Competition-based model of pheromone component ratio detection in the moth

For some moth species, especially those closely interrelated and sympatric, recognizing a specific pheromone component concentration ratio is essential for males to successfully locate conspecific females. We propose and determine the properties of a minimalist competition-based feed-forward neuronal model capable of detecting a certain ratio of pheromone components independently of overall concentration. This model represents an elementary recognition unit for the ratio of binary mixtures which we propose is entirely contained in the macroglomerular complex (MGC) of the male moth. A set of such units, along with projection neurons (PNs), can provide the input to higher brain centres. We found that (1) accuracy is mainly achieved by maintaining a certain ratio of connection strengths between olfactory receptor neurons (ORN) and local neurons (LN), much less by properties of the interconnections between the competing LNs proper. An exception to this rule is that it is beneficial if connections between generalist LNs (i.e. excited by either pheromone component) and specialist LNs (i.e. excited by one component only) have the same strength as the reciprocal specialist to generalist connections. (2) successful ratio recognition is achieved using latency-to-first-spike in the LN populations which, in contrast to expectations with a population rate code, leads to a broadening of responses for higher overall concentrations consistent with experimental observations. (3) when longer durations of the competition between LNs were observed it did not lead to higher recognition accuracy

Millifluidic culture improves human midbrain organoid vitality and differentiation

Human midbrain-specific organoids (hMOs) serve as an experimental in vitro model for studying the pathogenesis of Parkinson's disease (PD). In hMOs, neuroepithelial stem cells (NESCs) give rise to functional midbrain dopaminergic (mDA) neurons that are selectively degenerating during PD. A limitation of the hMO model is an under-supply of oxygen and nutrients to the densely packed core region, which leads eventually to a "dead core". To reduce this phenomenon, we applied a millifluidic culture system that ensures media supply by continuous laminar flow. We developed a computational model of oxygen transport and consumption in order to predict oxygen levels within the hMOs. The modelling predicts higher oxygen levels in the hMO core region under millifluidic conditions. In agreement with the computational model, a significantly smaller "dead core" was observed in hMOs cultured in a bioreactor system compared to those ones kept under conventional shaking conditions. Comparing the necrotic core regions in the organoids with those obtained from the model allowed an estimation of the critical oxygen concentration necessary for ensuring cell vitality. Besides the reduced "dead core" size, the differentiation efficiency from NESCs to mDA neurons was elevated in hMOs exposed to medium flow. Increased differentiation involved a metabolic maturation process that was further developed in the millifluidic culture. Overall, bioreactor conditions that improve hMO quality are worth considering in the context of advanced PD modelling

Data-driven honeybee antennal lobe model suggests how stimulus-onset asynchrony can aid odour segregation

Insects have a remarkable ability to identify and track odour sources in multi-odour backgrounds. Recent behavioural experiments show that this ability relies on detecting millisecond stimulus asynchronies between odourants that originate from different sources. Honeybees, Apis mellifera , are able to distinguish mixtures where both odourants arrive at the same time (synchronous mixtures) from those where odourant onsets are staggered (asynchronous mixtures) down to an onset delay of only 6 ms. In this paper we explore this surprising ability in a model of the insects' primary olfactory brain area, the antennal lobe. We hypothesize that a winner-take-all inhibitory network of local neurons in the antennal lobe has a symmetry-breaking effect, such that the response pattern in projection neurons to an asynchronous mixture is different from the response pattern to the corresponding synchronous mixture for an extended period of time beyond the initial odourant onset where the two mixture conditions actually differ. The prolonged difference between response patterns to synchronous and asynchronous mixtures could facilitate odour segregation in downstream circuits of the olfactory pathway. We present a detailed data-driven model of the bee antennal lobe that reproduces a large data set of experimentally observed physiological odour responses, successfully implements the hypothesised symmetry-breaking mechanism and so demonstrates that this mechanism is consistent with our current knowledge of the olfactory circuits in the bee brain

The metalloprotein YhcH is an anomerase providing N-acetylneuraminate aldolase with the open form of its substrate

N-acetylneuraminate (Neu5Ac), an abundant sugar present in glycans in vertebrates and some bacteria, can be used as an energy source by several prokaryotes, including Escherichia coli. In solution, more than 99% of Neu5Ac is in cyclic form (≈92% beta-anomer and ≈7% alpha-anomer), whereas <0.5% is in the open form. The aldolase that initiates Neu5Ac metabolism in E. coli, NanA, has been reported to act on the alphaanomer. Surprisingly, when we performed this reaction at pH 6 to minimize spontaneous anomerization, we found NanA and its human homolog NPL preferentially metabolize the open form of this substrate. We tested whether the E. coli Neu5Ac anomerase NanM could promote turnover, finding it stimulated the utilization of both beta and alpha-anomers by NanA in vitro. However, NanM is localized in the periplasmic space and cannot facilitate Neu5Ac metabolism by NanA in the cytoplasm in vivo. We discovered that YhcH, a cytoplasmic protein encoded by many Neu5Ac catabolic operons and belonging to a protein family of unknown function (DUF386), also facilitated Neu5Ac utilization by NanA and NPL and displayed Neu5Ac anomerase activity in vitro. YhcH contains Zn, and its accelerating effect on the aldolase reaction was inhibited by metal chelators. Remarkably, several transition metals accelerated Neu5Ac anomerization in the absence of enzyme. Experiments with E. coli mutants indicated that YhcH expression provides a selective advantage for growth on Neu5Ac. In conclusion, YhcH plays the unprecedented role of providing an aldolase with the preferred unstable open form of its substrate

Connecting environmental exposure and neurodegeneration using cheminformatics and high resolution mass spectrometry: potential and challenges

Connecting chemical exposures over a lifetime to complex chronic diseases with multifactorial causes such as neurodegenerative diseases is an immense challenge requiring a long-term, interdisciplinary approach. Rapid developments in analytical and data technologies, such as non-target high resolution mass spectrometry (NT-HR-MS), have opened up new possibilities to accomplish this, inconceivable 20 years ago. While NT-HR-MS is being applied to increasingly complex research questions, there are still many unidentified chemicals and uncertainties in linking exposures to human health outcomes and environmental impacts. In this perspective, we explore the possibilities and challenges involved in using cheminformatics and NT-HR-MS to answer complex questions that cross many scientific disciplines, taking the identification of potential (small molecule) neurotoxicants in environmental or biological matrices as a case study. We explore capturing literature knowledge and patient exposure information in a form amenable to high-throughput data mining, and the related cheminformatic challenges. We then briefly cover which sample matrices are available, which method(s) could potentially be used to detect these chemicals in various matrices and what remains beyond the reach of NT-HR-MS. We touch on the potential for biological validation systems to contribute to mechanistic understanding of observations and explore which sampling and data archiving strategies may be required to form an accurate, sustained picture of small molecule signatures on extensive cohorts of patients with chronic neurodegenerative disorders. Finally, we reflect on how NT-HR-MS can support unravelling the contribution of the environment to complex diseases

A biophysical model of the early olfactory system of honeybees

Experimental measurements often can only provide limited data from an animal’s sensory system. In addition, they exhibit large trial-to-trial and animal-to-animal variability. These limitations pose challenges to building mathematical models intended to make biologically relevant predictions. Here, we present a mathematical model of the early olfactory system of honeybees aiming to overcome these limitations. The model generates olfactory response patterns which conform to the statistics derived from experimental data for a variety of their properties. This allows considering the full dimensionality of the sensory input space as well as avoiding overfitting the underlying data sets. Several known biological mechanisms, including processes of chemical binding and activation of receptors, and spike generation and transmission in the antennal lobe network, are incorporated in the model at a minimal level. It can therefore be used to study how experimentally observed phenomena are shaped by these underlying biophysical processes. We verified that our model can replicate some key experimental findings that were not used when building it. Given appropriate data, our model can be generalized to the early olfactory systems of other insects. It hence provides a possible framework for future numerical and analytical studies of olfactory processing in insects

Tandem fluorescent protein timers for non-invasive relative protein lifetime measurement in plants

Targeted protein degradation is an important and pervasive regulatory mechanism in plants, required for perception and response to the environment as well as developmental signalling. Despite the significance of this process, relatively few studies have assessed plant protein turnover in a quantitative fashion. Tandem fluorescent protein timers (tFTs) offer a powerful approach for the assessment of in vivo protein turnover in distinct subcellular compartments of single or multiple cells. A tFT is a fusion of two different fluorescent proteins with distinct fluorophore maturation kinetics, which enable protein age to be estimated from the ratio of fluorescence intensities of the two fluorescent proteins. Here, we use short-lived auxin signalling proteins and model N-end rule pathway reporters to demonstrate the utility of tFTs for studying protein turnover in living plants. We present transient expression of tFTs as an efficient screen for relative protein lifetime, useful for testing the effects of mutations and different genetic backgrounds on protein stability, and demonstrate the potential for using stably expressed tFTs to study native protein dynamics with high temporal resolution in response to exogenous or endogenous stimuli

Identifying metabolites by integrating metabolome databases with mass spectrometry cheminformatics.

Novel metabolites distinct from canonical pathways can be identified through the integration of three cheminformatics tools: BinVestigate, which queries the BinBase gas chromatography-mass spectrometry (GC-MS) metabolome database to match unknowns with biological metadata across over 110,000 samples; MS-DIAL 2.0, a software tool for chromatographic deconvolution of high-resolution GC-MS or liquid chromatography-mass spectrometry (LC-MS); and MS-FINDER 2.0, a structure-elucidation program that uses a combination of 14 metabolome databases in addition to an enzyme promiscuity library. We showcase our workflow by annotating N-methyl-uridine monophosphate (UMP), lysomonogalactosyl-monopalmitin, N-methylalanine, and two propofol derivatives

Neurogenesis Drives Stimulus Decorrelation in a Model of the Olfactory Bulb

The reshaping and decorrelation of similar activity patterns by neuronal networks can enhance their discriminability, storage, and retrieval. How can such networks learn to decorrelate new complex patterns, as they arise in the olfactory system? Using a computational network model for the dominant neural populations of the olfactory bulb we show that fundamental aspects of the adult neurogenesis observed in the olfactory bulb -- the persistent addition of new inhibitory granule cells to the network, their activity-dependent survival, and the reciprocal character of their synapses with the principal mitral cells -- are sufficient to restructure the network and to alter its encoding of odor stimuli adaptively so as to reduce the correlations between the bulbar representations of similar stimuli. The decorrelation is quite robust with respect to various types of perturbations of the reciprocity. The model parsimoniously captures the experimentally observed role of neurogenesis in perceptual learning and the enhanced response of young granule cells to novel stimuli. Moreover, it makes specific predictions for the type of odor enrichment that should be effective in enhancing the ability of animals to discriminate similar odor mixtures