Biology subjects

Markovic, D.

Publications and source records attributed to Markovic, D..

14 recordsLinked to original sources

bioRxiv2026

Trans-kingdom delivery of aphid-derived small RNAs into Arabidopsis thaliana modulates plant immunity

Herbivore insects are on an evolutionary tug-of-war with plants. An important part of the plant-herbivore insect interaction is the exchange of molecules, in particular proteins (in the form of effectors) and RNA. Among RNAs, small RNAs have been identified as important molecules shaping the communication between different pests/parasites and their host, but the role of these molecules in the interaction between aphids and plants is not well understood. Here, we explored the role of aphid-derived sRNAs in the interaction between Arabidopsis thaliana and the green peach aphid, Myzus persicae. Using sRNA sequencing, we identified a significant amount of bona-fide aphid-derived sRNAs within Arabidopsis tissues. Using immunoprecipitation followed by sRNA sequencing and degradome sequencing we determined that these sRNAs are incorporated into endogenous AGO proteins, in particular AGO1, and induce the cleavage of transcripts involved in the modulation of the immune response against the aphid. Our results indicate that aphid sRNAs attenuate the immune response of Arabidopsis thaliana and can improve aphid performance. In addition, we identified that aphid-derived sRNAs are commonly injected into other aphid-host combinations. Accordingly, our work indicates that aphid-derived sRNAs are active players in aphid-host interactions.

Plant Biology↗

bioRxiv2026

STIMscope: centimeter-scale all-optical imaging and patterned optogenetic manipulation at single-cell resolution

Linking observation to intervention at cellular resolution makes it possible to move from measuring network activity to testing the contribution of defined neurons or ensembles within the same preparation. All-optical probing provides this capability by combining fluorescence-based readout with targeted optogenetic manipulation. Yet the platforms that deliver this capability remain complex, expensive, and difficult to maintain, requiring specialized expertise that has confined them to a small number of laboratories. They also typically provide fields of view too limited for studies of large, distributed neuronal populations. We address these constraints with the Spatiotemporal Illumination Microscope (STIMscope), a one-photon benchtop platform that integrates large-aperture tandem optics with a small-pixel back-illuminated CMOS sensor, a digital micromirror device for patterned illumination, and a GPU-based processing unit coordinated by a microcontroller for hardware-level synchronization. Ray-tracing simulations and point-spread-function measurements confirm cellular-scale resolution, with imaging lateral FWHM of 5.6 {micro}m at the field center and 5.8 {micro}m at the edge, and excitation lateral FWHM of 5.8 {micro}m at the center and 6.2 {micro}m at the edge, supporting fields of view as large as 14 mm x 11 mm in the demagnified configuration. The accompanying Closed-loop ready Real-time Imaging and Stimulation Pipeline (CRISPI) provides GPU-accelerated calibrated mask projection (26.3 ms latency), online ROI trace extraction, and modular ZeroMQ-based control, with a measured imaging-to-stimulation loop benchmark of 91.6 ms. We validate STIMscope in fixed mouse brain tissue, live iPSC-derived human neuronal cultures, and ex vivo organotypic slices of mouse auditory cortex. In organotypic slices, we show that both static and spatiotemporal stimulus identity can be decoded from population activity, revealing reservoir-like population dynamics, and that this decodability remains stable in the same neuronal population over hours. We further show that post-stimulus activity retains information about recent stimuli for several seconds, consistent with short-term memory dynamics. With a bill of materials under $5,000 USD and all mechanical designs, firmware, and software released open-source, STIMscope makes all-optical neuroscience experiments a routine capability accessible to laboratories without specialized optical engineering expertise.

bioengineering↗

bioRxiv2026

Translational profiling uncovers a tonoplast sugar transporter essential for vascular system development and cell wall composition

The inflorescence stem of Arabidopsis thaliana is a powerful model to study vascular development and carbon allocation, yet the translational landscape underlying xylem differentiation remains poorly defined. Here, we address this gap using tissue-specific Translating Ribosome Affinity Purification sequencing (TRAP-seq) to resolve the translatomes of xylem vessels, xylem parenchyma, and interfascicular fibers. We show that the very early stage of metaxylem differentiation involves coordinated activation of primary metabolism, plastid functions, and cell wall biosynthesis, supporting the metabolic demands of secondary wall formation. Xylem parenchyma displays enrichment in hormone signaling, stress, and immune pathways, consistent with a role in integrating environmental and metabolic cues. Interfascicular fibers exhibit increased translation of ribosomal proteins and spliceosome components, pointing to high translational activity and a role for alternative splicing during late development and programmed cell death. We further identify the tonoplast sugar transporter SUGAR WILL EVENTUALLY BE EXPORTED TRANSPORTER 2 (SWEET2) as a key regulator of stem radial growth. Preferentially translated in early stage of metaxylem differentiation, SWEET2 acts as a rate-limiting component of vacuolar sugar exchange, controlling cytosolic hexose availability for secondary wall biosynthesis. By contrast, SWEET16 and SWEET17 exert more specialized, tissue-specific functions. Together, our findings establish subcellular sugar partitioning as a central determinant of vascular development and highlight the power of translatome profiling.

plant biology↗

bioRxiv2025

Volatile compounds released by undamaged plants influence the adaptive growth strategies of neighboring plants

Plants constantly emit volatile organic compounds (VOCs) that can influence the physiology and behavior of neighboring plants. While the role of stress-induced VOCs in mediating plant-plant interactions is well established, the ecological significance of constitutive VOCs from undamaged plants remains less understood. We demonstrate that barley plants can detect the growth rate of their undamaged neighbors through constitutive VOCs and respond by adjusting their trade-off between growth and induced defense. Exposure to volatiles from cultivars with slower or faster growth triggered distinct shifts in biomass accumulation and gene expression in receiver plants, whereas exposure to VOCs from cultivars with similar growth rates elicited negligible responses. These divergent patterns reflect a trade-off between growth and induced defense, consistent with adaptive responses to anticipated competition. Our findings reveal a previously unrecognized adaptive function of constitutive VOCs in mediating receiver growth based on neighbor growth rate, emphasizing their role in shaping plant-plant interactions.

ecology↗

bioRxiv2022

Structural and functional properties of a plant NRAMP related aluminum transporter

The transport of transition metal ions by members of the SLC11/NRAMP family constitutes a ubiquitous mechanism for the uptake of Fe2+ and Mn2+ across all kingdoms of life. Despite the strong conservation of the family, two of its branches have evolved a distinct substrate preference with one mediating Mg2+ uptake in prokaryotes and another the transport of Al3+ into plant cells. Our previous work on the SLC11 transporter from Eggerthella lenta revealed the basis for its Mg2+ selectivity (Ramanadane et al., 2022). Here we have addressed the structural and functional properties of a putative Al3+ transporter from Setaria italica. We show that the protein transports diverse divalent metal ions and binds the trivalent ions Al3+ and Ga3+, which are both presumable substrates. Its cryo-EM structure displays an occluded conformation that is closer to an inward-than an outward-facing state with a binding site that is remodeled to accommodate the increased charge density of its transported substrate.

biophysics↗

bioRxiv2022

Revealing human sensitivity to a latent temporal structure of changes

Precisely timed behaviour and accurate time perception plays a critical role in our everyday lives, as our well-being and even survival can depend on well-timed decisions. Although the temporal structure of the world around us is essential for human decision making, we know surprisingly little about how representation of temporal structure of our everyday environment impacts decision making. How does the representation of temporal structure affect our ability to generate well-timed decisions? Here we address this question by using a well-established dynamic probabilistic learning task. Using computational modelling, we found that human subjects beliefs about temporal structure are reflected in their choices to either exploit their current knowledge or to explore novel options. The model-based analysis illustrates a large within-group and within-subject heterogeneity. To explain these results, we propose a normative model for how temporal structure is used in decision making, based on the semi-Markov formalism in an active inference framework. We discuss potential key applications of the presented approach to the fields of cognitive phenotyping and computational psychiatry.

neuroscience↗

bioRxiv2022

A wearable platform for closed-loop stimulation and recording of single- neuron and local field potential activity in freely-moving humans

Advances in technologies that can record and stimulate deep-brain activity in humans have led to impactful discoveries within the field of neuroscience and contributed to the development of novel therapies for neurological and psychiatric disorders. Further progress, however, has been hindered by device limitations in that recording of single-neuron activity during freely-moving behaviors in humans has not been possible. Additionally, implantable neurostimulation devices, currently approved for human use, have limited stimulation programmability and lack full-duplex bi-directional capability. Here, we developed a wearable bi-directional closed-loop neuromodulation system (Neuro-stack) and used it to record single-neuron and local field potential activity during stationary and ambulatory behavior in humans. Together with a highly flexible and customizable stimulation capability, the Neuro-stack provides an opportunity to investigate the neurophysiological basis of disease, develop improved responsive neuromodulation therapies, explore brain function during naturalistic behaviors in humans, and consequently, bridge decades of neuroscientific findings across species.

neuroscience↗

bioRxiv2021

Exophers are components of mammalian cell neurobiology in health and disease

Maintenance of cellular homeostasis is critically important for the survival of cells and organisms. Degradation and recycling of biomolecules and whole organelles is an essential mechanism for maintaining cellular homeostasis. The main systems responsible for these processes are the ubiquitin-proteasome system and autophagy-lysosome pathway. Another mechanism was reported in c. elegans--formation of large, membrane-enclosed projections called "exophers," into which cells direct debris and toxic protein aggregates1. Exophers were shown to act as large, temporary disposal compartments that disconnected from the cells within several hours. Here, we report the discovery of exophers in the mammalian brain, including the brains of humans and mice. Similar to those described in nematodes, the mammalian exophers appear to emanate from the cell body, initially connected by a nanotube, and eventually disconnect. Rare, innate exophers were found in healthy human brain and primary neurons from wild-type mice, presumably mediating transfer of large cargo between cells. The number of exophers increased as an adaptive response under proteostatic stress, e.g., in Alzheimers disease brain or in primary neurons from two tauopathy mouse models, where the exophers likely facilitated expulsion of proteotoxic material. Our findings suggest that exopherogenesis is a rare, innate house-keeping process that is elevated adaptively in response to proteostatic pressure and is a conserved mechanism from nematodes to humans.

cell biology↗

bioRxiv2021

Joint modeling of choices and reaction times based on Bayesian contextual behavioral control

In cognitive neuroscience and psychology, reaction times are an important behavioral measure. However, in instrumental learning and goal-directed decision making experiments, findings often rely only on choice probabilities from a value-based model, instead of reaction times. Recent advancements have shown that it is possible to connect value-based decision models with reaction time models. However, typically these models do not provide an integrated account of both value-based choices and reaction times, but simply link two types of models. Here, we propose a novel integrative joint model of both choices and reaction times by combining a mechanistic account of Bayesian sequential decision making with a sampling procedure. This allows us to describe how internal uncertainty in the planning process shapes reaction time distributions. Specifically, we use a recent context-specific Bayesian forward planning model which we extend by a Markov chain Monte Carlo (MCMC) sampler to obtain both choices and reaction times. As we will show this makes the sampler an integral part of the decision making process and enables us to reproduce, using simulations, well-known experimental findings in value based-decision making as well as classical inhibition and switching tasks. Specifically, we use the proposed model to explain both choice behavior and reaction times in instrumental learning and automatized behavior, in the Eriksen flanker task and in task switching. These findings show that the proposed joint behavioral model may describe common underlying processes in these different decision making paradigms. Author summaryMany influential results in psychology and cognitive neuroscience rest on reaction time effects in behavioral experiments, for example in studies about human decision making. For decisions that rest on planning, findings often rely on analyses using specific computational models. Until recently, these models did not allow for analysis of reaction times. In this article we introduce a new model of how to explain both choices and reaction times in decision making experiments that involve planning. Importantly, the model explains how the brain can make good decisions quickly, even in the face of many potential choices and in complex environments.

neuroscience↗

bioRxiv2020

Aphid feeding induces the relaxation of epigenetic control and the associated regulation of the defense response in Arabidopsis

Environmentally induced changes in the epigenome help individuals to quickly adapt to fluctuations in the conditions of their habitats. Here we explored those changes in Arabidopsis thaliana plants subjected to multiple biotic and abiotic stresses, and identified transposable element (TE) activation in plants infested with the green peach aphid, Myzus persicae. We performed a genome-wide analysis of DNA methylation, mRNA expression, mRNA degradation and small RNA accumulation. Our results demonstrate that aphid feeding induces loss of methylation of hundreds of loci, mainly TEs. This loss of methylation has the potential to regulate gene expression and we found evidence that it is involved in the control of key plant immunity genes. Accordingly, we find that mutant plants deficient in epigenetic silencing show increased resistance to M.persicae infestation. Collectively, our results show that changes in DNA methylation play a significant role in the regulation of the plant transcriptional response and induction of defense response against aphid feeding.

plant biology↗

bioRxiv2019

Meta-control of the exploration-exploitation dilemma emerges from probabilistic inference over a hierarchy of time scales

Cognitive control is typically understood as a set of mechanisms which enable humans to reach goals that require integrating the consequences of actions over longer time scales. Importantly, using routine beheavior or making choices beneficial only at a short time scales would prevent one from attaining these goals. During the past two decades, researchers have proposed various computational cognitive models that successfully account for behaviour related to cognitive control in a wide range of laboratory tasks. As humans operate in a dynamic and uncertain environment, making elaborate plans and integrating experience over multiple time scales is computationally expensive, the specific question of how uncertain consequences at different time scales are integrated into adaptive decisions remains poorly understood. Here, we propose that precisely the problem of integrating experience and forming elaborate plans over multiple time scales is a key component for better understanding how human agents solve cognitive control dilemmas such as the exploration-exploitation dilemma. In support of this conjecture, we present a computational model of probabilistic inference over hidden states and actions, which are represented as a hierarchy of time scales. Simulations of goal-reaching agents instantiating the model in an uncertain and dynamic task environment show how the exploration-exploitation dilemma may be solved by inferring meta-control states which adapt behaviour to changing contexts.

neuroscience↗

bioRxiv2019

Dynamic integration of forward planning and heuristic preferences during multiple goal pursuit

Selecting goals and successfully pursuing them in an uncertain and dynamic environment is an important aspect of human behaviour. In order to decide which goal to pursue at what point in time, one has to evaluate the consequences of ones actions over future time steps by forward planning. However, when the goal is still temporally distant, detailed forward planning can be prohibitively costly. One way to select actions at minimal computational costs is to use heuristics. It is an open question how humans mix heuristics with forward planning to balance computational costs with goal reaching performance. To test a hypothesis about dynamic mixing of heuristics with forward planning, we used a novel stochastic sequential two-goal task. Comparing participants decisions with an optimal full planning agent, we found that at the early stages of goal-reaching sequences, in which both goals are temporally distant and planning complexity is high, on average 42% (SD = 19%) of participants choices deviated from the agents optimal choices. Only towards the end of the sequence, participants behaviour converged to near optimal performance. Subsequent model-based analyses showed that participants used heuristic preferences when the goal was temporally distant and switched to forward planning when the goal was close. Author summaryWhen we pursue our goals, there is often a moment when we recognize that we did not make the progress that we hoped for. What should we do now? Persevere to achieve the original goal, or switch to another goal? Two features of real-world goal pursuit make these decisions particularly complex. First, goals can lie far into an unpredictable future and second, there are many potential goals to pursue. When potential goals are temporally distant, human decision makers cannot use an exhaustive planning strategy, rendering simpler rules of thumb more appropriate. An important question is how humans adjust the rule of thumb approach once they get closer to the goal. We addressed this question using a novel sequential two-goal task and analysed the choice data using a computational model which arbitrates between a rule of thumb and accurate planning. We found that participants decision making progressively improved as the goal came closer and that this improvement was most likely caused by participants starting to plan ahead.

animal behavior and cognition↗

bioRxiv2019

Balancing control: a Bayesian interpretation of habitual and goal-directed behavior

In everyday life, our behavior varies on a continuum from automatic and habitual to deliberate and goal-directed. Recent evidence suggests that habit formation and relearning of habits operate in a context-dependent manner: Habit formation is promoted when actions are performed in a specific context, while breaking off habits is facilitated after a context change. It is an open question how one can computationally model the brains balancing between context-specific habits and goal-directed actions. Here, we propose a hierarchical Bayesian approach for control of a partially observable Markov decision process that enables conjoint learning of habits and reward structure in a context-specific manner. In this model, habit learning corresponds to an updating of priors over policies and interacts with the learning of the outcome contingencies. Importantly, the model is solely built on probabilistic inference, which effectively provides a simple explanation of how the brain may balance contributions of habitual and goal-directed control. We illustrated the resulting behavior using agent-based simulated experiments, where we replicated several findings of devaluation, extinction, and renewal experiments, as well as the so-called two-step task which is typically used with human participants. In addition, we show how a single parameter, the habitual tendency, can explain individual differences in habit learning and the balancing between habitual and goal-directed control. Finally, we discuss the link of the proposed model to other habit learning models and implications for understanding specific phenomena in substance use disorder.

neuroscience↗

bioRxiv2018

Deterministic response strategies in trial-and-error learning

Trial-and-error learning is a universal strategy for establishing which actions are beneficial or harmful in new environments. However, learning stimulus-response associations solely via trial-and-error is often suboptimal, as in many settings dependencies among stimuli and responses can be exploited to increase learning efficiency. Previous studies have shown that in settings featuring such dependencies, humans typically engage high-level cognitive processes and employ advanced learning strategies to improve their learning efficiency. Here we analyze in detail the initial learning phase of a sample of human subjects (N = 85) performing a trial-and-error learning task with deterministic feedback and hidden stimulus-response dependencies. Using computational modeling, we find that the standard Q-learning model cannot sufficiently explain human learning strategies in this setting. Instead, newly introduced deterministic response models, which are theoretically optimal and transform stimulus sequences unambiguously into response sequences, provide the best explanation for 50.6% of the subjects. Most of the remaining subjects either show a tendency towards generic optimal learning (21.2%) or at least partially exploit stimulus-response dependencies (22.3%), while a few subjects (5.9%) show no clear preference for any of the employed models. After the initial learning phase, asymptotic learning performance during the subsequent practice phase is best explained by the standard Q-learning model. Our results show that human learning strategies in trial-and-error learning go beyond merely associating stimuli and responses via incremental reinforcement. Specifically during initial learning, high-level cognitive processes support sophisticated learning strategies that increase learning efficiency while keeping memory demands and computational efforts bounded. The good asymptotic fit of the Q-learning model indicates that these cognitive processes are successively replaced by the formation of stimulus-response associations over the course of learning.

animal behavior and cognition↗