Biology subjects

Aertsen, A.

Publications and source records attributed to Aertsen, A..

15 recordsLinked to original sources

bioRxiv2026

Inhibitory Gain and Hub Architecture Confer Dynamic Resilience to Microcircuit Degeneration

Neurodegeneration progressively removes synapses and neurons, yet neural circuits can retain stable collective dynamics despite substantial structural loss. Which structural principles confer this resilience remained unclear. Using large-scale spiking networks spanning empirical and synthetic microcircuit architectures, we systematically compared synaptic and neuronal modes of degeneration under controlled pruning strategies. We found that resilience was not determined by connectivity loss alone, but by how inhibitory gain was embedded within circuit architecture. Networks in which inhibitory neurons occupied structurally central positions robustly maintained health-like firing rates, levels of synchrony, and informational bandwidth across degeneration stages, whereas architectures lacking such embedding exhibited amplified dynamical disruption. Across regimes, the evolution of activity was organized by a compact set of weight-aware structural descriptors that generalized across network sizes and classes, with total effective synaptic coupling providing a dominant organizing axis. These results identified inhibitory architecture as a mechanistic determinant of circuit resilience and provided a predictive framework linking structural degeneration to collective dynamics.

neuroscience↗

bioRxiv2025

Delay selection by spike-timing-dependent plasticity shapes efficient networks for signal transmission

Information processing in the brain relies on efficient communication between different brain regions. Brain oscillations can control signal transmission in brain networks by modulating the timing and excitability of sender and receiver areas. For effective transmission, signals should arrive at target areas when their excitability is maximized. For reciprocally connected neural populations, this mechanism works if the transmission delay matches the period of their evoked oscillation. However, the mechanisms underlying such development of the connections with matched delays remain elusive. While transmission delays in brain networks change during development, the process by which delays are tuned for efficient transmission is unknown. Here, we demonstrate that the well-known Hebbian learning rule can provide a mechanism for selecting connections with delays that match the period of network oscillations. We consider a reciprocally connected bi-layer network of excitatory and inhibitory neurons that generate network-level oscillations spontaneously or in response to external stimuli. When exposed to spiketiming-dependent plasticity (STDP), the network self-organizes to potentiate connections with delays matching the oscillation period, while depressing those with non-matching delays. Our findings shed light on how transmission delays may evolve during learning and development to optimize the organization of brain networks for efficient signal transmission.

neuroscience↗

bioRxiv2025

Phage-mediated just-in-time circuit amplification and delivery for recombinant expression of toxic proteins

In order to address outstanding challenges in bacterial bioreactor bioprocessing, we have developed an approach that alleviates the need for chemical inducers and selective agents (such as antibiotics), and mitigates leaky expression of the production circuit. More specifically, temperate bacteriophage Lambda ({lambda}) was engineered as a chassis to just-in-time amplify and deliver a production circuit to a wild-type Escherichia coli population. Since the expression of the production pathway is engineered to be dependent on {lambda}s central regulatory switch towards the lysogenic state, {lambda} will first lytically amplify in the background without significantly altering overall bioreactor growth dynamics or gene expression. Only when {lambda} eventually starts outnumbering the host population, its natural switch to lysogeny will actively install and trigger the production circuit. As such, the {lambda} chassis serves as an intrinsic self-amplifying inducer that meanwhile rules out any leaky expression. The impact of the initial phage-to-host ratio on population dynamics and production efficiency was assessed experimentally and through ODE modeling, revealing that it can be used to fine-tune the trade-off between gene dosage and biomass conversion into virions. We demonstrate that this leakproof approach is particularly promising for the expression of toxic proteins such as Benzonase, a highly potent DNase and RNase.

synthetic biology↗

bioRxiv2025

Engineering non-exponential proliferation in Escherichia coli using functionalized protein aggregates

Uncurbed exponential proliferation might not always be required for genetically modified microorganisms, and might even cause unpredictable liabilities in their behavior and impact. We therefore constructed a bacterial chassis that adheres to linear proliferation for a finite number of generations, based on the asymmetric segregation of a gradually declining intracellular protein aggregate that is engineered to produce a conditionally essential metabolite such as cAMP.

synthetic biology↗

bioRxiv2024

Protein aggregation drives cell aging in a size-specific manner in Escherichia coli

Aging, the decline in physiological function over time, is marked by the intracellular accumulation of damaged components. It can be attributed to a trade-off between the investment into organismal maintenance and the production of high-quality offspring, where the parent accumulates damage over time and retains it upon reproduction, while the offspring is rejuvenated. Asymmetric damage partitioning has been observed even in simple unicellular organisms, such as Escherichia coli bacteria, that retain aggregates of misfolded proteins during cell division. However, recent studies presented conflicting evidence on the effect of protein aggregates on fitness, ranging from detrimental effects on cell growth to enhanced stress survival. Here, we show that the decisive factor driving growth decline in E. coli is not the presence of a protein aggregate, but the proportion of the intracellular space occupied by it. By following single-cell E. coli lineages expressing fluorescently labeled DnaK chaperones, we quantified damage accumulation and partitioning across generations in microfluidic devices. Our results suggest that the aggregation of damaged proteins allows cells to keep damage separate from vital processes and compensate for the lost intracellular space by growing to larger sizes. This process results in morphologically asymmetric divisions, a finding that counters the long-assumed symmetry of E. coli cell division. In line with other recent evidence, our findings point to a more complex role of protein aggregation, with implications for our understanding of the cellular mechanisms underlying aging as well as its evolutionary origins.

evolutionary biology↗

bioRxiv2024

Tuning and functionalization of logic gates for time resolved population programming

In order to increase our command over genetically engineered bacterial populations in bioprocessing and therapy, synthetic regulatory circuitry needs to enable the temporal programming of a number of consecutive functional tasks without external interventions. In this context, we have engineered a genetic circuit encoding an autonomous but chemically tunable timer in Escherichia coli, based on the concept of a transcription factor cascade mediated by the cytoplasmic dilution of repressors. As proof-of-concept, we used this circuit to impose a time-resolved two-staged synthetic pathway composed of a production-followed-by-lysis program, via a single input. Moreover, via a recombinase step, this synchronous timer was further engineered into an asynchronous timer in which the generational distance of differentiating daughter cells spawning off from a stem-cell like mother cell becomes a predictable driver and proxy for timer dynamics. Using this asynchronous timer circuit, a temporally defined population heterogeneity can be programmed in bacterial populations. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=116 SRC="FIGDIR/small/593743v2_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@bfccf4org.highwire.dtl.DTLVardef@28ce89org.highwire.dtl.DTLVardef@10fa754org.highwire.dtl.DTLVardef@b2a666_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2023

Spontaneous Necker-Cube Reversals are not that Spontaneous

During observation of the ambiguous Necker cube, our perception suddenly reverses between two about equally possible 3D interpretations. During passive observation, perceptual reversals seem to be sudden and spontaneous. A number of theoretical approaches postulate destabilization of neural representations as a precondition for spontaneous reversals of ambiguous figures. In the current study, we focused on possible EEG correlates of perceptual destabilization, that may allow to predict an upcoming perceptual reversal. We presented ambiguous Necker cube stimuli in an onset-paradigm and investigated the neural processes underlying endogenous reversals as compared to perceptual stability across two consecutive stimulus presentations. In a separate experimental condition, disambiguated cube variants were alternated randomly, to exogenously induce perceptual reversals. We compared the EEG immediately before and during endogenous Necker cube reversals with corresponding time windows during exogenously induced perceptual reversals of disambiguated cube variants. For the ambiguous Necker cube stimuli, we found the earliest differences in the EEG between reversal trials and stability trials already one second before a reversal occurred, at bilateral parietal electrodes. The traces remained similar until approximately 1100 ms before a perceived reversal, became maximally different at around 890 ms (p=7.59*10-6, Cohens d=1.35) and remained different until shortly before offset of the stimulus preceding the reversal. No such patterns were found in the case of disambiguated cube variants. The identified EEG effects may reflect destabilized states of neural representations, related to destabilized perceptual states preceding a perceptual reversal. They further indicate that spontaneous Necker cube reversals are most probably not as spontaneous as generally thought. Rather, the destabilization may occur over a longer time scale, at least one second before a reversal event.

neuroscience↗

bioRxiv2021

The expression of virulence increases outer-membrane permeability and sensitivity to envelope stress in Salmonella Typhimurium

Environmental cues modulate the expression of virulence in bacterial pathogens. However, while cues that upregulate virulence are often intuitive and mechanistically well understood, this is less so for cues that downregulate virulence. In this study, we noticed that upregulation of the HilD virulence regulon in Salmonella Typhimurium (S.Tm) sensitized cells to membrane stress mediated by cholate, Tris/EDTA or heat. Further monitoring of membrane status and stress resistance of S.Tm cells in relation to virulence expression, revealed that co-expressed virulence factors embedded in the envelope (including the Type Three Secretion System 1 and the flagella) increased permeability, and stress sensitivity of the membrane. Importantly, pretreating the bacteria by sublethal stress inhibited virulence expression and restored stress resistance. As such, these results demonstrate a trade-off between virulence and stress resistance, which explains the downregulation of virulence expression in response to harsh environments in S.Tm.

microbiology↗

bioRxiv2021

Relevance of network topology for the dynamics of biological neuronal networks

Complex random networks provide a powerful mathematical framework to study high-dimensional physical and biological systems. Several features of network structure (e.g. degree correlation, average path length, clustering coefficient) are correlated with descriptors of network dynamics and function. However, it is not clear which features of network structure relate to the dynamics of biological neuronal networks (BNNs), characterized by non-linear nodes with high in- and out degrees, but being weakly connected and communicating in an event-driven manner, i.e. only when neurons spike. To better understand the structure-dynamics relationship in BNNs, we analysed the structure and dynamics of > 9, 000 BNNs with different sizes and topologies. In addition, we also studied the effect of network degeneration on neuronal network structure and dynamics. Surprisingly, we found that the topological class (random, small-world, scale-free) was not an indicator of the BNNs activity state as quantified by the firing rate, network synchrony and spiking regularity. In fact, we show that different network topologies could result in similar activity dynamics. Furthermore, in most cases, the network activity changes did not depend on the rules according to which neurons or synapses were pruned from the networks. The analysis of dynamics and structure of the networks we studied revealed that the effective synaptic weight (ESW) was the most crucial feature in predicting the statistics of spiking activity in BNNs. ESW also explained why different synapse and neuron pruning strategies resulted in almost identical effects on the network dynamics. Thus, our findings provide new insights into the structure-dynamics relationships in BNNs. Moreover, we argue that network topology and rules by which BNNs degenerate are irrelevant for BNN activity dynamics. Beyond neuroscience, our results suggest that in large networks with non-linear nodes, the effective interaction strength among the nodes, instead of the topological network class, may be a better predictor of the network dynamics and information flow.

neuroscience↗

bioRxiv2019

Facilitating the propagation of spiking activity in feedforward networks by including feedback

Transient oscillations in the network activity upon sensory stimulation have been reported in different sensory areas. These evoked oscillations are the generic response of networks of excitatory and inhibitory neurons (EI-networks) to a transient external input. Recently, it has been shown that this resonance property of EI-networks can be exploited for communication in modular neuronal networks by enabling the transmission of sequences of synchronous spike volleys ( pulse packets), despite the sparse and weak connectivity between the modules. The condition for successful transmission is that the pulse packet (PP) intervals match the period of the modules resonance frequency. Hence, the mechanism was termed communication through resonance (CTR). This mechanism has three sever constraints, though. First, it needs periodic trains of PPs, whereas single PPs fail to propagate. Second, the inter-PP interval needs to match the network resonance. Third, transmission is very slow, because in each module, the network resonance needs to build-up over multiple oscillation cycles. Here, we show that, by adding appropriate feedback connections to the network, the CTR mechanism can be improved and the aforementioned constraints relaxed. Specifically, we show that adding feedback connections between two upstream modules, called the resonance pair, in an otherwise feedforward modular network can support successful propagation of a single PP throughout the entire network. The key condition for successful transmission is that the sum of the forward and backward delays in the resonance pair matches the resonance frequency of the network modules. The transmission is much faster, by more than a factor of two, than in the original CTR mechanism. Moreover, it distinctly lowers the threshold for successful communication by synchronous spiking in modular networks of weakly coupled networks. Thus, our results suggest a new functional role of bidirectional connectivity for the communication in cortical area networks.\n\nAuthor summaryThe cortex is organized as a modular system, with the modules (cortical areas) communicating via weak long-range connections. It has been suggested that the intrinsic resonance properties of population activities in these areas might contribute to enabling successful communication. A modules intrinsic resonance appears in the damped oscillatory response to an incoming spike volley, enabling successful communication during the peaks of the oscillation. Such communication can be exploited in feedforward networks, provided the participating networks have similar resonance frequencies. This, however, is not necessarily true for cortical networks. Moreover, the communication is slow, as it takes several oscillation cycles to build up the response in the downstream network. Also, only periodic trains of spikes volleys (and not single volleys) with matching intervals can propagate. Here, we present a novel mechanism that alleviates these shortcomings and enables propagation of synchronous spiking across weakly connected networks with not necessarily identical resonance frequencies. In this framework, an individual spike volley can propagate by local amplification through reverberation in a loop between two successive networks, connected by feedforward and feedback connections: the resonance pair. This overcomes the need for activity build-up in downstream networks, causing the volley to propagate distinctly faster and more reliably.

neuroscience↗

bioRxiv2019

Direct Pathway Neurons in Mouse Dorsolateral Striatum In Vivo Receive Stronger Synaptic Input than Indirect Pathway Neurons

Striatal projection neurons, the medium spiny neurons (MSNs), play a crucial role in various motor and cognitive functions. MSNs express either D1 or D2 type dopamine receptors and initiate the direct-pathway (dMSNs) or indirect pathways (iMSNs) of the basal ganglia, respectively. dMSNs have been shown to receive more inhibition than iMSNs from intrastriatal sources. Based on these findings, computational modelling of the striatal network has predicted that under healthy conditions dMSNs should receive more excitatory input than iMSNs. To test this prediction, we analyzed in vivo whole-cell recordings from dMSNs and iMSNs in healthy and dopamine-depleted (6OHDA) anaesthetized mice. By comparing their membrane potential fluctuations, we found that dMSNs exhibited considerably larger membrane potential fluctuations over a wide frequency range. Furthermore, by comparing the spike-triggered average membrane potentials, we found that dMSNs depolarized towards the spike threshold significantly faster than iMSNs did. Together, these finding corroborate the theoretical prediction that direct-pathway MSNs receive stronger input than indirect-pathway neurons. Finally, we found that dopamine-depleted mice exhibited no difference between the membrane potential fluctuations of dMSNs and iMSNs. These data provide new insights into the question how a lack of dopamine may lead to behavior deficits associated with Parkinsons disease.\n\nSignificance statementThe direct and indirect pathways of the basal ganglia originate from the D1 and D2 type dopamine receptor expressing medium spiny neurons (dMSNs and iMSNs), respectively. To understand the role of the striatum in brain function and dysfunction it is important to characterize the differences in synaptic inputs to the two MSN types. Theoretical results predicted that dMSNs should receive stronger excitatory input than iMSNs. Here, we studied membrane potential fluctuation statistics of MSNs recorded in vivo in anaesthetized mice and found that dMSNs, indeed, received stronger synaptic input than iMSNs. We corroborated this finding by spike-triggered membrane potential analysis, showing that dMSNs spiking required more synaptic input than iMSNs spiking did, as had been predicted by computational models.

neuroscience↗

bioRxiv2019

Host metabolic reprogramming of Pseudomonas aeruginosa by phage-based quorum sensing modulation

The Pseudomonas quinolone signal (PQS) is a multifunctional quorum sensing molecule of key importance to the P. aeruginosa metabolism. We here describe that the lytic Pseudomonas bacterial virus LUZ19 targets this population-density-dependent signaling system by expressing quorum sensing-associated acyltransferase (Qst) during early infection. Qst interacts with a key biosynthesis pathway enzyme PqsD, resulting in decreased metabolites levels of PQS and its precursor 2-heptyl-4(1H)-quinolone. The lack of a functional PqsD enzyme impairs the normal LUZ19 infection but is restored by external supplementation of 2-heptyl-4(1H)-quinolone, showing that LUZ19 exploits PQS to successfully achieve its infection. A functional interaction network, which includes enzymes of the central carbon metabolism (CoaC/ThiD) and a novel non-ribosomal peptide synthetase pathway (PA1217), suggests a broader functional context for Qst, which blocks P. aeruginosa cell division. Qst represents an exquisite example of intricate reprogramming of the bacterium, which may be exploited towards antibiotic target discovery for this bacterial pathogen.

microbiology↗

bioRxiv2018

From space to time: Spatial inhomogeneities lead to the emergence of spatio-temporal activity sequences in spiking neuronal networks

Spatio-temporal sequences of neuronal activity are observed in many brain regions in a variety of tasks and are thought to form the basis of any meaningful behavior. Mechanisms by which a neuronal network can generate spatio-temporal activity sequences have remained obscure. Existing models are biologically untenable because they require manual embedding of a feedforward network within a random network or supervised learning to train the connectivity of a network to generate sequences. Here, we propose a biologically plausible, generative rule to create spatio-temporal activity sequences in a network model of spiking neurons with distance dependent connectivity. We show that the emergence of spatio-temporal activity sequences requires: (1) individual neurons preferentially project a small fraction of their axons in a specific direction, and (2) the preferential projection direction of neighboring neurons is similar. Thus, an anisotropic but correlated connectivity of neuron groups suffices to generate spatio-temporal activity sequences in an otherwise random neuronal network model.

neuroscience↗

bioRxiv2017

Sensorimotor Processing In The Basal Ganglia Leads To Transient Beta Oscillations During Behavior

Brief epochs of beta oscillations have been implicated in sensorimotor control in the basal ganglia of task-performing healthy animals. However, which neural processes underlie their generation and how they are affected by sensorimotor processing remains unclear. To determine the mechanisms underlying transient beta oscillations in the local field potential (LFP), we combined computational modeling of the subthalamo-pallidal network for the generation of beta oscillations with realistic stimulation patterns derived from single unit data. The single unit data were recorded from different basal ganglia subregions in rats performing a cued choice task. In the recordings we found distinct firing patterns in the striatum, globus pallidus and subthalamic nucleus related to sensory and motor events during the behavioral task. Using these firing patterns to generate realistic inputs to our network model lead to transient beta oscillations with the same time course as the rat LFP data. In addition, our model can account for further non-intuitive aspects of beta modulation, including beta phase resets following sensory cues and correlations with reaction time. Overall, our model can explain how the combination of temporally regulated sensory responses of the subthalamic nucleus, ramping activity of the subthalamic nucleus, and movement-related activity of the globus pallidus, leads to transient beta oscillations during behavior.\n\nSignificance StatementTransient beta oscillations emerge in the normal functioning cortico-basal ganglia loop during behavior. In this work we employ a unique approach connecting a computational model closely with experimental data. In this way we achieve a simulation environment for our model that mimics natural input patterns in awake behaving animals. Using this approach we demonstrate that a computational model for beta oscillations in Parkinsons disease can also account for complex patterns of transient beta oscillations in healthy animals. Therefore, we propose that transient beta oscillations in healthy animals share the same mechanism with pathological beta oscillations in Parkinsons disease. This important result connects functional and pathological roles of beta oscillations in the basal ganglia.

neuroscience↗

bioRxiv2016

Activity Dynamics and Signal Representation in Striatal Network Model with Distance-dependent Connectivity

Striatum is predominantly inhibitory and the main input nucleus of the basal ganglia. A functional characterization of its activity dynamics is crucial for understanding the mechanisms underlying phenomenon such as action selection and initiation. Here, we investigated the effects of the spatial connectivity structure on the emergence and maintenance of localized bumps of activity in large-scale striatal networks (~10,000 neurons). We show that in striatal network model in which the distance-dependent connection probability varies in a Gaussian fashion (Gaussian networks), the activity remains asynchronous irregular (AI) and spatially homogeneous, independent of the background input. By contrast, when the distance-dependent connectivity varies according to a Gamma distribution (Gamma networks), with short-range connectivity suppressed, a repertoire of activity dynamics can be observed: While weak background inputs induce spatially homogeneous AI activity, stronger background inputs induce stable, spatially localized activity bumps as in winner-take-all (WTA) dynamics. Interestingly, for intermediate background inputs, the networks exhibit spatially localized, but unstable activity bumps (Transition Activity, TA), resembling the experimentally observed neuronal assembly dynamics in the striatum.\n\nAmong the three main regimes of network activity (AI, WTA, TA) we found that in the AI and TA regimes, network dynamics are flexible and can be easily modified by external stimuli. Moreover, the dynamical state of the network returns to the baseline after the stimulus is removed. By contrast, the dynamics in the WTA state are rigid and can only be changed by very strong external stimuli. These results support the hypothesis that the flexibility of the striatal network state in response to stimuli is important for its normal function and the rigid network states (WTA) correspond to brain disorders such as Parkinsons disease, where the striatum looses its repertoire of dynamic states and is only receptive to very strong inputs.

neuroscience↗