Biology subjects

Meijer, D.

Publications and source records attributed to Meijer, D..

9 recordsLinked to original sources

bioRxiv2026

RetroMol: Parsing a shared encoding from natural products and their biosynthetic gene clusters

Natural products such as polyketides and nonribosomal peptides (NRPs) are important sources of bioactive compounds, including many antibiotics. Many of them are assembled by modular enzyme complexes and further modified and diversified by tailoring reactions encoded by biosynthetic gene clusters (BGCs). Although natural products and their coding BGCs describe different data modalities of the same biochemical process, a unified language to jointly describe their biochemistry is lacking. Here we introduce a sequence-based representation of the core biosynthesis of modular natural products, which we call primary sequences, that bridges chemical structures and BGCs. We also present RetroMol, an algorithm that parses either natural product structures or their encoding BGCs into their primary sequences of natural product building blocks. RetroMol allows for similarity scoring between natural products and BGCs, enabling the retrieval of compounds, BGCs, and a combination of the two, based on their biosynthetic similarity. This can, for instance, be used to retrieve biosynthetically similar but structurally dissimilar compounds, or link natural products to candidate coding BGCs in large experimental datasets. We demonstrate the latter by rediscovering the nocardichelin B BGC as a proof of principle. We also exemplify the utility of biosynthetic similarity by showing various pairs of biosynthetically similar compounds with low structural similarity. Together, these results establish primary sequences as a shared biosynthetic encoding for natural product comparison and BGC prioritization.

bioinformatics↗

bioRxiv2025

Arousal-related mediation of perceptual belief updating across auditory domains

Belief updating refers to the integration of prior beliefs with incoming evidence and guides decision-making under uncertainty. In response to surprising events, this process is thought to be modulated by the locus-coeruleus-noradrenaline (LC-NA) arousal system, observable via pupil dilations (PDs). Pertinent literature has mostly focused on conscious, high-level decision-making and estimation processes, while assuming that the same principles apply to low-level sensory perceptual decision-making and generalize across tasks, domains and modalities. To address some of these assumptions, we devised a novel perceptual discrimination paradigm, investigating behavior and PDs across auditory domains. Participants were presented with auditory sequences of randomized length at a rapid pace, changing intermittently between two latent states: acceleration vs. deceleration (temporal group, N = 25) and clockwise vs. counterclockwise movement (spatial group, N = 22). Under high uncertainty, participants continuously inferred latent states to report the final state per sequence. To extract per-stimulus estimates of PDs during sequences, we fitted a deconvolution-based general linear model to the continuous pupil traces with a free amplitude parameter reflecting ongoing PDs. A Bayesian observer model was fitted to participants responses and used to estimate information gain and surprisal for every stimulus. Participants performance and PDs showed strong sensitivity to the occurrence of change-points. Both computational variables significantly predicted PDs in both domains, with information gain outperforming surprisal in a model comparison. Further model comparison revealed significant preference for models excluding possible domain-specific effects over models including them, pointing towards a constant effect over domains. We conclude that behavior and associated PDs observed in our purely perceptual auditory task align with Bayesian principles of belief updating. The observed lack of domain specificity supports the assumed generalizability of belief updating.

neuroscience↗

bioRxiv2025

PARAS: high-accuracy machine-learning of substrate specificities in nonribosomal peptide synthetases

Nonribosomal peptides are diverse natural products with important applications in medicine and agriculture. Bacterial and fungal genomes contain thousands of nonribosomal peptide biosynthetic gene clusters (BGCs) of unknown function, providing a promising resource for peptide discovery. Core structural features of such peptides can be inferred by predicting the substrate(s) of adenylation (A) domains in nonribosomal peptide synthetases (NRPSs). However, existing approaches to A domain prediction rely on limited datasets and often struggle with domains selecting large substrates or from less-studied taxa. Here, we systematically curate and computationally analyse 3,653 A domains and present two high-accuracy specificity predictors, PARAS and PARASECT. A type of A domain with unusually high L-tryptophan specificity was identified through the application of PARAS, and intact protein mass spectrometry to the corresponding NRPS showed it to direct the production of tryptopeptin-related metabolites in Streptomyces species. Together, these technologies will accelerate the characterisation of novel NRPSs and their metabolic products. PARAS and PARASECT are available at https://paras.bioinformatics.nl. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=106 SRC="FIGDIR/small/631717v2_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@8e48aaorg.highwire.dtl.DTLVardef@144c78dorg.highwire.dtl.DTLVardef@890c11org.highwire.dtl.DTLVardef@1776de9_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

How relevant is the prior? Bayesian causal inference for dynamic perception in volatile environments

Interpreting sensory prediction errors can be challenging in volatile environments because they can be caused by stochastic noise or by outdated predictions. Noisy signals should be integrated with prior beliefs to improve precision, but the two should be segregated when environmental changes render prior beliefs irrelevant. Bayesian causal inference provides a statistically optimal solution to deal with uncertainty about the causes of prediction errors. However, the method quickly becomes memory intensive and computationally intractable when applied sequentially. Here, we systematically evaluate the predictive performance of Bayesian causal inference for perceptual decisions in a spatial prediction task based on noisy audiovisual sequences with occasional changepoints. We elucidate the simplifying assumptions of a previously proposed reduced Bayesian observer model, and we compare it to an extensive set of models based on alternative simplification strategies. Model-free analyses revealed the hallmarks of Bayesian causal inference: participants seem to have integrated sensory evidence with prior beliefs to improve accuracy when prediction errors were small, but prior influence decreased gradually as prediction errors increased, signalling probable irrelevance of the priors due to changepoints. Model comparison results indicated that participants computed probability-weighted averages over the causal options (noise or changepoint), akin to the reduced Bayesian observer model. However, participants reliance on prior beliefs was systematically smaller than expected, and this was best explained by individually fitting lower-than-optimal parameters for the a-priori probability of prior relevance. We conclude that perceptual decision makers utilize priors flexibly to the extent that they are deemed relevant, though also conservatively with a lower tendency to bind than ideal observers. Simplified consecutive Bayesian causal inference predicts key characteristics of belief updating in changepoint environments and forms a suitable foundation for modelling dynamic perception in a changing world.

neuroscience↗

bioRxiv2024

Bayesian Prior Uncertainty and Surprisal Elicit Distinct Neural Patterns During Sound Localization in Dynamic Environments

Estimating the location of a stimulus is a key function in sensory processing, and widely considered to result from the integration of prior information and sensory input according to Bayesian principles. A deviation of sensory input from the prior elicits surprisal, depending on the uncertainty of the prior. While this mechanism is increasingly understood in the visual domain, much less is known about its implementation in audition, especially regarding spatial localization. Here, we combined human EEG with computational modeling to study auditory spatial inference in a noisy, volatile environment and analyzed behavioral and neural patterns associated with prior uncertainty and surprisal. First, our results demonstrate that participants indeed used prior information during periods of stable environmental statistics, but showed evidence of surprisal and discarded prior information following environmental changes. Second, we observed distinct EEG activity patterns associated with prior uncertainty and surprisal in both the time- and time-frequency domain, which are in line with previous studies using visual tasks. Third, these EEG activity patterns were predictive of our participants sound localization error, response uncertainty, and prior bias on a trial-by-trial basis. In summary, our work provides novel behavioral and neural evidence for Bayesian inference during dynamic auditory localization.

neuroscience↗

bioRxiv2023

rTMS to right parietal cortex decreases the precision of spatial priors in perceptual decision making

Throughout life human observers make perceptual decisions under uncertainty guided by prior knowledge about the worlds causal structure and properties. According to Bayesian probability theory, optimal decision making relies on integrating prior knowledge with current sensory inputs, weighted by their relative precisions (i.e., inverse of uncertainty). Thus, representing uncertainty is essential for optimal decisions. Although extensive research suggests that human perceptual decision making follows Bayesian principles, the neural underpinnings of priors and their uncertainties remain elusive. In this five-day study, we employed psychophysics, Bayesian causal inference models, and offline low-frequency (i.e., 1Hz) transcranial magnetic stimulation (TMS) to probe the role of right intraparietal sulcus (IPS), a key region for spatial processing, in the integration of prior knowledge with auditory/visual inputs for spatial decisions. Model-free and Bayesian modelling analyses consistently showed a reduction in the precision of observers long-term spatial prior and in the influence of their previous spatial choices on their current spatial decisions for right IPS-TMS compared to sham-TMS. In contrast, the causal prior and the auditory/visual uncertainties remained unaffected. The results show that offline IPS-TMS can selectively reduce the precision or influence of observers long-term spatial prior and their short-term spatial expectations on perceptual decisions, without affecting their causal prior or sensory uncertainties (i.e., likelihood). Our findings provide causal evidence for the role of parietal cortex, situated at the top of the audiovisual spatial processing hierarchy, in encoding the uncertainty of spatial - but not causal - priors during perceptual decision-making. Significance statementPerceptual decisions under uncertainty are pervasive in everyday life. Substantial evidence suggests that humans perform perceptual decisions near-optimally. They combine sensory inputs with prior knowledge about the signals causal structure and locations weighted by their uncertainties. Yet, the neural underpinnings remain elusive. Combining psychophysics, Bayesian models, and offline low-frequency inhibitory transcranial magnetic stimulation (TMS), we provide causal evidence that the parietal cortex is crucial for encoding the uncertainty of spatial - but not causal - priors during perceptual decision-making. Intriguingly, observers relied less on their long-term and short-term prior spatial expectations after parietal-TMS, as compared to sham-TMS. These results provide important insights into the neural substrates of priors and their uncertainties as key ingredients for near-optimal decisions consistent with normative Bayesian principles.

neuroscience↗

bioRxiv2023

Metacognition in the audiovisual McGurk illusion: perceptual and causal confidence

Almost all decisions in everyday life rely on multiple sensory inputs that can come from common or independent causes. These situations invoke perceptual uncertainty about environmental properties and the signals causal structure. Using the audiovisual McGurk illusion this study investigated how observers formed perceptual and causal confidence judgments in multisensory integration tasks under causal uncertainty. Observers were presented with spoken syllables, their corresponding articulatory lip movements, or their congruent and McGurk combinations (e.g. auditory B/P with visual G/K). Observers reported their perceived auditory syllable, the causal structure and confidence for each judgement. Observers were more accurate and confident on congruent than unisensory trials. Their perceptual and causal confidence were tightly related over trials as predicted by the interactive nature of perceptual and causal inference. Further, observers assigned comparable perceptual and causal confidence to veridical G/K percepts on audiovisual congruent trials and their causal and perceptual metamers on McGurk trials (i.e. illusory G/K percepts). Thus, observers metacognitively evaluate the integrated audiovisual percept with limited access to the conflicting unisensory stimulus components on McGurk trials. Collectively, our results suggest that observers form meaningful perceptual and causal confidence judgments about multisensory scenes that are qualitatively consistent with principles of Bayesian causal inference.

neuroscience↗

bioRxiv2022

A Bayesian model for human directional localization of broadband and static sound sources

Humans estimate sound-source directions by combining prior beliefs with sensory evidence. Prior beliefs represent statistical knowledge about the environment while sensory evidence is acquired from auditory features such as interaural disparities and monaural spectral shapes. Models of directional sound localization often impose constraints on the contribution of these features to either the horizontal or vertical dimension. Instead, we propose a Bayesian model that more flexibly incorporates each feature according to its spatial precision and integrates prior beliefs in the inference process. We applied the model to directional localization of a single, broadband, stationary sound source presented to a static human listener in an anechoic environment. We simplified interaural features to be broadband and compared two model variants, each considering a different type of monaural spectral features: magnitude profiles and gradient profiles. Both model variants were fitted to the baseline performance of five listeners and evaluated on the effects of localizing with non-individual head-related transfer functions (HRTFs) and sounds with rippled spectrum. The model variant with spectral gradient profiles outperformed other localization models. This model variant appears particularly useful for the evaluation of HRTFs and may serve as a basis for future extensions towards modeling dynamic listening conditions.

neuroscience↗

bioRxiv2021

BRN2 is a non-canonical melanoma tumor-suppressor

While the major drivers of melanoma initiation, including activation of NRAS/BRAF and loss of PTEN or CDKN2A, have been identified, the role of key transcription factors that impose altered transcriptional states in response to deregulated signaling is not well understood. The POU domain transcription factor BRN2 is a key regulator of melanoma invasion, yet its role in melanoma initiation remains unknown. Here, we show that BRN2 haplo-insufficiency is sufficient to promote melanoma initiation and metastasis, acting as a non-canonical tumor suppressor. Mechanistically, BRN2 directly modulates PTEN expression, and PI3K signaling, to drive tumor initiation and progression. Collectively our results reveal that somatic deletion of one BRN2 allele elicits melanoma initiation and progression. SIGNIFICANCEHere, we report frequent mono-allelic loss of the transcription factor BRN2 in human cutaneous melanoma metastases. We developed a mouse model for Brn2-deficient melanoma based on the most common alterations (BrafV600E and Pten loss) in human melanoma and established the role of Brn2 as a functional regulator of tumor initiation, tumor growth, and the formation of metastases in vivo. Mechanistically, BRN2 loss increases PI3K-signaling through PTEN repression, either via MITF induction or not. Overall, we describe a novel tumor suppressor of high prevalence in human melanoma that regulates several steps of in vivo melanomagenesis through two previously unknown molecular mechanisms.

cancer biology↗