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Static and dynamic balance abilities enable simple and complex movements and are determinants of top athletic performance. Balance abilities and their proficiency differ fundamentally with respect to age, gender, type of balance intervention, and type of sport. With this study, we aim to investigate whether 4weeks of dynamic balance training (DBT) improves static balance performance in school-aged gymnasts and football players. For this purpose, young male gymnasts (n=21) and male football players (n=20) completed an initial static balance assessment consisting of two one-legged stance (left and right foot) and two two-legged stance (eyes open and eyes closed) tasks. Subsequently, all participants underwent a 4-week intervention. DBT consisting of nine individual tasks was performed two times per week. Another static balance assessment followed 1day after the last training session and retention was assessed 2weeks later. Dynamic balance scores and total path length were analyzed via rank-based repeated measures designs using ANOVA-type statistics. The influence of factors GROUP and TIME on the static and dynamic balance performance was examined. Prior to DBT, young gymnasts showed better static balance performance than football players. However, after intervention, both groups improved in both one-legged stance tasks and also had high retention rates in these tasks. No significant improvements were seen in either group in the two-legged balance tests. Both groups improved in the dynamic balance tasks, although no differences in learning rates were evident. Our findings imply an inter-relationship between both static and dynamic balance components. Consequently, training regimes should include both balance components to facilitate early development of balance ability.

How cells adjust nutrient transport across their membranes is incompletely understood. Previously, we have shown that S. cerevisiae broadly re-configures the nutrient transporters at the plasma membrane in response to amino acid availability, through endocytosis of sugar- and amino acid transporters (AATs) (Müller et al., 2015). A genome-wide screen now revealed that the selective endocytosis of four AATs during starvation required the α-arrestin family protein Art2/Ecm21, an adaptor for the ubiquitin ligase Rsp5, and its induction through the general amino acid control pathway. Art2 uses a basic patch to recognize C-terminal acidic sorting motifs in AATs and thereby instructs Rsp5 to ubiquitinate proximal lysine residues. When amino acids are in excess, Rsp5 instead uses TORC1-activated Art1 to detect N-terminal acidic sorting motifs within the same AATs, which initiates exclusive substrate-induced endocytosis. Thus, amino acid excess or starvation activate complementary α-arrestin-Rsp5-complexes to control selective endocytosis and adapt nutrient acquisition.

We describe some general results that constrain the dynamical fluctuations that can occur in non-equilibrium steady states, with a focus on molecular dynamics. That is, we consider Hamiltonian systems, coupled to external heat baths, and driven out of equilibrium by non-conservative forces. We focus on the probabilities of rare events (large deviations). First, we discuss a PT (parity-time) symmetry that appears in ensembles of trajectories where a current is constrained to have a large (non-typical) value. We analyse the heat flow in such ensembles, and compare it with non-equilibrium steady states. Second, we consider pathwise large deviations that are defined by considering many copies of a system. We show how the probability currents in such systems can be decomposed into orthogonal contributions that are related to convergence to equilibrium and to dissipation. We discuss the implications of these results for modelling non-equilibrium steady states.

Abstract The Hierarchical Taxonomy of Psychopathology (HiTOP) arranges phenotypes of mental disorders based on empirical covariation, ranging from narrowly defined symptoms to higher-order spectra of psychopathology. Since the introduction of personality functioning (PF) in DSM-5 and ICD-11, several studies have identified PF as a predictor of transdiagnostic aspects of psychopathology. However, the role of PF in the HiTOP classification system has not been systematically examined. This study investigates how PF can be integrated into HiTOP, whether PF accounts for transdiagnostic variance captured in higher-order spectra, and how its predictive value for future affective well-being (AWB) and psychosocial impairment (PSI) compares to the predictive value of specific psychopathology beyond PF. To this end, we examined two years of ambulatory assessed data on psychopathology, PF, PSI, and AWB of N = 27,173 users of a mental health app. Results of bass-ackwards analyses largely aligned with the current HiTOP working model. Using bifactor modeling, aspects of PF were identified to capture most of the internalizing, thought disorder, and externalizing higher-order factor variance. In longitudinal prediction analyses employing bifactor-(S-1) modeling, PF explained 58.6% and 30.6% of variance in PSI and AWB when assessed across one year, respectively, and 33.1% and 23.2% of variance when assessed across two years. Results indicate that personality functioning may largely account for transdiagnostic variance captured in the higher-order components in HiTOP as well as longitudinal outcomes of PSI and AWB. Clinicians and their patients may benefit from assessing PF aspects such as identity problems or internal relationship models in a broad range of mental disorders. Further, incorporating measures of PF may advance research in biological psychiatry by providing empirically sound phenotypes.

Abstract Cholinergic striatal interneurons (ChIs) express the vesicular glutamate transporter 3 (VGLUT3) which allows them to regulate the striatal network with glutamate and acetylcholine (ACh). In addition, VGLUT3-dependent glutamate increases ACh vesicular stores through vesicular synergy. A missense polymorphism, VGLUT3-p.T8I, was identified in patients with substance use disorders (SUDs) and eating disorders (EDs). A mouse line was generated to understand the neurochemical and behavioral impact of the p.T8I variant. In VGLUT3T8I/T8I male mice, glutamate signaling was unchanged but vesicular synergy and ACh release were blunted. Mutant male mice exhibited a reduced DA release in the dorsomedial striatum but not in the dorsolateral striatum, facilitating habit formation and exacerbating maladaptive use of drug or food. Increasing ACh tone with donepezil reversed the self-starvation phenotype observed in VGLUT3T8I/T8I male mice. Our study suggests that unbalanced dopaminergic transmission in the dorsal striatum could be a common mechanism between SUDs and EDs.

Abstract Background We explored potential predictive biomarkers of immunotherapy response in patients with extensive-stage small-cell lung cancer (ES-SCLC) treated with durvalumab (D) + tremelimumab (T) + etoposide-platinum (EP), D + EP, or EP in the randomized phase 3 CASPIAN trial. Methods 805 treatment-naïve patients with ES-SCLC were randomized (1:1:1) to receive D + T + EP, D + EP, or EP. The primary endpoint was overall survival (OS). Patients were required to provide an archived tumor tissue block (or ≥ 15 newly cut unstained slides) at screening, if these samples existed. After assessment for programmed cell death ligand-1 expression and tissue tumor mutational burden, residual tissue was used for additional molecular profiling including by RNA sequencing and immunohistochemistry. Results In 182 patients with transcriptional molecular subtyping, OS with D ± T + EP was numerically highest in the SCLC-inflamed subtype (n = 10, median 24.0 months). Patients derived benefit from immunotherapy across subtypes; thus, additional biomarkers were investigated. OS benefit with D ± T + EP versus EP was greater with high versus low CD8A expression/CD8 cell density by immunohistochemistry, but with no additional benefit with D + T + EP versus D + EP. OS benefit with D + T + EP versus D + EP was associated with high expression of CD4 (median 25.9 vs. 11.4 months) and antigen-presenting and processing machinery (25.9 vs. 14.6 months) and MHC I and II (23.6 vs. 17.3 months) gene signatures, and with higher MHC I expression by immunohistochemistry. Conclusions These findings demonstrate the tumor microenvironment is important in mediating better outcomes with D ± T + EP in ES-SCLC, with canonical immune markers associated with hypothesized immunotherapy mechanisms of action defining patient subsets that respond to D ± T. Trial registration ClinicalTrials.gov, NCT03043872.

Abstract The strongly growing interest in digitalizing society requires simple and reliable strain‐sensing concepts. In this work, a highly sensitive stretchable sensor is presented using a straightforward and scalable printing method. The piezoresistive sensor consists of conductive core–shell microspheres embedded in an elastomer. As the elastomer, ethylene vinyl acetate (EVA) is employed as an efficient and cost‐effective alternative compared to polydimethylsiloxane (PDMS). EVA allows for a significantly lower percolation threshold and low hysteresis compared with PDMS. Using 35 µm microspheres, a detection limit of 0.01% is achieved. When using 4 µm microspheres, the sensor shows a detection limit of 0.015% and electromechanical robustness against 1000 cycles of 0–1% strain. The stretchable strain sensor is successfully implemented as an impact sensor and a diaphragm expansion monitoring sensor. Fast (20 ms) and high‐resolution response as well as mechanical robustness to strain values greater than the linear working range of the sensor are demonstrated. The results of this research indicate the promising potential of employing conductive microspheres embedded in the EVA matrix for fast and precise strain detection applications.

ABSTRACT Pseudomonas aeruginosa is a ubiquitous, opportunistic human pathogen. Since it often expresses multidrug resistance, new treatment options are urgently required. Such new treatments are usually assessed with one of the canonical laboratory strains, PAO1 or PA14. However, these two strains are unlikely representative of the strains infecting patients, because they have adapted to laboratory conditions and do not capture the enormous genomic diversity of the species. Here, we characterized the major P. aeruginosa clone type (mPact) panel. This panel consists of 20 strains, which reflect the species’ genomic diversity, cover all major clone types, and have both patient and environmental origins. We found significant strain variation in distinct responses toward antibiotics and general growth characteristics. Only few of the measured traits are related, suggesting independent trait optimization across strains. High resistance levels were only identified for clinical mPact isolates and could be linked to known antimicrobial resistance (AMR) genes. One strain, H01, produced highly unstable AMR combined with reduced growth under drug-free conditions, indicating an evolutionary cost to resistance. The expression of microcolonies was common among strains, especially for strain H15, which also showed reduced growth, possibly indicating another type of evolutionary trade-off. By linking isolation source, growth, and virulence to life history traits, we further identified specific adaptive strategies for individual mPact strains toward either host processes or degradation pathways. Overall, the mPact panel provides a reasonably sized set of distinct strains, enabling in-depth analysis of new treatment designs or evolutionary dynamics in consideration of the species’ genomic diversity.IMPORTANCENew treatment strategies are urgently needed for high-risk pathogens such as the opportunistic and often multidrug-resistant pathogen Pseudomonas aeruginosa. Here, we characterize the major P. aeruginosa clone type (mPact) panel. It consists of 20 strains with different origins that cover the major clone types of the species as well as its genomic diversity. This mPact panel shows significant variation in (i) resistance against distinct antibiotics, including several last resort antibiotics; (ii) related traits associated with the response to antibiotics; and (iii) general growth characteristics. We further developed a novel approach that integrates information on resistance, growth, virulence, and life-history characteristics, allowing us to demonstrate the presence of distinct adaptive strategies of the strains that focus either on host interaction or resource processing. In conclusion, the mPact panel provides a manageable number of representative strains for this important pathogen for further in-depth analyses of treatment options and evolutionary dynamics.

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.

Abstract Background Mental disorders (MDs) are one of the leading causes for workforce sickness absence and disability worldwide. The burden, costs and challenges are enormous for the individuals concerned, employers and society at large. Although most MDs are characterised by a high risk of relapse after treatment or by chronic courses, interventions that link medical-psychotherapeutic approaches with work-directed components to facilitate a sustainable return to work (RTW) are rare. This protocol describes the design of a study to evaluate the (cost-)effectiveness and implementation process of a multimodal, clinical and work-directed intervention, called RTW-PIA, aimed at employees with MDs to achieve sustainable RTW in Germany. Methods The study consists of an effectiveness, a health-economic and a process evaluation, designed as a two-armed, multicentre, randomised controlled trial, conducted in German psychiatric outpatient clinics. Sick-listed employees with MDs will receive either the 18-month RTW-PIA treatment in conjunction with care as usual, or care as usual only. RTW-PIA consists of a face-to-face individual RTW support, RTW aftercare group meetings, and web-based aftercare. Assessments will be conducted at baseline and 6, 12, 18 and 24 months after completion of baseline survey. The primary outcome is the employees´ achievement of sustainable RTW, defined as reporting less than six weeks of working days missed out due to sickness absence within 12 months after first RTW. Secondary outcomes include health-related quality of life, mental functioning, RTW self-efficacy, overall job satisfaction, severity of mental illness and work ability. The health-economic evaluation will be conducted from a societal and public health care perspective, as well as from the employer’s perspective in a cost–benefit analysis. The design will be supplemented by a qualitative effect evaluation using pre- and post-interviews, and a multimethod process evaluation examining various predefined key process indicators from different stakeholder perspectives. Discussion By applying a comprehensive, multimethodological evaluation design, this study captures various facets of RTW-PIA. In case of promising results for sustainable RTW, RTW-PIA may be integrated into standard care within German psychiatric outpatient clinics. Trial registration The study was prospectively registered with the German Clinical Trials Register ( DRKS00026232 , 1 September 2021).

[Introduction] The ICD-11 and DSM-5 are the leading systems for the classification of mental disorders, and their relevance for clinical work and research, as well as their impact for policy making and legal questions, has increased considerably. In recent years, other frameworks have been proposed to supplement or even replace the ICD and the DSM, raising many questions regarding clinical utility, scientific relevance, and, at the core, how best to conceptualize mental disorders. [Method] As examples of the new approaches that have emerged, here we introduce the Hierarchical Taxonomy of Psychopathology (HiTOP), the Research Domain Criteria (RDoC), systems and network approaches, process-based approaches, as well as a new approach to the classification of personality disorders. [Results and Discussion] We highlight main distinctions between these classification frameworks, largely related to different priorities and goals, and discuss areas of overlap and potential compatibility. Synergies among these systems may provide promising new avenues for research and clinical practice.

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.

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.

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.