Biology subjects

Faust, K.

Publications and source records attributed to Faust, K..

At least 19 recordsLinked to original sources

bioRxiv2025

PlasticEnz: An integrated database and screening tool combining homology and machine learning to identify plastic-degrading enzymes in meta-omics datasets

PlasticEnz is a new open-source tool for detecting plastic-degrading enzymes (plastizymes) in metagenomic data by combining sequence homology-based search with machine learning. It integrates custom Hidden Markov Models, DIAMOND alignments, and polymer-specific classifiers trained on ProtBERT embeddings to identify candidate depolymerases from contigs, genomes, or protein sequences. PlasticEnz supports 11 plastic polymers with ML classifiers for PET and PHB, achieving F1 > 0.7 on independent test sets. Applied to plastic-exposed microcosms and field metagenomes, the tool recovered known PETases and PHBases, distinguished plastic-contaminated from pristine environments, and clustered predictions with validated reference enzymes. PlasticEnz is fast, scalable, and user-friendly, providing a robust framework for exploring microbial plastic degradation potential in complex communities. Author SummaryPlastic pollution is a global problem, and one promising solution is using microbes that can break them down. However, finding the enzymes responsible for this in complex environmental samples is not easy. We developed PlasticEnz, a free and easy-to-use tool that helps researchers identify plastic-degrading enzymes or "plastizymes" in metagenomic data. PlasticEnz combines traditional sequence similarity search methods with machine learning models trained on known plastizymes. It works with protein sequences, contigs, or genomes with ML components optimised for detection of two common plastic polymers: PET and PHB. We tested PlasticEnz on both controlled lab experiments and real-world samples from plastic-polluted soils and clean environments. The tool successfully identified known plastic-degrading enzymes and even helped distinguish between polluted and pristine sites. By making plastizyme detection more accessible, PlasticEnz enables researchers to better explore the microbial potential for plastic degradation, which could support future bioremediation efforts.

bioinformatics↗

bioRxiv2025

μGrowthDB: querying, visualizing, and sharing microbial growth curve data

Microbial growth curve data contain valuable information about microorganisms physiology and metabolism. However, they are currently reported in various formats scattered across publications. A generic resource that allows querying and comparing microbial growth curve data is currently missing. For this reason, we developed GrowthDB, an open repository designed to store, visualize, analyze, and share quantitative measurements of species abundances and metabolite profiles from monocultures and communities of known composition. GrowthDB is designed to accommodate a range of experimental designs and measurement techniques. In addition to querying microbial growth curves by organism or metabolite, GrowthDB supports comparative visualisation of growth curves and offers a guided data submission process intended to make data upload as easy as possible. GrowthDB is available at: https://mgrowthdb.gbiomed.kuleuven.be/.

microbiology↗

bioRxiv2025

Dynamics of cortico-subthalamic neuronal patterns during dyskinesia in Parkinsons disease

Parkinson patients suffer from levodopa-induced dyskinesia, which occur adversely to chronic dopaminergic treatment. These abnormal involuntary movements can only partly be actively suppressed and affect quality of life. A lowered motor inhibition during hyperdopaminergic states, associated with structural and plasticity changes in the cortico-basal-ganglia motor network, is hypothesized to enable dyskinesia. Oscillatory cortico-subthalamic patterns associated with dyskinesia are proposed as adaptive neuromodulation biomarkers, but their dependence on behavioral states such as dyskinetic movement presence or suppression remains unknown. We studied cortico-subthalamic oscillations in 22 Parkinsons patients during dyskinesia-evoking protocols. We clinically differentiated between non-dyskinetic and dyskinetic periods, and defined movement presence with kinematics, leading to four behavioral states containing rest, voluntary movements, movement suppression during dyskinesia, and dyskinetic movements. Elevated subthalamic theta-activity and attenuated beta-activity was found during both dyskinetic movement suppression and execution, while cortico-subthalamic gamma-activity only increased during dyskinetic movement execution. Subthalamic spectral changes significantly predicted dyskinesia presence, and movement presence significantly affected the predictive performance. A movement-aware classifier enhanced dyskinesia detection based on movement-depending biomarkers containing cortical oscillations and gamma-bands. We propose movement execution during dyskinesia to be a distinct behavioral and neural microstate within a dopamine-depending dyskinetic macrostate, that can enhance dyskinesia classification for adaptive neuromodulation.

neuroscience↗

bioRxiv2025

Differential modulation of movement speed with state-dependent deep brain stimulation in Parkinson's disease

Subthalamic deep brain stimulation (STN-DBS) provides unprecedented spatiotemporal precision for the treatment of Parkinsons disease (PD), allowing for direct real-time state-specific adjustments. Inspired by findings from optogenetic stimulation in mice, we hypothesized that STN-DBS effects on movement speed depend on ongoing movement kinematics that patients exhibit during stimulation. To investigate this hypothesis, we implemented a motor state-dependent closed-loop neurostimulation algorithm, adapting DBS burst delivery to ongoing movement speed in 24 PD patients. We found a stronger anti-bradykinetic effect, raising movement speed to the level of healthy controls, when STN-DBS was applied during fast but not slow movements, while only stimulating 5% of overall movement time. To study underlying brain circuits and neurophysiological mechanisms, we investigated the behavioral effects with MRI connectomics and motor cortex electrocorticography. Finally, we demonstrate that machine learning-based brain signal decoding can be used to predict continuous movement speed for fully embedded state-dependent closed-loop algorithms. Our findings provide novel insights into the state-dependency of invasive neuromodulation, which could inspire advanced state-dependent neurostimulation algorithms for brain disorders.

neuroscience↗

bioRxiv2025

Beyond beta: Aperiodic broadband power reflects Parkinson's disease severity - a multicenter study

Parkinsons disease is linked to increased beta oscillations in the subthalamic nucleus, which correlate with motor symptoms. However, findings across studies have varied. Our standardized analysis of multicenter datasets shows that small sample sizes contributed to these discrepancies--a challenge we address by pooling datasets into one large cohort (n=119). Moving beyond beta power, we disentangled spectral components reflecting distinct neural processes. Combining aperiodic offset, low beta, and low gamma oscillations explained significantly more variance in symptom severity than beta alone. Moreover, interhemispheric within-patient analyses showed that, unlike beta oscillations, aperiodic broadband power-likely reflecting spiking activity-was increased in the more affected hemisphere. These findings identify aperiodic broadband power as a potential biomarker for adaptive deep brain stimulation and provide novel insights into the relationship between subthalamic hyperactivity and motor symptoms in human Parkinsons disease.

neuroscience↗

bioRxiv2024

Discovery of alternative stable states in a synthetic human gut microbial community

AbstractSeveral human-associated microbial communities exist in multiple configurations and can change their composition in response to perturbations, remaining in an altered state even after the perturbation ends. Multistability has been previously proposed to explain this behavior for gut microbiota in particular, but has not been clearly demonstrated experimentally. Here, we first investigated the life history strategies of three common human gut bacteria to identify mechanisms driving alternative states. We then used this data to build and parameterize a kinetic model, which predicted that alternative states emerge due to phenotype switching between subpopulations of the same species. Perturbation experiments supported these predictions, and confirmed the existence of alternative states. Finally, simulations showed that phenotype switching can also explain alternative states in larger communities. Thus, a transient perturbation combined with metabolic flexibility is sufficient for alternative communities to emerge, implying that they are not necessarily explained by differences between individuals. One-Sentence SummaryWe demonstrate the existence of alternative states in a human gut microbial community and propose phenotype switching as a mechanism explaining their emergence.

microbiology↗

bioRxiv2024

microbetag: simplifying microbial network interpretation through annotation, enrichment tests and metabolic complementarity analysis

Microbial co-occurrence network inference is often hindered by low accuracy and tool dependency. We introduce microbetag, a comprehensive software ecosystem designed to enhance network annotation. Nodes (taxa) are enriched with phenotypic traits, while edges represent metabolic complementarities, highlighting potential cross-feeding relationships. microbetags online version relies on microbetagDB, a database of 34,608 high-quality genomes with detailed annotations. A stand-alone tool allows users to apply microbetag to custom reference genomes/bins/MAGs. Additionally, MGG, a CytoscapeApp, offers a streamlined, user-friendly interface for network retrieval and visualization. microbetag effectively identified known metabolic interactions and serves as a robust hypothesis-generating tool.

systems biology↗

bioRxiv2024

Effects of microplastics on Daphnia-associated microbiomes in situ and in vitro

Microplastics (MP) pollution in aquatic environments is a growing global concern. MP, defined as plastic fragments smaller than 5mm, accumulate in freshwater reservoirs, especially those located in urban areas, impacting the resident biota. This study investigated the effects of MP on the performance and microbiome of Daphnia, a keystone organism in freshwater ecosystems, through both in situ sampling of freshwater ponds and a controlled 23-day in vitro exposure experiment. Using 16S rRNA gene sequencing and whole-genome shotgun sequencing, the microbiome community composition and functional capacity was analysed and correlated with MP pollution levels. Urban ponds showed higher MP concentrations in both water and sediment than natural ponds with significant differences in MP composition. Bacterioplankton communities were more diverse and richer than the Daphnia-associated microbiomes. Overall, the in situ study showed that the composition of the Daphnia-associated community co-varied with high MP levels but also with temperature and redox potential. Moreover, the functional analysis showed increased relative abundances of PET degradation enzymes and antibiotic resistance genes (ARGs) in microbiomes from high-MP ponds. In the in vitro experiment, the bacterioplankton inoculum source significantly influenced Daphnia survival and microbiome composition. Daphnia exposed to high MP concentrations inoculated with bacterioplankton pre-exposed to MP exhibited significantly higher survival rates, suggesting potential adaptive benefits from MP-associated microbiomes. Network analysis identified specific taxa associated with MP within the Daphnia microbiome. Our study suggests adaptive responses of freshwater host-associated microbiomes to MP pollution including biodegradation with potential benefits for the host.

ecology↗

bioRxiv2024

Shared pathway-specific network mechanisms of dopamine and deep brain stimulation for the treatment of Parkinson's disease

Deep brain stimulation is a brain circuit intervention that can modulate distinct neural pathways for the alleviation of neurological symptoms in patients with brain disorders. In Parkinsons disease, subthalamic deep brain stimulation clinically mimics the effect of dopaminergic drug treatment, but the shared pathway mechanisms on cortex - basal ganglia networks are unknown. To address this critical knowledge gap, we combined fully invasive neural multisite recordings in patients undergoing deep brain stimulation surgery with normative MRI-based whole-brain connectomics. Our findings demonstrate that dopamine and stimulation exert distinct mesoscale effects through modulation of local neural population activity. In contrast, at the macroscale, stimulation mimics dopamine in its suppression of excessive interregional network synchrony associated with indirect and hyperdirect cortex - basal ganglia pathways. Our results provide a better understanding of the circuit mechanisms of dopamine and deep brain stimulation, laying the foundation for advanced closed-loop neurostimulation therapies.

neuroscience↗

bioRxiv2024

PHARAOH: A collaborative crowdsourcing platform for PHenotyping And Regional Analysis Of Histology

Deep learning has proven to be capable of automating key aspects of histopathologic analysis, but its continual reliance on large expert-annotated training datasets hinders widespread adoption. Here, we present an online collaborative portal that streamlines tissue image annotation to promote the development and sharing of custom computer vision models for PHenotyping And Regional Analysis Of Histology (PHARAOH; https://www.pathologyreports.ai/). PHARAOH uses a weakly supervised active learning framework whereby patch-level image features are leveraged to organize large swaths of tissue into morphologically-uniform clusters for batched human annotation. By providing cluster-level labels on only a handful of cases, we show how custom PHARAOH models can be developed and used to guide the quantification of cellular features that correlate with molecular, pathologic and patient outcome data. Both custom model design and feature extraction pipelines are amenable to crowdsourcing making PHARAOH a fully scalable systems-level solution for the systematic expansion and cataloging of computational pathology applications.

bioinformatics↗

bioRxiv2023

Dopamine and neuromodulation shorten the latency from motor intention to execution in Parkinson's disease

The ability to initiate volitional action is fundamental to human behaviour. Loss of dopaminergic neurons in Parkinsons disease is associated with impaired action initiation, also termed akinesia. Both dopamine and subthalamic deep brain stimulation (DBS) can alleviate akinesia, but the underlying mechanisms are unknown. An important question is whether dopamine and DBS facilitate de novo build-up of neural dynamics for motor execution or accelerate existing cortical movement initiation signals through shared modulatory circuit effects. Answering these questions can provide the foundation for new closed-loop neurotherapies with adaptive DBS, but the objectification of neural processing delays prior to performance of volitional action remains a significant challenge. To overcome this challenge, we studied readiness potentials and trained brain signal decoders on invasive neurophysiology signals in 25 DBS patients (12 female) with Parkinsons disease during performance of self-initiated movements. Combined sensorimotor cortex electrocorticography (ECoG) and subthalamic local field potential (LFP) recordings were performed OFF therapy (N=22), ON dopaminergic medication (N=18) and ON subthalamic deep brain stimulation (N=8). This allowed us to compare their therapeutic effects on neural latencies between the earliest cortical representation of movement intention as decoded by linear discriminant analysis classifiers and onset of muscle activation recorded with electromyography (EMG). In the hypodopaminergic OFF state, we observed long latencies between motor intention and motor execution for readiness potentials and machine learning classifications. Both, dopamine and DBS significantly shortened these latencies, hinting towards a shared therapeutic mechanism for alleviation of akinesia. To investigate this further, we analysed directional cortico-subthalamic oscillatory communication with multivariate granger causality. Strikingly, we found that both therapies independently shifted cortico-subthalamic oscillatory information flow from antikinetic beta (13-35 Hz) to prokinetic theta (4-10 Hz) rhythms, which was correlated with latencies in motor execution. Our study reveals a shared brain network modulation pattern of dopamine and DBS that may underlie the acceleration of neural dynamics for augmentation of movement initiation in Parkinsons disease. Instead of producing or increasing preparatory brain signals, both therapies modulate oscillatory communication. These insights provide a link between the pathophysiology of akinesia and its therapeutic alleviation with oscillatory network changes in other non-motor and motor domains, e.g. related to hyperkinesia or effort and reward perception. In the future, our study may inspire the development of clinical brain computer interfaces based on brain signal decoders to provide temporally precise support for action initiation in patients with brain disorders.

neuroscience↗

bioRxiv2023

Starvation response strategies of human gut bacteria Bacteroides thetaiotaomicron and Roseburia intestinalis and their impact on co-culture dynamics

Bacterial growth often alters the environment, which in turn can impact interspecies interactions among bacteria. Here, we used an in vitro batch system containing mucin beads to emulate the dynamic host environment and to study its impact on the interactions between two abundant and prevalent human gut bacteria, the primary fermenter Bacteroides thetaiotaomicron and the butyrate producer Roseburia intestinalis. By combining machine learning and flow cytometry, we found that the number of viable B. thetaiotaomicron cells decreases with glucose consumption due to acid production, while R. intestinalis survives post-glucose depletion by entering a slow growth mode. Both species attach to mucin beads, but only viable cell counts of B. thetaiotaomicron increase significantly. The number of viable co-culture cells varies significantly over time compared to those of monocultures. A combination of targeted metabolomics and RNA-seq showed that the slow growth mode of R. intestinalis represents a diauxic shift towards acetate and lactate consumption, whereas B. thetaiotaomicron survives glucose depletion and low pH by foraging on mucin sugars. In addition, most of the mucin monosaccharides we tested inhibited the growth of R. intestinalis but not B. thetaiotaomicron. We encoded these causal relationships in a kinetic model, which reproduced the observed dynamics. In summary, we explored how R. intestinalis and B. thetaiotaomicron respond to nutrient scarcity and how this affects their dynamics. We highlight the importance of understanding bacterial metabolic strategies to effectively modulate microbial dynamics in changing conditions.

microbiology↗

bioRxiv2022

Characterisation of neonatal Staphylococcus capitis NRCS-A isolates compared with non NRCS-A Staphylococcus capitis from neonates and adults.

Staphylococcus capitis is a frequent cause of Late-Onset Sepsis (LOS) in neonates admitted to Neonatal Intensive Care Units (NICU). The NRCS-A clone of S. capitis has been isolated from NICUs globally although the reasons for the global success of this clone are not understood. We analysed a collection of S. capitis colonising babies admitted to two NICUs, one in the UK and one in Germany as well as corresponding pathological clinical isolates. Genome analysis identified 3 groups; non-NRCS-A isolates, NRCS-A isolates, and a group of proto NRCS-A - isolates closely related to NRCS-A but not associated with neonatal infection. All bloodstream isolates belonged to the NRCS-A group and were indistinguishable from strains carried on the skin or in the gut. NRCS-A isolates showed increased tolerance to chlorhexidine and antibiotics relative to the other S. capitis as well as enhanced ability to grow at higher pH values. Analysis of 138 pangenomes of the clades identified characteristic nsr and tarJ genes in the NRCS-A and proto groups with a CRISPR-cas system only seen in NRCS-A isolates which also showed enrichment of genes for metal acquisition and transport. We found evidence for transmission of S. capitis NRCS-A within NICU, with related isolates shared between babies and multiple acquisitions by some babies. Our data show NRCS-A strains commonly colonise uninfected babies in NICU representing a potential reservoir for potential infection. This work provides more evidence that adaptation to survive in the gut and on skin facilitates spread of NRCS-A, and that metal acquisition and tolerance may be important to the biology of NRCS-A. Understanding how NRCS-A survives in NICUs can help develop infection control procedures against this clone.

genomics↗

bioRxiv2022

Subthalamic beta bursts correlate with dopamine-dependent motor symptoms in 106 Parkinson's patients

Pathologically increased beta power has been described as a biomarker for Parkinsons disease (PD) and related to prolonged bursts of subthalamic beta synchronization. Here, we investigate the association between subthalamic beta dynamics and motor impairment in a cohort of 106 Parkinsons patients in the ON- and OFF-medication state, suing two different methods of beta burst determination. We report a frequency-specific correlation of low beta power and burst duration with motor impairment OFF dopaminergic medication. Furthermore, reduction of power and burst duration correlated significantly with symptom alleviation through dopaminergic medication. Importantly, qualitatively similar results were yielded with two different methods of beta burst definition. Our findings validate the robustness of previous results on pathological changes in subcortical oscillations both in the frequency-as well as in the time-domain in the largest cohort of PD patients to date with important implications for next-generation adaptive deep brain stimulation control algorithms.

neuroscience↗

bioRxiv2021

Quantifying the impact of ecological memory on the dynamics of interacting communities

Ecological memory refers to the influence of past events on the response of an ecosystem to exogenous or endogenous changes. Memory has been widely recognized as a key contributor to the dynamics of ecosystems and other complex systems, yet quantitative community models often ignore memory and its implications. Recent modeling studies have shown how interactions between community members can lead to the emergence of resilience and multistability under environmental perturbations. We demonstrate how memory can be introduced in such models using the framework of fractional calculus. We study how the outcomes of a well-characterized interaction model are affected by gradual increases in ecological memory under varying initial conditions, perturbations, and stochasticity. Our results highlight the implications of memory on several key aspects of community dynamics. In general, memory introduces inertia into the dynamics. This favors species coexistence under perturbation, enhances system resistance to state shifts, mitigates hysteresis, and can affect system resilience both ways depending on the time scale considered. Memory also promotes long transient dynamics, such as long-standing oscillations and delayed regime shifts, and contributes to the emergence and persistence of alternative stable states. Our study highlights the fundamental role of memory on ecological communities, and provides quantitative tools to introduce it in ecological models and analyse its impact under varying conditions. Author summaryAn ecosystem is said to exhibit ecological memory when its future states do not only depend on its current state but also on its initial state and trajectory. Memory may arise through various mechanisms as organisms adapt to their environment, modify it, and accumulate biotic and abiotic material. It may also emerge from phenotypic heterogeneity at the population level. Despite its commonness in nature, ecological memory and its potential influence on ecosystem dynamics have been so far overlooked in many applied contexts. Here, we use modeling to investigate how memory can influence the dynamics, composition, and stability landscape of ecological communities. We incorporate long-term memory effects into a multi-species model recently introduced to investigate alternative stable states in microbial communities. We assess the impact of memory on key aspects of model behavior and validate our findings using a model parameterized by empirical data from the human gut microbiota. Our approach for modeling memory and studying its implications has the potential to improve our understanding of microbial community dynamics and ultimately our ability to predict, manipulate, and experimentally design microbial ecosystems. It could also be applied more broadly in the study of systems composed of interacting components.

systems biology↗

bioRxiv2021

Disentangling environmental effects in microbial association networks

BackgroundEcological interactions among microorganisms are fundamental for ecosystem function, yet they are mostly unknown or poorly understood. High-throughput-omics can indicate microbial interactions through associations across time and space, which can be represented as association networks. Associations could result from either ecological interactions between microorganisms, or from environmental selection, where the associations are environmentally-driven. Therefore, before downstream analysis and interpretation, we need to distinguish the nature of the association, particularly if it is due to environmental selection or not. ResultsWe present EnDED (Environmentally-Driven Edge Detection), an implementation of four approaches as well as their combination to predict which links between microorganisms in an association network are environmentally-driven. The four approaches are Sign Pattern, Overlap, Interaction Information, and Data Processing Inequality. We tested EnDED on networks from simulated data of 50 microorganisms. The networks contained on average 50 nodes and 1087 edges, of which 60 were true interactions but 1026 false associations (i.e. environmentally-driven or due to chance). Applying each method individually, we detected a moderate to high number of environmentally-driven edges--87% Sign Pattern and Overlap, 67% Interaction Information, and 44% Data Processing Inequality. Combining these methods in an intersection approach resulted in retaining more interactions, both true and false (32% of environmentally-driven associations). After validation with the simulated datasets, we applied EnDED on a marine microbial network inferred from 10 years of monthly observations of microbial-plankton abundance. The intersection combination predicted that 8.3% of the associations were environmentally-driven, while individual methods predicted 24.8% (Data Processing Inequality), 25.7% (Interaction Information), and up to 84.6% (Sign Pattern as well as Overlap). The fraction of environmentally-driven edges among negative microbial associations in the real network increased rapidly with the number of environmental factors. ConclusionsTo reach accurate hypotheses about ecological interactions, it is important to determine, quantify, and remove environmentally-driven associations in marine microbial association networks. For that, EnDED offers up to four individual methods as well as their combination. However, especially for the intersection combination, we suggest using EnDED with other strategies to reduce the number of false associations and consequently the number of potential interaction hypotheses.

microbiology↗

bioRxiv2021

Successional stages in infant gut microbiota maturation

BackgroundDisturbances in the primary colonization of the infant gut can result in life-long consequences and have been associated with a range of host conditions. Although early life factors have been shown to affect the infant gut microbiota development, our current understanding of the human gut colonization in early life remains limited. To gain more insights in the unique dynamics of this rapidly evolving ecosystem, we investigated the microbiota over the first year of life in eight densely sampled infants (total number of samples, n=303). To evaluate gut microbiota maturation transition towards an adult configuration, we compared the microbiome composition of the infants to the Flemish Gut Flora Project population (n=1,106). ResultsWe observed the infant gut microbiota to mature through three distinct, conserved stages of ecosystem development. Across these successional gut microbiota maturation stages, genus predominance was observed to shift from Escherichia over Bifidobacterium to Bacteroides. Both disease and antibiotic treatment were observed to be associated occasionally with gut microbiota maturation stage regression, a transient setback in microbiota maturation dynamics. Although the studied microbiota trajectories evolved to more adult-like constellations, microbiome community typing against the background of the Flemish Gut Flora Project (FGFP) cohort clustered all infant samples within the (in adults) potentially dysbiotic Bact2 enterotype. ConclusionWe confirmed similarities between infant gut microbial colonization and adult dysbiosis. A profound knowledge about the primary gut colonization process in infants might provide crucial insights into how the secondary colonization of a dysbiotic adult gut can be redirected.

microbiology↗

bioRxiv2020

A framework for comparing microbial networks reveals core associations

Microbial network construction and analysis is an important tool in microbial ecology. As microbial interactions are challenging to infer experimentally, such networks are often constructed from statistically inferred associations and may not represent ecological interactions. Hence, microbial association networks contain a large number of errors and their derived properties do not necessarily reflect true community structure. Such errors can be identified with the use of appropriate null models. We have developed anuran, a toolbox for investigation of noisy networks with null models, for identification of non-random patterns in groups of association networks. This toolbox compares multiple networks to identify conserved subsets (core association networks, CANs) and other network properties that are shared across all networks. Such groups of networks can be generated from a collection of time series data or from cross-sectional sample sets. We use data from the Global Sponge Project to demonstrate that different orders of sponges have a larger CAN than expected at random.

microbiology↗