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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2022

Synchrotron-based DEI and DEI-CT systems to image chick pea seeds, plant anatomy and the associated physiology at 30 keV.

SummaryO_LIThe study the effect of contrast on seeds, growth and the associated anatomy and physiology, with upgraded imaging systems. The use of phase information to explore new information at various stage of the growth. C_LIO_LIThis work benefits, the use of Synchrotron-based DEI and DEI-CT systems to enhance the contrast in plant root architecture and contrast mechanisms, visibility of fine structures of root architecture growth and some aspects of physiology at acceptable level. These non-destructive, imaging systems available at the X-15A beamline, at NSLS, BNL, USA, are utilized. C_LIO_LINoticed detailed anatomical and physiological observations, contrast mechanisms, with these upgraded systems, compared to other conventional techniques, equipped with tube source of X-rays. Examined the potential of these systems to quantify the plant roots in situ. The acquired images provided good contrast, anatomical structures and physiology of the plant root micro-architecture. We observed some of the complex plant traits, such as growth, development, root architecture and the associated physiology. The interior structure, root architecture, root morphology, growth of laterals and subsequent laterals can be visualized directly by synchrotron-based imaging techniques. C_LIO_LIRoot architecture of the plant grown from seeds provides new information about the structure and enhancement of some desired property, for example, interior micro-structure of the root laterals and the subsequent laterals and the clear visibility of the leaves in detail. This way, it will be possible to differentiate the weakly and strongly attenuation of the signal traversing within the sample, clearly reflects the acceptable visibility in root laterals, subsequent laterals and the associated opaque matrix with enhanced contrast. The sample has a thin layer of hard structure outside and protein inside. Extinction properties of these samples will be characterized by Sy-DEI and Sy-DEI-CT. This way, we may be able to differentiate softly and weakly attenuation within the sample, to know more about the contrast mechanisms. The visibility, contrast and porosity, with finer details, can be noticed, with Sy-DEI-CT systems as distinguished from Sy-DEI. However, limited field of view, may limit the problems associated with Sy-DEI-CT. C_LI

plant biology↗
bioRxiv2022

Common brain areas for processing physiologically and socially 'needed' stimuli

We looked at the overlap between brain areas related to perception of physiologically and socially (non-physiological) needed stimuli and how they might regulate serotonin levels. First, we conducted separate ALE meta-analyses on published results pertaining to brain activation patterns when participants perceived food while hungry or water while thirsty, and social interactions while being excluded. This allowed us to identify common consistent brain activation patterns for physiological and social needed stimuli. We also looked at significant spatial association between the common network and serotonin receptor distribution. We found that regions within the mid-posterior insula, the anterior cingulate cortex and the caudate are at the intersection of physiological (hunger and thirst) and social (exclusion) aspects of needing. Furthermore, we found a significant positive spatial correlation between that common network and 5HT4 receptor among serotonin receptors. While this was the highest for serotonin receptors, it was not the highest of all receptors. Our study suggests there is a common brain pattern during the processing of physiologically and socially needed stimuli, and discusses their spatial association with serotonin receptors and its possible implication.

neuroscience↗
bioRxiv2022

A high-impact COL6A3 mutation alters the response of chondrocytes in neo-cartilage organoids to hyper-physiologic mechanical loading

ObjectivesThe etiology of osteoarthritis revolves around the interplay between genetic predisposition and perturbing environmental cues, such as mechanical stress. The pericellular matrix, with its hallmark proteins collagen type VI and fibronectin, surrounds chondrocytes and is critical in transducing the biomechanical cues. The objective is to study the functional effects of an OA disease-risk mutation in COL6A3 in interaction with hyper-physiological mechanical cues in a tailored human induced pluripotent stem cells (hiPSCs) derived cartilage organoid model. MethodTo identify pathogenic OA mutations exome sequencing in symptomatic OA patients was performed. To study functional effects, CRISPR-Cas9 genome engineering was used to introduce the mutation in our established human induced pluripotent stem cell-derived in-vitro neo-cartilage organoid model in interaction with hyper-physiological mechanical loading conditions. ResultsA high-impact mutation in COL6A3 was identified that resulted in significantly lower binding between the PCM proteins COLVI and fibronectin (FN) and provoked an osteoarthritic chondrocyte state. Moreover, aberrant function of the PCM, secondary to the COL6A3 mutation, abolished the initial stress responses marked particularly by upregulation of PTGS2 encoding cyclooxygenase-2 (COX-2), after hyper-physiological mechanical loading conditions. ConclusionThese findings demonstrate that ablating the characteristic transient COX-2 response after injurious mechanical cues may have a direct negative impact on chondrocyte health. What is already knownO_LIThe etiology of osteoarthritis revolves around the interplay between genetic predisposition and perturbing environmental cues, such as mechanical stress. C_LIO_LIThe pericellular matrix, with its hallmark proteins collagen type VI and fibronectin, surrounds the chondrocytes and is critical in transducing biomechanical cues from the extracellular matrix to chondrocytes henceforth it determines the chondrocyte mechanical environment. C_LIO_LIThe mechanical environment of the chondrocytes is a critical factor that influences chondrocyte health as it determines the balance between synthesis and degradation of the articular cartilage extracellular matrix. C_LI What this study addsO_LIA sustainable human induced pluripotent stem cell-derived in-vitro neo-cartilage organoid model that is tailored to study detailed biologic effects of mechanical cues to chondrocytes. C_LIO_LIAn OA disease-risk mutation in COL6A3 reduces the binding between collagen type VI to fibronectin and provoked an osteoarthritic chondrocyte state. C_LIO_LIUpon hyper-physiological mechanical loading, aberrant function of the pericellular matrix, secondary to the COL6A3 mutation, ablates the initial transient inflammatory response, characterized particularly by PTGS2 encoding cyclooxygenase-2 (COX-2). C_LI How this study might affect research practice or policyO_LIInhibiting COX-2, as an important transient inflammatory response after hyper-physiological mechanical cues, could worsen the loss of structural integrity of the cartilage in osteoarthritis patients. Henceforth, prescription of COX-2 inhibitors as pain treatment for OA patients should be reconsidered. C_LI

molecular biology↗
bioRxiv2023

Thermodynamic limitations on brain oxygen metabolism: physiological implications

A recent hypothesis is that maintaining the brain tissue ratio of O2 to CO2 is critical for preserving the entropy increase available from oxidative metabolism of glucose, with a fall of that available entropy leading to a reduction of the phosphorylation potential and impairment of brain energy metabolism. The hypothesis suggests that physiological responses under different conditions can be understood as preserving tissue O2/CO2. To test this idea, a mathematical model of O2 and CO2 transport was used to calculate how well different physiological responses maintain tissue O2/CO2, showing good agreement with reported experimental measurements for increased neural activity, hypercapnia and hypoxia. The results highlight the importance of thinking about brain blood flow as a way to modulate tissue O2/CO2, rather than simply in terms of O2 delivery to the capillary bed. The hypoxia modeling focused on humans at high altitude, including acclimatized lowlanders and adapted populations, with a primary finding that decreasing CO2 by increasing ventilation rate is much more effective for preserving tissue O2/CO2 than increasing blood hemoglobin content. The modeling provides a new framework and perspective for understanding how blood flow and other physiological factors support energy metabolism in the brain under a wide range of conditions. Key points summaryO_LIRecent thermodynamic modeling suggests that preserving the O2/CO2 ratio in brain tissue is critical for preserving the entropy change available from the oxidative metabolism of glucose and the phosphorylation potential underlying energy metabolism. C_LIO_LIThe hypothesis tested is that normal physiological responses (notably blood flow changes) often act to preserve this ratio under changing conditions. C_LIO_LIUsing a detailed model to calculate tissue O2/CO2 we found good agreement with the predictions of the hypothesis and reported experimental results during hypoxia, hypercapnia and increased oxygen metabolic rate in response to increased neural activity. C_LIO_LIFor the hypoxia modeling we considered high altitude acclimatization and adaptation in humans, showing the critical role of reducing CO2 in preserving tissue O2/CO2. C_LIO_LIThe tissue O2/CO2 hypothesis provides a useful perspective for understanding the function of observed physiological responses under different conditions in terms of preserving brain energy metabolism, although the mechanisms underlying these functions are not well understood. C_LI

neuroscience↗
bioRxiv2023

Comparing data-driven physiological denoising approaches for resting-state fMRI: Implications for the study of aging

Physiological nuisance contributions by cardiac and respiratory signals has a significant impact on resting-state fMRI data quality. As these physiological signals are often not recorded, data-driven denoising methods are commonly used to estimate and remove physiological noise from fMRI data. To investigate the efficacy of these denoising methods, one of the first steps is to accurately capture the cardiac and respiratory signals, which requires acquiring fMRI data with high temporal resolution. In this study, we used such high-temporal resolution fMRI data to evaluate the effectiveness of several data-driven denoising methods, including global-signal regression (GSR), white matter and cerebrospinal fluid regression (WM-CSF), anatomical (aCompCor) and temporal CompCor (tCompCor), ICA-AROMA. Our analysis focused on each methods ability to remove cardiac and respiratory signal power, as well as its ability to preserve low-frequency signals and age-related functional connectivity (fcMRI) differences. Our findings revealed that ICA-AROMA and GSR consistently remove more heart-beat and respiratory frequencies, but also the most low-frequency signals. Our results confirm that the ICA-AROMA and GSR removed the most physiological noise at the expense of meaningful age-related fcMRI differences. On the other hand, aCompCor and tCompCor seem to provide a good balance between removing physiological signals and preserving fcMRI information. Lastly, methods differ in performance on young- and older-adult data sets. While this study cautions direct comparisons of fcMRI results based on different denoising methods in the study of aging, it also informs the choice of denoising method for broader fcMRI applications.

neuroscience↗
bioRxiv2023

libpspm: A feature-rich numerical package for solving physiologically structured population models

For a vast majority of organisms, life-history processes depend on their physiological state, such as body size, as well as on their environment. Size-structured population models, or more generally, physiologically structured population models (PSPMs), have emerged as powerful tools for modelling the population dynamics of organisms, as they account for the dependences of growth, mortality, and fecundity rates on an organisms physiological state and capture feedbacks between a populations structure and its environment, including all types of density regulation. However, despite their widespread appeal across biological disciplines, few numerical packages exist for solving PSPMs in an accessible and computationally efficient way. The main reason for this is that PSPMs typically involve solving partial differential equations (PDEs), and no single numerical method works universally best, or even at all, for all PDEs. Here, we present libpspm, a general-purpose numerical library for solving user-defined PSPMs. libpspm provides eight different methods for solving the PDEs underlying PSPMs, including four semi-implicit solvers that can be used for solving stiff problems. Users can choose the desired method without changing the code specifying the PSPM. libpspm allows for predicting the dynamics of multiple physiologically structured or unstructured species, each of which can have its own distinct set of physiological states and demographic functions. By separating model definition from model solution, libpspm can make PSPM-based modelling accessible to non-specialists and thus promote the widespread adoption of PSPMs.

ecology↗
bioRxiv2023

Novel isolates expand the physiological diversity of Prochlorococcus and illuminate its macroevolution

Prochlorococcus is a diverse picocyanobacterial genus and the most abundant phototroph on Earth. Its photosynthetic diversity divides it into high- or low-light adapted groups representing broad phylogenetic grades - each composed of several monophyletic clades. Here we physiologically characterize four new Prochlorococcus strains isolated from below the deep chlorophyll maximum in the North Pacific Ocean and combine this information with genomic and evolutionary analyses. The isolates belong to deeply-branching low-light adapted clades that have no other cultivated representatives and display some unusual characteristics. For example, despite its otherwise low-light adapted physiological characteristics, strain MIT1223 has low chl b2 content similar to high-light adapted strains. Isolate genomes revealed that each strain contains a unique arsenal of pigment biosynthesis and binding alleles that have been horizontally acquired, contributing to the observed physiological diversity. Comparative genomic analysis of all picocyanobacteria reveals that Pcb, the major pigment carrying protein in Prochlorococcus, greatly increased in copy number and diversity per genome along a branch that coincides with the loss of facultative particle attachment. Collectively, these observations add support to the current macroevolutionary model of picocyanobacteria, where niche constructing radiations allowed ancestral lineages to transition from a particle-attached to planktonic lifestyle and broadly colonize the water column, followed by adaptive radiations near the surface that pushed ancestral lineages deeper in the euphotic zone resulting in modern depth-abundance profiles. Originality-Significance StatementThe marine cyanobacterium, Prochlorococcus, is among the Earths most abundant organisms, and much of its genetic and physiological diversity remains uncharacterized. While field studies help reveal the scope of diversity, cultured isolates allow us to link genomic potential to physiological processes, illuminate eco-evolutionary feedbacks, and test theories arising from comparative genomics of wild cells. Here, we report the isolation and characterization of novel low-light (LL) adapted Prochlorococcus strains that fill in multiple evolutionary gaps. These new strains are the first cultivated representatives of the LLVII and LLVIII paraphyletic grades of Prochlorococcus, which are broadly distributed in the lower regions of the ocean euphotic zone. Each of these grades is a unique, highly diverse section of the Prochlorococcus tree that separates distinct ecological groups: the LLVII grade branches between monophyletic clades that have facultatively particle-associated and constitutively planktonic lifestyles, while the LLVIII grade lies along the branch that leads to all high-light (HL) adapted clades. Characterizing strains and genomes from these grades yields insights into the large-scale evolution of Prochlorococcus. The new LLVII and LLVIII strains are adapted to growth at very low irradiance levels and possess unique light-harvesting gene signatures and pigmentation. The LLVII strains represent the most basal Prochlorococcus group with a major expansion in photosynthetic antenna genes. Further, a strain from the LLVIII grade challenges the paradigm that all LL-adapted Prochlorococcus exhibit high ratios of chl b:a2. These findings provide insights into major transitions in Prochlorococcus evolution, from the benthos to a fully planktonic lifestyle and from growth at low irradiances to the rise of the HL-adapted clades that dominate the modern ocean.

microbiology↗
bioRxiv2024

Physiology-informed regularization enables training of universal differential equation systems for biological applications

Systems biology tackles the challenge of understanding the high complexity in the internal regulation of homeostasis in the human body through mathematical modelling. These models can aid in the discovery of disease mechanisms and potential drug targets. However, on one hand the development and validation of knowledge-based mechanistic models is time-consuming and does not scale well with increasing features in medical data. On the other hand, more data-driven approaches such as machine learning models require large volumes of data to produce generalizable models. The integration of neural networks and mechanistic models, forming universal differential equation (UDE) models, enables the automated learning of unknown model terms with less data than the neural network alone. Nevertheless, estimating parameters for these hybrid models remains difficult with sparse data and limited sampling durations that are common in biological applications. In this work, we propose the use of physiology-informed regularization, penalizing biologically implausible model behavior to guide the UDE towards more physiologically plausible regions of the solution space. In a simulation study we show that physiology-informed regularization not only results in a more accurate forecasting of model behaviour, but also supports training with less data. We also applied this technique to learn a representation of the rate of glucose appearance in the glucose minimal model using meal response data measured in healthy people. In that case, the inclusion of regularization reduces variability between UDE-embedded neural networks that were trained from different initial parameter guesses. Author summarySystems biology concerns the modelling and analysis of biological processes, by viewing these as interconnected systems. Modelling is typically done either using mechanistic differential equations that are derived from experiments and known biology, or using machine learning on large biological datasets. While mathematical modelling from biological experiments can provide useful insights with limited data, building and validating these models takes a long time and often requires highly invasive measurements in humans. Efforts to combine this classical technique with machine learning have resulted in a framework termed universal differential equations, where the model equations contain a neural network to describe unknown biological interactions. While these methods have shown success in numerous fields, applications in biology are more challenging due to limited data-availability, high data sparsity. In this work, we have introduced physiology-informed regularization to overcome these instabilities and to constrain the model to biologically plausible behavior. Our results show that by using physiology-informed regularization, we can accurately predict future unseen observations in a simulated example, with much more limited data than a similar model without regularization. Additionally, we show an application of this technique on human data, applying a neural network to learn the appearance of glucose in the blood plasma after a meal.

systems biology↗
bioRxiv2024

Evaluating Physiological Indicators in Detecting Deception using the Comparison Question Test (CQT)

Despite significant advancements in deception detection, traditional methods often fall short in real-world applications. This study addresses these limitations by evaluating the effectiveness of various physiological measures -- pupil response, electrodermal activity (EDA), heart rate (HR), and facial temperature changes -- in predicting deception using the Comparison Question Test (CQT). It also fills a critical research gap by validating these methods within an Asian context. Employing a between-subjects design, data was collected from a diverse sample of 118 participants from Singapore, including Chinese, Indian, and Malay individuals. The research aims to identify which physiological indicators, in combination, offer the most robust predictions of deceptive behavior. Key innovations include the adaptation of the CQT with a modified directed lie paradigm and an expanded sample size to assess the relative importance of each physiological measure. The studys findings reveal that pupil response is the most significant predictor of deception, with EDA enhancing the models explanatory power. HR, while relevant, adds limited value when combined with pupil response and EDA, and facial temperature changes were statistically non-significant. The study highlights the need for further research into the interactions among physiological measures and their application in varied contexts. This research contributes valuable insights into improving deception detection methodologies and sets the stage for future investigations that could incorporate additional physiological indicators and explore real-world applications.

bioengineering↗
bioRxiv2024

Mitochondrial morphodynamics are modulated by physiological range of temperature and influence host cell outcomes during influenza infection

Influenza viruses replicate in both the cooler, upper portions of the airway and the warmer, lower portions of the respiratory tract. This study investigates how physiological ranges of temperature, specifically 33{degrees}C and 37{degrees}C, impact host cell biology and how temperature-dependent differences in host cells influence outcomes during influenza A virus infection. This study prioritizes describing mitochondrial networks due to their importance in maintaining cellular homeostasis and mediating immune responses to viral infection. The temperature at which cells are incubated significantly influences mitochondrial network morphology and mitochondrial function. Moreover, temperature-dependent changes to mitochondrial networks prior to infection result in temperature-specific changes to host cell outcomes during infection. These findings indicate that mitochondrial structure alone can modulate host cell outcomes during viral infection and that both the form and function of mitochondria directly impact influenza A virus production. While not all mitochondrial processes were shown to be affected by temperature or infection, these results highlight the importance of using physiologically relevant temperatures in respiratory pathogen research and elucidate how mitochondrial dynamics contribute to host cell outcomes during influenza A virus infection. ImportanceRespiratory viruses infect the upper and lower respiratory tract but rarely is the impact of physiological ranges of temperature (33{degrees}C to 37{degrees}C) considered. Mitochondria are central mediators of numerous physiological pathways, and their functions are often modified by virus infection. Physiological ranges of temperature can alter mitochondrial form and function, which is further impacted by virus infection. The study sheds light on how temperature can impact mitochondrial form and function in concert with virus infection.

microbiology↗
bioRxiv2024

Transcriptional Regulation of Neuropeptide Receptors Decodes Complexity of Peptidergic Modulation of Behavior and Physiology.

The modulation of complex behaviors in response to environmental and physiological contexts is a fundamental aspect of animal biology, with neuropeptides (NPs) playing a crucial role in this process. This study investigates the transcriptional regulation of neuropeptide receptors (NPRs) as a mechanism for context-dependent neuropeptidergic modulation of physiology and behavior. We hypothesize that the transcriptional control of NPR genes, rather than the NPs themselves, is a critical determinant in the context-dependent modulation of behavior and physiology. Using a multi-faceted approach, including comparative genomics, transcription factor network analysis, and empirical validation in model organisms such as Drosophila melanogaster, we reveal a complex regulatory landscape where NPR expression is tightly controlled. Our findings demonstrate that NPR genes exhibit a higher number of enhancers, CTCF-binding sites, and open chromatin regions compared to NP genes, suggesting a greater susceptibility to transcriptional modulation. This regulatory architecture allows for precise control over neuropeptidergic signaling, enabling dynamic and context-specific behavioral and physiological responses. Our results highlight the importance of NPR-expressing cells by transcriptional regulation in mediating the effects of NPs on behavior and physiology. We show that this regulation is conserved across species, indicating an evolutionarily significant mechanism for fine-tuning neuropeptidergic signaling. Furthermore, our study provides insights into the distinct regulatory mechanisms underlying the multifunctionality of NPs and their receptors, offering a novel perspective on the transcriptional control of complex behaviors. In conclusion, this study advances our understanding of neuropeptidergic signaling by focusing on the transcriptional regulation of NPRs. Our findings have broad implications for the development of therapeutic strategies targeting neuropeptidergic systems in various neurological and behavioral disorders.

neuroscience↗
bioRxiv2025

Dual process impairments in reinforcement learning and working memory systems underlie learning deficits in physiological anxiety

Anxiety has been robustly linked to deficits in frontal executive function including working memory (WM) and attentional control processes. However, although anxiety has also been associated with impaired performance on learning tasks, computational investigations of reinforcement learning (RL) impairment in anxiety have yielded mixed results. WM processes are known to contribute to learning behavior in parallel to RL processes and to modulate the effective learning rate as a function of load. However, WM processes have typically not been modeled in investigations of anxiety and RL. In the current study, we leveraged an experimental paradigm (RLWM) which manipulates the relative contributions of WM and RL processes in a reinforcement learning and retention task using multiple stimulus set sizes. Using a computational model of interactive RL and WM processes, we investigated whether individual differences in physiological or cognitive anxiety impacted task performance via deficits in RL or WM. Elevated physiological, but not cognitive, anxiety scores were strongly associated with worse performance during learning and retention testing across all set sizes. Computationally, higher physiological anxiety scores were significantly related to reduced learning rate and increased rate of WM decay. To highlight the importance of modeling WM contributions to learning, we considered the effect of fitting RL models without WM modules to the data. Here we found that reduced learning performance for higher physiological anxiety was at least partially misattributed to stochastic decision noise in 9 out of 10 RL-only models considered. These findings reveal a dual-process impairment in learning in anxiety that is linked to a more physiological than cognitive anxiety phenotype. More broadly, this work also points to the importance of accounting for the contribution of WM to RL when investigating psychopathology-related deficits in learning.

neuroscience↗
bioRxiv2025

The Physiological Component of the BOLD Signal: Impact of Age and Heart Rate Variability Biofeedback Training

Aging is associated with declines in autonomic nervous system (ANS) function, including reduced heart rate variability (HRV), impaired neurovascular coupling, and diminished cerebrovascular responsiveness--factors that may contribute to cognitive decline and neurodegenerative diseases. Understanding how aging alters physiological signal integration in the brain is crucial for identifying potential interventions to promote brain health. This study examines age-related differences in how cardiac and respiratory fluctuations influence the blood oxygenation level-dependent (BOLD) signal, using two independent resting-state fMRI datasets with concurrent physiological recordings from younger and older adults. Our findings reveal significant age-related reductions in the percent variance of the BOLD signal explained by heart rate (HR), respiratory variation (RV), and end-tidal CO2, particularly in regions involved in autonomic regulation, including the orbitofrontal cortex, anterior cingulate cortex, insula, basal ganglia, and white matter. Cross-correlation analysis also revealed that younger adults exhibited stronger HR-BOLD coupling in white matter, as well as a more rapid BOLD response to RV and CO2 in gray matter. Additionally, we investigated the effects of heart rate variability biofeedback (HRV-BF) training, a non-invasive intervention designed to modulate heart rate oscillations. The intervention altered physiological-BOLD coupling in an age- and training-dependent manner: older adults who underwent HRV-BF to enhance HR oscillations exhibited a shift toward younger-like HR-BOLD coupling patterns, while younger adults who trained to suppress HR oscillations showed increased CO2-BOLD coupling. These findings suggest that HRV-BF may help mitigate age-related declines in autonomic or cerebrovascular function. Overall, this study underscores the role of physiological dynamics in brain aging and highlights the importance of considering autonomic function when interpreting BOLD signals. By demonstrating that HRV-BF can modulate physiological-BOLD interactions, our findings suggest a potential pathway for enhancing cerebrovascular function and preserving brain health across the lifespan.

neuroscience↗
bioRxiv2025

Stealth replication of SARS-CoV-2 Omicron in the nasal epithelium at physiological temperature

The COVID-19 pandemic was marked by successive waves of SARS-CoV-2 variants with distinct properties. The Omicron variant that emerged in late 2021 showed a major antigenic shift and rapidly spread worldwide. Since then, Omicron-derived variants have maintained their global dominance, for reasons that remain incompletely understood. We report that the original Omicron variant BA.1 evolved several traits that converged in facilitating viral spread. First, Omicron displayed an early replicative advantage over previous variants when grown in a reconstructed nasal epithelium model based on primary human cells. The increase in Omicron replication was more marked at the 33{degrees}C temperature characteristic of human nasal passages, resulting in a physiologically relevant advantage. Omicron also caused a decrease in epithelial integrity, as measured by transepithelial electrical resistance and caspase-3 activation. Furthermore, Omicron caused a more marked loss of motile cilia at 33{degrees}C than other variants, suggesting a capacity to impair mucociliary clearance. RNAseq analysis showed that Omicron induced a broad transcriptional downregulation of ciliary genes but only a limited upregulation of host innate defense genes at 33{degrees}C. The lower production of type I and type III interferons in epithelia infected by Omicron compared to those infected by the Delta variant, at 33{degrees}C as well as 37{degrees}C, confirmed the increased capacity of Omicron to evade the innate antiviral response. Thus, Omicron combined replication speed, motile cilia impairment, and limited induction of innate antiviral responses when propagated in reconstructed nasal epithelia at physiological temperature. Omicron has the capacity to propagate efficiently but stealthily in the upper respiratory tract, which likely contributed to the evolutionary success of this SARS-CoV-2 variant. AUTHOR SUMMARYThe COVID-19 pandemic was initially characterized by a rapid succession of viral variants that emerged independently of each other, with each of these variants outcompeting previous ones and rising to regional or global dominance. A major evolutionary shift occurred in late 2021, with the emergence of the highly divergent Omicron BA.1 variant. Since then, all the dominant SARS-CoV-2 variants have been derived from Omicron, for reasons that remain incompletely understood. In this study, we chose to compare the replication of SARS-CoV-2 variants in a human nasal epithelium model grown at 37{degrees}C but also at 33{degrees}C, a more physiological temperature that approximates that found in the nasal cavity. In this model, Omicron showed an early replicative advantage that was more marked at nasal physiological temperature. Omicron also markedly impaired the layer of motile cilia that normally contributes to the clearance of inhaled particles from the nasal mucosa. Even though it caused tissue damage, Omicron triggered only a minimal antiviral interferon response from epithelia grown at 33{degrees}C. Thus, Omicron has the capacity to propagate rapidly but stealthily in the nasal epithelium at physiological temperature, which helps account for the efficient dissemination of this variant worldwide.

microbiology↗
bioRxiv2025

Contrasting needle physiological strategies to soil nutrient scarcity in radiata pine plantations

Intensive European forestry practices contribute to soil degradation and nutrient depletion, compromising tree health and ecosystem stability. However, the influence of soil nutrient scarcity on tree physiological responses during drought remains unclear, particularly in Pinus radiata D. Don plantations, where it may impair needle function and overall tree health. We investigated how soil nutrient availability influences leaf-level physiological strategies during drought by comparing same-aged needles from two P. radiata stands with contrasting management stages and ages: (i) trees ({approx}20 years), likely to be clear-cut at 30-35 years (herein managed), and (ii) trees (>45 years) not clear-cut at the typical rotation age (herein abandoned). During the severe summer drought of 2022, both stands exhibited downregulation of the photosynthetic apparatus, indicating impaired photosynthetic performance under drought. However, their leaf-physiological responses to nutrient scarcity diverged. Managed trees exhibited dependence on soil nutrients, with reduced photosynthetic performance under nutrient-poor conditions. In contrast, abandoned trees showed relative independence from soil nutrients, maintaining photosynthetic function even under nutrient-poor conditions. This highlights a trade-off: younger, managed trees may enhance photosynthesis under optimal nutrient conditions but are more susceptible to nutrient imbalances and environmental stress. In turn, older, abandoned trees appeared to buffer the effects of drought and nutrient scarcity through age-related physiological traits. Our findings underscore the physiological value of mature stands. Furthermore, higher soil organic carbon and vegetation diversity in abandoned stands suggest that management cessation may enhance long-term ecosystem resilience. These results emphasise the importance of integrating soil-leaf interactions into sustainable forest management.

plant biology↗
bioRxiv2025

A clinical grade neurostimulation implant for hierarchical control of physiological activity

Bioelectronic implants for neurostimulation aim to steer disordered neurophysiological processes back towards a healthy state. However, physiology is subject to biological rhythms, including the circadian rhythm and the sleep-wake cycle. These predictable rhythms affect disease symptomatology, biomarkers used in closed-loop therapies, and a physiological systems expected response to stimulation. Therefore, therapeutic devices should incorporate feedforward elements to align algorithm parameters with predictable changes in physiological state, as a parallel of physiological rheostatic control. Here we introduce the DyNeuMo-2c, the first clinical-grade implant capable of delivering closed-loop neurostimulation while flexibly changing its functional configuration according to time of day. The device can chronically measure brain activity and motion state to track potential biomarker patterns in natural, out-of-clinic settings, allowing identification and targeting of patient-specific chronotypes. The system implements a hierarchical control flow, with baseline therapy set by a circadian scheduler, and adaptive policies layered to take effect based on specific biomarkers indicating patient and disease state. Using a benchtop validation setup, we demonstrate that the system has the required capabilities for delivering time-contingent closed-loop therapy in two established clinical use cases: Parkinsons disease and epilepsy. Next, we deploy the system in vivo to deliver closed-loop deep brain stimulation in a healthy non-human primate model of vigilance, highlighting the importance of synchronisation between device operation and physiological state in various conditions (task performance, unconstrained behaviour, and sleep). Time-of-day-dependent adaptation of closed-loop stimulation enabled modulation of both vigilance and behaviour. Overall, the novel device architecture provides a proof-of-concept for delivering time-contingent therapy in chronic therapeutic settings where biological rhythms are of key importance.

neuroscience↗
bioRxiv2018

Empirical Models for Anatomical and Physiological Changes in a Human Mother and Fetus During Pregnancy and Gestation

Many parameters treated as constants in traditional physiologically based pharmacokinetic models must be formulated as time-varying quantities when modeling pregnancy and gestation due to the dramatic physiological and anatomical changes that occur during this period. While several collections of empirical models for such parameters have been published, each has shortcomings. We sought to create a repository of empirical models for tissue volumes, blood flow rates, and other quantities that undergo substantial changes in a human mother and her fetus during the time between conception and birth, and to address deficiencies with similar, previously published repositories. We used maximum likelihood estimation to calibrate various models for the time-varying quantities of interest, and then used the Akaike information criterion to select an optimal model for each quantity. For quantities of interest for which time-course data were not available, we constructed composite models using percentages and/or models describing related quantities. In this way, we developed a comprehensive collection of formulae describing parameters essential for constructing a PBPK model of a human mother and her fetus throughout the approximately 40 weeks of pregnancy and gestation. We included models describing blood flow rates through various fetal blood routes that have no counterparts in adults. Our repository of mathematical models for anatomical and physiological quantities of interest provides a basis for PBPK models of human pregnancy and gestation, and as such, it can ultimately be used to support decision-making with respect to optimal pharmacological dosing and risk assessment for pregnant women and their developing fetuses. The views expressed in this article are those of the authors and do not necessarily represent the views or policies of the U.S. Environmental Protection Agency.\n\nAUTHOR SUMMARYPhysiologically based pharmacokinetic modeling is a well-known technique for making predictions about internal time-course concentrations of a substance that has entered an organism. This tool is widely used in both pharmaceutical research and human health risk assessment because it harnesses one of the fundamental tenets of both pharmacology and toxicology: it is the concentrations of an active chemical that reach internal target tissues, rather than externally applied \"doses\", that govern the extent of the response (whether beneficial or adverse). Constructing physiologically based pharmacokinetic models for pregnancy and gestation presents a considerable challenge because many of the required parameters (such as blood flow rates or tissue volumes) that are typically assumed to be constant in adult models or short-duration simulations cannot be assumed to be constant when modeling pregnancy. Here we present models, stated as functions of gestational age, for anatomical and physiological changes that occur in a human mother and fetus during pregnancy and gestation. We evaluated and selected models by applying a consistent statistical technique, and where possible, we compared results produced by our models to those produced by previously-published models. The collection of pregnancy parameter models presented here represents the most comprehensive such collection to date.

developmental biology↗
bioRxiv2018

Individual Behavioral and Physiological Responses During Different Experimental Situations - Consistency over Time and Effects of Context

In a number of species, consistent behavioral differences between individuals have been described in standardized tests, e.g. novel object exploration, open field test. Different behavioral expressions are reflective of different coping strategies of individuals in stressful situations. A causal link between behavioral responses and the activation of the physiological stress response is assumed but not thoroughly studied. Also, most standard paradigms investigating individual behavioral differences, are framed in a fearful context, therefore the present study aimed to add a test in a more positive context, the feeding context. We assessed individual differences in physiological (heart rate, HR) and behavioral responses (presence or absence of pawing, startle response, defecation, snorting) of twenty domestic horses (Equus caballus) in two behavioral experiments, a novel object presentation and a pre-feeding excitement test. Experiments were conducted twice, in summer and autumn. Both experiments caused higher mean HR in the first ten seconds after stimulus presentation compared to a control condition, but mean HR did not differ between the experimental conditions. Interestingly, in the novel object experiment, horses displaying stress-related behaviors during the experiments also showed a significantly higher HR increase compared to horses which did not display any stress-related behaviors, reflecting a correlation between behavioral and physiological responses to the novel object. On the contrary, in the pre-feeding experiments, horses that showed fewer behavioral responses had a greater HR increase, indicating the physiological response being due to emotional arousal and not behavioral activity. Moreover, HR response to experimental situations varied significantly between individuals, and although we found HR to be significantly repeatable across experiments, repeatability indices were low. In conclusion, our findings show that horses behavioral and physiological responses differed between test situations and that high emotional reactivity, shown via mean HR and HR increase, is not always displayed behaviorally.

animal behavior and cognition↗