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.
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.
Harnessing the power of microbial consortia is integral to a diverse range of sectors, from healthcare to biotechnology to environmental remediation. To fully realize this potential, it is critical ...to understand the mechanisms behind the interactions that structure microbial consortia and determine their functions. Constraint-based reconstruction and analysis (COBRA) approaches, employing genome-scale metabolic models (GEMs), have emerged as the state-of-the-art tool to simulate the behavior of microbial communities from their constituent genomes. In the last decade, many tools have been developed that use COBRA approaches to simulate multi-species consortia, under either steady-state, dynamic, or spatiotemporally varying scenarios. Yet, these tools have not been systematically evaluated regarding their software quality, most suitable application, and predictive power. Hence, it is uncertain which tools users should apply to their system and what are the most urgent directions that developers should take in the future to improve existing capacities. This study conducted a systematic evaluation of COBRA-based tools for microbial communities using datasets from two-member communities as test cases. First, we performed a qualitative assessment in which we evaluated 24 published tools based on a list of FAIR (Findability, Accessibility, Interoperability, and Reusability) features essential for software quality. Next, we quantitatively tested the predictions in a subset of 14 of these tools against experimental data from three different case studies: a) syngas fermentation by C. autoethanogenum and C. kluyveri for the static tools, b) glucose/xylose fermentation with engineered E. coli and S. cerevisiae for the dynamic tools, and c) a Petri dish of E. coli and S. enterica for tools incorporating spatiotemporal variation. Our results show varying performance levels of the best qualitatively assessed tools when examining the different categories of tools. The differences in the mathematical formulation of the approaches and their relation to the results were also discussed. Ultimately, we provide recommendations for refining future GEM microbial modeling tools.
The LoPF-Q 12–18 (Levels of Personality Functioning Questionnaire) was designed for clinical use and to promote early detection of personality disorder (PD). It is a self-report measure with 97 items ...to assess personality functioning in adolescents from 12 years up. It operationalizes the dimensional concept of personality disorder (PD) severity used in the Alternative DSM-5 Model for Personality Disorders and the ICD-11. In this study, we investigated the factorial structure of the LoPF-Q 12–18. Additionally, a short version was developed to meet the need of efficient screening for PD in clinical and research applications. To investigate the factorial structure, several confirmatory factor analysis models were compared. A bifactor model with a strong general factor and four specific factors showed the best nominal fit (CFI = .91, RMSEA = .04, SRMR = .07). The short version was derived using the ant colony optimization algorithm. This procedure resulted in a 20-item version with excellent fit for a hierarchical model with four first order factors to represent the domains and a secondary higher order factor to represent personality functioning (CFI = .98, RMSEA = .05, SRMR = .04). Clinical validity (effect size d = 3.1 between PD patients and controls) and clinical utility (cutoff ≥ 36 providing 87.5% specificity and 80.2% sensitivity) for detecting patients with PD were high for the short version. Both, the long and short LoPF-Q 12–18 version are ready to be used for research and diagnostic purposes.
Major depression affects over 300 million people worldwide, but cases are often detected late or remain undetected. This increases the risk of symptom deterioration and chronification. Consequently, ...there is a high demand for low threshold but clinically sound approaches to depression detection. Recent studies show a great willingness among users of mobile health apps to assess daily depression symptoms. In this pilot study, we present a provisional validation of the depression screening app Moodpath. The app offers a 14-day ambulatory assessment (AA) of depression symptoms based on the ICD-10 criteria as well as ecologically momentary mood ratings that allow the study of short-term mood dynamics.
N = 113 Moodpath users were selected through consecutive sampling and filled out the Patient Health Questionnaire (PHQ-9) after completing 14 days of AA with 3 question blocks (morning, midday, and evening) per day. The psychometric properties (sensitivity, specificity, accuracy) of the ambulatory Moodpath screening were assessed based on the retrospective PHQ-9 screening result. In addition, several indicators of mood dynamics (e.g. average, inertia, instability), were calculated and investigated for their individual and incremental predictive value using regression models.
We found a strong linear relationship between the PHQ-9 score and the AA Moodpath depression score (r = .76, p < .001). The app-based screening demonstrated a high sensitivity (.879) and acceptable specificity (.745). Different indicators of mood dynamics covered substantial amounts of PHQ-9 variance, depending on the number of days with mood data that were included in the analyses.
AA and PHQ-9 shared a large proportion of variance but may not measure exactly the same construct. This may be due to the differences in the underlying diagnostic systems or due to differences in momentary and retrospective assessments. Further validation through structured clinical interviews is indicated. The results suggest that ambulatory assessed mood indicators are a promising addition to multimodal depression screening tools. Improving app-based AA screenings requires adapted screening algorithms and corresponding methods for the analysis of dynamic processes over time.
Caenorhabditis elegans is associated in nature with a species-rich, distinct microbiota, which was characterized only recently 1. Thus, our understanding of the relevance of the microbiota for ...nematode fitness is still at its infancy. One major benefit that the intestinal microbiota can provide to its host is protection against pathogen infection 2. However, the specific strains conferring the protection and the underlying mechanisms of microbiota-mediated protection are often unclear 3. Here, we identify natural C. elegans microbiota isolates that increase C. elegans resistance to pathogen infection. We show that isolates of the Pseudomonas fluorescens subgroup provide paramount protection from infection with the natural pathogen Bacillus thuringiensis through distinct mechanisms. We found that the P. lurida isolates MYb11 and MYb12 (members of the P. fluorescens subgroup) protect C. elegans against B. thuringiensis infection by directly inhibiting growth of the pathogen both in vitro and in vivo. Using genomic and biochemical analyses, we further demonstrate that MYb11 and MYb12 produce massetolide E, a cyclic lipopeptide biosurfactant of the viscosin group 4, 5, which is active against pathogenic B. thuringiensis. In contrast to MYb11 and MYb12, P. fluorescens MYb115-mediated protection involves increased resistance without inhibition of pathogen growth and most likely depends on indirect, host-mediated mechanisms. This work provides new insight into the functional significance of the C. elegans natural microbiota and expands our knowledge of bacteria-derived compounds that can influence pathogen colonization in the intestine of an animal.
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•Natural C. elegans microbiota confer protection against pathogen infection•Different Pseudomonas isolates protect C. elegans through distinct mechanisms•P. lurida isolates produce massetolide E and directly inhibit pathogen growth•P. fluorescens-mediated protection may depend on indirect, host-mediated mechanisms
Kissoyan et al. provide novel insights into the function of the native microbiota of the model nematode C. elegans. Their work highlights the role of microbes in supporting C. elegans defense responses and the diversity of immune-protective mechanisms, including the involvement of microbiota-derived metabolites.
The use of biomaterials and bioinspired concepts in electronics will enable the fabrication of transient and disposable technologies within areas ranging from smart packaging and advertisement to ...healthcare applications. In this work, the use of a nonhalogenated biodegradable solid polymer electrolyte based on poly(ε‐caprolactone‐co‐trimethylene carbonate) and tetrabutylammonium bis‐oxalato borate in light‐emitting electrochemical cells (LECs) is presented. It is shown that the spin‐cast devices exhibit current efficiencies of ≈2 cd A−1 with luminance over ≈12 000 cd m−2, an order of magnitude higher than previous bio‐based LECs. By a combination of industrially relevant techniques (i.e., inkjet printing and blade coating), the fabrication of LEC devices on a cellulose‐based flexible biodegradable substrate showing lifetimes compatible with transient applications is demonstrated. The presented results have direct implications toward the industrial manufacturing of biomaterial‐based light‐emitting devices with potential use in future biodegradable/biocompatible electronics.
Light‐emitting electrochemical cells comprising a biodegradable nonhalogenated solid polymer electrolyte are deposited on a cellulose‐based substrate. The maximum luminance of reference devices reaches ≈104 cd m−2 with maximum current efficiencies of 2 cd A−1. The printed devices, fabricated by a combination of industrially relevant techniques on cellulose diacetate, demonstrate the potential cost‐efficient processability of biomaterials in optoelectronic applications.
The core machinery of synaptic vesicle fusion consists of three soluble N-ethylmaleimide-sensitive factor attachment receptor (SNARE) proteins, the two t-SNAREs at the plasma membrane (SNAP-25, ...Syntaxin 1) and the vesicle-bound v-SNARE synaptobrevin 2 (VAMP2). Formation of the trans-oriented four-α-helix bundle between these SNAREs brings vesicle and plasma membrane in close proximity and prepares the vesicle for fusion. The t-SNAREs are thought to be necessary for vesicle fusion. Whether the v-SNAREs are required for fusion is still unclear, as substantial vesicle priming and spontaneous release activity remain in mammalian mass-cultured synaptobrevin/cellubrevin-deficient neurons. Using the autaptic culture system from synaptobrevin 2 knockout neurons of mouse hippocampus, we found that the majority of cells were devoid of any evoked or spontaneous release and had no measurable readily releasable pool. A small subpopulation of neurons, however, displayed release, and their release activity correlated with the presence and amount of v-SNARE synaptobrevin 1 expressed. Comparison of synaptobrevin 1 and 2 in rescue experiments demonstrates that synaptobrevin 1 can substitute for the other v-SNARE, but with a lower efficiency in neurotransmitter release probability. Release activity in synaptobrevin 2-deficient mass-cultured neurons was massively reduced by a knockdown of synaptobrevin 1, demonstrating that synaptobrevin 1 is responsible for the remaining release activity. These data support the hypothesis that both t- and v-SNAREs are absolutely required for vesicle priming and evoked release and that differential expression of SNARE paralogs can contribute to differential synaptic coding in the brain.
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.
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