Biology subjects

Lu, R.

Publications and source records attributed to Lu, R..

At least 55 records · Page 3Linked to original sources

bioRxiv2025

Visual pathway origins: an electron microscopic volume for connectomic analysis of the human foveal retina

The fovea is a unique specialization of the primate retina and is a promising site for obtaining the first complete connectome of a human central nervous system (CNS) structure. Within the fovea, neural cells and circuits have been miniaturized and compressed during evolution to sample the visual image at highest spatial resolution and begin the neural processing that serves human form, color, and motion perception. Here we present a comprehensive analysis of a sample of human foveal retina using deep learning-based segmentation to reconstruct all cells and synaptic connections at nanoscale resolution. We classified [~]3,000 cells into 51 distinct morphological types based on their structural features and connectivity patterns. Our observations reveal novel synaptic pathways absent in non-human primates, suggesting specialized circuits contribute uniquely to human trichromatic color vision. A biophysical model of the distinct connectomes made by gap junctions (electrical synapses) between short- (S) and medium-long- (ML) wavelength-sensitive cone photoreceptors, suggests chromatic interactions between S and ML cones prior to the first chemical synapse. Segmentation of retinal ganglion cells (RGCs) suggests the presence of only 11 visual pathways, with 5 high-density RGC pathways accounting for over 95% of foveal output to the brain: a dramatic contrast to the 40+ ganglion cell types recognized in mouse retina. Our connectomic analysis reveals distinctive features of human neural circuitry and demonstrates how AI-based computational approaches can advance understanding of human brain structure and function. Significant statementVision begins in the retina, an accessible outpost of the brain located at the back of the eye. When we directly view an object, we use the fovea--a tiny region containing over 50 distinct, densely packed neuron types. How these diverse cells interconnect through synapses to transmit visual information has been difficult to determine. Here, we used artificial intelligence-based computational methods to create a complete wiring diagram--a connectome--mapping every neuron and synapse in a sample of human fovea. This connectomic resource will be freely available to researchers worldwide. Discoveries so far include synaptic circuits linked to human color vision, demonstrating how connectomics can reveal what makes the human nervous system unique.

neuroscience↗

bioRxiv2025

Distinct and complementary mechanisms of oscillatory and aperiodic alpha activity in visuospatial attention

Alpha oscillations are thought to play a key role in visuospatial attention, particularly through lateralisation mechanisms. However, whether this function is driven purely by oscillatory activity or also involves aperiodic neural components remains unclear, making it difficult to develop precise theoretical models of alpha function and attention. Using EEG and concurrent TMS-EEG, this study aimed to (1) disentangle the contributions of oscillatory and aperiodic alpha activity to visuospatial attention and (2) examine their causal roles by differentially modulating aperiodic and oscillatory components. First, across three independent EEG datasets, we found that both oscillatory and aperiodic responses in the alpha band contribute to spatial attention encoding and univariate lateralisation effects. The two signals were uncorrelated across electrodes and their combination yielded stronger effects than either signal separately, suggesting that they may play complementary roles. Then, we used concurrent TMS-EEG to modulate the two signals. Compared to arrhythmic TMS (ar-TMS), rhythmic TMS (rh-TMS), enhanced oscillatory alpha power, especially at the stimulated area, while decreasing aperiodic alpha power across the scalp. Despite these opposing effects, rh-TMS improved visuospatial attention representation carried by both oscillatory and aperiodic alpha signals, suggesting that both signals may support attentional processing through different mechanisms. Moreover, TMS-induced changes in oscillatory and aperiodic alpha decoding differentially predicted behavioural performance, with TMS-induced changes in oscillatory alpha decoding correlating with response errors and changes in aperiodic alpha decoding correlating with response speed. Together these findings reveal a functional dissociation between oscillatory and aperiodic activity in the alpha band. We suggest a dual mechanism for alpha band activity in supporting visuospatial attention, where the two components have distinct but complementary roles. Oscillatory components may primarily support attentional filtering and target prioritization, while aperiodic components may reflect overall neural excitability and cognitive efficiency. Both of these mechanisms contribute to successful visuospatial attention.

neuroscience↗

bioRxiv2024

Interpretable PROTAC degradation prediction with structure-informed deep ternary attention framework

Proteolysis Targeting Chimeras (PROTACs) are heterobifunctional ligands that form ternary complexes with Protein Of Interests (POIs) and E3 ligases, exploiting the ubiquitin-proteasome system to degrade disease-associated proteins, promising to drug the undruggable. While PROTAC research primarily relies on costly and time-consuming wet experimental approaches, deep learning offers a promising avenue to accelerate development and reduce expenses. However, existing deep learning methods for PROTAC degradation prediction often overlook the significance of hierarchical molecular representation and protein structural information, hindering effective data modeling. Moreover, their black-box nature limits the interpretability of computational outcomes, failing to provide intuitive insights into substructure interactions within the PROTAC system. This study introduces PROTAC-STAN, a structure-informed deep ternary attention network (STAN) framework for interpretable PROTAC degradation prediction. PROTAC-STAN represents PROTAC molecules across atom, molecule, and property hierarchies and incorporates structure information for POIs and E3 ligases using a protein language model infused with structural data. Furthermore, it simulates interactions among three entities at the atom and amino acid levels via a novel ternary attention network tailored for the PROTAC system, providing unprecedented insights into the degradation mechanism. By integrating hierarchical PROTAC molecule representation, structural embedding of POI and E3 ligase, and ternary attention network modeling interactions, our approach substantially improves prediction accuracy by 10.95% while enabling significant model interpretability via atomic and residue level visualization of molecule and complex. Experiments on the refined public PROTAC dataset demonstrate that PROTAC-STAN outperforms state-of-the-art baselines in overall performance. The excellent performance of PROTAC-STAN is anticipated to establish it as a foundational tool in future research on PROTAC-related drugs, thereby accelerating the development of this field.

bioinformatics↗

bioRxiv2024

Antibody-Fab and -Fc features promote Mycobacterium tuberculosis restriction.

Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis (TB), is a leading cause of death by an infectious disease globally, with no efficacious vaccine. Antibodies are implicated in Mtb control, but the mechanisms of antibody action remain poorly understood. We assembled a library of TB monoclonal antibodies (mAb) and screened for the ability to restrict Mtb in mice, identifying protective antibodies targeting known and novel antigens. To dissect the mechanism of mAb-mediated Mtb restriction, we optimized a protective lipoarabinomannan-specific mAb through Fc-swapping. In vivo analysis of these Fc-variants revealed a critical role for Fc-effector function in Mtb restriction. Restrictive Fc-variants altered distribution of Mtb across innate immune cells. Single-cell transcriptomics highlighted distinctly activated molecular circuitry within innate immune cell subpopulations, highlighting early activation of neutrophils as a key signature of mAb-mediated Mtb restriction. Therefore, improved antibody-mediated restriction of Mtb is associated with reorganization of the tissue-level immune response to infection and depends on the collaboration of antibody Fab and Fc.

immunology↗

bioRxiv2024

Cellular and molecular networks of intercellular signaling in bone marrow hematopoiesis

Hematopoietic stem and progenitor cells (HSPCs) rely on intercellular signaling to maintain and adjust their production of blood and immune cells. This process occurs in the semi-fluidic bone marrow, hosting dozens of cell types that constantly migrate and interact. To elucidate the dynamic network of cell-cell interaction and signaling transduction underlying hematopoiesis, we developed an algorithm to measure Cell-cell Spatial Interaction Probability (CellSIP) by integrating data on ligand and receptor expression, cell type abundance, and cellular spatial positioning. Using new and published mouse datasets, we validated CellSIP and uncovered signaling transductions indicating feedback mechanisms underlying hematopoiesis. Moreover, we identified significant correlations between signaling pathways across individual HSPCs at the same hematopoiesis stage. These pathway correlations illuminate the organization of cellular and signaling networks underlying hematopoiesis, revealing new regulators through their associations with established ones. The signaling quantification and correlation data are available through the Hematopoiesis Intercellular Signaling Explorer (HISE).

bioinformatics↗

bioRxiv2024

Dissecting the oncogenic mechanisms of POT1 cancer mutations through deep scanning mutagenesis

Mutations in the shelterin protein POT1 are associated with diverse cancers, but their role in cancer progression remains unclear. To resolve this, we performed deep scanning mutagenesis in POT1 locally haploid human stem cells to assess the impact of POT1 variants on cellular viability and cancer-associated telomeric phenotypes. Though POT1 is essential, frame-shift mutants are rescued by chemical ATR inhibition, indicating that POT1 is not required for telomere replication or lagging strand synthesis. In contrast, a substantial fraction of clinically-validated pathogenic mutations support normal cellular proliferation, but still drive ATR-dependent telomeric DNA damage signaling and ATR-independent telomere elongation. Moreover, this class of cancer-associated POT1 variants elongates telomeres more rapidly than POT1 frame-shifts, indicating they actively drive oncogenesis and are not simple loss-of-function mutations.

cancer biology↗

bioRxiv2024

Aperiodic and oscillatory systems underpinning human domain-general cognition

Domain-general cognitive systems are essential for adaptive human behaviour, supporting various cognitive tasks through flexible neural mechanisms. While fMRI studies link the frontoparietal network activation to increasing demands across various tasks, the electrophysiological mechanisms underlying this domain-general response to demand remain unclear. Here, we used MEG/EEG, with aperiodic and oscillatory components separated, to examine their roles in domain-general cognition across three cognitive tasks using multivariate analysis. We found that both aperiodic (broadband power, slope, and intercept) and oscillatory (theta, alpha, and beta power) components coded task demand and content across all subtasks. Aperiodic broadband power in particular strongly coded task demand, in a manner that generalised across all subtasks. Source estimation suggested that increasing cognitive demand decreased aperiodic broadband power across the brain, with the strongest modulations overlapping with the frontoparietal network. In contrast, oscillatory activity showed more localised patterns of modulation, primarily in frontal or occipital regions. These results provide insights into the electrophysiological underpinnings of human domain-general cognition, highlighting the critical role of aperiodic broadband power.

neuroscience↗

bioRxiv2024

Non-autonomous insulin signaling regulates the duration of mitosis in C. elegans germline stem and progenitor cells

Stem and progenitor cell mitosis is essential for tissue development and homeostasis. How these cells ensure proper chromosome segregation, and thereby maintain mitotic fidelity, in the complex physiological environment of a living animal is poorly understood. Here we use in situ live-cell imaging of C. elegans germline stem and progenitor cells (GSPCs) to ask how the signaling environment influences stem and progenitor cell mitosis in vivo. Through a candidate screen we identify a new role for the insulin/IGF receptor (IGFR), daf-2, during GSPC mitosis. Mitosis is delayed in daf-2/IGFR mutants, and these delays require canonical, DAF-2/IGFR to DAF-16/FoxO insulin signaling, here acting cell non-autonomously from the soma. Interestingly, mitotic delays in daf-2/IGFR mutants depend on the spindle assembly checkpoint but are not accompanied by a loss of mitotic fidelity. Correspondingly, we show that caloric restriction, which delays GSPC mitosis and compromises mitotic fidelity, does not act via the canonical insulin signaling pathway, and instead requires AMP-activated kinase (AMPK). Together this work demonstrates that GSPC mitosis is influenced by at least two genetically separable signaling pathways and highlights the importance of signaling networks for proper stem and progenitor cell mitosis in vivo. Author SummaryStem and progenitor cells drive tissue development and sustain adult tissue turnover by producing new daughter cells via cell division, the success of which depends on proper chromosome segregation during mitosis. Stem and progenitor cells perform mitosis in the complex environment of a living animal, yet relatively little is known about how events during mitosis are influenced by this in vivo context. In particular, whether signaling pathways that coordinate other aspects of stem and progenitor cell behavior with animal physiology also play a role during mitosis is poorly understood. Here we took advantage of the germline stem and progenitor cells of the model nematode C. elegans to address this question. Through live-cell imaging of germline stem and progenitor cell mitosis, we uncover a new role for the insulin signaling pathway. We find that reducing insulin signaling delays germline stem and progenitor cell mitosis, but, surprisingly, these delays are not accompanied by a loss of mitotic fidelity. In addition, we find that reducing insulin signaling in somatic tissues is sufficient to delay germline stem and progenitor mitosis, indicating that the pathway acts non-autonomously. Finally, while insulin signaling is known to link cell division with nutritional status in many species, we found that it did not mediate the effects of caloric restriction on germline stem and progenitor cell mitosis. Instead, caloric restriction acts via the conserved energy-sensing regulator AMPK. These results uncover new regulators of germline stem and progenitor cell mitosis and emphasize the importance of signaling pathways for proper stem and progenitor cell mitosis in vivo.

developmental biology↗

bioRxiv2024

Recognition of RNA secondary structures with a programmable peptide nucleic acid-based platform

RNA secondary structures comprise double-stranded (ds) and single-stranded (ss) regions. Antisense peptide nucleic acids (asPNAs) enable the targeting of ssRNAs and weakly formed dsRNAs. Nucleobase-modified dsRNA-binding PNAs (dbPNAs) allow for targeting of relatively stable dsRNAs. A programmable RNA structure-specific targeting strategy is needed for simultaneous recognition of dsRNAs and ssRNAs. Here, we report on combining dbPNAs and asPNAs (designated as daPNAs) for the targeting of dsRNA-ssRNA junctions. Our binding and modeling data suggest that combining traditional asPNA (with a 4-letter code: T, C, A, and G) and dbPNA (with a 4-letter code: T or s2U, L, Q, and E) scaffolds facilitates RNA structure-specific tight binding (nM to M) under physiologically-relevant conditions. We further applied our daPNAs in substrate specific inhibition of Dicer acting on pre-miR-198 in a cell-free assay and regulating ribosomal frameshifting induced by model hairpins in both cell-free and cell culture assays. daPNAs would be a useful platform for developing chemical probes and therapeutic ligands targeting RNA. HighlightO_LIWe demonstrated that sequence- and structure-specific targeting of RNA can be facilitated by nucleobase-modified dsRNA-binding PNAs (dbPNAs) platform in combination with antisense PNAs (asPNAs). We name the novel PNAs as daPNAs. C_LIO_LIdaPNAs can be used in a programmable way for targeting RNAs by formation of a short triplex next to a short duplex at a dsRNA-ssRNA junction. C_LIO_LIWe applied our daPNAs in substrate specific inhibition of Dicer acting on pre-miR-198 in a cell-free assay and regulating ribosomal frameshifting induced by model hairpins in both cell-free and cell culture assays. C_LIO_LIThe daPNAs platform would serve as useful junction-specific molecular glues for the targeting of many biologically important RNA structures in transcriptomes. C_LI

biochemistry↗

bioRxiv2024

Enhanced Recognition of a Herbal Compound Epiberberine by a DNA Quadruplex-Duplex Structure

The small molecule epiberberine (EPI) is a natural alkaloid with versatile bioactivities against several diseases, including cancer and bacterial infection. EPI can induce the formation of a unique binding pocket at the 5' side of a human telomeric G-quadruplex (HTG) sequence Q4, resulting in a nanomolar binding affinity (KD approximately 26 nM) with significant fluorescence enhancement upon binding. It is important to understand (1) how EPI binding affects HTG structural stability and (2) how enhanced EPI binding may be achieved through the engineering of the DNA binding pocket. In this work, the EPI binding-induced HTG structure stabilization effect was probed by a peptide nucleic acid (PNA) invasion assay in combination with a series of biophysical techniques. We show that the PNA invasion-based method may be useful for the characterization of compounds binding to DNA (and RNA) structures in physiological conditions without the need to vary the solution temperature or buffer components, which are typically needed for structural stability characterization. Importantly, the combination of theoretical modeling and experimental quantification allows us to successfully engineer the Q4 derivative Q4-ds-A by a simple extension of a duplex structure to Q4 at the 5' end. Q4-ds-A is a superb EPI binder with a KD of 8 nM, with the binding enhancement achieved through the preformation of a binding pocket and a reduced dissociation rate. The tight binding of Q4 and Q4-ds-A with EPI allows us to develop a novel magnetic bead-based affinity purification system to effectively extract EPI from Rhizoma coptidis (Huang Lian) extracts.

biochemistry↗

bioRxiv2024

An Efficient and Principled Model to Jointly Learn the Agnostic and Multifactorial Effect in Large-Scale Biological Data

The rich information contained in biological data is often distorted by multiple interacting intrinsic or extrinsic factors. Modeling the effects of these factors is necessary to uncover the underlying true signals. However, this is challenging in real applications, because biological data usually consist of tens of thousands or millions of factors, and no reliable prior knowledge is available on how these factors exert the effect, to what degree the effect is, as well as how they interact with each other. Thus, the existing approaches rely on excessive simplification or unrealistic assumptions such as the probabilistic independence among factors. In this paper, we report the finding that after reformulating the data as a contingency tensor the problem can be well addressed by a fundamental machine learning principle, Maximum Entropy, with an extra effort of designing an efficient algorithm to solve the large-scale optimization problem. Based on the principle of maximum entropy, and by constraining the marginals of the contingency tensor using the observed values, our Conditional Multifactorial Contingency (CMC) model imposes minimum but essential assumptions about the multifactorial joint effects and leads to a conceptually simple distribution, which informs how these factors exert the effects and interact with each other. By replacing hard constraints with expected values, CMC avoids the NP-hard problem and results in a theoretically solvable convex problem. However, due to the large scale of variables and constraints, the standard convex solvers do not work. Exploring the special properties of the CMC model we developed an efficient iterative optimizer, which reduces the running time from infeasible to minutes or from days to seconds. We applied CMC to quite a few cutting-edge biological applications, including the detection of driving transcription factor, scRNA-seq normalization, cancer-associated gene identification, GO-term activity transformation, and quantification of single-cell-level similarity. CMC gained much better performance than other methods with respect to various evaluation criteria. Our source code of CMC as well as its example applications can be found at https://github.com/yu-lab-vt/CMC. One-Sentence SummaryCMC jointly learns intertwined effects of numerous factors in biologival data and outperform existing methods in multiple cutting-edge biological applications.

bioinformatics↗

bioRxiv2023

The causal roles of parietal alpha oscillations and evoked potentials in coding task-relevant information during selective attention

Selective attention is a fundamental cognitive mechanism that allows people to prioritise task-relevant information while ignoring irrelevant information. Previous research has suggested key roles of parietal evoked potentials and alpha oscillatory responses in spatial attention tasks. However, the informational content of these signals is less clear, and their causal effects on the coding of multiple task elements are yet unresolved. Here, we used concurrent TMS-EEG to causally manipulate parietal alpha power and evoked potentials and investigate their roles in coding multiple task features (where to attend, what to attend to, and visual stimulus) in a selective attention task. First, using EEG-only data, we found that evoked potentials coded all three types of task-relevant information with distinct temporal dynamics, and alpha oscillations carried information regarding both where to attend and what to attend to. Then, we applied rhythmic-TMS (rTMS) at individual alpha frequency over the right intraparietal sulcus (IPS), while concurrently measuring EEG. Compared with control arrhythmic-TMS, alpha rTMS increased alpha power and inter-trial phase coherence and yielded more negative posterior-contralateral evoked potentials. Moreover, alpha rTMS causally and specifically improved multivariate decoding of the information about where to attend (but not what to attend to or feature information) during task performance, with decoding improvements predicting changes in behavioural performance. These findings illuminate the dynamics with which the complementary aspects of a selective attention task are encoded in evoked and oscillatory brain activity. Moreover, they reveal a specific and causal role of IPS-controlled evoked and oscillatory activity in carrying behaviour-driving information about where to focus attention.

neuroscience↗

bioRxiv2023

Monocytes reprogrammed by 4-PBA potently contribute to the resolution of inflammation and atherosclerosis

BackgroundChronic inflammation initiated by inflammatory monocytes underlies the pathogenesis of atherosclerosis. However, approaches that can effectively resolve chronic low-grade inflammation targeting monocytes are not readily available. The small chemical compound 4-phenylbutyric acid (4-PBA) exhibits broad anti-inflammatory effects in reducing atherosclerosis. Selective delivery of 4-PBA reprogrammed monocytes may hold novel potential in providing targeted and precision therapeutics for the treatment of atherosclerosis. MethodsSystems analyses integrating single-cell RNA-sequencing and complementary immunological approaches characterized key resolving characteristics as well as defining markers of reprogrammed monocytes trained by 4-PBA. Molecular mechanisms responsible for monocyte reprogramming was assessed by integrated biochemical and genetic approaches. The inter-cellular propagation of homeostasis resolution was evaluated by co-culture assays with donor monocytes trained by 4-PBA and recipient naive monocytes. The in vivo effects of monocyte resolution and atherosclerosis prevention by 4-PBA were assessed with the high fat diet-fed ApoE-/- mouse model with i.p. 4-PBA administration. Furthermore, the selective efficacy of 4-PBA trained monocytes were examined by i.v. transfusion of ex vivo trained monocytes by 4-PBA into recipient high fat diet-fed ApoE-/- mice. ResultsIn this study, we found that monocytes can be potently reprogrammed by 4-PBA into an immune-resolving state characterized by reduced adhesion and enhanced expression of anti-inflammatory mediator CD24. Mechanistically, 4-PBA reduced the expression of ICAM-1 via reducing peroxisome stress and attenuating SYK-mTOR signaling. Concurrently, 4-PBA enhanced the expression of resolving mediator CD24 through promoting PPAR{gamma} neddylation mediated by TOLLIP. 4-PBA trained monocytes can effectively propagate anti-inflammation activity to neighboring monocytes through CD24. Our data further demonstrated that 4-PBA trained monocytes effectively reduce atherosclerosis pathogenesis when administered in vivo. ConclusionOur study describes a robust and effective approach to generate resolving monocytes, characterizes novel mechanisms for targeted monocyte reprogramming, and offers a precision-therapeutics for atherosclerosis based on delivering reprogrammed resolving monocytes.

immunology↗

bioRxiv2023

The Synaptic Architecture of Layer 5 Thick Tufted Excitatory Neurons in the Visual Cortex of Mice

The neocortex is one of the most critical structures that makes us human, and it is involved in a variety of cognitive functions from perception to sensory integration and motor control. Composed of repeated modules, or microcircuits, the neocortex relies on distinct cell types as its fundamental building blocks. Despite significant progress in characterizing these cell types 1-5, an understanding of the complete synaptic partners associated with individual excitatory cell types remain elusive. Here, we investigate the connectivity of arguably the most well recognized and studied excitatory neuron in the neocortex: the thick tufted layer 5 pyramidal cell 6-10 also known as extra telencephalic (ET) 11 neurons. Although the synaptic interactions of ET neurons have been extensively explored, a comprehensive characterization of their local connectivity remains lacking. To address this knowledge gap, we leveraged a 1 mm3 electron microscopic (EM) dataset. We found that ET neurons primarily establish connections with inhibitory cells in their immediate vicinity. However, when they extend their axons to other cortical regions, they tend to connect more with excitatory cells. We also find that the inhibitory cells targeted by ET neurons are a specific group of cell types, and they preferentially inhibit ET cells. Finally, we observed that the most common excitatory targets of ET neurons are layer 5 IT neurons and layer 6 pyramidal cells, whereas synapses with other ET neurons are not as common. These findings challenge current views of the connectivity of ET neurons and suggest a circuit design that involves local competition among ET neurons and collaboration with other types of excitatory cells. Our results also highlight a specific circuit pattern where a subclass of excitatory cells forms a network with specific inhibitory cell types, offering a framework for exploring the connectivity of other types of excitatory cells.

neuroscience↗

bioRxiv2023

Discovery of a new activator of Slack potassium channels with robust efficacy in models of histamine-independent and chronic itch

Various disorders are accompanied by histamine-independent itching, which is often resistant to the currently available therapies. In this study, we hypothesized that pharmacological activation of Slack (Kcnt1, KNa1.1), a potassium channel highly expressed in itch-sensitive sensory neurons, has therapeutic potential for the treatment of itching. Based on the Slack-activating antipsychotic drug, loxapine, we designed a series of new derivatives with improved pharmacodynamic and pharmacokinetic profiles that enabled us to validate Slack as a pharmacological target in vivo. One of these new Slack activators, compound 6, exhibited negligible dopamine D2 and D3 receptor binding, unlike loxapine. We found that compound 6 displayed potent on-target antipruritic activity in multiple mouse models of acute histamine-independent and chronic itch without motor side effects. These properties make compound 6 a lead molecule for the development of new antipruritic therapies targeting Slack.

pharmacology and toxicology↗

bioRxiv2023

Global and local adaptation to aridity in a desert plant Gymnocarpos przewalskii

In order to thrive and survive plant species need to combine stability in the long term and rapid response to environmental challenges in the short term. The former would be reflected by global adaptation across species and the latter by pronounced local adaptation among populations of the same species. It remains unclear how much overlap is to be expected between the parts of the genome associated to these two contrasted adaptation processes. In the present study, we generated a high-quality genome and re-sequenced 177 individuals for Gymnocarpos przewalskii, an important desert plant species from North-West China, to detect local adaptation. To test for global adaptation to aridity at the molecular level we compared genomic data of 15 species that vary in their ability to withstand drought. A total of 118 genes were involved in global adaptation to aridity. Sixty-five G. przewalskii genes were shared across all xerophytic species, of which sixty-three were under stabilizing selection and two under directional selection. While 20% of G. przewalskii genome showed signatures of local adaptation to aridity during population divergence, only 13 of those genes were also under global adaptation. Hence, our results suggest that long-term stability is crucial for adaptation to extreme environmental stress but is only maintained in a small group of highly pleiotropic genes while a rapid response to recent changes elicits a genome-wide response, including gene family expansion. The overlap between the two evolutionary mechanisms appears limited.

evolutionary biology↗

bioRxiv2023

CAVE: Connectome Annotation Versioning Engine

Advances in Electron Microscopy, image segmentation and computational infrastructure have given rise to large-scale and richly annotated connectomic datasets which are increasingly shared across communities. To enable collaboration, users need to be able to concurrently create new annotations and correct errors in the automated segmentation by proofreading. In large datasets, every proofreading edit relabels cell identities of millions of voxels and thousands of annotations like synapses. For analysis, users require immediate and reproducible access to this constantly changing and expanding data landscape. Here, we present the Connectome Annotation Versioning Engine (CAVE), a computational infrastructure for immediate and reproducible connectome analysis in up-to petascale datasets ([~]1mm3) while proofreading and annotating is ongoing. For segmentation, CAVE provides a distributed proofreading infrastructure for continuous versioning of large reconstructions. Annotations in CAVE are defined by locations such that they can be quickly assigned to the underlying segment which enables fast analysis queries of CAVEs data for arbitrary time points. CAVE supports schematized, extensible annotations, so that researchers can readily design novel annotation types. CAVE is already used for many connectomics datasets, including the largest datasets available to date.

neuroscience↗

bioRxiv2023

Neuronal wiring diagram of an adult brain

Connections between neurons can be mapped by acquiring and analyzing electron microscopic (EM) brain images. In recent years, this approach has been applied to chunks of brains to reconstruct local connectivity maps that are highly informative, yet inadequate for understanding brain function more globally. Here, we present the first neuronal wiring diagram of a whole adult brain, containing 5x107 chemical synapses between [~]130,000 neurons reconstructed from a female Drosophila melanogaster. The resource also incorporates annotations of cell classes and types, nerves, hemilineages, and predictions of neurotransmitter identities. Data products are available by download, programmatic access, and interactive browsing and made interoperable with other fly data resources. We show how to derive a projectome, a map of projections between regions, from the connectome. We demonstrate the tracing of synaptic pathways and the analysis of information flow from inputs (sensory and ascending neurons) to outputs (motor, endocrine, and descending neurons), across both hemispheres, and between the central brain and the optic lobes. Tracing from a subset of photoreceptors all the way to descending motor pathways illustrates how structure can uncover putative circuit mechanisms underlying sensorimotor behaviors. The technologies and open ecosystem of the FlyWire Consortium set the stage for future large-scale connectome projects in other species.

neuroscience↗