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At least 973 records · Page 54

Functional analysis of a conserved site mutation in the DNA end processing enzyme PNKP leading to ataxia with oculomotor apraxia type 4 in humans

Polynucleotide kinase 3'-phosphatase (PNKP), an essential DNA end-processing enzyme in mammals with 3'-phosphatase and 5'-kinase activities, plays a pivotal role in multiple DNA repair pathways. Its functional deficiency has been etiologically linked to various neurological disorders. Recent reports have shown that mutation at a conserved glutamine (Gln) in PNKP leads to late-onset ataxia with oculomotor apraxia type 4 (AOA4) in humans and embryonic lethality in pigs. However, the molecular mechanism underlying such phenotypes remains elusive. Here, we report that the enzymatic activities of the mutant versus WT PNKP are comparable; however, cells expressing mutant PNKP and peripheral blood mononuclear cells (PBMCs) of AOA4 patients showed a significant amount of DNA double-strand break accumulation and consequent activation of the DNA damage response. Further investigation revealed that the nuclear localization of mutant PNKP is severely abrogated, and the mutant proteins remain primarily in the cytoplasm. Western blot analysis of AOA4 patient-derived PBMCs also revealed the presence of mutated PNKP predominantly in the cytoplasm. To understand the molecular determinants, we identified that mutation at a conserved Gln residue impedes the interaction of PNKP with importin alpha but not with importin beta, two highly conserved proteins that mediate the import of proteins from the cytoplasm into the nucleus. Collectively, our data suggest that the absence of PNKP in the nucleus leads to constant activation of the DNA damage response due to persistent accumulation of double-strand breaks in the mutant cells, triggering death of vulnerable brain cells—a potential cause of neurodegeneration in AOA4 patients.

59 BASIC BIOLOGICAL SCIENCES↗

Determining the impacts of venoarterial extracorporeal membrane oxygenation on cerebral oxygenation using a one-dimensional blood flow simulator

Venoarterial extracorporeal membrane oxygenation (VA-ECMO) is a mechanical system that provides rapid and short-term support for patients with cardiac failure. In many patients, pulmonary function is also impaired, resulting in poorly-oxygenated cardiac outflow competing against well-oxygenated VA-ECMO outflow, a condition known as North-South syndrome. North-South syndrome is a primary concern because of its potential to cause cerebral hypoxia, which has a critical influence on neurological complications often seen in this patient population. In order to reduce ischemic neurological complications, it is important to understand how clinical decisions regarding VA-ECMO parameters influence blood oxygenation. In our work, we studied the impacts of flow rate and cannulation site on oxygenation using a one-dimensional (1D) model to simulate blood flow. Our model was initially tested by comparing blood flow results to those observed from experimental work in VA-ECMO patients. The 1D model was combined with a two-phase flow model to simulate oxygenation. Additionally, the influence of various other clinician-tunable parameters on oxygenation in the common carotid arteries (CCAs) were tested, including, blood viscosity, cannula position within the insertion artery, heart rate, and systemic vascular resistance (SVR), as well as geometrical changes such as arterial radius and length. Our results indicated that blood oxygenation to the brain strongly depended on the cannula insertion site and the VA-ECMO flow rate with a weaker but potentially significant dependence on arterial radius. During femoral cannulation, VA-ECMO flow rates greater than ~4.9 L/min were needed to perfuse the CCAs. However, axillary and central cannulation began to perfuse the CCAs at significantly lower flow (~1 L/min). These results may help explain the incidence of cerebral hypoxia in this patient population and the common need to change cannulation strategies during treatment to address this clinical problem. While this work describes patient-averaged results, determining these relationships between VA-ECMO parameters and cerebral hypoxia is an important step towards future work to develop patient-specific models that clinicians can use to improve outcomes.

1D blood flow↗

Explicit physics-informed neural networks for nonlinear closure: The case of transport in tissues

In upscaling methods, closures for nonlinear problems present a well-known challenge. While a number of theoretical methods have been proposed for handling such closures, nonlinearities still remain a significant obstacle for many problems. In this work, we use a combination of formal upscaling and data-driven machine learning for explicitly closing a nonlinear transport and reaction process in multiscale tissues. The classical effectiveness factor model is used to formulate the macroscale reaction kinetics. We train a multilayer perceptron network using training data generated by direct numerical simulations over microscale examples. Once trained, the network is used in an algorithm for numerically solving the upscaled (coarse-grained) differential equation describing mass transport and reaction in two example tissues. The network is described as being explicit in the sense that the network is trained using macroscale concentrations and gradients of concentration as components of the feature space rather than incorporating them as part of a constraint in the optimization process. Network training and solutions to the macroscale transport equations were computed for two different tissues. The two tissue types (brain and liver) exhibit markedly different geometrical complexity and spatial scale (cell size and sample size). The upscaled solutions for the average concentration are compared with numerical solutions derived from the microscale concentration fields by a posteriori averaging. There are three outcomes of this work of particular note. 1) Our overall approach results in an upscaled nonlinear PDE. The PDE is closed using a neural network, and our approach results in the definition of the classical effectiveness factor for effecting closure. 2) We identify particular source terms for the closure problem that are important for representing the structure of the closure. These source terms involve macroscale concentrations and their gradients. We adopt these source terms to use as explicit features in the learning algorithm. We find the trained networks that include the macroscale source terms generate models that are able to predict the correction factor with increased fidelity over those that do not. 3) We find that the trained network exhibits good generalizability, and it is able to predict the effectiveness factor with high fidelity for realistically-structured tissues despite the significantly different scale and geometrical complexity of the two example tissue types. This latter result emphasizes our purposeful connection between conventional averaging methods with the use of machine learning for closure; this contrasts with some machine learning methods for upscaling where the exact form of the macroscale equation remains unknown.

97 MATHEMATICS AND COMPUTING↗

Lipid and lipoprotein metabolism in microglia: Alzheimer’s disease mechanisms and interventions

Alzheimer's disease (AD) presents a significant challenge owing to its widespread prevalence and complex neuropathogenesis, affecting millions worldwide. Current therapeutic strategies that predominantly target amyloid-beta accumulation are insufficient, particularly for ApoE4 carriers. Alterations in lipid composition are well documented in AD, characterized by reductions in phospholipids and sulfatides, along with increases in cholesterol, cholesteryl esters, and triglycerides (TGs). Microglia, the brain's resident immune cells, link dysfunctional lipid processing to AD neuropathogenesis. For example, genetic studies have pointed to microglial lipid and lipoprotein processing gene variants as some of the strongest risk factors for AD. In addition, microglial dysfunction, characterized by lipid droplet accumulation, increased cholesterol and TG levels, and altered lipid transport, may exacerbate the pathological hallmarks of AD, such as amyloid-beta and tau accumulation. Conversely, emerging studies have shown that strategies aimed at inhibiting lipid droplet accumulation in microglia, reducing TG synthesis, and promoting the activity of lipoprotein receptors expressed by microglia can improve cell functions and markers of AD pathology. This review dissects the interplay between microglial lipid metabolism and AD, highlighting the significance of lipid transport and trafficking within the CNS. Given the intrinsic link between microglial metabolism and AD progression, emerging and potential therapeutic strategies aimed at restoring lipid handling and improving microglial function are explored. This review provides a comprehensive examination of the emerging literature, detailing the current state of knowledge on microglial lipid metabolism, its genetic underpinnings, and the potential for novel interventions targeting these mechanisms to ameliorate AD pathology.

Alzheimer's disease↗

Structural Insights into the Mechanisms and Pharmacology of K 2P Potassium Channels

Leak currents, defined as voltage and time independent flows of ions across cell membranes, are central to cellular electrical excitability control. The K 2P (KCNK) potassium channel class comprises an ion channel family that produces potassium leak currents that oppose excitation and stabilize the resting membrane potential in cells in the brain, cardiovascular system, immune system, and sensory organs. Due to their widespread tissue distribution, K 2P s contribute to many physiological and pathophysiological processes including anesthesia, pain, arrythmias, ischemia, hypertension, migraine, intraocular pressure regulation, and lung injury responses. Structural studies of six homomeric K 2P s have established the basic architecture of this channel family, revealed key moving parts involved in K 2P function, uncovered the importance of asymmetric pinching and dilation motions in the K 2P selectivity filter (SF) C-type gate, and defined two K 2P structural classes based on the absence or presence of an intracellular gate. Further, a series of structures characterizing K 2P :modulator interactions have revealed a striking polysite pharmacology housed within a relatively modestly sized (~70 kDa) channel. Binding sites for small molecules or lipids that control channel function are found at every layer of the channel structure, starting from its extracellular side through the portion that interacts with the membrane bilayer inner leaflet. This framework provides the basis for understanding how gating cues sensed by different channel parts control function and how small molecules and lipids modulate K 2P activity. Such knowledge should catalyze development of new K 2P modulators to probe function and treat a wide range of disorders.

59 BASIC BIOLOGICAL SCIENCES↗

Two-dimensional materials for bio-realistic neuronal computing networks

Two-dimensional (2D) van der Waals materials have found broad utility in a diverse range of applications including electronics, optoelectronics, renewable energy, and quantum information technologies. Meanwhile, exponentially growing digital data coupled with the ubiquity of artificial intelligence algorithms have generated significant interest in edge neuromorphic computing as an alternative to centralized cloud computing. The drive to incorporate neuroscience principles into computing hardware is motivated by the low power consumption, parallel processing, and reconfigurability of the human brain. The diverse library of 2D materials with atomic-level thicknesses, exceptional electrostatic tunability, and integration versatility is particularly well-suited for realizing bio-realistic synaptic and neuronal functionality. Here, we summarize past and present work in this field and outline the frontier challenges that have not yet been overcome. Here we also delineate potential solutions and suggest that the neuroscience principles of criticality and synchrony have the potential to inspire breakthrough applications of 2D materials in neuronal computing networks.

36 MATERIALS SCIENCE↗

Spatially Resolved Top-Down Proteomics of Tissue Sections Based on a Microfluidic Nanodroplet Sample Preparation Platform

Conventional proteomics measures the averaged signal from mixed cell populations or bulk tissues, leading to the dilution of significant changes in subpopulations of cells that might serve as important biomarkers. Recent developments in bottom-up proteomics have enabled spatial mapping of cellular heterogeneity in tissue microenvironments. However, bottom-up proteomics cannot precisely infer the abundance changes of intact proteins, which are presented as proteoforms. Herein, we described a spatially resolved top-down proteomics (TDP) platform for proteoform identification and quantification directly on thin tissue sections. The spatial TDP platform consisted of a nanoPOTS (nanodroplet Processing in One pot for Trace Samples)-based sample preparation system and an LCM (laser capture microdissection)-based cell isolation system. We improved the nanoPOTS sample preparation by adding benzonase in the extraction buffer to enhance the coverage of nucleus proteins. Using ~200 cultured cells as model samples, the improved approach increased proteoform identifications from 493 to 700; newly identified proteoforms primarily corresponded to nuclear proteins. To demonstrate the spatial TDP platform in tissue samples, we analyzed LCM-isolated tissue voxels from rat brain cortex and hypothalamus regions. We quantified 426 proteoforms by combining identifications from TopPIC and TDPortal with the quantitation from ProMex. Several proteoforms corresponding to the same gene exhibited mixed abundance profiles between two tissue regions, suggesting potential PTM-specific spatial distributions. The spatial TDP workflow has prospects for biomarker discovery at proteoform level from small tissue sections.

59 BASIC BIOLOGICAL SCIENCES↗

Detect and correct bias in multi-site neuroimaging datasets

The desire to train complex machine learning algorithms and to increase the statistical power in association studies drives neuroimaging research to use ever-larger datasets. The most obvious way to increase sample size is by pooling scans from independent studies. However, simple pooling is often ill-advised as selection, measurement, and confounding biases may creep in and yield spurious correlations. In this work, we combine 35,320 magnetic resonance images of the brain from 17 studies to examine bias in neuroimaging. In the first experiment, Name That Dataset, we provide empirical evidence for the presence of bias by showing that scans can be correctly assigned to their respective dataset with 71.5% accuracy. Given such evidence, we take a closer look at confounding bias, which is often viewed as the main shortcoming in observational studies. In practice, we neither know all potential confounders nor do we have data on them. Hence, we model confounders as unknown, latent variables. Kolmogorov complexity is then used to decide whether the confounded or the causal model provides the simplest factorization of the graphical model. Finally, we present methods for dataset harmonization and study their ability to remove bias in imaging features. In particular, we propose an extension of the recently introduced ComBat algorithm to control for global variation across image features, inspired by adjusting for unknown population stratification in genetics. Overall, our results demonstrate that harmonization can reduce dataset-specific information in image features. Further, confounding bias can be reduced and even turned into a causal relationship. However, harmonization also requires caution as it can easily remove relevant subject-specific information. Code is available at https://github.com/ai-med/Dataset-Bias.

42 ENGINEERING↗

Susceptibility weighted MRI pinpoints spontaneous intracerebral hemorrhage in stroke-prone spontaneously hypertensive rats

Purpose: To find magnetic resonance imaging (MRI) precursors of spontaneous intracerebral hemorrhage in stroke-prone spontaneously hypertensive rats (SHRSP). Method: SHRSP rats were used with both a low/high salt (n = 18 or 11) Japanese diet and salty drinking water to generate spontaneous intracerebral hemorrhage (ICH). Various MRI sequences, and in particular, susceptibility weighted imaging (SWI), were used and combined with a gadolinium (Gd) contrast agent or oxygen gas to identify the rupture of the blood brain barrier (BBB) and the temporal ICH. Results: Most rats developed hypertensive ICH stroke in the high salt group during the 10–13 week period compared to only one third of rats in the low salt group during the 14–18 week period. The location of stroke for both the low/high-salt groups was highest in the striatum (58%/43%), followed by the cortex (21%/30%). The edematous enhancement on T2 weighted (T2W) imaging or Gd based T1 weighted (Gd-T1W) imaging due to the ruptured BBB preceded the striatal hemorrhages seen on SWI. The most recent bleeds were observed on temporal SWI or on oxygen-enhanced SWI. The mode of the volume of bleeds was 0.4 mm 3 . A positive correlation between susceptibility x volume and R2* x volume of the bleeds was observed. Conclusions: SHRSP rats with the high salt diet effectively generated a hypertensive hemorrhagic stroke which could be monitored by various MRI sequences. The venous dilation on SWI may precede any abnormality on T2W or Gd-T1W imaging. The edematous enhancement on T2W or Gd-T1W indicated a BBB breakdown that may precede striatal ICH by several days. This suggests the need for immediate treatment to improve outcome if this finding is observed. Here, the use of oxygen with SWI was able to help differentiate old bleeds from very recent bleeds.

60 APPLIED LIFE SCIENCES↗

Cerebellar dysfunction in a mouse model of childhood-onset manganese-induced dystonia parkinsonism

Humans with pathogenic variants of the manganese (Mn) transporter gene SLC39A14 exhibit highly elevated brain Mn concentrations and childhood-onset dystonia-parkinsonism. Here we show that Slc39a14-knockout (KO) mice, a preclinical model of the disease with elevated Mn concentrations in the CB, express deficits in physiological tremor implicating cerebellar (CB) dysfunction. Imaging of intracellular Mn in Purkinje cells (PCs) using synchrotron-based X-ray fluorescence microscopy confirmed highly elevated Mn concentrations in the PCs of Slc39a14-KO mice. To determine biological pathways altered in the CB of Slc39a14-KO mice relative to wildtype (WT), we performed RNA sequencing and discovered significant upregulation of pathways and genes regulating immune response and cell death. To substantiate these findings, we performed quantitative autoradiography of the neuroinflammation biomarker Translocator Protein 18 kDa (TSPO) which was significantly increased in the CB of Slc39a14-KO mice relative to WT. The latter findings were confirmed via immunostaining with the microglial marker Iba-1, revealing widespread microglia activation and clustering in the CB cortex. Immunostaining for cleaved caspase-3 (cCASP3), a marker of apoptosis, showed increased number of PCs with positive immunolabeling for cCASP3 in Slc39a14-KO mice relative to WT. Degeneration of PCs was confirmed by Hematoxylin and Eosin (H&E) staining. Lastly, functional electrophysiological assessment of CB neurocircuitry revealed a marked decrease in firing rates of cerebellar nuclei (CN) neurons and increased variability of PC simple spikes firing. Collectively, these findings show, for the first time, Mn-induced PC degeneration and dysfunctional CB circuitry in Slc39a14-KO mice providing additional evidence for the pathological underpinnings of the dystonia-like movements, balance, and gait abnormalities in SLC39A14 mutation carriers.

36 MATERIALS SCIENCE↗

Benchmarking the Performance of Neuromorphic and Spiking Neural Network Simulators

Software simulators play a critical role in the development of new algorithms and system architectures in any field of engineering. Neuromorphic computing, which has shown potential in building brain-inspired energy-efficient hardware, suffers a slow-down in the development cycle due to a lack of flexible and easy-to-use simulators of either neuromorphic hardware itself or of spiking neural networks (SNNs), the type of neural network computation executed on most neuromorphic systems. While there are several openly available neuromorphic or SNN simulation packages developed by a variety of research groups, they have mostly targeted computational neuroscience simulations, and only a few have targeted small-scale machine learning tasks with SNNs. Evaluations or comparisons of these simulators have often targeted computational neuroscience-style workloads. In this work, we seek to evaluate the performance of several publicly available SNN simulators with respect to non-computational neuroscience workloads, in terms of speed, flexibility, and scalability. We evaluate the performance of the NEST, Brian2, Brian2GeNN, BindsNET and Nengo packages under a common front-end neuromorphic framework. Our evaluation tasks include a variety of different network architectures and workload types to mimic the computation common in different algorithms, including feed-forward network inference, genetic algorithms, and reservoir computing. We also study the scalability of each of these simulators when running on different computing hardware, from single core CPU workstations to multi-node supercomputers. Our results show that the BindsNET simulator has the best speed and scalability for most of the SNN workloads (sparse, dense, and layered SNN architectures) on a single core CPU. However, when comparing the simulators leveraging the GPU capabilities, Brian2GeNN outperforms the others for these workloads in terms of scalability. NEST performs the best for small sparse networks and is also the most flexible simulator in terms of reconfiguration capability NEST shows a speedup of at least 2x compared to the other packages when running evolutionary algorithms for SNNs. The multi-node and multi-thread capabilities of NEST show at least 2x speedup compared to the rest of the simulators (single core CPU or GPU based simulators) for large and sparse networks. We conclude our work by providing a set of recommendations on the suitability of employing these simulators for different tasks and scales of operations. We also present the characteristics for a future generic ideal SNN simulator for different neuromorphic computing workloads.

97 MATHEMATICS AND COMPUTING↗

Locus coeruleus catecholamines link neuroticism and vulnerability to tau pathology in aging

Higher neuroticism is a risk factor for Alzheimer's disease (AD), and is implicated in disordered stress responses. The locus coeruleus (LC)-catecholamine system is activated during perceived threat and is a centerpiece of developing models of the pathophysiology of AD, as it is the first brain region to develop abnormal tau. We examined relationships among the "Big 5" personality traits, LC catecholamine synthesis capacity measured with [ 18 F]Fluoro-m-tyrosine PET, and tau burden measured with [ 18 F]Flortaucipir PET in cognitively normal older adults (n = 47). β-amyloid (Aβ) status was determined using [ 11 C]Pittsburgh compound B PET (n = 14 Aβ positive). Lower LC catecholamine synthesis capacity was associated with higher neuroticism, more depressive symptoms as measured by the Geriatric Depression Scale, and higher amygdala tau-PET binding. Exploratory analyses with other personality traits revealed that low trait conscientiousness was also related to both lower LC catecholamine synthesis capacity, and more depressive symptoms. A significant indirect path linked both high neuroticism and low conscientiousness to greater amygdala tau burden via their mutual association with low LC catecholamine synthesis capacity. Together, these findings reveal LC catecholamine synthesis capacity to be a promising marker of affective health and pathology burden in aging, and identifies candidate neurobiological mechanisms for the effect of personality on increased vulnerability to dementia.

59 BASIC BIOLOGICAL SCIENCES↗

Regional specialization in prefrontal cortex manifests in the reliability of task progression codes

The brain has the remarkable ability to guide the performance of complex tasks. Distinct prefrontal cortical areas make specific contributions to this ability, with the orbitofrontal cortex (OFC) critical for processing information related to trial outcomes and the dorsomedial prefrontal cortex (dmPFC) critical for sustained effort and selecting the right action at the right time. Yet, in both areas, neural activity represents both outcome- and action-related quantities. How similar neural representations support different functions remains unclear. Here, we compared OFC and dmPFC activity in rats performing a spatial alternation task. We show that, in contrast to other task-related variables, task progression is represented in both areas, but with distinct patterns of across-trial reliability that match each area’s previously documented functional specialization. Our results indicate that the engagement of reliable, task-phase-specific activity patterns differs across prefrontal regions in a manner well suited to engage different computations at different times.

Biological and medical sciences↗

YOLO2U-Net: Detection-guided 3D instance segmentation for microscopy

Microscopy imaging techniques are instrumental for characterization and analysis of biological structures. As these techniques typically render 3D visualization of cells by stacking 2D projections, issues such as out-of-plane excitation and low resolution in the z-axis may pose challenges (even for human experts) to detect individual cells in 3D volumes as these non-overlapping cells may appear as overlapping. In this paper a comprehensive method for accurate 3D instance segmentation of cells in the brain tissue is introduced. The proposed method combines the 2D YOLO detection method with a multi-view fusion algorithm to construct a 3D localization of the cells. Next, the 3D bounding boxes along with the data volume are input to a 3D U-Net network that is designed to segment the primary cell in each 3D bounding box, and in turn, to carry out instance segmentation of cells in the entire volume. The promising performance of the proposed method is shown in comparison with current deep learning-based 3D instance segmentation methods.

3D instance segmentation↗

Evaluation of a rapid, generic human gestational dose model

Chemical risk assessment considers potentially susceptible populations including pregnant women and developing fetuses. Humans encounter thousands of chemicals in their environments, few of which have been fully characterized. Toxicokinetic (TK) information is needed to relate chemical exposure to potentially bioactive tissue concentrations. Observational data describing human gestational exposures are unavailable for most chemicals, but physiologically based TK (PBTK) models estimate such exposures. Development of chemical-specific PBTK models requires considerable time and resources. As an alternative, generic PBTK approaches describe a standardized physiology and characterize chemicals with a set of standard physical and TK descriptors – primarily plasma protein binding and hepatic clearance. Here we report and evaluate a generic PBTK model of a human mother and developing fetus. We used a published set of formulas describing the major anatomical and physiological changes that occur during pregnancy to augment the High-Throughput Toxicokinetics (httk) software package. We simulated the ratio of concentrations in maternal and fetal plasma and compared to literature in vivo measurements. We evaluated the model with literature in vivo time-course measurements of maternal plasma concentrations in pregnant and non-pregnant women. Finally, we prioritized chemicals measured in maternal serum based on predicted fetal brain concentrations. This new model can be used for TK simulations of 859 chemicals with existing human-specific in vitro TK data as well as any new chemicals for which such data become available. Furthermore, this gestational model may allow for in vitro to in vivo extrapolation of point of departure doses relevant to reproductive and developmental toxicity.

60 APPLIED LIFE SCIENCES↗

Structures of drug-specific monoclonal antibodies bound to opioids and nicotine reveal a common mode of binding

Opioid-related fatal overdoses have reached epidemic proportions. Because existing treatments for opioid use disorders offer limited long-term protection, accelerating the development of newer approaches is critical. Monoclonal antibodies (mAbs) are an emerging treatment strategy that targets and sequesters selected opioids in the bloodstream, reducing drug distribution across the blood-brain barrier, thus preventing or reversing opioid toxicity. We previously identified a series of murine mAbs with high affinity and selectivity for oxycodone, morphine, fentanyl, and nicotine. To determine their binding mechanism, we used X-ray crystallography to solve the structures of mAbs bound to their respective targets, to 2.2 Å resolution or higher. Structural analysis showed a critical convergent hydrogen bonding mode that is dependent on a glutamic acid residue in the mAbs’ heavy chain and a tertiary amine of the ligand. Further, characterizing drug-mAb complexes represents a significant step toward rational antibody engineering and future manufacturing activities to support clinical evaluation.

60 APPLIED LIFE SCIENCES↗

Conducting polymer-based electrochemical sensors: Progress, challenges, and future perspectives

Conducting polymers are promising due to their unique properties, such as excellent electrical and optical properties, physical and chemical stability, high conductivity, and effective redox properties with high-temperature stability and biocompatibility. Due to these properties, conducting polymers are useful in diverse applications like sensors, batteries, oil industries, biosensors, biomedicines, catalysis, cancer treatment, etc. This review article aims to discuss the recent trends and analysis of conducting polymer-based electrochemical sensors in diverse areas with all required sensor characteristics, such as the derived limit of detection, utilized techniques for the sensing analysis and derived linear dynamic range with the stability of the sensors. Conducting polymers and their nanocomposites-based electrochemical sensors have demonstrated exceptional capabilities towards detecting various biomolecules, heavy metals, pesticides, and viruses like SARS-COV-2. Incorporation of redox mediators, use of conducting hydrogels, and molecular imprinting are promising strategies for better performance of the derived sensor. The article has demonstrated the existing challenges and limitations and provided solutions in the field. In the future, conducting polymers-based electrochemical sensors can be utilized in wearable sensors and integrated with IoT devices for better reach in real-time applications. They can also be made more accessible with precise control and data output by following specific methodologies. Utilizing green and sustainable conducting polymers can be crucial in advancing eco-friendly practices in the future. Conducting polymer-based electrochemical sensors has affectivity in neurochemical and pathogen sensing, which is essential for brain function and mental health.

42 ENGINEERING↗

Phylogenomic insights into the taxonomy, ecology, and mating systems of the lorchel family Discinaceae (Pezizales, Ascomycota)

Lorchels, also known as false morels (Gyromitra sensu lato), are iconic due to their brain-shaped mushrooms and production of gyromitrin, a deadly mycotoxin. Molecular phylogenetic studies have hitherto failed to resolve deep-branching relationships in the lorchel family, Discinaceae, hampering our ability to settle longstanding taxonomic debates and to reconstruct the evolution of toxin production. We generated 75 draft genomes from cultures and ascomata (some collected as early as 1960), conducted phylogenomic analyses using 1542 single-copy orthologs to infer the early evolutionary history of lorchels, and identified genomic signatures of trophic mode and mating-type loci to better understand lorchel ecology and reproductive biology. Our phylogenomic tree was supported by high gene tree concordance, facilitating taxonomic revisions in Discinaceae. We recognized 10 genera across two tribes: tribe Discineae (Discina, Maublancomyces, Neogyromitra, Piscidiscina, and Pseudodiscina) and tribe Gyromitreae (Gyromitra, Hydnotrya, Paragyromitra, Pseudorhizina, and Pseudoverpa); Piscidiscina was newly erected and 26 new combinations were formalized. Paradiscina melaleuca and Marcelleina donadinii formed their own family-level clade sister to Morchellaceae, which merits further taxonomic study. Genome size and CAZyme content were consistent with a mycorrhizal lifestyle for the truffle species (Hydnotrya spp.), whereas the other Discinaceae genera possessed genomic properties of a saprotrophic habit. Lorchels were found to be predominantly heterothallic-either MAT1-1 or MAT1-2-but a single occurrence of colocalized mating-type idiomorphs indicative of homothallism was observed in Gyromitra esculenta strain CBS101906 and requires additional confirmation and follow-up study. Lastly, we confirmed that gyromitrin has a phylogenetically discontinuous distribution, having been detected exclusively in two distantly related genera (Gyromitra and Piscidiscina) belonging to separate tribes. Our genomic dataset will facilitate further investigations into the gyromitrin biosynthesis genes and their evolutionary history. With additional sampling of Geomoriaceae and Helvellaceae-two closely related families with no publicly available genomes-these data will enable comprehensive studies on the independent evolution of truffles and ecological diversification in an economically important group of pezizalean fungi.

Dirks, Alden C↗