Whole-body metabolic modelling reveals microbiome and genomic interactions on reduced urine formate levels in Alzheimer's disease

In this study, we aimed to understand the potential role of the gut microbiome in the development of Alzheimer's disease (AD). We took a multi-faceted approach to investigate this relationship. Urine metabolomics were examined in individuals with AD and controls, revealing decreased formate and fumarate concentrations in AD. Additionally, we utilised whole-genome sequencing (WGS) data obtained from a separate group of individuals with AD and controls. This information allowed us to create and investigate host-microbiome personalised whole-body metabolic models. Notably, AD individuals displayed diminished formate microbial secretion in these models. Additionally, we identified specific reactions responsible for the production of formate in the host, and interestingly, these reactions were linked to genes that have correlations with AD. This study suggests formate as a possible early AD marker and highlights genetic and microbiome contributions to its production. The reduced formate secretion and its genetic associations point to a complex connection between gut microbiota and AD. This holistic understanding might pave the way for novel diagnostic and therapeutic avenues in AD management.

Replication Protein A, the Main Eukaryotic Single-Stranded DNA Binding Protein, a Focal Point in Cellular DNA Metabolism

Replication protein A (RPA) is a heterotrimeric protein complex and the main single-stranded DNA (ssDNA)-binding protein in eukaryotes. RPA has key functions in most of the DNA-associated metabolic pathways and DNA damage signalling. Its high affinity for ssDNA helps to stabilise ssDNA structures and protect the DNA sequence from nuclease attacks. RPA consists of multiple DNA-binding domains which are oligonucleotide/oligosaccharide-binding (OB)-folds that are responsible for DNA binding and interactions with proteins. These RPA-ssDNA and RPA-protein interactions are crucial for DNA replication, DNA repair, DNA damage signalling, and the conservation of the genetic information of cells. Proteins such as ATR use RPA to locate to regions of DNA damage for DNA damage signalling. The recruitment of nucleases and DNA exchange factors to sites of double-strand breaks are also an important RPA function to ensure effective DNA recombination to correct these DNA lesions. Due to its high affinity to ssDNA, RPA's removal from ssDNA is of central importance to allow these metabolic pathways to proceed, and processes to exchange RPA against downstream factors are established in all eukaryotes. These faceted and multi-layered functions of RPA as well as its role in a variety of human diseases will be discussed.

Causal inference on microbiome-metabolome relations in observational host-microbiome data via in silico in vivo association pattern analyses

Understanding the effects of the microbiome on the host's metabolism is core to enlightening the role of the microbiome in health and disease. Herein, we develop the paradigm of in silico in vivo association pattern analyses, combining microbiome metabolome association studies with in silico constraint-based community modeling. Via theoretical dissection of confounding and causal paths, we show that in silico in vivo association pattern analyses allow for causal inference on microbiome-metabolome relations in observational data. We justify the corresponding theoretical criterion by structural equation modeling of host-microbiome systems, integrating deterministic microbiome community modeling into population statistics approaches. We show the feasibility of our approach on a published multi-omics dataset (n = 347), demonstrating causal microbiome-metabolite relations for 26 out of 54 fecal metabolites. In summary, we generate a promising approach for causal inference in metabolic host-microbiome interactions by integrating hypothesis-free screening association studies with knowledge-based in silico modeling.

APOLLO: A genome-scale metabolic reconstruction resource of 247,092 diverse human microbes spanning multiple continents, age groups, and body sites

Computational modelling of microbiome metabolism has proved instrumental to catalyse our understanding of diet-host-microbiome-disease interactions through the interrogation of mechanistic, strain- and molecule-resolved metabolic models. We present APOLLO, a resource of 247,092 human microbial genome-scale metabolic reconstructions spanning 19 phyla and accounting for microbial genomes from 34 countries, all age groups, and five body sites. We explored the metabolic potential of the reconstructed strains and developed a machine learning classifier able to predict with high accuracy the taxonomic strain assignments. We also built 14,451 sample-specific microbial community models, which could be stratified by body site, age, and disease states. Finally, we predicted faecal metabolites enriched or depleted in gut microbiomes of people with Crohn's disease, Parkinson disease, and undernourished children. APOLLO is compatible with the human whole-body models, and thus, provide unprecedented opportunities for systems-level modelling of personalised host-microbiome co-metabolism. APOLLO will be freely available under https://www.vmh.life/.

Whole-body modelling reveals microbiome and genomic interactions on reduced urine formate levels in Alzheimer's disease

In this study, we aimed to understand the potential role of the gut microbiome in the development of Alzheimer's disease (AD). We took a multi-faceted approach to investigate this relationship. Urine metabolomics were examined in individuals with AD and controls, revealing decreased formate and fumarate concentrations in AD. Additionally, we utilized whole-genome sequencing (WGS) data obtained from a separate group of individuals with AD and controls. This information allowed us to create and investigate host-microbiome personalized models. Notably, AD individuals displayed diminished formate microbial secretion in these models. Additionally, we identified specific reactions responsible for the production of formate in the host, and interestingly, these reactions were linked to genes that have correlations with AD. This study suggests formate as a possible early AD marker and highlights genetic and microbiome contributions to its production. The reduced formate secretion and its genetic associations point to a complex connection between gut microbiota and AD. This holistic understanding might pave the way for novel diagnostic and therapeutic avenues in AD management.

Cardinality optimization in constraint-based modelling: application to human metabolism

MOTIVATION

Several applications in constraint-based modelling can be mathematically formulated as cardinality optimization problems involving the minimization or maximization of the number of nonzeros in a vector. These problems include testing for stoichiometric consistency, testing for flux consistency, testing for thermodynamic flux consistency, computing sparse solutions to flux balance analysis problems and computing the minimum number of constraints to relax to render an infeasible flux balance analysis problem feasible. Such cardinality optimization problems are computationally complex, with no known polynomial time algorithms capable of returning an exact and globally optimal solution.

RESULTS

By approximating the zero-norm with nonconvex continuous functions, we reformulate a set of cardinality optimization problems in constraint-based modelling into a difference of convex functions. We implemented and numerically tested novel algorithms that approximately solve the reformulated problems using a sequence of convex programs. We applied these algorithms to various biochemical networks and demonstrate that our algorithms match or outperform existing related approaches. In particular, we illustrate the efficiency and practical utility of our algorithms for cardinality optimization problems that arise when extracting a model ready for thermodynamic flux balance analysis given a human metabolic reconstruction.

AVAILABILITY AND IMPLEMENTATION

Open source scripts to reproduce the results are here https://github.com/opencobra/COBRA.papers/2023_cardOpt with general purpose functions integrated within the COnstraint-Based Reconstruction and Analysis toolbox: https://github.com/opencobra/cobratoolbox.

Circulating metabolites modulated by diet are associated with depression

Metabolome reflects the interplay of genome and exposome at molecular level and thus can provide deep insights into the pathogenesis of a complex disease like major depression. To identify metabolites associated with depression we performed a metabolome-wide association analysis in 13,596 participants from five European population-based cohorts characterized for depression, and circulating metabolites using ultra high-performance liquid chromatography/tandem accurate mass spectrometry (UHPLC/MS/MS) based Metabolon platform. We tested 806 metabolites covering a wide range of biochemical processes including those involved in lipid, amino-acid, energy, carbohydrate, xenobiotic and vitamin metabolism for their association with depression. In a conservative model adjusting for life style factors and cardiovascular and antidepressant medication use we identified 8 metabolites, including 6 novel, significantly associated with depression. In individuals with depression, increased levels of retinol (vitamin A), 1-palmitoyl-2-palmitoleoyl-GPC (16:0/16:1) (lecithin) and mannitol/sorbitol and lower levels of hippurate, 4-hydroxycoumarin, 2-aminooctanoate (alpha-aminocaprylic acid), 10-undecenoate (11:1n1) (undecylenic acid), 1-linoleoyl-GPA (18:2) (lysophosphatidic acid; LPA 18:2) are observed. These metabolites are either directly food derived or are products of host and gut microbial metabolism of food-derived products. Our Mendelian randomization analysis suggests that low hippurate levels may be in the causal pathway leading towards depression. Our findings highlight putative actionable targets for depression prevention that are easily modifiable through diet interventions.

Genome-scale metabolic reconstruction of 7,302 human microorganisms for personalized medicine

The human microbiome influences the efficacy and safety of a wide variety of commonly prescribed drugs. Designing precision medicine approaches that incorporate microbial metabolism would require strain- and molecule-resolved, scalable computational modeling. Here, we extend our previous resource of genome-scale metabolic reconstructions of human gut microorganisms with a greatly expanded version. AGORA2 (assembly of gut organisms through reconstruction and analysis, version 2) accounts for 7,302 strains, includes strain-resolved drug degradation and biotransformation capabilities for 98 drugs, and was extensively curated based on comparative genomics and literature searches. The microbial reconstructions performed very well against three independently assembled experimental datasets with an accuracy of 0.72 to 0.84, surpassing other reconstruction resources and predicted known microbial drug transformations with an accuracy of 0.81. We demonstrate that AGORA2 enables personalized, strain-resolved modeling by predicting the drug conversion potential of the gut microbiomes from 616 patients with colorectal cancer and controls, which greatly varied between individuals and correlated with age, sex, body mass index and disease stages. AGORA2 serves as a knowledge base for the human microbiome and paves the way to personalized, predictive analysis of host-microbiome metabolic interactions.

The Effects of Hospitalisation on the Serum Metabolome in COVID-19 Patients

COVID-19, a systemic multi-organ disease resulting from infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is known to result in a wide array of disease outcomes, ranging from asymptomatic to fatal. Despite persistent progress, there is a continued need for more accurate determinants of disease outcomes, including post-acute symptoms after COVID-19. In this study, we characterised the serum metabolomic changes due to hospitalisation and COVID-19 disease progression by mapping the serum metabolomic trajectories of 71 newly hospitalised moderate and severe patients in their first week after hospitalisation. These 71 patients were spread out over three hospitals in Switzerland, enabling us to meta-analyse the metabolomic trajectories and filter consistently changing metabolites. Additionally, we investigated differential metabolite-metabolite trajectories between fatal, severe, and moderate disease outcomes to find prognostic markers of disease severity. We found drastic changes in serum metabolite concentrations for 448 out of the 901 metabolites. These results included markers of hospitalisation, such as environmental exposures, dietary changes, and altered drug administration, but also possible markers of physiological functioning, including carboxyethyl-GABA and fibrinopeptides, which might be prognostic for worsening lung injury. Possible markers of disease progression included altered urea cycle metabolites and metabolites of the tricarboxylic acid (TCA) cycle, indicating a SARS-CoV-2-induced reprogramming of the host metabolism. Glycerophosphorylcholine was identified as a potential marker of disease severity. Taken together, this study describes the metabolome-wide changes due to hospitalisation and COVID-19 disease progression. Moreover, we propose a wide range of novel potential biomarkers for monitoring COVID-19 disease course, both dependent and independent of the severity.

Systems biology elucidates the distinctive metabolic niche filled by the human gut microbe Eggerthella lenta

Human gut bacteria perform diverse metabolic functions with consequences for host health. The prevalent and disease-linked Actinobacterium Eggerthella lenta performs several unusual chemical transformations, but it does not metabolize sugars and its core growth strategy remains unclear. To obtain a comprehensive view of the metabolic network of E. lenta, we generated several complementary resources: defined culture media, metabolomics profiles of strain isolates, and a curated genome-scale metabolic reconstruction. Stable isotope-resolved metabolomics revealed that E. lenta uses acetate as a key carbon source while catabolizing arginine to generate ATP, traits which could be recapitulated in silico by our updated metabolic model. We compared these in vitro findings with metabolite shifts observed in E. lenta-colonized gnotobiotic mice, identifying shared signatures across environments and highlighting catabolism of the host signaling metabolite agmatine as an alternative energy pathway. Together, our results elucidate a distinctive metabolic niche filled by E. lenta in the gut ecosystem. Our culture media formulations, atlas of metabolomics data, and genome-scale metabolic reconstructions form a freely available collection of resources to support further study of the biology of this prevalent gut bacterium.

A nutrition algorithm to optimize feed and medium composition using genome-scale metabolic models

The optimization of animal feeds and cell culture media are problems of interest to a wide range of industries and scientific disciplines. Both problems are dictated by the properties of an organism's metabolism. However, due to the tremendous complexity of metabolic systems, it can be difficult to predict how metabolism will respond to changes in nutrient availability. A common tool used to capture the complexity of metabolism in a computational framework is a genome-scale metabolic model (GEM). GEMs are useful for predicting the fluxes of reactions within an organism's metabolism. To optimize feed or media, in silico experiments can be performed with GEMs by systematically varying nutritional constraints and predicting metabolic activity. In this way, the influence of various nutritional changes on metabolic outcomes can be evaluated. However, this methodology does not guarantee an optimal solution. Here, we develop a nutrition algorithm that utilizes linear programming to search the entire flux solution space of possible dietary intervention strategies to identify the most efficient changes to nutrition for a desirable metabolic outcome. We illustrate the utility of the nutrition algorithm on GEMs of Atlantic salmon (Salmo salar) and Chinese hamster ovary (CHO) cell metabolism and find that the nutrition algorithm makes predictions that not only align with experimental findings but reveal new insights into promising feeding strategies. We show that the nutrition algorithm is highly versatile and customizable to meet the user's needs. For instance, we demonstrate that the nutrition algorithm can be used to predict feed/media compositions that maximize profit margins. While the nutrition algorithm can be used to define an optimal feed/medium ab initio, it can also identify minimal changes to be made to an existing feed/medium to drive the largest metabolic shift. Moreover, the nutrition algorithm can target multiple metabolic pathways simultaneously with only a marginal increase in computational expense. While the nutrition algorithm has its limitations, we believe that this tool can be leveraged in a broad range of biotechnological applications to enhance the feed/medium optimization process.

Longitudinal flux balance analyses of a patient with episodic colonic inflammation reveals microbiome metabolic dynamics

We report the first use of constraint-based microbial community modeling on a single individual with episodic inflammation of the gastrointestinal tract, who has a well documented set of colonic inflammatory biomarkers, as well as metagenomically-sequenced fecal time series covering seven dates over 16 months. Between the first two time steps the individual was treated with both steroids and antibiotics. Our methodology enabled us to identify numerous time-correlated microbial species and metabolites. We found that the individual's dynamical microbial ecology in the disease state led to time-varying in silico overproduction, compared to healthy controls, of more than 24 biologically important metabolites, including methane, thiamine, formaldehyde, trimethylamine N-oxide, folic acid, serotonin, histamine, and tryptamine. The microbe-metabolite contribution analysis revealed that some Dialister species changed metabolic pathways according to the inflammation phases. At the first time point, characterized by the highest levels of serum (complex reactive protein) and fecal (calprotectin) inflammation biomarkers, they produced L-serine or formate. The production of the compounds, through a cascade effect, was mediated by the interaction with pathogenic Escherichia coli strains and Desulfovibrio piger. We integrated the microbial community metabolic models of each time point with a male whole-body, organ-resolved model of human metabolism to track the metabolic consequences of dysbiosis at different body sites. The presence of D. piger in the gut microbiome influenced the sulfur metabolism with a domino effect affecting the liver. These results revealed large longitudinal variations in an individual's gut microbiome ecology and metabolite production, potentially impacting other organs in the body. Future simulations with more time points from an individual could permit us to assess how external drivers, such as diet change or medical interventions, drive microbial community dynamics.

MetaboAnnotator: An efficient toolbox to annotate metabolites in genome-scale metabolic reconstructions

MOTIVATION

Genome-scale metabolic reconstructions have been assembled for thousands of organisms using a wide-range of tools. However, metabolite annotations, required to compare and link metabolites between reconstructions remain incomplete. Here, we aim to further extend metabolite annotation coverage using various databases and chemoinformatic approaches.

RESULTS

We developed a COBRA toolbox extension, deemed MetaboAnnotator, which facilitates the comprehensive annotation of metabolites with database independent and dependent identifiers, obtains molecular structure files, and calculates metabolite formula and charge at pH 7.2. The resulting metabolite annotations allow for subsequent cross-mapping between reconstructions and mapping of, e.g., metabolomic data.

AVAILABILITY

MetaboAnnotator and tutorials are freely available at https://github.com/opencobra.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Maternal Microbial Metabolites and Risk of Fetal Growth Extremes: A Longitudinal Multi-Racial/Ethnic Cohort Study

Objectives

Fetal growth extremes [small- and large-for-gestational age (SGA and LGA)] represent high-risk phenotypes for cardiometabolic disorders. Metabolomic profiling of the in-utero milieu may elucidate pathophysiology of fetal growth extremes and inform dietary recommendations and upstream risk factors. We aimed to identify microbiome-derived metabolites associated with SGA and LGA.

Methods

A random sample of 140 SGA, 134 LGA and 140 appropriate for gestational age (AGA) was drawn from the Pregnancy Environment and Lifestyle Study cohort. Using fasting serum at gestational weeks (GW) 10–13 and 16–19,1167 known metabolites were measured by gas and liquid chromatography (LC)/time-of-flight mass spectrometry (TOF-MS) and hydrophilic interaction LC/quadrupole TOF-MS, of which 165 microbial metabolites were linked with the Virtual Metabolic Human Database. After adjusting for sociodemographic, lifestyle, reproductive history, and clinical factors, we identified significant pathways associated with risk of SGA and LGA vs. AGA using chemical similarity enrichment analysis, controlling for the false discovery rate (P_FDR).

Results

At GW 10–13, branched-chain amino acids (AA), dicarboxylic acids (DA) and medium-chain hydroxy acids were positively, while phosphatidylcholines, saturated fatty acids (FA) and glucogenic AA were inversely associated with SGA risk (all P_FDR < 0.01). At GW 16–19, positive associations of branched-chain AA and DA and inverse associations of saturated FA with SGA risk persisted, while unsaturated triglycerides (TG) were inversely associated with SGA risk (all P_FDR < 0.01). At GW 10–13, glucogenic AA, DA, hippurates, phenylacetylglutamine and cresols were positively, whereas phosphatidylcholine and unsaturated TG were inversely associated with LGA risk (all P_FDR < 0.04). At GW 16–19, carnitine, saturated TG, cyclic AA, DA, glyceric acids, phenylacetylglutamine and cresols were positively, while aromatic, basic and sulfur AA, sugar alcohols, phosphatidylcholines and unsaturated TG were inversely associated with LGA risk (all P_FDR < 0.05).

Conclusions

Distinct microbial metabolites in early to mid-pregnancy are associated with SGA and LGA risk, calling for further investigation into microbiome-metabolome-host interactions.

Funding Sources

The research was supported by NIDDK, NIEHS, and NIH Office of Directors.

Microbiome Modelling Toolbox 2.0: efficient, tractable modelling of microbiome communities

MOTIVATION

Constraint-Based Reconstruction and Analysis (COBRA) is a widely used approach for the interrogation and stratification of microbiome samples, yet applications to large-scale cohorts are hampered by limited scalability and efficiency of simulations.

RESULTS

We substantially improved the computation speed and scalability of a previous implementation for the construction and interrogation of personalized constraint-based microbiome models as well as implemented additional functionalities for analysis and visualization.

AVAILABILITY AND IMPLEMENTATION

Microbiome Modelling Toolbox and tutorials are freely available as part of the COBRA Toolbox at https://git.io/microbiomeModelingToolbox.

The gut microbial metabolite formate exacerbates colorectal cancer progression

The gut microbiome is a key player in the immunomodulatory and protumorigenic microenvironment during colorectal cancer (CRC), as different gut-derived bacteria can induce tumour growth. However, the crosstalk between the gut microbiome and the host in relation to tumour cell metabolism remains largely unexplored. Here we show that formate, a metabolite produced by the CRC-associated bacterium Fusobacterium nucleatum, promotes CRC development. We describe molecular signatures linking CRC phenotypes with Fusobacterium abundance. Cocultures of F. nucleatum with patient-derived CRC cells display protumorigenic effects, along with a metabolic shift towards increased formate secretion and cancer glutamine metabolism. We further show that microbiome-derived formate drives CRC tumour invasion by triggering AhR signalling, while increasing cancer stemness. Finally, F. nucleatum or formate treatment in mice leads to increased tumour incidence or size, and Th17 cell expansion, which can favour proinflammatory profiles. Moving beyond observational studies, we identify formate as a gut-derived oncometabolite that is relevant for CRC progression.

NMR Metabolomics Reveal Urine Markers of Microbiome Diversity and Identify Benzoate Metabolism as a Mediator between High Microbial Alpha Diversity and Metabolic Health

Microbial metabolites measured using NMR may serve as markers for physiological or pathological host–microbe interactions and possibly mediate the beneficial effects of microbiome diversity. Yet, comprehensive analyses of gut microbiome data and the urine NMR metabolome from large general population cohorts are missing. Here, we report the associations between gut microbiota abundances or metrics of alpha diversity, quantified from stool samples using 16S rRNA gene sequencing, with targeted urine NMR metabolites measures from 951 participants of the Study of Health in Pomerania (SHIP). We detected significant genus–metabolite associations for hippurate, succinate, indoxyl sulfate, and formate. Moreover, while replicating the previously reported association between hippurate and measures of alpha diversity, we identified formate and 4-hydroxyphenylacetate as novel markers of gut microbiome alpha diversity. Next, we predicted the urinary concentrations of each metabolite using genus abundances via an elastic net regression methodology. We found profound associations of the microbiome-based hippurate prediction score with markers of liver injury, inflammation, and metabolic health. Moreover, the microbiome-based prediction score for hippurate completely mediated the clinical association pattern of microbial diversity, hinting at a role of benzoate metabolism underlying the positive associations between high alpha diversity and healthy states. In conclusion, large-scale NMR urine metabolomics delivered novel insights into metabolic host–microbiome interactions, identifying pathways of benzoate metabolism as relevant candidates mediating the beneficial health effects of high microbial alpha diversity.

Early-Life Adversity Leaves Its Imprint on the Oral Microbiome for More Than 20 Years and Is Associated with Long-Term Immune Changes

The early-life microbiome (ELM) interacts with the psychosocial environment, in particular during early-life adversity (ELA), defining life-long health trajectories. The ELM also plays a significant role in the maturation of the immune system. We hypothesised that, in this context, the resilience of the oral microbiomes, despite being composed of diverse and distinct communities, allows them to retain an imprint of the early environment. Using 16S amplicon sequencing on the EpiPath cohort, we demonstrate that ELA leaves an imprint on both the salivary and buccal oral microbiome 24 years after exposure to adversity. Furthermore, the changes in both communities were associated with increased activation, maturation, and senescence of both innate and adaptive immune cells, although the interaction was partly dependent on prior herpesviridae exposure and current smoking. Our data suggest the presence of multiple links between ELA, Immunosenescence, and cytotoxicity that occur through long-term changes in the microbiome.

Integration of constraint-based modeling with fecal metabolomics reveals large deleterious effects of Fusobacterium spp. on community butyrate production

Characterizing the metabolic functions of the gut microbiome in health and disease is pivotal for translating alterations in microbial composition into clinical insights. Two major analysis paradigms have been used to explore the metabolic functions of the microbiome but not systematically integrated with each other: statistical screening approaches, such as metabolome-microbiome association studies, and computational approaches, such as constraint-based metabolic modeling. To combine the strengths of the two analysis paradigms, we herein introduce a set of theoretical concepts allowing for the population statistical treatment of constraint-based microbial community models. To demonstrate the utility of the theoretical framework, we applied it to a public metagenomic dataset consisting of 365 colorectal cancer (CRC) cases and 251 healthy controls, shining a light on the metabolic role of Fusobacterium spp. in CRC. We found that (1) glutarate production capability was significantly enriched in CRC microbiomes and mechanistically linked to lysine fermentation in Fusobacterium spp., (2) acetate and butyrate production potentials were lowered in CRC, and (3) Fusobacterium spp. presence had large negative ecological effects on community butyrate production in CRC cases and healthy controls. Validating the model predictions against fecal metabolomics, the in silico frameworks correctly predicted in vivo species metabolite correlations with high accuracy. In conclusion, highlighting the value of combining statistical association studies with in silico modeling, this study provides insights into the metabolic role of Fusobacterium spp. in the gut, while providing a proof of concept for the validity of constraint-based microbial community modeling.

DEMETER: Efficient simultaneous curation of genome-scale reconstructions guided by experimental data and refined gene annotations

Motivation

Manual curation of genome-scale reconstructions is laborious, yet existing automated curation tools do not typically take species-specific experimental and curated genomic data into account.

Results

We developed Data-drivEn METabolic nEtwork Refinement (DEMETER), a Constraint-Based Reconstruction and Analysis (COBRA) Toolbox extension, which enables the efficient, simultaneous refinement of thousands of draft genome-scale reconstructions, while ensuring adherence to the quality standards in the field, agreement with available experimental data and refinement of pathways based on manually refined genome annotations.

Availability and implementation

DEMETER and tutorials are freely available at https://github.com/opencobra.

Supplementary information

Supplementary data are available at Bioinformatics online.

Integration of a physiologically-based pharmacokinetic model with a whole-body, organ-resolved genome-scale model for characterization of ethanol and acetaldehyde metabolism

Ethanol is one of the most widely used recreational substances in the world and due to its ubiquitous use, ethanol abuse has been the cause of over 3.3 million deaths each year. In addition to its effects, ethanol's primary metabolite, acetaldehyde, is a carcinogen that can cause symptoms of facial flushing, headaches, and nausea. How strongly ethanol or acetaldehyde affects an individual depends highly on the genetic polymorphisms of certain genes. In particular, the genetic polymorphisms of mitochondrial aldehyde dehydrogenase, ALDH2, play a large role in the metabolism of acetaldehyde. Thus, it is important to characterize how genetic variations can lead to different exposures and responses to ethanol and acetaldehyde. While the pharmacokinetics of ethanol metabolism through alcohol dehydrogenase have been thoroughly explored in previous studies, in this paper, we combined a base physiologically-based pharmacokinetic (PBPK) model with a whole-body genome-scale model (WBM) to gain further insight into the effect of other less explored processes and genetic variations on ethanol metabolism. This combined model was fit to clinical data and used to show the effect of alcohol concentrations, organ damage, ALDH2 enzyme polymorphisms, and ALDH2-inhibiting drug disulfiram on ethanol and acetaldehyde exposure. Through estimating the reaction rates of auxiliary processes with dynamic Flux Balance Analysis, The PBPK-WBM was able to navigate around a lack of kinetic constants traditionally associated with PK modelling and demonstrate the compensatory effects of the body in response to decreased liver enzyme expression. Additionally, the model demonstrated that acetaldehyde exposure increased with higher dosages of disulfiram and decreased ALDH2 efficiency, and that moderate consumption rates of ethanol could lead to unexpected accumulations in acetaldehyde. This modelling framework combines the comprehensive steady-state analyses from genome-scale models with the dynamics of traditional PK models to create a highly personalized form of PBPK modelling that can push the boundaries of precision medicine.

Genome-Scale Metabolic Modeling of the Human Microbiome in the Era of Personalized Medicine

The human microbiome plays an important role in human health and disease. Meta-omics analyses provide indispensable data for linking changes in microbiome composition and function to disease etiology. Yet, the lack of a mechanistic understanding of, e.g., microbiome-metabolome links hampers the translation of these findings into effective, novel therapeutics. Here, we propose metabolic modeling of microbial communities through constraint-based reconstruction and analysis (COBRA) as a complementary approach to meta-omics analyses. First, we highlight the importance of microbial metabolism in cardiometabolic diseases, inflammatory bowel disease, colorectal cancer, Alzheimer disease, and Parkinson disease. Next, we demonstrate that microbial community modeling can stratify patients and controls, mechanistically link microbes with fecal metabolites altered in disease, and identify host pathways affected by the microbiome. Finally, we outline our vision for COBRA modeling combined with meta-omics analyses and multivariate statistical analyses to inform and guide clinical trials, yield testable hypotheses, and ultimately propose novel dietary and therapeutic interventions. Expected final online publication date for the Annual Review of Microbiology, Volume 75 is October 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Methanogenic granule growth and development is a continual process characterized by distinct morphological features

Up-flow anaerobic bioreactors are widely applied for high-rate digestion of industrial wastewaters and rely on formation, and retention, of methanogenic granules, comprising of dense, fast-settling, microbial aggregates (approx. 0.5-4.0 mm in diameter). Granule formation (granulation) mechanisms have been reasonably well hypothesized and documented. However, this study used laboratory-scale bioreactors, inoculated with size-separated granular sludge to follow new granule formation, maturation, disintegration and re-formation. Temporal size profiles, volatile solids content, settling velocity, and ultrastructure of granules were determined from each of four bioreactors inoculated only with small granules, four with only large granules, and four with a full complement of naturally-size-distributed granules. Constrained granule size profiles shifted toward the natural distribution, which was associated with maximal bioreactor performance. Distinct morphological features characterized different granule sizes and biofilm development stages, including 'young', 'juvenile', 'mature' and 'old'. The findings offer opportunities toward optimizing management of high-rate, anaerobic digesters by shedding light on the rates of granule growth, the role of flocculent sludge in granulation and how shifting size distributions should be considered when setting upflow velocities.

Metabolic modelling reveals broad changes in gut microbial metabolism in inflammatory bowel disease patients with dysbiosis

Inflammatory bowel diseases, such as Crohn's Disease, are characterised by an altered blood and faecal metabolome, and changes in gut microbiome composition. Here, we present an efficient, scalable, tractable systems biology framework to mechanistically link microbial strains and faecal metabolites. We retrieve strain-level relative abundances from metagenomics data from a cohort of paediatric Crohn's Disease patients with and without dysbiosis and healthy control children and construct and interrogate a personalised microbiome model for each sample. Predicted faecal secretion profiles and strain-level contributions to each metabolite vary broadly between healthy, dysbiotic, and non-dysbiotic microbiomes. The reduced microbial diversity in IBD results in reduced numbers of secreted metabolites, especially in sulfur metabolism. We demonstrate that increased potential to synthesise amino acids is linked to Proteobacteria contributions, in agreement with experimental observations. The established modelling framework yields testable hypotheses that may result in novel therapeutic and dietary interventions targeting the host-gut microbiome-diet axis.

Dynamic flux balance analysis of whole-body metabolism for type 1 diabetes

Type 1 diabetes (T1D) mellitus is a systemic disease triggered by a local autoimmune inflammatory reaction in insulin-producing cells that induce organ-wide, long-term metabolic effects. Mathematical modeling of the whole-body regulatory bihormonal system has helped to identify therapeutic interventions but is limited to a coarse-grained representation of metabolism. To extend the depiction of T1D, we developed a whole-body model of organ-specific regulation and metabolism that highlighted chronic inflammation as a hallmark of the disease, identified processes related to neurodegenerative disorders and suggested calcium channel blockers as adjuvants for diabetes control. In addition, whole-body modeling of a patient population allowed for the assessment of between-individual variability to insulin and suggested that peripheral glucose levels are degenerate biomarkers of the internal metabolic state. Taken together, the organ-resolved, dynamic modeling approach enables modeling and simulation of metabolic disease at greater levels of coverage and precision and the generation of hypothesis from a molecular level up to the population level.

Personalized whole-body models integrate metabolism, physiology, and the gut microbiome

Comprehensive molecular‐level models of human metabolism have been generated on a cellular level. However, models of whole‐body metabolism have not been established as they require new methodological approaches to integrate molecular and physiological data. We developed a new metabolic network reconstruction approach that used organ‐specific information from literature and omics data to generate two sex‐specific whole‐body metabolic (WBM) reconstructions. These reconstructions capture the metabolism of 26 organs and six blood cell types. Each WBM reconstruction represents whole‐body organ‐resolved metabolism with over 80,000 biochemical reactions in an anatomically and physiologically consistent manner. We parameterized the WBM reconstructions with physiological, dietary, and metabolomic data. The resulting WBM models could recapitulate known inter‐organ metabolic cycles and energy use. We also illustrate that the WBM models can predict known biomarkers of inherited metabolic diseases in different biofluids. Predictions of basal metabolic rates, by WBM models personalized with physiological data, outperformed current phenomenological models. Finally, integrating microbiome data allowed the exploration of host–microbiome co‐metabolism. Overall, the WBM reconstructions, and their derived computational models, represent an important step toward virtual physiological humans.

Rare genetic variants affecting urine metabolite levels link population variation to inborn errors of metabolism

Metabolite levels in urine may provide insights into genetic mechanisms shaping their related pathways. We therefore investigate the cumulative contribution of rare, exonic genetic variants on urine levels of 1487 metabolites and 53,714 metabolite ratios among 4864 GCKD study participants. Here we report the detection of 128 significant associations involving 30 unique genes, 16 of which are known to underlie inborn errors of metabolism. The 30 genes are strongly enriched for shared expression in liver and kidney (odds ratio = 65, p-FDR = 3e-7), with hepatocytes and proximal tubule cells as driving cell types. Use of UK Biobank whole-exome sequencing data links genes to diseases connected to the identified metabolites. In silico constraint-based modeling of gene knockouts in a virtual whole-body, organ-resolved metabolic human correctly predicts the observed direction of metabolite changes, highlighting the potential of linking population genetics to modeling. Our study implicates candidate variants and genes for inborn errors of metabolism.

Laser Wakefield Driven Generation of Isolated Carrier-Envelope-Phase Tunable Intense Subcycle Pulses

Sources of intense, ultrashort electromagnetic pulses enable applications such as attosecond pulse generation, control of electron motion in solids, and the observation of reaction dynamics at the electronic level. For such applications, both high intensity and carrier-envelope-phase (CEP) tunability are beneficial, yet hard to obtain with current methods. In this Letter, we present a new scheme for generation of isolated CEP tunable intense subcycle pulses with central frequencies that range from the midinfrared to the ultraviolet. It utilizes an intense laser pulse that drives a wake in a plasma, copropagating with a long-wavelength seed pulse. The moving electron density spike of the wake amplifies the seed and forms a subcycle pulse. Controlling the CEP of the seed pulse or the delay between driver and seed leads to CEP tunability, while frequency tunability can be achieved by adjusting the laser and plasma parameters. Our 2D and 3D particle-in-cell simulations predict laser-to-subcycle-pulse conversion efficiencies up to 1%, resulting in relativistically intense subcycle pulses.

The contribution of gut bacterial metabolites in the human immune signaling pathway of non-communicable diseases

The interaction disorder between gut microbiota and its host has been documented in different non-communicable diseases (NCDs) such as metabolic syndrome, neurodegenerative disease, and autoimmune disease. The majority of these altered interactions arise through metabolic cross-talk between gut microbiota and host immune system, inducing a low-grade chronic inflammation that characterizes all NCDs. In this review, we discuss the contribution of bacterial metabolites to immune signaling pathways involved in NCDs. We then review recent advances that aid to rationally design microbial therapeutics. A deeper understanding of these intersections between host and gut microbiota metabolism using metabolomics-based system biology platform promises to reveal the fundamental mechanisms that drive metabolic predispositions to disease and suggest new avenues to use microbial therapeutic opportunities for NCDs treatment and prevention. Abbreviations: NCDs: non-communicable disease, IBD: inflammatory bowel disease, IL: interleukin, T2D: type 2 diabetes, SCFAs: short-chain fatty acids, HDAC: histone deacetylases, GPCR: G-protein coupled receptors, 5-HT: 5-hydroxytryptamine receptor signaling, DCs: dendritic cells, IECs: intestinal epithelial cells, T-reg: T regulatory cell, NF-κB: nuclear factor κB, TNF-α: tumor necrosis factor alpha, Th: T helper cell, CNS: central nervous system, ECs: enterochromaffin cells, NSAIDs: non-steroidal anti-inflammatory drugs, AhR: aryl hydrocarbon receptor, IDO: indoleamine 2,3-dioxygenase, QUIN: quinolinic acid, PC: phosphatidylcholine, TMA: trimethylamine, TMAO: trimethylamine N-oxide, CVD: cardiovascular disease, NASH: nonalcoholic steatohepatitis, BAs: bile acids, FXR: farnesoid X receptor, CDCA: chenodeoxycholic acid, DCA: deoxycholic acid, LCA: lithocholic acid, UDCA: ursodeoxycholic acid, CB: cannabinoid receptor, COBRA: constraint-based reconstruction and analysis.

Metabolic Network Analysis Reveals Altered Bile Acid Synthesis and Metabolism in Alzheimer's Disease

Increasing evidence suggests Alzheimer's disease (AD) pathophysiology is influenced by primary and secondary bile acids, the end product of cholesterol metabolism. We analyze 2,114 post-mortem brain transcriptomes and identify genes in the alternative bile acid synthesis pathway to be expressed in the brain. A targeted metabolomic analysis of primary and secondary bile acids measured from post-mortem brain samples of 111 individuals supports these results. Our metabolic network analysis suggests that taurine transport, bile acid synthesis, and cholesterol metabolism differ in AD and cognitively normal individuals. We also identify putative transcription factors regulating metabolic genes and influencing altered metabolism in AD. Intriguingly, some bile acids measured in brain tissue cannot be explained by the presence of enzymes responsible for their synthesis, suggesting that they may originate from the gut microbiome and are transported to the brain. These findings motivate further research into bile acid metabolism in AD to elucidate their possible connection to cognitive decline.

Optical Bloch modeling of femtosecond-laser-induced electron dynamics in dielectrics

A model based on optical Bloch equations is developed to describe the interaction of femtosecond laser pulses with dielectric solids, accounting for optical-cycle-resolved electron dynamics. It includes the main physical processes at play: photoionization, impact ionization, direct and collisional laser heating, and recombination. By using an electron band structure, this approach also accounts for material optical properties as nonlinear polarization response. Various studies are performed, shedding light on the contribution of various processes to the full electron dynamics depending on laser intensity and wavelength. In particular, the standard influence of the impact ionization process is retrieved.

Gain of electron orbital angular momentum in a direct laser acceleration process

Three-dimensional "particle in cell" simulations show that a quasistatic magnetic field can be generated in a plasma irradiated by a linearly polarized Laguerre-Gauss beam with a nonzero orbital angular momentum (OAM). Perturbative analysis of the electron dynamics in the low intensity limit and detailed numerical analysis predict a laser to electrons OAM transfer. Plasma electrons gain angular velocity thanks to the dephasing process induced by the combined action of the ponderomotive force and the laser induced-radial oscillation. Similar to the "direct laser acceleration," where Gaussian laser beams transmit part of its axial momentum to electrons, Laguerre-Gaussian beams transfer a part of their orbital angular momentum to electrons through the dephasing process.

The Microbiome Modeling Toolbox: from microbial interactions to personalized microbial communities

Motivation

The application of constraint-based modeling to functionally analyze metagenomic data has been limited so far, partially due to the absence of suitable toolboxes.

Results

To address this gap, we created a comprehensive toolbox to model (i) microbe–microbe and host–microbe metabolic interactions, and (ii) microbial communities using microbial genome-scale metabolic reconstructions and metagenomic data. The Microbiome Modeling Toolbox extends the functionality of the constraint-based reconstruction and analysis toolbox.

Availability and implementation

The Microbiome Modeling Toolbox and the tutorials at https://git.io/microbiomeModelingToolbox.

Parkinson's disease-associated alterations of the gut microbiome predict disease-relevant changes in metabolic functions

BACKGROUND

Parkinson's disease (PD) is a systemic disease clinically defined by the degeneration of dopaminergic neurons in the brain. While alterations in the gut microbiome composition have been reported in PD, their functional consequences remain unclear. Herein, we addressed this question by an analysis of stool samples from the Luxembourg Parkinson's Study (n = 147 typical PD cases, n = 162 controls).

RESULTS

All individuals underwent detailed clinical assessment, including neurological examinations and neuropsychological tests followed by self-reporting questionnaires. Stool samples from these individuals were first analysed by 16S rRNA gene sequencing. Second, we predicted the potential secretion for 129 microbial metabolites through personalised metabolic modelling using the microbiome data and genome-scale metabolic reconstructions of human gut microbes. Our key results include the following. Eight genera and seven species changed significantly in their relative abundances between PD patients and healthy controls. PD-associated microbial patterns statistically depended on sex, age, BMI, and constipation. Particularly, the relative abundances of Bilophila and Paraprevotella were significantly associated with the Hoehn and Yahr staging after controlling for the disease duration. Furthermore, personalised metabolic modelling of the gut microbiomes revealed PD-associated metabolic patterns in the predicted secretion potential of nine microbial metabolites in PD, including increased methionine and cysteinylglycine. The predicted microbial pantothenic acid production potential was linked to the presence of specific non-motor symptoms.

CONCLUSION

Our results suggest that PD-associated alterations of the gut microbiome can translate into substantial functional differences affecting host metabolism and disease phenotype.

The Virtual Metabolic Human database: integrating human and gut microbiome metabolism with nutrition and disease

A multitude of factors contribute to complex diseases and can be measured with ‘omics’ methods. Databases facilitate data interpretation for underlying mechanisms. Here, we describe the Virtual Metabolic Human (VMH, www.vmh.life) database encapsulating current knowledge of human metabolism within five interlinked resources ‘Human metabolism’, ‘Gut microbiome’, ‘Disease’, ‘Nutrition’, and ‘ReconMaps’. The VMH captures 5180 unique metabolites, 17 730 unique reactions, 3695 human genes, 255 Mendelian diseases, 818 microbes, 632 685 microbial genes and 8790 food items. The VMH’s unique features are (i) the hosting of the metabolic reconstructions of human and gut microbes amenable for metabolic modeling; (ii) seven human metabolic maps for data visualization; (iii) a nutrition designer; (iv) a user-friendly webpage and application-programming interface to access its content; (v) user feedback option for community engagement and (vi) the connection of its entities to 57 other web resources. The VMH represents a novel, interdisciplinary database for data interpretation and hypothesis generation to the biomedical community.

Arterio-venous metabolomics exploration reveals major changes across liver and intestine in the obese Yucatan minipig

Blood circulation mainly aims at distributing the nutrients required for tissue metabolism and collecting safely the by-products of all tissues to be further metabolized or eliminated. The simultaneous study of arterial (A) and venous (V) specific metabolites therefore has appeared to be a more relevant approach to understand and study the metabolism of a given organ. We propose to implement this approach by applying a metabolomics (NMR) strategy on paired AV blood across the intestine and liver on high fat/high sugar (HFHS)-fed minipigs. Our objective was to unravel kinetically and sequentially the metabolic adaptations to early obesity/insulin resistance onset specifically on these two tissues. After two months of HFHS feeding our study of AV ratios of the metabolome highlighted three major features. First, the hepatic metabolism switched from carbohydrate to lipid utilization. Second, the energy demand of the intestine increased, resulting in an enhanced uptake of glutamine, glutamate, and the recruitment of novel energy substrates (choline and creatine). Third, the uptake of methionine and threonine was considered to be driven by an increased intestine turnover to cope with the new high-density diet. Finally, the unique combination of experimental data and modelling predictions suggested that HFHS feeding was associated with changes in tryptophan metabolism and fatty acid β-oxidation, which may play an important role in lipid hepatic accumulation and insulin sensitivity.

Comparative Genomic Analysis Reveals Novel Microcompartment-Associated Metabolic Pathways in the Human Gut Microbiome

Bacterial microcompartments are self-assembling subcellular structures surrounded by a semipermeable protein shell and found only in bacteria, but not archaea or eukaryotes. The general functions of the bacterial microcompartments are to concentrate enzymes, metabolites, and cofactors for multistep pathways; maintain the cofactor ratio; protect the cell from toxic metabolic intermediates; and protect the encapsulated pathway from unwanted side reactions. The bacterial microcompartments were suggested to play a significant role in organisms of the human gut microbiome, especially for various pathogens. Here, we used a comparative genomics approach to analyze the bacterial microcompartments in 646 individual genomes of organisms commonly found in the human gut microbiome. The bacterial microcompartments were found in 150 (23.2%) analyzed genomes. These microcompartments include previously known ones for the utilization of ethanolamine, 1,2-propanediol, choline, and fucose/rhamnose. Moreover, we reconstructed two novel pathways associated with the bacterial microcompartments. These pathways are catabolic pathways for the utilization of 1-amino-2-propanol/1-amino-2-propanone and xanthine. Remarkably, the xanthine utilization pathway does not demonstrate similarity to previously known microcompartment-associated pathways. Thus, we describe a novel type of bacterial microcompartment.

Dynamic genome-scale cell-specific metabolic models reveal novel inter-cellular and intra-cellular metabolic communications during ovarian follicle development

BACKGROUND

The maturation of the female germ cell, the oocyte, requires the synthesis and storing of all the necessary metabolites to support multiple divisions after fertilization. Oocyte maturation is only possible in the presence of surrounding, diverse, and changing layers of somatic cells. Our understanding of metabolic interactions between the oocyte and somatic cells has been limited due to dynamic nature of ovarian follicle development, thus warranting a systems approach.

RESULTS

Here, we developed a genome-scale metabolic model of the mouse ovarian follicle. This model was constructed using an updated mouse general metabolic model (Mouse Recon 2) and contains several key ovarian follicle development metabolic pathways. We used this model to characterize the changes in the metabolism of each follicular cell type (i.e., oocyte, granulosa cells, including cumulus and mural cells), during ovarian follicle development in vivo. Using this model, we predicted major metabolic pathways that are differentially active across multiple follicle stages. We identified a set of possible secreted and consumed metabolites that could potentially serve as biomarkers for monitoring follicle development, as well as metabolites for addition to in vitro culture media that support the growth and maturation of primordial follicles.

CONCLUSIONS

Our systems approach to model follicle metabolism can guide future experimental studies to validate the model results and improve oocyte maturation approaches and support growth of primordial follicles in vitro.

Predicting gastrointestinal drug effects using contextualized metabolic models

Gastrointestinal side effects are among the most common classes of adverse reactions associated with orally absorbed drugs. These effects decrease patient compliance with the treatment and induce undesirable physiological effects. The prediction of drug action on the gut wall based on in vitro data solely can improve the safety of marketed drugs and first-in-human trials of new chemical entities. We used publicly available data of drug-induced gene expression changes to build drug-specific small intestine epithelial cell metabolic models. The combination of measured in vitro gene expression and in silico predicted metabolic rates in the gut wall was used as features for a multilabel support vector machine to predict the occurrence of side effects. We showed that combining local gut wall-specific metabolism with gene expression performs better than gene expression alone, which indicates the role of small intestine metabolism in the development of adverse reactions. Furthermore, we reclassified FDA-labeled drugs with respect to their genetic and metabolic profiles to show hidden similarities between seemingly different drugs. The linkage of xenobiotics to their transcriptomic and metabolic profiles could take pharmacology far beyond the usual indication-based classifications.

Systematic assessment of secondary bile acid metabolism in gut microbes reveals distinct metabolic capabilities in inflammatory bowel disease

BACKGROUND

The human gut microbiome performs important functions in human health and disease. A classic example for host-gut microbial co-metabolism is host biosynthesis of primary bile acids and their subsequent deconjugation and transformation by the gut microbiome. To understand these system-level host-microbe interactions, a mechanistic, multi-scale computational systems biology approach that integrates the different types of omic data is needed. Here, we use a systematic workflow to computationally model bile acid metabolism in gut microbes and microbial communities.

RESULTS

Therefore, we first performed a comparative genomic analysis of bile acid deconjugation and biotransformation pathways in 693 human gut microbial genomes and expanded 232 curated genome-scale microbial metabolic reconstructions with the corresponding reactions (available at https://vmh.life ). We then predicted the bile acid biotransformation potential of each microbe and in combination with other microbes. We found that each microbe could produce maximally six of the 13 secondary bile acids in silico, while microbial pairs could produce up to 12 bile acids, suggesting bile acid biotransformation being a microbial community task. To investigate the metabolic potential of a given microbiome, publicly available metagenomics data from healthy Western individuals, as well as inflammatory bowel disease patients and healthy controls, were mapped onto the genomes of the reconstructed strains. We constructed for each individual a large-scale personalized microbial community model that takes into account strain-level abundances. Using flux balance analysis, we found considerable variation in the potential to deconjugate and transform primary bile acids between the gut microbiomes of healthy individuals. Moreover, the microbiomes of pediatric inflammatory bowel disease patients were significantly depleted in their bile acid production potential compared with that of controls. The contributions of each strain to overall bile acid production potential across individuals were found to be distinct between inflammatory bowel disease patients and controls. Finally, bottlenecks limiting secondary bile acid production potential were identified in each microbiome model.

CONCLUSIONS

This large-scale modeling approach provides a novel way of analyzing metagenomics data to accelerate our understanding of the metabolic interactions between the host and gut microbiomes in health and diseases states. Our models and tools are freely available to the scientific community.

Wavelength scaling of terahertz pulse energies delivered by two-color air plasmas

We address the long-standing problem of anomalous growth observed in the terahertz (THz) energy yield from air plasmas created by two-color laser pulses, as the fundamental wavelength λ₀ is increased. Using two distinct optical parametric amplifiers (OPAs), we report THz energies scaling like λ0α with large exponents 5.6≤α≤14.3, which departs from the growth in λ02 expected from photocurrent theory. By means of comprehensive 3D simulations, we demonstrate that the changes in the laser beam size, pulse duration, and phase-matching conditions in the second-harmonic generation process when tuning the OPA's carrier wavelength can lead to these high scaling powers. The value of the phase angle between the two colors reached at the exit of the doubling crystal turns out to be crucial and even explains non-monotonic behaviors in the measurements.

Electron Beam Driven Generation of Frequency-Tunable Isolated Relativistic Subcycle Pulses

We propose a novel scheme for frequency-tunable subcycle electromagnetic pulse generation. To this end a pump electron beam is injected into an electromagnetic seed pulse as the latter is reflected by a mirror. The electron beam is shown to be able to amplify the field of the seed pulse while upshifting its central frequency and reducing its number of cycles. We demonstrate the amplification by means of 1D and 2D particle-in-cell simulations. In order to explain and optimize the process, a model based on fluid theory is proposed. We estimate that using currently available electron beams and terahertz pulse sources, our scheme is able to produce millijoule-strong midinfrared subcycle pulses.

Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0

Constraint-based reconstruction and analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental molecular systems biology data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive desktop software suite of interoperable COBRA methods. It has found widespread application in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. This protocol is an update to the COBRA Toolbox v.1.0 and v.2.0. Version 3.0 includes new methods for quality-controlled reconstruction, modeling, topological analysis, strain and experimental design, and network visualization, as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimization solvers for multi-scale, multi-cellular, and reaction kinetic modeling, respectively. This protocol provides an overview of all these new features and can be adapted to generate and analyze constraint-based models in a wide variety of scenarios. The COBRA Toolbox v.3.0 provides an unparalleled depth of COBRA methods.

Finding useful biomarkers for Parkinson's disease

The recent advent of an "ecosystem" of shared biofluid sample biorepositories and data sets will focus biomarker efforts in Parkinson's disease, boosting the therapeutic development pipeline and enabling translation with real-world impact.