scholarly journals Quantitative prediction of conditional vulnerabilities in regulatory and metabolic networks using PRIME

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Selva Rupa Christinal Immanuel ◽  
Mario L. Arrieta-Ortiz ◽  
Rene A. Ruiz ◽  
Min Pan ◽  
Adrian Lopez Garcia de Lomana ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Immune System ◽  
Mortality Rate ◽  
Predictive Model ◽  
Metabolic Networks ◽  
Performance Evaluations ◽  
Quantitative Prediction ◽  
Genome Wide ◽  
Context Specific ◽  
Extensive Performance

AbstractThe ability of Mycobacterium tuberculosis (Mtb) to adopt heterogeneous physiological states underlies its success in evading the immune system and tolerating antibiotic killing. Drug tolerant phenotypes are a major reason why the tuberculosis (TB) mortality rate is so high, with over 1.8 million deaths annually. To develop new TB therapeutics that better treat the infection (faster and more completely), a systems-level approach is needed to reveal the complexity of network-based adaptations of Mtb. Here, we report a new predictive model called PRIME (Phenotype of Regulatory influences Integrated with Metabolism and Environment) to uncover environment-specific vulnerabilities within the regulatory and metabolic networks of Mtb. Through extensive performance evaluations using genome-wide fitness screens, we demonstrate that PRIME makes mechanistically accurate predictions of context-specific vulnerabilities within the integrated regulatory and metabolic networks of Mtb, accurately rank-ordering targets for potentiating treatment with frontline drugs.

scholarly journals Quantitative prediction of conditional vulnerabilities in regulatory and metabolic networks of Mycobacterium tuberculosis

2021 ◽  
Author(s):  
Selva Rupa Christinal Immanuel ◽  
Mario L. Arrieta-Ortiz ◽  
Rene A. Ruiz ◽  
Min Pan ◽  
Adrian Lopez Garcia de Lomana ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Immune System ◽  
Mortality Rate ◽  
Predictive Model ◽  
Metabolic Networks ◽  
Performance Evaluations ◽  
Quantitative Prediction ◽  
Genome Wide ◽  
Context Specific ◽  
Extensive Performance

AbstractThe ability of Mycobacterium tuberculosis (Mtb) to adopt heterogeneous physiological states, underlies its success in evading the immune system and tolerating antibiotic killing. Drug tolerant phenotypes are a major reason why the tuberculosis (TB) mortality rate is so high, with over 1.8 million deaths annually. To develop new TB therapeutics that better treat the infection (faster and more completely), a systems-level approach is needed to reveal the complexity of network-based adaptations of Mtb. Here, we report a new predictive model called PRIME (Phenotype of Regulatory influences Integrated with Metabolism and Environment) to uncover environment-specific vulnerabilities within the regulatory and metabolic networks of Mtb. Through extensive performance evaluations using genome-wide fitness screens, we demonstrate that PRIME makes mechanistically accurate predictions of context-specific vulnerabilities within the integrated regulatory and metabolic networks of Mtb, accurately rank-ordering targets for potentiating treatment with frontline drugs.

scholarly journals The thick waxy coat of mycobacteria, a protective layer against antibiotics and the host's immune system

2020 ◽  
Vol 477 (10) ◽  
pp. 1983-2006 ◽  
Author(s):  
Sarah M. Batt ◽  
David E. Minnikin ◽  
Gurdyal S. Besra
Keyword(s):  
Infectious Disease ◽  
Cell Wall ◽  
Mycobacterium Tuberculosis ◽  
Immune System ◽  
Mortality Rate ◽  
Cell Envelope ◽  
Protective Layer ◽  
Structure And Function ◽  
Complex Lipids ◽  
And Function

Tuberculosis, caused by the pathogenic bacterium Mycobacterium tuberculosis (Mtb), is the leading cause of death from an infectious disease, with a mortality rate of over a million people per year. This pathogen's remarkable resilience and infectivity is largely due to its unique waxy cell envelope, 40% of which comprises complex lipids. Therefore, an understanding of the structure and function of the cell wall lipids is of huge indirect clinical significance. This review provides a synopsis of the cell envelope and the major lipids contained within, including structure, biosynthesis and roles in pathogenesis.

scholarly journals Dynamic histone acetylation in floral volatile synthesis and emission in petunia flowers

2021 ◽  
Author(s):  
Ryan M Patrick ◽  
Xing-Qi Huang ◽  
Natalia Dudareva ◽  
Ying Li
Keyword(s):  
Transcriptional Activation ◽  
Metabolic Pathways ◽  
Transcriptional Repression ◽  
Metabolic Networks ◽  
Underlying Mechanism ◽  
Expression Of Genes ◽  
Genome Wide ◽  
Chemical Inhibitors ◽  
Volatile Organic ◽  
Floral Volatile

Abstract Biosynthesis of secondary metabolites relies on primary metabolic pathways to provide precursors, energy, and cofactors, thus requiring coordinated regulation of primary and secondary metabolic networks. However, to date, it remains largely unknown how this coordination is achieved. Using Petunia hybrida flowers, which emit high levels of phenylpropanoid/benzenoid volatile organic compounds (VOCs), we uncovered genome-wide dynamic deposition of histone H3 lysine 9 acetylation (H3K9ac) during anthesis as an underlying mechanism to coordinate primary and secondary metabolic networks. The observed epigenome reprogramming is accompanied by transcriptional activation at gene loci involved in primary metabolic pathways that provide precursor phenylalanine, as well as secondary metabolic pathways to produce volatile compounds. We also observed transcriptional repression among genes involved in alternative phenylpropanoid branches that compete for metabolic precursors. We show that GNAT family histone acetyltransferase(s) (HATs) are required for the expression of genes involved in VOC biosynthesis and emission, by using chemical inhibitors of HATs, and by knocking down a specific HAT gene, ELP3, through transient RNAi. Together, our study supports that regulatory mechanisms at chromatin level may play an essential role in activating primary and secondary metabolic pathways to regulate VOC synthesis in petunia flowers.

scholarly journals Biofilm formation in the lung contributes to virulence and drug tolerance of Mycobacterium tuberculosis

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Poushali Chakraborty ◽  
Sapna Bajeli ◽  
Deepak Kaushal ◽  
Bishan Dass Radotra ◽  
Ashwani Kumar
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Immune System ◽  
Biofilm Formation ◽  
Antimycobacterial Activity ◽  
Drug Tolerance ◽  
Host Immune System ◽  
Tissue Sections ◽  
Slow Growing

AbstractTuberculosis is a chronic disease that displays several features commonly associated with biofilm-associated infections: immune system evasion, antibiotic treatment failures, and recurrence of infection. However, although Mycobacterium tuberculosis (Mtb) can form cellulose-containing biofilms in vitro, it remains unclear whether biofilms are formed during infection in vivo. Here, we demonstrate the formation of Mtb biofilms in animal models of infection and in patients, and that biofilm formation can contribute to drug tolerance. First, we show that cellulose is also a structural component of the extracellular matrix of in vitro biofilms of fast and slow-growing nontuberculous mycobacteria. Then, we use cellulose as a biomarker to detect Mtb biofilms in the lungs of experimentally infected mice and non-human primates, as well as in lung tissue sections obtained from patients with tuberculosis. Mtb strains defective in biofilm formation are attenuated for survival in mice, suggesting that biofilms protect bacilli from the host immune system. Furthermore, the administration of nebulized cellulase enhances the antimycobacterial activity of isoniazid and rifampicin in infected mice, supporting a role for biofilms in phenotypic drug tolerance. Our findings thus indicate that Mtb biofilms are relevant to human tuberculosis.

scholarly journals Identifying Personalized Metabolic Signatures in Breast Cancer

Metabolites ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 20
Author(s):  
Priyanka Baloni ◽  
Wikum Dinalankara ◽  
John C. Earls ◽  
Theo A. Knijnenburg ◽  
Donald Geman ◽  
...  
Keyword(s):  
Drug Targets ◽  
Metabolic Networks ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Metabolic Reprogramming ◽  
The Cancer Genome Atlas ◽  
Estrogen Metabolism ◽  
Acid Oxidation ◽  
A Genome ◽  
Context Specific

Cancer cells are adept at reprogramming energy metabolism, and the precise manifestation of this metabolic reprogramming exhibits heterogeneity across individuals (and from cell to cell). In this study, we analyzed the metabolic differences between interpersonal heterogeneous cancer phenotypes. We used divergence analysis on gene expression data of 1156 breast normal and tumor samples from The Cancer Genome Atlas (TCGA) and integrated this information with a genome-scale reconstruction of human metabolism to generate personalized, context-specific metabolic networks. Using this approach, we classified the samples into four distinct groups based on their metabolic profiles. Enrichment analysis of the subsystems indicated that amino acid metabolism, fatty acid oxidation, citric acid cycle, androgen and estrogen metabolism, and reactive oxygen species (ROS) detoxification distinguished these four groups. Additionally, we developed a workflow to identify potential drugs that can selectively target genes associated with the reactions of interest. MG-132 (a proteasome inhibitor) and OSU-03012 (a celecoxib derivative) were the top-ranking drugs identified from our analysis and known to have anti-tumor activity. Our approach has the potential to provide mechanistic insights into cancer-specific metabolic dependencies, ultimately enabling the identification of potential drug targets for each patient independently, contributing to a rational personalized medicine approach.

scholarly journals Genome-Wide Associations for Microscopic Differential Somatic Cell Count and Specific Mastitis Pathogens in Holstein Cows in Compost-Bedded Pack and Cubicle Farming Systems

Animals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1839
Author(s):  
Patricia Wagner ◽  
Tong Yin ◽  
Kerstin Brügemann ◽  
Petra Engel ◽  
Christina Weimann ◽  
...  
Keyword(s):  
Immune System ◽  
Somatic Cell ◽  
Candidate Genes ◽  
Farming Systems ◽  
Farming System ◽  
Environmental Stressors ◽  
Udder Health ◽  
Genome Wide ◽  
Cell Fractions ◽  
Mastitis Pathogens

The aim of the present study was to detect significant SNP (single-nucleotide polymorphism) effects and to annotate potential candidate genes for novel udder health traits in two different farming systems. We focused on specific mastitis pathogens and differential somatic cell fractions from 2198 udder quarters of 537 genotyped Holstein Friesian cows. The farming systems comprised compost-bedded pack and conventional cubicle barns. We developed a computer algorithm for genome-wide association studies allowing the estimation of main SNP effects plus consideration of SNPs by farming system interactions. With regard to the main effect, 35 significant SNPs were detected on 14 different chromosomes for the cell fractions and the pathogens. Six SNPs were significant for the interaction effect with the farming system for most of the udder health traits. We inferred two possible candidate genes based on significant SNP interactions. HEMK1 plays a role in the development of the immune system, depending on environmental stressors. CHL1 is regulated in relation to stress level and influences immune system mechanisms. The significant interactions indicate that gene activity can fluctuate depending on environmental stressors. Phenotypically, the prevalence of mastitis indicators differed between systems, with a notably lower prevalence of minor bacterial indicators in compost systems.

Abstract 62: Molecular Basis of Regulatory Variation at Coronary Heart Disease-Associated Loci

2015 ◽  
Vol 35 (suppl_1) ◽  
Author(s):  
Clint L Miller ◽  
Milos Pjanic ◽  
Jonathan D Lee ◽  
Boxiang Liu ◽  
William J Greenleaf ◽  
...  
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Chromatin Immunoprecipitation ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Regulatory Mechanisms ◽  
Tgf Beta ◽  
Genome Wide ◽  
Context Specific ◽  
Accessible Chromatin

Genome-wide association studies have identified 46 replicated genetic loci for coronary heart disease (CHD), and 104 loci associated at a 5% false discovery rate. However, the regulatory mechanisms of these associations largely remain elusive. Given that the majority of these CHD-associated loci reside in non-coding regions, they are predicted to function via context-specific gene regulation. Recent high-throughput assays of regulatory function include the assay for transposase-accessible chromatin using sequencing (ATAC-seq) and chromatin immunoprecipitation-sequencing (ChIP-seq). ATAC-seq utilizes a Tn5 transposase to fragment and tag accessible DNA sequences, which are often coupled to transcription factor occupancy identified by ChIP-seq. Importantly, this assay may reveal the spatio-temporal regulatory profiles in limited numbers of primary cells. Using ATAC-seq in human coronary artery smooth muscle cells (HCASMC) we identified 147,173 accessible chromatin peaks in control versus 198,976 peaks in TGF-beta-stimulated cells (136,446 shared peaks). Using de novo motif enrichment analysis we identified significant enrichment of specific AP-1 family members (29.2% vs. 5.1% background), chromatin remodeling, and SMC differentiation transcription factors. Using functional enrichment analysis of ChIP-seq and CHD-overlapping regions we observed enrichment of the hypoxia inducible factor 1 (HIF-1) and TGF-beta signaling pathways (1.5x10 -22 and 5.6x10 -18 , respectively) and relevant phenotypes, including cell migration and blood vessel morphology. Finally, we utilized these regulatory maps to explore the causal mechanisms underlying CHD-associated variants at four loci using haplotype-specific chromatin immunoprecipitation (haploChIP) and luciferase reporter assays. Taken together, these results suggest that genome-wide approaches such as ATAC-seq can be leveraged to map context-specific regulatory mechanisms of non-coding variants associated with complex diseases such as CHD, and reveal new biological and molecular insights into targeting heritable disease risk.

Molecular Bases for Pharmacotherapy of COVID -19

2021 ◽  
Vol 6 (5) ◽  
Keyword(s):  
T Cells ◽  
Immune System ◽  
Mortality Rate ◽  
Large Scale ◽  
Tissue Injury ◽  
Diffuse Alveolar Damage ◽  
New Drugs ◽  
Patient Death ◽  
Double Positive ◽  
Host Cell Membrane

The most large-scale challenge aroused at the beginning of Y2020 was the global spread of the coronavirus disease 2019 (COVID-19), caused by a zoonotic beta-coronavirus. One year after we have nearly 270 thousand confirmed cases with mortality rate 1.3% in Georgia, and almost 120 billion confirmed cases with mortality rate 2.2% worldwide. As it is known, COVID-19 is triggered by coronavirus species 2 or SARS-CoV-2, which inters in the human body by binding to the angiotensin-converting enzyme 2 (ACE2) molecule on the host cell membrane via the viral spike protein and expresses complex pathological changes in many organs linked with vascular injuries. The most severe expression of this disease exposed by microscopic examination is bilateral diffuse alveolar damage with fibroblasts exudates, indicating Acute Respiratory Distress Syndrome (ARDS). Immune system plays crucial role in tissue damage. As clinical researches showed, the number of peripheral CD4+ and CD8 + T cells were significantly reduced, while their activity was hyper-expressed as evidenced by the high proportions of HLADR (CD4 3•47%) and CD38 (CD8 39•4%) double-positive fractions. Moreover, there was identified an amplified concentration of highly pro inflammatory CCR6+ Th17 in CD4 T cells. This date explains that severe tissue injury in later stages of COVID-19 is depend on the immune system abnormalities, but not on SARS-CoV-2 direct cell destruction. In the same time the scientists and doctors found out abnormalities in coagulation function in most of the severe COVID-19 patients, which were expressed in elevation of D-Dimer level and prolongation of prothrombin time, some of whom terminated in disseminated intravascular coagulation (DIC), deep venous thrombosis (DVT) or fatal pulmonary thromboembolism (PTE). At the later stage in some severe patients it was identified thrombocytopenia as a result of excessive platelets consuming, which significantly affected on treatment and prognosis. More than 300 drugs are used for the treatment of COVID-19 worldwide. Now, the most popular treatments include Remdesivir, Hydroxychloroquine, Betamethasone, Tocilizumab, anti HIV drugs, and convalescent plasma. In the same time, WHO supports vaccines distribution for immunization. Currently, almost 8 vaccines are approved by different countries and more than 180 vaccines are under the clinical trails. Conclusion & Significance: Up till now it is challenging problem to combat SARS-CoV-2 with not well-defined origin and inexplicable biological characteristics as well as to control a pandemic of COVID-19 with such a high R0, a long incubation period and different disease outcomes. Unfortunately, we have limited understandings of particular mechanisms running to abnormal expression of immune system and coagulation processes. In the same time, we don’t have complete picture of vasculopathy leading to the tissue injury and patient death. Therefore, it is problematic to manage SARS-CoV-2 induced processes successfully using available drugs with no significant restoring effect on the organ damages in severe COVID-19 patients. So, we need new targets and new drugs for the prophylaxes and treatment of COVID-19 even we have vaccines available.

scholarly journals Intricate genetic programs controlling dormancy in Mycobacterium tuberculosis

2019 ◽  
Author(s):  
Abrar A. Abidi ◽  
Eliza J. R. Peterson ◽  
Mario L. Arrieta-Ortiz ◽  
Boris Aguilar ◽  
James T. Yurkovich ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Time Course ◽  
Experimental System ◽  
Topology Analysis ◽  
Transcriptional Dynamics ◽  
Regulatory Circuits ◽  
Genome Wide ◽  
Gene Regulatory ◽  
Location Map ◽  
Favorable Conditions

AbstractMycobacterium tuberculosis (MTB), responsible for the deadliest infectious disease worldwide, displays the remarkable ability to transition in and out of dormancy, a hallmark of the pathogen’s capacity to evade the immune system and opportunistically exploit immunocompromised individuals. Uncovering the gene regulatory programs that underlie the dramatic phenotypic shifts in MTB during disease latency and reactivation has posed an extraordinary challenge. We developed a novel experimental system to precisely control dissolved oxygen levels in MTB cultures in order to capture the chain of transcriptional events that unfold as MTB transitions into and out of hypoxia-induced dormancy. Using a comprehensive genome-wide transcription factor binding location map and insights from network topology analysis, we identified regulatory circuits that deterministically drive sequential transitions across six transcriptionally and functionally distinct states encompassing more than three-fifths of the MTB genome. The architecture of the genetic programs explains the transcriptional dynamics underlying synchronous entry of cells into a dormant state that is primed to infect the host upon encountering favorable conditions.One Sentence SummaryHigh-resolution transcriptional time-course reveals six-state genetic program that enables MTB to enter and exit hypoxia-induced dormancy.

scholarly journals DEXOM: Diversity-based enumeration of optimal context-specific metabolic networks

2020 ◽  
Author(s):  
Pablo Rodríguez-Mier ◽  
Nathalie Poupin ◽  
Carlo de Blasio ◽  
Laurent Le Cam ◽  
Fabien Jourdan
Keyword(s):  
Metabolic Network ◽  
Metabolic Networks ◽  
Relevant Information ◽  
Experimental Information ◽  
Correct Identification ◽  
Good Representation ◽  
Metabolic State ◽  
Whole Space ◽  
Context Specific ◽  
Optimal Networks

AbstractThe correct identification of metabolic activity in tissues or cells under different environmental or genetic conditions can be extremely elusive due to mechanisms such as post-transcriptional modification of enzymes or different rates in protein degradation, making difficult to perform predictions on the basis of gene expression alone. Context-specific metabolic network reconstruction can overcome these limitations by leveraging the integration of multi-omics data into genome-scale metabolic networks (GSMN). Using the experimental information, context-specific models are reconstructed by extracting from the GSMN the sub-network most consistent with the data, subject to biochemical constraints. One advantage is that these context-specific models have more predictive power since they are tailored to the specific organism and condition, containing only the reactions predicted to be active in such context. A major limitation of this approach is that the available information does not generally allow for an unambiguous characterization of the corresponding optimal metabolic sub-network, i.e., there are usually many different sub-network that optimally fit the experimental data. This set of optimal networks represent alternative explanations of the possible metabolic state. Ignoring the set of possible solutions reduces the ability to obtain relevant information about the metabolism and may bias the interpretation of the true metabolic state. In this work, we formalize the problem of enumeration of optimal metabolic networks, we implement a set of techniques that can be used to enumerate optimal networks, and we introduce DEXOM, a novel strategy for diversity-based extraction of optimal metabolic networks. Instead of enumerating the whole space of optimal metabolic networks, which can be computationally intractable, DEXOM samples solutions from the set of optimal metabolic sub-networks maximizing diversity in order to obtain a good representation of the possible metabolic state. We evaluate the solution diversity of the different techniques using simulated and real datasets, and we show how this method can be used to improve in-silico gene essentiality predictions in Saccharomyces Cerevisiae using diversity-based metabolic network ensembles. Both the code and the data used for this research are publicly available on GitHub1.

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