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Article ; Online: MICOM

Christian Diener / Sean M. Gibbons / Osbaldo Resendis-Antonio

mSystems, Vol 5, Iss 1, p e00606-

Metagenome-Scale Modeling To Infer Metabolic Interactions in the Gut Microbiota

2020 Volume 19

Abstract: The bacterial communities that live within the human gut have been linked to health and disease. However, we are still just beginning to understand how those bacteria interact and what potential interventions to our gut microbiome can make us healthier. ... ...

Abstract	The bacterial communities that live within the human gut have been linked to health and disease. However, we are still just beginning to understand how those bacteria interact and what potential interventions to our gut microbiome can make us healthier. Here, we present a mathematical modeling framework (named MICOM) that can recapitulate the growth rates of diverse bacterial species in the gut and can simulate metabolic interactions within microbial communities. We show that MICOM can unravel the ecological rules that shape the microbial landscape in our gut and that a given dietary or probiotic intervention can have widely different effects in different people.Compositional changes in the gut microbiota have been associated with a variety of medical conditions such as obesity, Crohn’s disease, and diabetes. However, connecting microbial community composition to ecosystem function remains a challenge. Here, we introduce MICOM, a customizable metabolic model of the human gut microbiome. By using a heuristic optimization approach based on L2 regularization, we were able to obtain a unique set of realistic growth rates that corresponded well with observed replication rates. We integrated adjustable dietary and taxon abundance constraints to generate personalized metabolic models for individual metagenomic samples. We applied MICOM to a balanced cohort of metagenomes from 186 people, including a metabolically healthy population and individuals with type 1 and type 2 diabetes. Model results showed that individual bacterial genera maintained conserved niche structures across humans, while the community-level production of short-chain fatty acids (SCFAs) was heterogeneous and highly individual specific. Model output revealed complex cross-feeding interactions that would be difficult to measure in vivo. Metabolic interaction networks differed somewhat consistently between healthy and diabetic subjects. In particular, MICOM predicted reduced butyrate and propionate production in a diabetic cohort, with restoration of SCFA production profiles found in healthy subjects following metformin treatment. Overall, we found that changes in diet or taxon abundances have highly personalized effects. We believe MICOM can serve as a useful tool for generating mechanistic hypotheses for how diet and microbiome composition influence community function. All methods are implemented in an open-source Python package, which is available at https://github.com/micom-dev/micom.
Keywords	flux balance analysis ; gut microbiome ; metagenome ; systems biology ; Microbiology ; QR1-502
Subject code	612
Language	English
Publishing date	2020-01-01T00:00:00Z
Publisher	American Society for Microbiology
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: MICOM

Christian Diener / Sean M. Gibbons / Osbaldo Resendis-Antonio

mSystems, Vol 5, Iss

Metagenome-Scale Modeling To Infer Metabolic Interactions in the Gut Microbiota

2020 Volume 1

Abstract: ABSTRACT Compositional changes in the gut microbiota have been associated with a variety of medical conditions such as obesity, Crohn’s disease, and diabetes. However, connecting microbial community composition to ecosystem function remains a challenge. ... ...

Abstract	ABSTRACT Compositional changes in the gut microbiota have been associated with a variety of medical conditions such as obesity, Crohn’s disease, and diabetes. However, connecting microbial community composition to ecosystem function remains a challenge. Here, we introduce MICOM, a customizable metabolic model of the human gut microbiome. By using a heuristic optimization approach based on L2 regularization, we were able to obtain a unique set of realistic growth rates that corresponded well with observed replication rates. We integrated adjustable dietary and taxon abundance constraints to generate personalized metabolic models for individual metagenomic samples. We applied MICOM to a balanced cohort of metagenomes from 186 people, including a metabolically healthy population and individuals with type 1 and type 2 diabetes. Model results showed that individual bacterial genera maintained conserved niche structures across humans, while the community-level production of short-chain fatty acids (SCFAs) was heterogeneous and highly individual specific. Model output revealed complex cross-feeding interactions that would be difficult to measure in vivo. Metabolic interaction networks differed somewhat consistently between healthy and diabetic subjects. In particular, MICOM predicted reduced butyrate and propionate production in a diabetic cohort, with restoration of SCFA production profiles found in healthy subjects following metformin treatment. Overall, we found that changes in diet or taxon abundances have highly personalized effects. We believe MICOM can serve as a useful tool for generating mechanistic hypotheses for how diet and microbiome composition influence community function. All methods are implemented in an open-source Python package, which is available at https://github.com/micom-dev/micom. IMPORTANCE The bacterial communities that live within the human gut have been linked to health and disease. However, we are still just beginning to understand how those bacteria interact and what potential ...
Keywords	flux balance analysis ; gut microbiome ; metagenome ; systems biology ; Microbiology ; QR1-502
Subject code	612
Language	English
Publishing date	2020-02-01T00:00:00Z
Publisher	American Society for Microbiology
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Systems Biology and the Challenge of Deciphering the Metabolic Mechanisms Underlying Cancer

Christian Diener / Osbaldo Resendis-Antonio

2017

Abstract: Since the discovery of the Warburg effect in the 1920s cancer has been tightly associated with the genetic and metabolic state of the cell. One of the hallmarks of cancer is the alteration of the cellular metabolism in order to promote proliferation and ... ...

Abstract	Since the discovery of the Warburg effect in the 1920s cancer has been tightly associated with the genetic and metabolic state of the cell. One of the hallmarks of cancer is the alteration of the cellular metabolism in order to promote proliferation and undermine cellular defense mechanisms such as apoptosis or detection by the immune system. However, the strategies by which this is achieved in different cancers and sometimes even in different patients of the same cancer is very heterogeneous, which hinders the design of general treatment options.Recently, there has been an ongoing effort to study this phenomenon on a genomic scale in order to understand the causality underlying the disease. Hence, current “omics” technologies have contributed to identify and monitor different biological pieces at different biological levels, such as genes, proteins or metabolites. These technological capacities have provided us with vast amounts of clinical data where a single patient may often give rise to various tissue samples, each of them being characterized in detail by genomescale data on the sequence, expression, proteome and metabolome level. Data with such detail poses the imminent problem of extracting meaningful interpretations and translating them into specific treatment options. To this purpose, Systems Biology provides a set of promising computational tools in order to decipher the mechanisms driving a healthy cell’s metabolism into a cancerous one. However, this enterprise requires bridging the gap between large data resources, mathematical analysis and modeling specifically designed to work with the available data. This is by no means trivial and requires high levels of communication and adaptation between the experimental and theoretical side of research.
Keywords	Systems Biology ; Metabolism ; Cancer ; Metabolic alterations ; Modeling ; Computational Biology
Subject code	610
Language	English
Publisher	Frontiers Media SA
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Editorial

Osbaldo Resendis-Antonio / Christian Diener

Frontiers in Physiology, Vol

Systems Biology and the Challenge of Deciphering the Metabolic Mechanisms Underlying Cancer

2017 Volume 8

Keywords	systems biology ; cancer ; modeling and simulation ; metabolic modeling ; dynamical systems ; Physiology ; QP1-981
Language	English
Publishing date	2017-07-01T00:00:00Z
Publisher	Frontiers Media S.A.
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A network perspective on the ecology of gut microbiota and progression of type 2 diabetes

Diego A. Esquivel-Hernández / Yoscelina Estrella Martínez-López / Jean Paul Sánchez-Castañeda / Daniel Neri-Rosario / Cristian Padrón-Manrique / David Giron-Villalobos / Cristian Mendoza-Ortíz / Osbaldo Resendis-Antonio

Frontiers in Endocrinology, Vol

Linkages to keystone taxa in a Mexican cohort

2023 Volume 14

Abstract: IntroductionThe human gut microbiota (GM) is a dynamic system which ecological interactions among the community members affect the host metabolism. Understanding the principles that rule the bidirectional communication between GM and its host, is one of ... ...

Abstract	IntroductionThe human gut microbiota (GM) is a dynamic system which ecological interactions among the community members affect the host metabolism. Understanding the principles that rule the bidirectional communication between GM and its host, is one of the most valuable enterprise for uncovering how bacterial ecology influences the clinical variables in the host.MethodsHere, we used SparCC to infer association networks in 16S rRNA gene amplicon data from the GM of a cohort of Mexican patients with type 2 diabetes (T2D) in different stages: NG (normoglycemic), IFG (impaired fasting glucose), IGT (impaired glucose tolerance), IFG + IGT (impaired fasting glucose plus impaired glucose tolerance), T2D and T2D treated (T2D with a 5-year ongoing treatment).ResultsBy exploring the network topology from the different stages of T2D, we observed that, as the disease progress, the networks lose the association between bacteria. It suggests that the microbial community becomes highly sensitive to perturbations in individuals with T2D. With the purpose to identify those genera that guide this transition, we computationally found keystone taxa (driver nodes) and core genera for a Mexican T2D cohort. Altogether, we suggest a set of genera driving the progress of the T2D in a Mexican cohort, among them Ruminococcaceae NK4A214 group, Ruminococcaceae UCG-010, Ruminococcaceae UCG-002, Ruminococcaceae UCG-005, Alistipes, Anaerostipes, and Terrisporobacter.DiscussionBased on a network approach, this study suggests a set of genera that can serve as a potential biomarker to distinguish the distinct degree of advances in T2D for a Mexican cohort of patients. Beyond limiting our conclusion to one population, we present a computational pipeline to link ecological networks and clinical stages in T2D, and desirable aim to advance in the field of precision medicine.
Keywords	gut microbiota ; microbial ecology ; systems biology ; type 2 diabetes ; network analysis ; Diseases of the endocrine glands. Clinical endocrinology ; RC648-665
Subject code	590
Language	English
Publishing date	2023-04-01T00:00:00Z
Publisher	Frontiers Media S.A.
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Effect of metformin and metformin/linagliptin on gut microbiota in patients with prediabetes.

Estrella, Martínez-López Yoscelina / Daniel, Neri-Rosario / Armando, Esquivel-Hernández Diego / Cristian, Padron-Manrique / Aarón, Vázquez-Jiménez / Paul, Sánchez-Castañeda Jean / David, Girón-Villalobos / Cristian, Mendoza-Ortíz / de Lourdes, Reyes-Escogido María / Lola, Evia-Viscarra Maria / Alberto, Aguilar-Garcia / Osbaldo, Resendis-Antonio / Rodolfo, Guardado-Mendoza

Scientific reports

2024 Volume 14, Issue 1, Page(s) 9678

Abstract: Lifestyle modifications, metformin, and linagliptin reduce the incidence of type 2 diabetes (T2D) in people with prediabetes. The gut microbiota (GM) may enhance such interventions' efficacy. We determined the effect of linagliptin/metformin (LM) vs ... ...

Abstract	Lifestyle modifications, metformin, and linagliptin reduce the incidence of type 2 diabetes (T2D) in people with prediabetes. The gut microbiota (GM) may enhance such interventions' efficacy. We determined the effect of linagliptin/metformin (LM) vs metformin (M) on GM composition and its relationship to insulin sensitivity (IS) and pancreatic β-cell function (Pβf) in patients with prediabetes. A cross-sectional study was conducted at different times: basal, six, and twelve months in 167 Mexican adults with prediabetes. These treatments increased the abundance of GM SCFA-producing bacteria M (Fusicatenibacter and Blautia) and LM (Roseburia, Bifidobacterium, and [Eubacterium] hallii group). We performed a mediation analysis with structural equation models (SEM). In conclusion, M and LM therapies improve insulin sensitivity and Pβf in prediabetics. GM is partially associated with these improvements since the SEM models suggest a weak association between specific bacterial genera and improvements in IS and Pβf.
MeSH term(s)	Humans ; Metformin/pharmacology ; Metformin/therapeutic use ; Gastrointestinal Microbiome/drug effects ; Prediabetic State/drug therapy ; Prediabetic State/microbiology ; Male ; Female ; Middle Aged ; Cross-Sectional Studies ; Linagliptin/therapeutic use ; Linagliptin/pharmacology ; Hypoglycemic Agents/pharmacology ; Hypoglycemic Agents/therapeutic use ; Diabetes Mellitus, Type 2/drug therapy ; Diabetes Mellitus, Type 2/microbiology ; Diabetes Mellitus, Type 2/metabolism ; Insulin Resistance ; Adult ; Insulin-Secreting Cells/drug effects ; Insulin-Secreting Cells/metabolism ; Aged
Chemical Substances	Metformin (9100L32L2N) ; Linagliptin (3X29ZEJ4R2) ; Hypoglycemic Agents
Language	English
Publishing date	2024-04-27
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-024-60081-y
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Filling kinetic gaps

Osbaldo Resendis-Antonio

PLoS ONE, Vol 4, Iss 3, p e

dynamic modeling of metabolism where detailed kinetic information is lacking.

2009 Volume 4967

Abstract: Integrative analysis between dynamical modeling of metabolic networks and data obtained from high throughput technology represents a worthy effort toward a holistic understanding of the link among phenotype and dynamical response. Even though the ... ...

Abstract	Integrative analysis between dynamical modeling of metabolic networks and data obtained from high throughput technology represents a worthy effort toward a holistic understanding of the link among phenotype and dynamical response. Even though the theoretical foundation for modeling metabolic network has been extensively treated elsewhere, the lack of kinetic information has limited the analysis in most of the cases. To overcome this constraint, we present and illustrate a new statistical approach that has two purposes: integrate high throughput data and survey the general dynamical mechanisms emerging for a slightly perturbed metabolic network.This paper presents a statistic framework capable to study how and how fast the metabolites participating in a perturbed metabolic network reach a steady-state. Instead of requiring accurate kinetic information, this approach uses high throughput metabolome technology to define a feasible kinetic library, which constitutes the base for identifying, statistical and dynamical properties during the relaxation. For the sake of illustration we have applied this approach to the human Red blood cell metabolism (hRBC) and its capacity to predict temporal phenomena was evaluated. Remarkable, the main dynamical properties obtained from a detailed kinetic model in hRBC were recovered by our statistical approach. Furthermore, robust properties in time scale and metabolite organization were identify and one concluded that they are a consequence of the combined performance of redundancies and variability in metabolite participation.In this work we present an approach that integrates high throughput metabolome data to define the dynamic behavior of a slightly perturbed metabolic network where kinetic information is lacking. Having information of metabolite concentrations at steady-state, this method has significant relevance due its potential scope to analyze others genome scale metabolic reconstructions. Thus, I expect this approach will significantly contribute to explore the ...
Keywords	Medicine ; R ; Science ; Q
Subject code	006
Language	English
Publishing date	2009-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: “Gestaltomics”

Nora A. Gutierrez Najera / Osbaldo Resendis-Antonio / Humberto Nicolini

Frontiers in Physiology, Vol

Systems Biology Schemes for the Study of Neuropsychiatric Diseases

2017 Volume 8

Abstract: The integration of different sources of biological information about what defines a behavioral phenotype is difficult to unify in an entity that reflects the arithmetic sum of its individual parts. In this sense, the challenge of Systems Biology for ... ...

Abstract	The integration of different sources of biological information about what defines a behavioral phenotype is difficult to unify in an entity that reflects the arithmetic sum of its individual parts. In this sense, the challenge of Systems Biology for understanding the “psychiatric phenotype” is to provide an improved vision of the shape of the phenotype as it is visualized by “Gestalt” psychology, whose fundamental axiom is that the observed phenotype (behavior or mental disorder) will be the result of the integrative composition of every part. Therefore, we propose the term “Gestaltomics” as a term from Systems Biology to integrate data coming from different sources of information (such as the genome, transcriptome, proteome, epigenome, metabolome, phenome, and microbiome). In addition to this biological complexity, the mind is integrated through multiple brain functions that receive and process complex information through channels and perception networks (i.e., sight, ear, smell, memory, and attention) that in turn are programmed by genes and influenced by environmental processes (epigenetic). Today, the approach of medical research in human diseases is to isolate one disease for study; however, the presence of an additional disease (co-morbidity) or more than one disease (multimorbidity) adds complexity to the study of these conditions. This review will present the challenge of integrating psychiatric disorders at different levels of information (Gestaltomics). The implications of increasing the level of complexity, for example, studying the co-morbidity with another disease such as cancer, will also be discussed.
Keywords	systems biology ; psychiatry ; lung cancer ; diagnosis ; omics ; Physiology ; QP1-981
Subject code	004
Language	English
Publishing date	2017-05-01T00:00:00Z
Publisher	Frontiers Media S.A.
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Spermiogenesis alterations in the absence of CTCF revealed by single cell RNA sequencing

Ulises Torres-Flores / Fernanda Díaz-Espinosa / Tayde López-Santaella / Rosa Rebollar-Vega / Aarón Vázquez-Jiménez / Ian J. Taylor / Rosario Ortiz-Hernández / Olga M. Echeverría / Gerardo H. Vázquez-Nin / María Concepción Gutierrez-Ruiz / Inti Alberto De la Rosa-Velázquez / Osbaldo Resendis-Antonio / Abrahan Hernández-Hernandez

Frontiers in Cell and Developmental Biology, Vol

2023 Volume 11

Abstract: CTCF is an architectonic protein that organizes the genome inside the nucleus in almost all eukaryotic cells. There is evidence that CTCF plays a critical role during spermatogenesis as its depletion produces abnormal sperm and infertility. However, ... ...

Abstract	CTCF is an architectonic protein that organizes the genome inside the nucleus in almost all eukaryotic cells. There is evidence that CTCF plays a critical role during spermatogenesis as its depletion produces abnormal sperm and infertility. However, defects produced by its depletion throughout spermatogenesis have not been fully characterized. In this work, we performed single cell RNA sequencing in spermatogenic cells with and without CTCF. We uncovered defects in transcriptional programs that explain the severity of the damage in the produced sperm. In the early stages of spermatogenesis, transcriptional alterations are mild. As germ cells go through the specialization stage or spermiogenesis, transcriptional profiles become more altered. We found morphology defects in spermatids that support the alterations in their transcriptional profiles. Altogether, our study sheds light on the contribution of CTCF to the phenotype of male gametes and provides a fundamental description of its role at different stages of spermiogenesis.
Keywords	ScRNA-seq ; mouse testis ; spermatogenesis ; CTCF ; sperm ; Biology (General) ; QH301-705.5
Language	English
Publishing date	2023-03-01T00:00:00Z
Publisher	Frontiers Media S.A.
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Unveiling functional heterogeneity in breast cancer multicellular tumor spheroids through single-cell RNA-seq

Erick Andrés Muciño-Olmos / Aarón Vázquez-Jiménez / Ugo Avila-Ponce de León / Meztli Matadamas-Guzman / Vilma Maldonado / Tayde López-Santaella / Abrahan Hernández-Hernández / Osbaldo Resendis-Antonio

Scientific Reports, Vol 10, Iss 1, Pp 1-

2020 Volume 16

Abstract: Abstract Heterogeneity is an intrinsic characteristic of cancer. Even in isogenic tumors, cell populations exhibit differential cellular programs that overall supply malignancy and decrease treatment efficiency. In this study, we investigated the ... ...

Abstract	Abstract Heterogeneity is an intrinsic characteristic of cancer. Even in isogenic tumors, cell populations exhibit differential cellular programs that overall supply malignancy and decrease treatment efficiency. In this study, we investigated the functional relationship among cell subtypes and how this interdependency can promote tumor development in a cancer cell line. To do so, we performed single-cell RNA-seq of MCF7 Multicellular Tumor Spheroids as a tumor model. Analysis of single-cell transcriptomes at two-time points of the spheroid growth, allowed us to dissect their functional relationship. As a result, three major robust cellular clusters, with a non-redundant complementary composition, were found. Meanwhile, one cluster promotes proliferation, others mainly activate mechanisms to invade other tissues and serve as a reservoir population conserved over time. Our results provide evidence to see cancer as a systemic unit that has cell populations with task stratification with the ultimate goal of preserving the hallmarks in tumors.
Keywords	Medicine ; R ; Science ; Q
Language	English
Publishing date	2020-07-01T00:00:00Z
Publisher	Nature Publishing Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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