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Abstract Background Inflammation is characterized by a metabolic switch promoting glycolysis and lactate production. Hexokinases (HK) catalyze the first reaction of glycolysis and inhibition of epithelial HK2 protected from colitis in mice. HK2 expression has been described as elevated in patients with intestinal inflammation; however, there is conflicting data from few cohorts especially with severely inflamed individuals; thus, systematic studies linking disease activity with HK2 levels are needed. Methods We examined the relationship between HK2 expression and inflammation severity using bulk transcriptome data derived from the mucosa of thoroughly phenotyped inflammatory bowel disease (IBD) patients of two independent cohorts including both subtypes Crohn’s disease (CD) and ulcerative colitis (UC). Publicly available single-cell RNA sequencing data were analyzed, and immunofluorescence staining on colonic biopsies of unrelated patients with intestinal inflammation was performed to confirm the RNA-based findings on cellular and protein level. Results HK2 expression gradually increased from mild to intermediate inflammation, yet strongly declined at high inflammation scores. Expression of epithelial marker genes also declined at high inflammation scores, whereas that of candidate immune marker genes increased, indicating a cellular remodeling of the mucosa during inflammation with an infiltration of HK2-negative immune cells and a loss of terminal differentiated epithelial cells in the apical epithelium—the main site of HK2 expression. Normalizing for the enterocyte loss clearly identified epithelial HK2 expression as gradually increasing with disease activity and remaining elevated at high inflammation scores. HK2 protein expression was mostly restricted to brush border enterocytes, and these cells along with HK2 levels vanished with increasing disease severity. Conclusions Our findings clearly define dysregulated epithelial HK2 expression as an indicator of disease activity in intestinal inflammation and suggest targeted HK2-inhibition as a potential therapeutic avenue.

Abstract Fecal calprotectin is an established marker of gut inflammation in inflammatory bowel disease (IBD). Elevated levels of fecal calprotectin as well as gut microbial dysbiosis have also been observed in other clinical conditions. However, systemic and multi-omics alterations linked to elevated fecal calprotectin in older individuals remain unclear. This study comprehensively investigated the relationship between fecal calprotectin levels, gut microbiome composition, serum inflammation and targeted metabolomics markers, and relevant lifestyle and medical data in a large sample of older individuals (n = 735; mean age ± SD: 68.7 ± 6.3) from the TREND cohort study. Low (0–50 μg/g; n = 602), moderate (> 50–100 μg/g; n = 64) and high (> 100 μg/g; n = 62) fecal calprotectin groups were stratified. Several pro-inflammatory gut microbial genera were significantly increased and short-chain fatty acid producing genera were decreased in high vs. low calprotectin groups. In serum, IL-17C, CCL19 and the toxic metabolite indoxyl sulfate were increased in high vs. low fecal calprotectin groups. These changes were partially mediated by the gut microbiota. Moreover, the high fecal calprotectin group showed increased BMI and a higher disease prevalence of heart attack and obesity. Our findings contribute to the understanding of fecal calprotectin as a marker of gut dysbiosis and its broader systemic and clinical implications in older individuals.

Abstract Although DNA methylation data yields highly accurate age predictors, little is known about the dynamics of this quintessential epigenomic biomarker during lifespan. To narrow the gap, we investigate the methylation trajectories of male mouse colon at five different time points of aging. Our study indicates the existence of sudden hypermethylation events at specific stages of life. Precisely, we identify two epigenomic switches during early-to-midlife (3-9 months) and mid-to-late-life (15-24 months) transitions, separating the rodents’ life into three stages. These nonlinear methylation dynamics predominantly affect genes associated with the nervous system and enrich in bivalently marked chromatin regions. Based on groups of nonlinearly modified loci, we construct a clock-like classifier STageR (STage of aging estimatoR) that accurately predicts murine epigenetic stage. We demonstrate the universality of our clock in an independent mouse cohort and with publicly available datasets.

ABSTRACTThe microbiome expresses a variety of functions that influence host biology. The range of functions depends on the microbiome’s composition, which can change during the host’s lifetime due to neutral assembly processes, host-mediated selection, and environmental conditions. To date, the exact dynamics of microbiome assembly, the underlying determinants, and the effects on host-associated functions remain poorly understood. Here, we used the nematode Caenorhabditis elegans and a defined community of fully sequenced, naturally associated bacteria to study microbiome dynamics and functions across a major part of the worm’s lifetime of hosts under controlled experimental conditions. Bacterial community composition initially shows strongly declining levels of stochasticity, which increases during later time points, suggesting selective effects in younger animals as opposed to more random processes in older animals. The adult microbiome is enriched in genera Ochrobactrum and Enterobacter compared to the direct substrate and a host-free control environment. Using pathway analysis, metabolic, and ecological modeling, we further find that the lifetime assembly dynamics increase competitive strategies and gut-associated functions in the host-associated microbiome, indicating that the colonizing bacteria benefit the worm. Overall, our study introduces a framework for studying microbiome assembly dynamics based on stochastic, ecological, and metabolic models, yielding new insights into the processes that determine host-associated microbiome composition and function.IMPORTANCEThe microbiome plays a crucial role in host biology. Its functions depend on the microbiome composition that can change during a host’s lifetime. To date, the dynamics of microbiome assembly and the resulting functions still need to be better understood. This study introduces a new approach to characterize the functional consequences of microbiome assembly by modeling both the relevance of stochastic processes and metabolic characteristics of microbial community changes. The approach was applied to experimental time-series data obtained for the microbiome of the nematode Caenorhabditis elegans across the major part of its lifetime. Stochastic processes played a minor role, whereas beneficial bacteria as well as gut-associated functions enriched in hosts. This indicates that the host might actively shape the composition of its microbiome. Overall, this study provides a framework for studying microbiome assembly dynamics and yields new insights into C. elegans microbiome functions.

ABSTRACTWhile numerous health-beneficial interactions between host and microbiota have been identified, there is still a lack of targeted approaches for modulating these interactions. Thus, we here identify precision prebiotics that specifically modulate the abundance of a microbiome member species of interest. In the first step, we show that defining precision prebiotics by compounds that are only taken up by the target species but no other species in a community is usually not possible due to overlapping metabolic niches. Subsequently, we use metabolic modeling to identify precision prebiotics for a two-member Caenorhabditis elegans microbiome community comprising the immune-protective target species Pseudomonas lurida MYb11 and the persistent colonizer Ochrobactrum vermis MYb71. We experimentally confirm four of the predicted precision prebiotics, L-serine, L-threonine, D-mannitol, and γ-aminobutyric acid, to specifically increase the abundance of MYb11. L-serine was further assessed in vivo, leading to an increase in MYb11 abundance also in the worm host. Overall, our findings demonstrate that metabolic modeling is an effective tool for the design of precision prebiotics as an important cornerstone for future microbiome-targeted therapies.IMPORTANCEWhile various mechanisms through which the microbiome influences disease processes in the host have been identified, there are still only few approaches that allow for targeted manipulation of microbiome composition as a first step toward microbiome-based therapies. Here, we propose the concept of precision prebiotics that allow to boost the abundance of already resident health-beneficial microbial species in a microbiome. We present a constraint-based modeling pipeline to predict precision prebiotics for a minimal microbial community in the worm Caenorhabditis elegans comprising the host-beneficial Pseudomonas lurida MYb11 and the persistent colonizer Ochrobactrum vermis MYb71 with the aim to boost the growth of MYb11. Experimentally testing four of the predicted precision prebiotics, we confirm that they are specifically able to increase the abundance of MYb11 in vitro and in vivo. These results demonstrate that constraint-based modeling could be an important tool for the development of targeted microbiome-based therapies against human diseases.

ABSTRACTInflammatory bowel disease (IBD) is a persistent inflammatory condition that affects the gastrointestinal tract and presents significant challenges in its management and treatment. Despite the knowledge that within-host bacterial evolution occurs in the intestine, the disease has rarely been studied from an evolutionary perspective. In this study, we aimed to investigate the evolution of resident bacteria during intestinal inflammation and whether- and how disease-related bacterial genetic changes may present trade-offs with potential therapeutic importance. Here, we perform an in vivo evolution experiment of E. coli in a gnotobiotic mouse model of IBD, followed by multiomic analyses to identify disease-specific genetic and phenotypic changes in bacteria that evolved in an inflamed versus a non-inflamed control environment. Our results demonstrate distinct evolutionary changes in E. coli specific to inflammation, including a single nucleotide variant that independently reached high frequency in all inflamed mice. Using ex vivo fitness assays, we find that these changes are associated with a higher fitness in an inflamed environment compared to isolates derived from non-inflamed mice. Further, using large-scale phenotypic assays, we show that bacterial adaptation to inflammation results in clinically relevant phenotypes, which intriguingly include collateral sensitivity to antibiotics. Bacterial evolution in an inflamed gut yields specific genetic and phenotypic signatures. These results may serve as a basis for developing novel evolution-informed treatment approaches for patients with intestinal inflammation.

IntroductionBy 2050, the worldwide percentage of people 65 years and older is assumed to have doubled compared to current numbers. Therefore, finding ways of promoting healthy (cognitive) aging is crucial. Physical activity is considered an effective approach to counteract not only physical but also cognitive decline. However, the underlying mechanisms that drive the benefits of regular physical activity on cognitive function are not fully understood. This randomized controlled trial aims to analyze the effect of an eight-week standardized physical activity training program in older humans on cognitive, brain, and gut-barrier function as well as the relationship between the resulting changes.Methods and analysisOne-hundred healthy participants aged 60 to 75 years will be recruited. First, participants will undergo an extensive baseline assessment consisting of neurocognitive tests, functional and structural brain imaging, physical fitness tests, and gut-microbiome profiling. Next, participants will be randomized into either a multi-component physical activity group (experimental condition) or a relaxation group (active control condition), with each training lasting 8 weeks and including an equal number and duration of exercises. The whole intervention will be online-based, i.e., participants will find their intervention schedule and all materials needed on the study website. After the intervention phase, participants will have their post-intervention assessment, which consists of the same measures and tests as the baseline assessment. The primary outcome of this study is the change in the cognitive parameter of visual processing speed from baseline to post-measurement, which will on average take place 10 weeks after the randomization. Secondary outcomes related to cognitive, brain, and microbiome data will be analyzed exploratory.Clinical trial registration:https://drks.de/search/de/trial/DRKS00028022

Current classifications (World Health Organization-HAEM5/ICC) define up to 26 molecular B-cell precursor acute lymphoblastic leukemia (BCP-ALL) disease subtypes by genomic driver aberrations and corresponding gene expression signatures. Identification of driver aberrations by transcriptome sequencing (RNA-Seq) is well established, while systematic approaches for gene expression analysis are less advanced. Therefore, we developed ALLCatchR, a machine learning-based classifier using RNA-Seq gene expression data to allocate BCP-ALL samples to all 21 gene expression-defined molecular subtypes. Trained on n = 1869 transcriptome profiles with established subtype definitions (4 cohorts; 55% pediatric / 45% adult), ALLCatchR allowed subtype allocation in 3 independent hold-out cohorts (n = 1018; 75% pediatric / 25% adult) with 95.7% accuracy (averaged sensitivity across subtypes: 91.1% / specificity: 99.8%). High-confidence predictions were achieved in 83.7% of samples with 98.9% accuracy. Only 1.2% of samples remained unclassified. ALLCatchR outperformed existing tools and identified novel driver candidates in previously unassigned samples. Additional modules provided predictions of samples blast counts, patient’s sex, and immunophenotype, allowing the imputation in cases where these information are missing. We established a novel RNA-Seq reference of human B-lymphopoiesis using 7 FACS-sorted progenitor stages from healthy bone marrow donors. Implementation in ALLCatchR enabled projection of BCP-ALL samples to this trajectory. This identified shared proximity patterns of BCP-ALL subtypes to normal lymphopoiesis stages, extending immunophenotypic classifications with a novel framework for developmental comparisons of BCP-ALL. ALLCatchR enables RNA-Seq routine application for BCP-ALL diagnostics with systematic gene expression analysis for accurate subtype allocation and novel insights into underlying developmental trajectories.

Parkinson’s disease (PD) is a chronic and progressive neurodegenerative disease with the major symptoms comprising loss of movement coordination (motor dysfunction) and non-motor dysfunction, including gastrointestinal symptoms. Alterations in the gut microbiota composition have been reported in PD patients vs. controls. However, it is still unclear how these compositional changes contribute to disease etiology and progression. Furthermore, most of the available studies have focused on European, Asian, and North American cohorts, but the microbiomes of PD patients in Latin America have not been characterized. To address this problem, we obtained fecal samples from Colombian participants (n = 25 controls, n = 25 PD idiopathic cases) to characterize the taxonomical community changes during disease via 16S rRNA gene sequencing. An analysis of differential composition, diversity, and personalized computational modeling was carried out, given the fecal bacterial composition and diet of each participant. We found three metabolites that differed in dietary habits between PD patients and controls: carbohydrates, trans fatty acids, and potassium. We identified six genera that changed significantly in their relative abundance between PD patients and controls, belonging to the families Lachnospiraceae, Lactobacillaceae, Verrucomicrobioaceae, Peptostreptococcaceae, and Streptococcaceae. Furthermore, personalized metabolic modeling of the gut microbiome revealed changes in the predicted production of seven metabolites (Indole, tryptophan, fructose, phenylacetic acid, myristic acid, 3-Methyl-2-oxovaleric acid, and N-Acetylneuraminic acid). These metabolites are associated with the metabolism of aromatic amino acids and their consumption in the diet. Therefore, this research suggests that each individual’s diet and intestinal composition could affect host metabolism. Furthermore, these findings open the door to the study of microbiome–host interactions and allow us to contribute to personalized medicine.

Recent rodent microbiome experiments suggest that besides Akkermansia, Parasutterella sp. are important in type 2 diabetes and obesity development. In the present translational human study, we aimed to characterize Parasutterella in our European cross-sectional FoCus cohort (n = 1,544) followed by validation of the major results in an independent Canadian cohort (n = 438). In addition, we examined Parasutterella abundance in response to a weight loss intervention (n = 55). Parasutterella was positively associated with BMI and type 2 diabetes independently of the reduced microbiome α/β diversity and low-grade inflammation commonly found in obesity. Nutritional analysis revealed a positive association with the dietary intake of carbohydrates but not with fat or protein consumption. Out of 126 serum metabolites differentially detectable by untargeted HPLC-based MS-metabolomics, L-cysteine showed the strongest reduction in subjects with high Parasutterella abundance. This is of interest, since Parasutterella is a known high L-cysteine consumer and L-cysteine is known to improve blood glucose levels in rodents. Furthermore, metabolic network enrichment analysis identified an association of high Parasutterella abundance with the activation of the human fatty acid biosynthesis pathway suggesting a mechanism for body weight gain. This is supported by a significant reduction of the Parasutterella abundance during our weight loss intervention. Together, these data indicate a role for Parasutterella in human type 2 diabetes and obesity, whereby the link to L-cysteine might be relevant in type 2 diabetes development and the link to the fatty acid biosynthesis pathway for body weight gain in response to a carbohydrate-rich diet in obesity development.

Abstract Genome-scale metabolic models of microorganisms are powerful frameworks to predict phenotypes from an organism’s genotype. While manual reconstructions are laborious, automated reconstructions often fail to recapitulate known metabolic processes. Here we present gapseq ( https://github.com/jotech/gapseq ), a new tool to predict metabolic pathways and automatically reconstruct microbial metabolic models using a curated reaction database and a novel gap-filling algorithm. On the basis of scientific literature and experimental data for 14,931 bacterial phenotypes, we demonstrate that gapseq outperforms state-of-the-art tools in predicting enzyme activity, carbon source utilisation, fermentation products, and metabolic interactions within microbial communities.

Abstract Background The intestinal microbiota plays a crucial role in protecting the host from pathogenic microbes, modulating immunity and regulating metabolic processes. We studied the simplified human intestinal microbiota (SIHUMIx) consisting of eight bacterial species with a particular focus on the discovery of novel small proteins with less than 100 amino acids (= sProteins), some of which may contribute to shape the simplified human intestinal microbiota. Although sProteins carry out a wide range of important functions, they are still often missed in genome annotations, and little is known about their structure and function in individual microbes and especially in microbial communities. Results We created a multi-species integrated proteogenomics search database (iPtgxDB) to enable a comprehensive identification of novel sProteins. Six of the eight SIHUMIx species, for which no complete genomes were available, were sequenced and de novo assembled. Several proteomics approaches including two earlier optimized sProtein enrichment strategies were applied to specifically increase the chances for novel sProtein discovery. The search of tandem mass spectrometry (MS/MS) data against the multi-species iPtgxDB enabled the identification of 31 novel sProteins, of which the expression of 30 was supported by metatranscriptomics data. Using synthetic peptides, we were able to validate the expression of 25 novel sProteins. The comparison of sProtein expression in each single strain versus a multi-species community cultivation showed that six of these sProteins were only identified in the SIHUMIx community indicating a potentially important role of sProteins in the organization of microbial communities. Two of these novel sProteins have a potential antimicrobial function. Metabolic modelling revealed that a third sProtein is located in a genomic region encoding several enzymes relevant for the community metabolism within SIHUMIx. Conclusions We outline an integrated experimental and bioinformatics workflow for the discovery of novel sProteins in a simplified intestinal model system that can be generically applied to other microbial communities. The further analysis of novel sProteins uniquely expressed in the SIHUMIx multi-species community is expected to enable new insights into the role of sProteins on the functionality of bacterial communities such as those of the human intestinal tract. Video abstract

The study of microbiomes by sequencing has revealed a plethora of correlations between microbial community composition and various life-history characteristics of the corresponding host species. However, inferring causation from correlation is often hampered by the sheer compositional complexity of microbiomes, even in simple organisms. Synthetic communities offer an effective approach to infer cause-effect relationships in host-microbiome systems. Yet the available communities suffer from several drawbacks, such as artificial (thus non-natural) choice of microbes, microbe-host mismatch (e.g., human microbes in gnotobiotic mice), or hosts lacking genetic tractability. Here we introduce CeMbio, a simplified natural Caenorhabditis elegans microbiota derived from our previous meta-analysis of the natural microbiome of this nematode. The CeMbio resource is amenable to all strengths of the C. elegans model system, strains included are readily culturable, they all colonize the worm gut individually, and comprise a robust community that distinctly affects nematode life-history. Several tools have additionally been developed for the CeMbio strains, including diagnostic PCR primers, completely sequenced genomes, and metabolic network models. With CeMbio, we provide a versatile resource and toolbox for the in-depth dissection of naturally relevant host-microbiome interactions in C. elegans.

Aspergillus fumigatus is an important human fungal pathogen and its conidia are constantly inhaled by humans. In immunocompromised individuals, conidia can grow out as hyphae that damage lung epithelium. The resulting invasive aspergillosis is associated with devastating mortality rates. Since infection is a race between the innate immune system and the outgrowth of A. fumigatus conidia, we use dynamic optimization to obtain insight into the recruitment and depletion of alveolar macrophages and neutrophils. Using this model, we obtain key insights into major determinants of infection outcome on host and pathogen side. On the pathogen side, we predict in silico and confirm in vitro that germination speed is an important virulence trait of fungal pathogens due to the vulnerability of conidia against host defense. On the host side, we found that epithelial cells, which have been underappreciated, play a role in fungal clearance and are potent mediators of cytokine release. Both predictions were confirmed by in vitro experiments on established cell lines as well as primary lung cells. Further, our model affirms the importance of neutrophils in invasive aspergillosis and underlines that the role of macrophages remains elusive. We expect that our model will contribute to improvement of treatment protocols by focusing on the critical components of immune response to fungi but also fungal virulence traits.

Neurodegenerative diseases such as Parkinson’s (PD) and Alzheimer’s disease (AD), the prevalence of which is rapidly rising due to an aging world population and westernization of lifestyles, are expected to put a strong socioeconomic burden on health systems worldwide. Clinical trials of therapies against PD and AD have only shown limited success so far. Therefore, research has extended its scope to a systems medicine point of view, with a particular focus on the gastrointestinal–brain axis as a potential main actor in disease development and progression. Microbiome and metabolome studies have already revealed important insights into disease mechanisms. Both the microbiome and metabolome can be easily manipulated by dietary and lifestyle interventions, and might thus offer novel, readily available therapeutic options to prevent the onset as well as the progression of PD and AD. This review summarizes our current knowledge on the interplay between microbiota, metabolites, and neurodegeneration along the gastrointestinal–brain axis. We further illustrate state-of-the art methods of microbiome and metabolome research as well as metabolic modeling that facilitate the identification of disease pathomechanisms. We conclude with therapeutic options to modulate microbiome composition to prevent or delay neurodegeneration and illustrate potential future research directions to fight PD and AD.

Cognitive decline is one of the greatest health threats of old age and the maintenance of optimal brain function across a lifespan remains a big challenge. The hippocampus is considered particularly vulnerable but there is cross-species consensus that its functional integrity benefits from the early and continuous exercise of demanding physical, social and mental activities, also referred to as environmental enrichment (EE). Here, we investigated the extent to which late-onset EE can improve the already-impaired cognitive abilities of lifelong deprived C57BL/6 mice and how it affects gene expression in the hippocampus. To this end, 5- and 24-month-old mice housed in standard cages (5mSC and 24mSC) and 24-month-old mice exposed to EE in the last 2 months of their life (24mEE) were subjected to a Barnes maze task followed by next-generation RNA sequencing of the hippocampal tissue. Our analyses showed that late-onset EE was able to restore deficits in spatial learning and short-term memory in 24-month-old mice. These positive cognitive effects were reflected by specific changes in the hippocampal transcriptome, where late-onset EE affected transcription much more than age (24mSC vs. 24mEE: 1311 DEGs, 24mSC vs. 5mSC: 860 DEGs). Remarkably, a small intersection of 72 age-related DEGs was counter-regulated by late-onset EE. Of these, Bcl3, Cttnbp2, Diexf, Esr2, Grb10, Il4ra, Inhba, Rras2, Rps6ka1 and Socs3 appear to be particularly relevant as key regulators involved in dendritic spine plasticity and in age-relevant molecular signaling cascades mediating senescence, insulin resistance, apoptosis and tissue regeneration. In summary, our observations suggest that the brains of aged mice in standard cage housing preserve a considerable degree of plasticity. Switching them to EE proved to be a promising and non-pharmacological intervention against cognitive decline.

Urinary tract infections (UTIs) are frequent in humans, affecting the upper and lower urinary tract. Present diagnosis relies on the positive culture of uropathogenic bacteria from urine and clinical markers of inflammation of the urinary tract. The bladder is constantly challenged by adverse environmental stimuli which influence urinary tract physiology, contributing to a dysbiotic environment. Simultaneously, pathogens are primed by environmental stressors such as antibiotics, favoring recurrent UTIs (rUTIs), resulting in chronic illness. Due to different confounders for UTI onset, a greater understanding of the fundamental environmental mechanisms and microbial ecology of the human urinary tract is required. Such advancements could promote the tandem translation of bench and computational studies for precision treatments and clinical management of UTIs. Therefore, there is an urgent need to understand the ecological interactions of the human urogenital microbial communities which precede rUTIs. This review aims to outline the mechanistic aspects of rUTI ecology underlying dysbiosis between both the human microbiome and host physiology which predisposes humans to rUTIs. By assessing the applications of next generation and systems level methods, we also recommend novel approaches to elucidate the systemic consequences of rUTIs which requires an integrated approach for successful treatment. To this end, we will provide an outlook towards the so-called ‘uncomplicated environment of UTIs’, a holistic and systems view that applies ecological principles to define patient-specific UTIs. This perspective illustrates the need to withdraw from traditional reductionist perspectives in infection biology and instead, a move towards a systems-view revolving around patient-specific pathophysiology during UTIs.

The molecular foundation of chronic inflammatory diseases (CIDs) can differ markedly between individuals. As our understanding of the biochemical mechanisms underlying individual disease manifestations and progressions expands, new strategies to adjust treatments to the patient’s characteristics will continue to profoundly transform clinical practice. Nutrition has long been recognized as an important determinant of inflammatory disease phenotypes and treatment response. Yet empirical work demonstrating the therapeutic effectiveness of patient-tailored nutrition remains scarce. This is mainly due to the challenges presented by long-term effects of nutrition, variations in inter-individual gastrointestinal microbiota, the multiplicity of human metabolic pathways potentially affected by food ingredients, nutrition behavior, and the complexity of food composition. Historically, these challenges have been addressed in both human studies and experimental model laboratory studies primarily by using individual nutrition data collection in tandem with large-scale biomolecular data acquisition (e.g. genomics, metabolomics, etc.). This review highlights recent findings in the field of precision nutrition and their potential implications for the development of personalized treatment strategies for CIDs. It emphasizes the importance of computational approaches to integrate nutritional information into multi-omics data analysis and to predict which molecular mechanisms may explain how nutrients intersect with disease pathways. We conclude that recent findings point towards the unexhausted potential of nutrition as part of personalized medicine in chronic inflammation.

Both intrinsic and extrinsic mechanisms regulating bacterial expression have been elucidated and described, however, such studies have mainly focused on local effects on the two-dimensional structure of the prokaryote genome while long-range as well as spatial interactions influencing gene expression are still only poorly understood. In this paper, we investigate the association between co-expression and distance between genes, using RNA-seq data at multiple growth phases in order to illuminate whether such conserved patterns are an indication of a gene regulatory mechanism relevant for prokaryotic cell proliferation, adaption, and evolution. We observe recurrent sinusoidal patterns in correlation of pairwise expression as function of genomic distance and rule out that these are caused by transcription-induced supercoiling gradients, gene clustering in operons, or association with regulatory transcription factors (TFs). By comparing spatial proximity for pairs of genomic bins with their correlation of pairwise expression, we further observe a high co-expression proportional with the spatial proximity. Based on these observations, we propose that the observed patterns are related to nucleoid structure as a product of transcriptional spilling, where genes actively influence transcription of spatially proximal genes through increases within shared local pools of RNA polymerases (RNAP), and actively spilling transcription onto neighboring genes.

The reconstruction and application of genome-scale metabolic network models is a central topic in the field of systems biology with numerous applications in biotechnology, ecology, and medicine. However, there is no agreed upon standard for the definition of the nutritional environment for these models. The objective of this article is to provide a guideline and a clear paradigm on how to translate nutritional information into an in-silico representation of the chemical environment. Step-by-step procedures explain how to characterise and categorise the nutritional input and to successfully apply it to constraint-based metabolic models. In parallel, we illustrate the proposed procedure with a case study of the growth of Escherichia coli in a complex nutritional medium and show that an accurate representation of the medium is crucial for physiological predictions. The proposed framework will assist researchers to expand their existing metabolic models of their microbial systems of interest with detailed representations of the nutritional environment, which allows more accurate and reproducible predictions of microbial metabolic processes.

Inflammation is considered a possible cause of cognitive decline during aging. This study investigates the influence of physical activity and social isolation in old mice on their cognitive functions and inflammation. The Barnes maze task was performed to assess spatial learning and memory in 3, 9, 15, 24, and 28 months old male C57BL/6 mice as well as following voluntary wheel running (VWR) and social isolation (SI) in 20 months old mice. Inflammatory gene expression was analyzed in hippocampal and colonic samples by qPCR. Cognitive decline occurs in mice between 15 and 24 months of age. VWR improved cognitive functions while SI had negative effects. Expression of inflammatory markers changed during aging in the hippocampus (Il1a/Il6/S100b/Iba1/Adgre1/Cd68/Itgam) and colon (Tnf/Il6/Il1ra/P2rx7). VWR attenuates inflammaging specifically in the colon (Ifng/Il10/Ccl2/S100b/Iba1), while SI regulates intestinal Il1b and Gfap. Inflammatory markers in the hippocampus were not altered following VWR and SI. The main finding of our study is that both the hippocampus and colon exhibit an increase in inflammatory markers during aging, and that voluntary wheel running in old age exclusively attenuates intestinal inflammation. Based on the existence of the gut-brain axis, our results extend therapeutic approaches preserving cognitive functions in the elderly to the colon.

Ageing is associated with a pronounced shift in mortality from cancer to degenerative diseases. Here, the authors show that in concordance with this shift, conserved transcriptional alterations during ageing across four vertebrates align with degenerative diseases but are opposite to those in cancer.

Parkinson’s Disease (PD) is a neurodegenerative disease, leading to motor and non-motor complications. Autonomic alterations, including gastrointestinal symptoms, precede motor defects and act as early warning signs. Chronic exposure to dietary, environmental heavy metals impacts the gastrointestinal system and host-associated microbiome, eventually affecting the central nervous system. The correlation between dysbiosis and PD suggests a functional and bidirectional communication between the gut and the brain. The bioaccumulation of metals promotes stress mechanisms by increasing reactive oxygen species, likely altering the bidirectional gut–brain link. To better understand the differing molecular mechanisms underlying PD, integrative modeling approaches are necessary to connect multifactorial perturbations in this heterogeneous disorder. By exploring the effects of gut microbiota modulation on dietary heavy metal exposure in relation to PD onset, the modification of the host-associated microbiome to mitigate neurological stress may be a future treatment option against neurodegeneration through bioremediation. The progressive movement towards a systems toxicology framework for precision medicine can uncover molecular mechanisms underlying PD onset such as metal regulation and microbial community interactions by developing predictive models to better understand PD etiology to identify options for novel treatments and beyond. Several methodologies recently addressed the complexity of this interaction from different perspectives; however, to date, a comprehensive review of these approaches is still lacking. Therefore, our main aim through this manuscript is to fill this gap in the scientific literature by reviewing recently published papers to address the surrounding questions regarding the underlying molecular mechanisms between metals, microbiota, and the gut–brain-axis, as well as the regulation of this system to prevent neurodegeneration.

The biology of all organisms is influenced by the associated community of microorganisms. In spite of its importance, it is usually not well understood how exactly this microbiota affects host functions and what are the underlying molecular processes. To rectify this knowledge gap, we took advantage of the nematode Caenorhabditis elegans as a tractable, experimental model system and assessed the inducible transcriptome response after colonization with members of its native microbiota. For this study, we focused on two isolates of the genus Ochrobactrum. These bacteria are known to be abundant in the nematode’s microbiota and are capable of colonizing and persisting in the nematode gut, even under stressful conditions. The transcriptome response was assessed across development and three time points of adult life, using general and C. elegans-specific enrichment analyses to identify affected functions. Our assessment revealed an influence of the microbiota members on the nematode’s dietary response, development, fertility, immunity, and energy metabolism. This response is mainly regulated by a GATA transcription factor, most likely ELT-2, as indicated by the enrichment of (i) the GATA motif in the promoter regions of inducible genes and (ii) of ELT-2 targets among the differentially expressed genes. We compared our transcriptome results with a corresponding previously characterized proteome data set, highlighting a significant overlap in the differentially expressed genes, the affected functions, and ELT-2 target genes. Our analysis further identified a core set of 86 genes that consistently responded to the microbiota members across development and adult life, including several C-type lectin-like genes and genes known to be involved in energy metabolism or fertility. We additionally assessed the consequences of induced gene expression with the help of metabolic network model analysis, using a previously established metabolic network for C. elegans. This analysis complemented the enrichment analyses by revealing an influence of the Ochrobactrum isolates on C. elegans energy metabolism and furthermore metabolism of specific amino acids, fatty acids, and also folate biosynthesis. Our findings highlight the multifaceted impact of naturally colonizing microbiota isolates on C. elegans life history and thereby provide a framework for further analysis of microbiota-mediated host functions.

Metabolism is one of the attributes of life and supplies energy and building blocks to organisms. Therefore, understanding metabolism is crucial for the understanding of complex biological phenomena. Despite having been in the focus of research for centuries, our picture of metabolism is still incomplete. Metabolomics, the systematic analysis of all small molecules in a biological system, aims to close this gap. In order to facilitate such investigations a blueprint of the metabolic network is required. Recently, several metabolic network reconstructions for the model organism Caenorhabditis elegans have been published, each having unique features. We have established the WormJam Community to merge and reconcile these (and other unpublished models) into a single consensus metabolic reconstruction. In a series of workshops and annotation seminars this model was refined with manual correction of incorrect assignments, metabolite structure and identifier curation as well as addition of new pathways. The WormJam consensus metabolic reconstruction represents a rich data source not only for in silico network-based approaches like flux balance analysis, but also for metabolomics, as it includes a database of metabolites present in C. elegans, which can be used for annotation. Here we present the process of model merging, correction and curation and give a detailed overview of the model. In the future it is intended to expand the model toward different tissues and put special emphasizes on lipid metabolism and secondary metabolism including ascaroside metabolism in accordance to their central role in C. elegans physiology.

The bacterial nucleoid is highly condensed and forms compartment-like structures within the cell. Much attention has been devoted to investigating the dynamic topology and organization of the nucleoid. In contrast, the specific nucleoid organization, and the relationship between nucleoid structure and function is often neglected with regard to importance for adaption to changing environments and horizontal gene acquisition. In this review, we focus on the structure-function relationship in the bacterial nucleoid. We provide an overview of the fundamental properties that shape the chromosome as a structured yet dynamic macromolecule. These fundamental properties are then considered in the context of the living cell, with focus on how the informational flow affects the nucleoid structure, which in turn impacts on the genetic output. Subsequently, the dynamic living nucleoid will be discussed in the context of evolution. We will address how the acquisition of foreign DNA impacts nucleoid structure, and conversely, how nucleoid structure constrains the successful and sustainable chromosomal integration of novel DNA. Finally, we will discuss current challenges and directions of research in understanding the role of chromosomal architecture in bacterial survival and adaptation.

In this work, we investigate optimality principles behind synthesis strategies for protein complexes using a dynamic optimization approach. We show that the cellular capacity of protein synthesis has a strong influence on optimal synthesis strategies reaching from a simultaneous to a sequential synthesis of the subunits of a protein complex. Sequential synthesis is preferred if protein synthesis is strongly limited, whereas a simultaneous synthesis is optimal in situations with a high protein synthesis capacity. We confirm the predictions of our optimization approach through the analysis of the operonic organization of protein complexes in several hundred prokaryotes. Thereby, we are able to show that cellular protein synthesis capacity is a driving force in the dissolution of operons comprising the subunits of a protein complex. Thus, we also provide a tested hypothesis explaining why the subunits of many prokaryotic protein complexes are distributed across several operons despite the presumably less precise co-regulation.

Recent advances focusing on the metabolic interactions within and between cellular populations have emphasized the importance of microbial communities for human health. Constraint-based modeling, with flux balance analysis in particular, has been established as a key approach for studying microbial metabolism, whereas individual-based modeling has been commonly used to study complex dynamics between interacting organisms. In this study, we combine both techniques into the R package BacArena (https://cran.r-project.org/package=BacArena) to generate novel biological insights into Pseudomonas aeruginosa biofilm formation as well as a seven species model community of the human gut. For our P. aeruginosa model, we found that cross-feeding of fermentation products cause a spatial differentiation of emerging metabolic phenotypes in the biofilm over time. In the human gut model community, we found that spatial gradients of mucus glycans are important for niche formations which shape the overall community structure. Additionally, we could provide novel hypothesis concerning the metabolic interactions between the microbes. These results demonstrate the importance of spatial and temporal multi-scale modeling approaches such as BacArena.

A precise and rapid adjustment of fluxes through metabolic pathways is crucial for organisms to prevail in changing environmental conditions. Based on this reasoning, many guiding principles that govern the evolution of metabolic networks and their regulation have been uncovered. To this end, methods from dynamic optimization are ideally suited since they allow to uncover optimality principles behind the regulation of metabolic networks. We used dynamic optimization to investigate the influence of toxic intermediates in connection with the efficiency of enzymes on the regulation of a linear metabolic pathway. Our results predict that transcriptional regulation favors the control of highly efficient enzymes with less toxic upstream intermediates to reduce accumulation of toxic downstream intermediates. We show that the derived optimality principles hold by the analysis of the interplay between intermediate toxicity and pathway regulation in the metabolic pathways of over 5000 sequenced prokaryotes. Moreover, using the lipopolysaccharide biosynthesis in Escherichia coli as an example, we show how knowledge about the relation of regulation, kinetic efficiency and intermediate toxicity can be used to identify drug targets, which control endogenous toxic metabolites and prevent microbial growth. Beyond prokaryotes, we discuss the potential of our findings for the development of antifungal drugs.

The original version of this Article contained an error in the spelling of the author Jule Müller, which was incorrectly given as Julia Müller. Additionally, in Fig. 4a, the blue-red colour scale for fold change in ageing/disease regulation included a blue stripe in place of a red stripe at the right-hand end of the scale. These errors have been corrected in both the PDF and HTML versions of the Article.

Bacterial communities are taxonomically highly diverse, yet the mechanisms that maintain this diversity remain poorly understood. We hypothesized that an obligate and mutual exchange of metabolites, as is very common among bacterial cells, could stabilize different genotypes within microbial communities. To test this, we developed a cellular automaton to model interactions among six empirically characterized genotypes that differ in their ability and propensity to produce amino acids. By systematically varying intrinsic (i.e. benefit-to-cost ratio) and extrinsic parameters (i.e. metabolite diffusion level, environmental amino acid availability), we show that obligate cross-feeding of essential metabolites is selected for under a broad range of conditions. In spatially structured environments, positive assortment among cross-feeders resulted in the formation of cooperative clusters, which limited exploitation by non-producing auxotrophs, yet allowed them to persist at the clusters' periphery. Strikingly, cross-feeding helped to maintain genotypic diversity within populations, while amino acid supplementation to the environment decoupled obligate interactions and favored auxotrophic cells that saved amino acid production costs over metabolically autonomous prototrophs. Together, our results suggest that spatially structured environments and limited nutrient availabilities should facilitate the evolution of metabolic interactions, which can help to maintain genotypic diversity within natural microbial populations.

Abstract While previous studies have shed light on the link between the structure of metabolism and its transcriptional regulation, the extent to which transcriptional regulation controls metabolism has not yet been fully explored. In this work, we address this problem by integrating a large number of experimental data sets with a model of the metabolism of Escherichia coli. Using a combination of computational tools including the concept of elementary flux patterns, methods from network inference and dynamic optimization, we find that transcriptional regulation of pathways reflects the protein investment into these pathways. While pathways that are associated to a high protein cost are controlled by fine‐tuned transcriptional programs, pathways that only require a small protein cost are transcriptionally controlled in a few key reactions. As a reason for the occurrence of these different regulatory strategies, we identify an evolutionary trade‐off between the conflicting requirements to reduce protein investment and the requirement to be able to respond rapidly to changes in environmental conditions.

The question whether fatty acids can be converted into glucose in humans has a long standing tradition in biochemistry, and the expected answer is "No". Using recent advances in Systems Biology in the form of large-scale metabolic reconstructions, we reassessed this question by performing a global investigation of a genome-scale human metabolic network, which had been reconstructed on the basis of experimental results. By elementary flux pattern analysis, we found numerous pathways on which gluconeogenesis from fatty acids is feasible in humans. On these pathways, four moles of acetyl-CoA are converted into one mole of glucose and two moles of CO₂. Analyzing the detected pathways in detail we found that their energetic requirements potentially limit their capacity. This study has many other biochemical implications: effect of starvation, sports physiology, practically carbohydrate-free diets of inuit, as well as survival of hibernating animals and embryos of egg-laying animals. Moreover, the energetic loss associated to the usage of gluconeogenesis from fatty acids can help explain the efficiency of carbohydrate reduced and ketogenic diets such as the Atkins diet.

Background. The question whether even-chain fatty acids can be converted into glucose has a long-standing tradition in biochemistry. Since the glyoxylate shunt is absent from mammals, the question has been considered to be solved. It is of particular relevance for understanding the metabolic state of natives of the arctic regions due to the very high fat content of their traditional diet only containing negligible amounts of carbohydrates. Methods & Results. Using an in silico approach, we discovered several hitherto unknown routes in human metabolism that allow the conversion of even-chain fatty acids into carbohydrates in humans. These pathways proceed via ketogenesis over the intermediate of acetone and produce the gluconeogenic precursor pyruvate. While these pathways can make a contribution to glucose production during times of limited carbohydrate supply, we found that their capacity might be limited due to a high demand in reducing equivalents in acetone degradation. Considering the traditional diet of natives of the arctic regions, the detected pathways are not only important in order to improve carbohydrate supply, but moreover reduce the amount of protein that needs to be used for gluconeogenesis. Conclusion. In summary, our study sheds new light on our understanding of the metabolic state of natives from the arctic regions on their traditional diet. Moreover, they provide an avenue for new analyses that can reveal how humans have adapted metabolically to a practically carbohydrate-free diet.