methods Across Diet, Microbiome, and Health.

Food and human health are inextricably linked. As such, revolutionary impacts on health have been derived from advances in the production and distribution of food relating to food safety and fortification with micronutrients. During the past two decades, it has become apparent that the human microbiome has the potential to modulate health, including in ways that may be related to diet and the composition of specific foods. Despite the excitement and potential surrounding this area, the complexity of the gut microbiome, the chemical composition of food, and their interplay in situ remains a daunting task to fully understand. However, recent advances in high-throughput sequencing, metabolomics profiling, compositional analysis of food, and the emergence of electronic health records provide new sources of data that can contribute to addressing this challenge. Computational science will play an essential role in this effort as it will provide the foundation to integrate these data layers and derive insights capable of revealing and understanding the complex interactions between diet, gut microbiome, and health. Here, we review the current knowledge on diet-health-gut microbiota, relevant data sources, bioinformatics tools, machine learning capabilities, as well as the intellectual property and legislative regulatory landscape. We provide guidance on employing machine learning and data analytics, identify gaps in current methods, and describe new scenarios to be unlocked in the next few years in the context of current knowledge

Uncovering the genetic architecture and metabolic basis of amino acid composition in maize kernels using multi-omics integration

Seeds are a major source of protein in human and livestock diets. Cereal grains are some of the most consumed seeds by both humans and livestock worldwide, with maize, wheat, and rice alone accounting for ~70% of the total cereal production. Maize is one of the major staple crops used for food, feed, and fuel. A mature maize kernel contains small embryo (10% of the volume) and a large endosperm (~90% of its volume). In terms of composition, majority of the kernel proportion contains around 90% of starch and around 8-10% of protein. Nine of the twenty amino acids cannot be synthesized by monogastric animals, including humans, and must be obtained through the diet and are considered essential amino acids (EAA): lysine, isoleucine, leucine, histidine, methionine, phenylalanine, threonine, tryptophan, and valine. The protein quality is poor in maize endosperm as the primary storage proteins are severely deficient in EAA such as lysine, tryptophan, and methionine. Such deficiencies can be detrimental since corn provides an important source of proteins for food in developing countries and for feed in developed countries such as the U.S. Failure to consume sufficient levels of EAA per day leads to severe malnutrition, even if one's calories requirements are met. Many attempts to increase the EAA has demonstrated only limited success since seed can rebalance their amino acids composition even when major changes are introduced in their proteome. One possible approach to solve this applied problem is by seed EAA biofortification; however, many attempts at this task fall short and strongly indicates that even though we know most of the metabolic pathways of amino acids, we know very little about their regulation especially in seed. Therefore, the first step towards efficient amino acid biofortification is to increase our fundamental understanding of their function, as well as the metabolic regulation and the biology of the plant seeds. Despite the tight regulation within any given genotype seed amino acid composition display extensive natural variation which can be utilized to uncover the genetic basis and identify new targets for seed amino acids biofortification. Hence to uncover the genetic architecture of amino acids composition in maize kernels we used Goodman-Buckler maize association panel that consists of 282 diverse maize inbred lines including stiff stalk, non-stiff stalk, tropical and subtropical, sweetcorn and popcorn lines. I performed genome wide association study (GWAS) on both the protein bound amino acids (PBAA) and free amino acids (FAA). Although, GWAS is widely used to dissect the genetic architecture of complex traits, oftentimes the GWAS outputs the extensive list of genes particularly when using multiple phenotypic traits. To overcome this, I used an integrative multi-omics approach that combines GWAS and co-expression networks modules obtained from ten seed filling stages of B73 to uncover novel key regulatory genes, characterize biological process and prioritized the candidate genes that involved in shaping the natural variation of amino acid composition. Chapter one of the dissertation is the general introduction and literature review on the seed amino acids. It briefly discuss the general introduction of PBAA and FAA, previous attempts done to improve seed PBAA and FAA composition, natural variation used to uncover the genetic architecture of complex traits including metabolic traits such as amino acids and finally discuss the multi-omics integration to uncover the genetic basis of complex traits. Chapter two elaborates the comprehensive genetic basis of PBAA in maize kernels using integrative analysis of 76 PBAA GWAS with protein co-expression network modules. Previous studies have shown that manipulation of storage proteins and amino acid pathway genes have contributed in the improvement of quality protein maize however, my study strongly suggests that in addition to the manipulation of storage protein and amino acid metabolic genes, specific ribosomal genes along with other translation machinery could be the novel target for seed amino acids biofortification. Chapter three discusses the genetic basis of FAA in maize kernels using integrative analysis of 109 FAA GWAS with protein co-expression network modules. I have presented here the comprehensive list of SNPs as well as the candidate genes and several biological processes including the translational machinery responsible for shaping the genetic architecture of FAA in seed. Chapter four includes the conclusion and future works. Maize is an important crop used for both food and feed and possesses great genotypic and phenotypic diversity. The results from my study has validated several previous characterized genes and identified novel key genes that regulate and shape the PBAA and FAA in maize kernels, which could be used further to target for amino acid biofortification.Includes bibliographical references

Intestinal microbiome landscaping : insight in community assemblage and implications for microbial modulation strategies

High individuality, large complexity and limited understanding of the mechanisms underlying human intestinal microbiome function remain the major challenges for designing beneficial modulation strategies. Exemplified by the analysis of intestinal bacteria in a thousand Western adults, we discuss key concepts of the human intestinal microbiome landscape, i.e. the compositional and functional 'core', the presence of community types and the existence of alternative stable states. Genomic investigation of core taxa revealed functional redundancy, which is expected to stabilize the ecosystem, as well as taxa with specialized functions that have the potential to shape the microbiome landscape. The contrast between Prevotella-and Bacteroides-dominated systems has been well described. However, less known is the effect of not so abundant bacteria, for example, Dialister spp. that have been proposed to exhibit distinct bistable dynamics. Studies employing time-series analysis have highlighted the dynamical variation in the microbiome landscape with and without the effect of defined perturbations, such as the use of antibiotics or dietary changes. We incorporate ecosystem-level observations of the human intestinal microbiota and its keystone species to suggest avenues for designing microbiome modulation strategies to improve host health.Peer reviewe

Systems Biology of the human microbiome

© The Author(s), 2017. This is the author's version of the work and is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Current Opinion in Biotechnology 51 (2018): 146-153, doi:10.1016/j.copbio.2018.01.018.Recent research has shown that the microbiome—a collection of microorganisms, including bacteria, fungi, and viruses, living on and in a host—are of extraordinary importance in human health, even from conception and development in the uterus. Therefore, to further our ability to diagnose disease, to predict treatment outcomes, and to identify novel therapeutics, it is essential to include microbiome and microbial metabolic biomarkers in Systems Biology investigations. In clinical studies or, more precisely, Systems Medicine approaches, we can use the diversity and individual characteristics of the personal microbiome to enhance our resolution for patient stratification. In this review, we explore several Systems Medicine approaches, including Microbiome Wide Association Studies to understand the role of the human microbiome in health and disease, with a focus on ‘preventive medicine’ or P4 (i.e., personalized, predictive, preventive, participatory) medicine.BPB is funded by the Arnold and Mabel Beckman Foundation (Arnold O. Beckman Postdoctoral Fellow)2019-02-1

Multi-omics gut microbiome signatures in obese women: role of diet and uncontrolled eating behavior

Background: Obesity and related co-morbidities represent a major health challenge nowadays, with a rapidly increasing incidence worldwide. The gut microbiome has recently emerged as a key modifier of human health that can affect the development and progression of obesity, largely due to its involvement in the regulation of food intake and metabolism. However, there are still few studies that have in-depth explored the functionality of the human gut microbiome in obesity and even fewer that have examined its relationship to eating behaviors. Methods: In an attempt to advance our knowledge of the gut-microbiome-brain axis in the obese phenotype, we thoroughly characterized the gut microbiome signatures of obesity in a well-phenotyped Italian female cohort from the NeuroFAST and MyNewGut EU FP7 projects. Fecal samples were collected from 63 overweight/obese and 37 normal-weight women and analyzed via a multi-omics approach combining 16S rRNA amplicon sequencing, metagenomics, metatranscriptomics, and lipidomics. Associations with anthropometric, clinical, biochemical, and nutritional data were then sought, with particular attention to cognitive and behavioral domains of eating. Results: We identified four compositional clusters of the gut microbiome in our cohort that, although not distinctly associated with weight status, correlated differently with eating habits and behaviors. These clusters also differed in functional features, i.e., transcriptional activity and fecal metabolites. In particular, obese women with uncontrolled eating behavior were mostly characterized by low-diversity microbial steady states, with few and poorly interconnected species (e.g., Ruminococcus torques and Bifidobacterium spp.), which exhibited low transcriptional activity, especially of genes involved in secondary bile acid biosynthesis and neuroendocrine signaling (i.e., production of neurotransmitters, indoles and ligands for cannabinoid receptors). Consistently, high amounts of primary bile acids as well as sterols were found in their feces. Conclusions: By finding peculiar gut microbiome profiles associated with eating patterns, we laid the foundation for elucidating gut-brain axis communication in the obese phenotype. Subject to confirmation of the hypotheses herein generated, our work could help guide the design of microbiome-based precision interventions, aimed at rewiring microbial networks to support a healthy diet-microbiome-gut-brain axis, thus counteracting obesity and related complications

Meeting report: a hard look at the state of enamel research.

The Encouraging Novel Amelogenesis Models and Ex vivo cell Lines (ENAMEL) Development workshop was held on 23 June 2017 at the Bethesda headquarters of the National Institute of Dental and Craniofacial Research (NIDCR). Discussion topics included model organisms, stem cells/cell lines, and tissues/3D cell culture/organoids. Scientists from a number of disciplines, representing institutions from across the United States, gathered to discuss advances in our understanding of enamel, as well as future directions for the field

Comparative Analysis of Muscle Transcriptome between Pig Genotypes Identifies Genes and Regulatory Mechanisms Associated to Growth, Fatness and Metabolism.

Iberian ham production includes both purebred (IB) and Duroc-crossbred (IBxDU) Iberian pigs, which show important differences in meat quality and production traits, such as muscle growth and fatness. This experiment was conducted to investigate gene expression differences, transcriptional regulation and genetic polymorphisms that could be associated with the observed phenotypic differences between IB and IBxDU pigs. Nine IB and 10 IBxDU pigs were slaughtered at birth. Morphometric measures and blood samples were obtained and samples from Biceps femoris muscle were employed for compositional and transcriptome analysis by RNA-Seq technology. Phenotypic differences were evident at this early age, including greater body size and weight in IBxDU and greater Biceps femoris intramuscular fat and plasma cholesterol content in IB newborns. We detected 149 differentially expressed genes between IB and IBxDU neonates (p < 0.01 and Fold-Change > 1. 5). Several were related to adipose and muscle tissues development (DLK1, FGF21 or UBC). The functional interpretation of the transcriptomic differences revealed enrichment of functions and pathways related to lipid metabolism in IB and to cellular and muscle growth in IBxDU pigs. Protein catabolism, cholesterol biosynthesis and immune system were functions enriched in both genotypes. We identified transcription factors potentially affecting the observed gene expression differences. Some of them have known functions on adipogenesis (CEBPA, EGRs), lipid metabolism (PPARGC1B) and myogenesis (FOXOs, MEF2D, MYOD1), which suggest a key role in the meat quality differences existing between IB and IBxDU hams. We also identified several polymorphisms showing differential segregation between IB and IBxDU pigs. Among them, non-synonymous variants were detected in several transcription factors as PPARGC1B and TRIM63 genes, which could be associated to altered gene function. Taken together, these results provide information about candidate genes, metabolic pathways and genetic polymorphisms potentially involved in phenotypic differences between IB and IBxDU pigs associated to meat quality and production traits

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment

COST Action CA18131 Cierva Grant IJC2019-042188-I (LM-Z) Estonian Research Council grant PUT 1371The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.publishersversionpublishe