Miracle-dz/UniFAR-FTUs huggingface dataset:
Can neurogenesis act as a neural regularizer?

New neurons are continuously generated in the subgranular zone of the dentate gyrus throughout adulthood. These new neurons gradually integrate into hippocampal circuits, forming new naïve synapses. Viewed from this perspective, these new neurons may represent a significant source of ‘wiring’ noise in hippocampal networks. In machine learning, such noise injection is commonly used as a regularization technique. Regularization techniques help prevent overfitting training data, and allow models to generalize learning to new, unseen data. Using a computational modeling approach, here we ask whether a neurogenesis-like process similarly acts as a regularizer, facilitating generalization in a category learning task. In a convolutional neural network (CNN) trained on the CIFAR-10 object recognition dataset, we modeled neurogenesis as a replacement/turnover mechanism, where weights for a randomly chosen small subset of neurons in a chosen hidden layer were re-initialized to new values as the model learned to categorize 10 different classes of objects. We found that neurogenesis enhanced generalization on unseen test data compared to networks with no neurogenesis. Moreover, neurogenic networks either outperformed or performed similarly to networks with conventional noise injection (i.e., dropout, weight decay, and neural noise). These results suggest that neurogenesis can enhance generalization in hippocampal learning through noise-injection, expanding on the roles that neurogenesis may have in cognition. Author Summary In deep neural networks, various forms of noise injection are used as regularization techniques to prevent overfitting and promote generalization on unseen test data. Here, we were interested in whether adult neurogenesis– the lifelong production of new neurons in the hippocampus– might similarly function as a regularizer in the brain. We explored this question computationally, assessing whether implementing a neurogenesis-like process in a hidden layer within a convolutional neural network trained in a category learning task would prevent overfitting and promote generalization. We found that neurogenesis regularization was as least as effective as, or more effective than, conventional regularizers (i.e., dropout, weight decay and neural noise) in improving model performance. These results suggest that optimal levels of hippocampal neurogenesis may improve memory-guided decision making by preventing overfitting, thereby promoting the formation of more generalized memories that can be applied in a broader range of circumstances. We outline how these predictions may be evaluated behaviorally in rodents with altered hippocampal neurogenesis.
Multimodal deep neural decoding of visual object representation in humans

Perception and categorization of objects in a visual scene are essential to grasp the surrounding situation. However, it is unclear how neural activities in spatially-distributed brain regions, especially in the aspect of temporal dynamics, represent visual objects. To address this issue, we explored spatial and temporal organization of visual object representations using concurrent functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), combined with neural decoding by deep neural networks (DNNs). Visualization of the fMRI DNN revealed that visual categorization (faces or non-face objects) occurred in brain-wide cortical regions, including the ventral temporal cortex. Interestingly, the EEG DNN valued the earlier phase of neural responses for categorization and the later phase of neural responses for sub-categorization. Combination of both DNNs improved classification performance for both categorization and sub-categorization. These deep learning-based results demonstrate a categorization principle in which visual objects are represented in a spatially organized and coarse-to-fine manner.
Assessing TMS-evoked cognitive network responses depending on neurocognitive and oscillatory brain state: A simultaneous TMS-EEG-fMRI project

Human cognition arises from information exchange within and between functionally connected brain networks. Alterations in such signal propagation across networks are linked to numerous disorders. Brain-wide signal propagation can be experimentally studied with simultaneous transcranial magnetic stimulation (TMS) and functional magnetic resonance imaging (fMRI), where TMS pulses introduce a signal at a certain network node and fMRI charts its propagation through the network. Yet, this approach ignores the fact that the (network) impact of a TMS pulse depends on brain state, where brain state fluctuates spontaneously from moment to moment (e.g. oscillatory state) as well as depending on what a participant does (neurocognitive state). Here, we assessed TMS-evoked fMRI activations as a function of neurocognitive state (eyes open versus eyes closed in complete darkness) and oscillatory state (low versus high pre-TMS alpha power, as measured with simultaneous electroencephalography (EEG)). We applied supra-versus sub-threshold triple-pulse TMS to the right posterior parietal cortex in eight participants, while simultaneously recording EEG and fMRI during two different ocular states. In this first application of the multimodal TMS-EEG-fMRI paradigm to a cognitive network hub, we did not find evidence for a brain state modulation of TMS-induced signal propagation. Instead, we found state-independent TMS-evoked fMRI responses mostly in sensory areas such as the insula, superior temporal gyrus, anterior cingulate cortex, and thalamus, but also in the frontal eye fields. Interestingly, neurocognitive state did seem to modulate the fMRI response to indirect TMS effects such as sensory stimulation. These results lead to several important insights for future cognitive multimodal TMS experiments.
Loss of the repressor REST affects progesterone receptor function and promotes uterine leiomyoma pathogenesis

Uterine Leiomyomas (UL) are benign tumors that arise in the myometrial layer of the uterus. The standard treatment option for UL is hysterectomy, although hormonal therapies such as selective progesterone receptor modulators are often used as temporary treatment options to reduce symptoms or slow the growth of tumors. However, since the pathogenesis of UL is poorly understood and most hormonal therapies are not based on UL-specific, divergent hormone signaling pathways, hallmarks that predict long-term efficacy and safety of pharmacotherapies remain largely undefined. In a previous study, we reported aberrant expression of REST/NRSF target genes activate UL growth due to the near ubiquitous loss of REST. Here, we show that ablation of the Rest gene in mouse uterus leads to UL phenotype and gene expression patterns analogous to UL, including altered estrogen and progesterone signaling pathways. We demonstrate that many of the genes dysregulated in UL harbor cis-regulatory elements bound by REST and progesterone receptor (PGR) adjacent to each other. Crucially, we identify an interaction between REST and PGR in healthy myometrium and present a putative mechanism for the dysregulation of progesterone responsive genes in UL ensuing the loss of REST. Using three Rest conditional knockout mouse lines, we provide a comprehensive picture of the impact loss of REST has in UL pathogenesis and in altering the response of UL to steroid hormones. Significance statement Ablation of Rest gene in the mouse uterus, modelling the loss REST in uterine fibroids, results in tumor formation and gene expression patterns analogous to human UL, including altered estrogen and progesterone receptor (PGR) pathways. The current study provides a putative mechanism for the aberrant function of PGR in UL.
Social determinants of health and disease in companion dogs: a cohort study from the Dog Aging Project

Exposure to social environmental adversity is associated with health and survival in many social species, including humans. However, little is known about if and how these health and mortality effects vary across the lifespan, largely due to the difficulty of studying long-lived organisms across much of their lifespan. Here, we leveraged a relatively new and powerful model for human aging, the companion dog, to investigate which components of the social environment are associated with dog health and how these associations vary across the lifespan. We drew on comprehensive survey data collected on 21,410 dogs from the Dog Aging Project and identified five factors that together explained 33.7% of the variation in a dog’s social environment. Factors capturing financial and household adversity were associated with poorer health and lower physical mobility in companion dogs, while factors that captured social support, such as living with other dogs, were associated with better health when controlling for dog age and weight. Some of these associations differed across a dog’s lifespan, including a stronger relationship between owner age and health in younger (as compared to older) dogs. Taken together, these findings suggest the importance of income, stability, and owner age on owner-reported health outcomes in companion dogs and point to potential behavioral and/or environmental modifiers that can be used to promote healthy aging across species.
Evolutionary history mediates population response to rapid environmental change through within-generational and transgenerational plasticity

Rapid environmental change is affecting many organisms; some are coping well but many species are in decline. A key mechanism for facilitating success following environmental change is phenotypic plasticity. Organisms use cues to respond phenotypically to environmental conditions; many incorporate recent information (within-generation plasticity) and information from previous generations (transgenerational plasticity). We extend an existing evolutionary model where organisms utilize within-generational plasticity, transgenerational plasticity, rapid evolution, and bet-hedging. We show how, when rapid evolution of plasticity is not possible, the effect of environmental change (altering the environment mean, variance, or autocorrelation, or cue reliability) on population growth rate depends on selection for within-generation plasticity and transgenerational plasticity under historical environmental conditions. We then evaluate the predictions that populations adapted to highly variable environments, or with greater within-generational plasticity, are more likely to successfully respond to environmental change. We identify when these predictions fail, and show environmental change is most detrimental when previously reliable cues become unreliable. When multiple cues become unreliable, environmental change can cause deleterious effects regardless of the population’s evolutionary history. Overall, this work provides a general framework for understanding the role of plasticity in population responses to rapid environmental change.
In situ single particle classification reveals distinct 60S maturation intermediates in cells

Electron cryo-microscopy (cryo-EM) can generate high-resolution views of cells with faithful preservation of molecular structure. In situ cryo-EM, therefore, has enormous potential to reveal the atomic details of biological processes in their native context. However, in practice, the utility of in situ cryo-EM is limited by the difficulty of reliably locating and confidently identifying molecular targets (particles) and their conformational states in the crowded cellular environment. We recently showed that 2DTM, a fine-grained template-based search applied to cryo-EM micrographs, can localize particles in two-dimensional views of cells with high precision. Here we demonstrate that the signal-to-noise ratio (SNR) observed with 2DTM can be used to differentiate related complexes in focused ion beam (FIB)-milled cell sections. We apply this method in two contexts to locate and classify related intermediate states of 60S ribosome biogenesis in the Saccharomyces cerevisiae cell nucleus. In the first, we separate the nuclear pre-60S population from the cytoplasmic mature 60S population, using the subcellular localization to validate assignment. In the second, we show that relative 2DTM SNRs can be used to separate mixed populations of nuclear pre-60S that are not visually separable. We use a maximum likelihood approach to define the probability of each particle belonging to each class, thereby establishing a statistic to describe the confidence of our classification. Without the need to generate 3D reconstructions, 2DTM can be applied even when only a few target particles exist in a cell.
Short-course but not prolonged treatment with ATR inhibitor AZD6738 integrates with radiotherapy to generate a tumor antigen-specific CD8+ T cell expansion in the periphery

ATR kinase is a central regulator of the DNA damage response. While ATR kinase inhibitors (ATRi’s) are known to sensitize cancer cells to DNA damage, their effect on immune cells is not known. Here we show in mice that short-course AZD6738 (ATRi) on days 1-3 decreases proliferating T cells in the tumor and periphery and that cessation of ATRi causes a proliferative rebound. Integrating radiation on days 1-2 (RT) with ATRi on days 1-3 increases IFN-β in the tumor and activates tumor antigen-specific CD8+ T cells in the tumor-draining lymph node (DLN). RT with short-course ATRi induces an expansion of tumor antigen-specific CD8+ T cells in the DLN. In contrast, RT with prolonged daily ATRi blocks expansion of antigen-specific CD8+ T cells, despite increased IFN-β and activation of CD8+ T cells. Our data identifies critical schedule considerations for ATRi with RT, immunotherapy and genotoxic therapies.
Decoding Distinct Ganglioside Patterns of Native and Differentiated Mesenchymal Stem Cells by a Novel Glycolipidomics Profiling Strategy

Gangliosides are an indispensable glycolipid class concentrated on cell surfaces with a critical role in stem cell differentiation. Nonetheless, owing to the lack of suitable methods for scalable analysis covering the full scope of ganglioside molecular diversity, their mechanistic properties in signaling and differentiation remain undiscovered to a large extent. This work introduces a sensitive and comprehensive ganglioside assay based on liquid chromatography, high-resolution mass spectrometry, and multistage fragmentation. Complemented by an open-source data evaluation workflow, we provide automated in-depth lipid species-level and molecular species-level annotation based on decision rule sets for all major ganglioside classes. Compared to conventional state-of-the-art methods, the presented ganglioside assay offers (1) increased sensitivity, (2) superior structural elucidation, and (3) the possibility to detect novel ganglioside species. A major reason for the highly improved sensitivity is the optimized spectral readout based on the unique capability of two parallelizable mass analyzers for multistage fragmentation. In addition to the significant technological advance, we identified 263 ganglioside species including cell-state-specific markers and previously unreported gangliosides in native and differentiated human mesenchymal stem cells. A general increase of the ganglioside numbers upon differentiation was observed as well as cell-state-specific clustering based on the ganglioside species patterns. By proving the predictive power of gangliosides as ubiquitous cell state-specific markers, we demonstrated the high throughput universal capability of our novel analytical strategy, which comes with new insights on the biological role of gangliosides in stem cell differentiation. Our analytical workflow will pave the way for new ganglioside- and glycolipid-based clusters of differentiation markers to determine stem cell phenotypes.
Unmasking hidden changes in intrinsic properties in neurons that coordinate oscillatory networks

Neurons utilize gain control to efficiently encode shifting inputs with maximum sensitivity. While gain control is present in a multitude of neurons, research on the underlying mechanisms is still sparse. We focused on one coordinating neuron (ASCE) in the crayfish swimmeret system that is both necessary and sufficient to coordinate distributed motor circuits. This neuron encodes properties of the motor activity in its home ganglion and sends the information as burst of spikes to its anterior target ganglia. Previous research has shown that ASCE encodes the strength of the motor activity by its spike number, and that the range of the number of spikes per burst adapts to the average motor activity. In turn, the motor activity can be activated and modulated via cholinergic pathways. We explored ASCE’s response to changing levels of motor system excitation, which was induced with cholinergic agonists, both when the neuron was part of the full swimmeret system’s circuit and when it was isolated from the network. The cholinergic agonist carbachol directly depolarized ASCE’s membrane potential via nicotinic receptors when the neuron was isolated. In the intact circuit, ASCE’s membrane potential did not change but its input resistance decreased. This indicated an additional indirect action of carbachol, presumably via presynaptic central pattern generating neurons at the core of each motor circuit. The balance of direct and indirect effects could allow ASCE to match its spiking range to the system’s excitation level. 1. Summary statement A balance of direct and indirect cholinergic effects precisely controls the membrane potential, and therefore the gain, of a neuron that is involved in the coordination of distributed motor circuits.
Ecological and evolutionary drivers of phenotypic and genetic variation in the European crabapple (Malus sylvestris (L.) Mill.), a wild relative of the cultivated apple

Characterizing the phenotypic and genetic variation among populations of crop wild relatives help understanding the ecological and evolutionary processes involved in population divergence, and better harness their diversity to mitigate the impact of climate change on crops. We assessed genetic and phenotypic diversity of the European crabapple, Malus sylvestris, a main contributor to the cultivated apple genome (Malus domestica), and investigated for ecological divergence. We assessed variation in growth rate and traits related to carbon uptake between seedlings measured in a common garden, and related it to the genetic ancestry of the seedlings, assessed using 13 microsatellite loci and Bayesian clustering method. The occurrence of patterns of isolation-by-distance, -by-climate and -by-adaptation that might have caused genetic and phenotypic differentiation among M. sylvestris populations was also tested. Seedlings belonged to seven M. sylvestris populations in Europe, with 11.6% of seedlings introgressed by M. domestica. Significant trait variation among M. sylvestris populations was observed, which for some was of moderate to high heritability. Lack of association between trait and genetic divergence suggests that this significant phenotypic variation is not adaptive, but strong association between genetic variation and the climate during the last glacial maximum suggests local adaptation of M. sylvestris to past climates. This study provides an insight into the ecological and evolutionary drivers of phenotypic and genetic differentiation among populations of a wild apple species and relative of cultivated apples, which is a starting point for future breeding programs. Societal impact Statement Apple is a major fruit crop worldwide and a model species for understanding the evolutionary processes underlying perennial crop domestication. Several wild species have contributed to the genetic make-up of the cultivated apple, yet phenotypic and genetic diversity data across their natural distribution is lacking. This study revealed phenotypic variation between populations of the European crabapple, and showed that both geography, and surprisingly, past but not current climate, shaped its genetic structure. We provide a starting point for harnessing wild apple diversity for apple breeding programs to mitigate the impact of climate change on this perennial crop.
Identification of a novel AP2 transcription factor in zygotes with an essential role in Plasmodium ookinete development

The sexual phase of Plasmodium represents a crucial step in malaria transmission, during which these parasites fertilize and form ookinetes to infect mosquitoes. Plasmodium development after fertilization is thought to proceed with female-stored mRNAs until the formation of a retort-form ookinete; thus, transcriptional activity in zygotes has previously been considered quiescent. In this study, we reveal the essential role of transcriptional activity in zygotes by investigating the function of a newly identified AP2 transcription factor, AP2-Z. ap2-z was previously reported as a female transcriptional regulator gene whose disruption resulted in developmental arrest at the retort stage of ookinetes. In this study, although ap2-z was transcribed in females, we show that it was translationally repressed by the DOZI complex and translated after fertilization with peak expression at the zygote stage. ChIP-seq analysis of AP2-Z shows that it binds on specific DNA motifs, targeting the majority of genes known as an essential component of ookinetes, which largely overlap with the AP2-O targets, as well as genes that are unique among the targets of other sexual transcription factors. The results of this study also indicate the existence of a cascade of transcription factors, beginning with AP2-G, that proceeds from gametocytogenesis to ookinete formation. Author summary Sexual development in Plasmodium parasites, a causative agent of malaria, is essential for their transmission from vertebrate hosts to mosquitoes. This important developmental process proceeds as follows: formation of a gametocyte/gamete, fertilization and conversion of the zygote into the mosquito midgut invasive stage, called the ookinete. As a target of transmission blocking strategies, it is important to understand the mechanisms regulating Plasmodium sexual development. In this study, we assessed transcriptional regulation after fertilization by investigating the function of a novel transcription factor, AP2-Z. The results revealed the essential role of de novo transcription activated by AP2-Z in zygotes for promoting ookinete development. As transcriptional activity during the zygote stage has previously been considered silent in Plasmodium, novel genes important for ookinete formation can now be explored in the target genes of AP2-Z. Investigating the functions of these genes can help us understand the mechanisms of Plasmodium zygote/ookinete development and identify new targets for transmission blocking vaccines.