Biology subjects

Ding, X.

Publications and source records attributed to Ding, X..

At least 91 records · Page 5Linked to original sources

bioRxiv2023

Projectome-defined subtypes and modular intra-hypothalamic subnetworks of peptidergic neurons

The hypothalamus plays a vital role in coordinating essential neuroendocrine, autonomic, and somatomotor responses for survival and reproduction. While previous studies have explored population-level projections of hypothalamic neurons, the specific innervation patterns of individual hypothalamic axons remain unclear. To understand the organization of hypothalamic axon projections, we conducted a comprehensive reconstruction of single-cell projectomes from 7,180 mouse hypothalamic neurons expressing specific neuropeptides. Our analysis identified 31 distinct subtypes based on projectome-defined characteristics, with many exhibiting long-range axon collateral projections to multiple brain regions. Notably, these subtypes selectively targeted specific subdomains within downstream areas, either unilaterally or bilaterally. Furthermore, we observed that individual peptidergic neuronal types encompassed multiple projectome-defined subtypes, explaining their diverse functional roles. Additionally, by examining intra-hypothalamic axon projections, we uncovered six modular subnetworks characterized by enriched intramodular connections and distinct preferences for downstream targets. This modular organization of the intra-hypothalamic network likely contributes to the coordinated organization of hypothalamic outputs. In summary, our comprehensive projectome analysis reveals the organizational principles governing hypothalamic axon projections, providing a framework for understanding the neural circuit mechanisms underlying the diverse and coordinated functions of the hypothalamus.

neuroscience↗

bioRxiv2023

Information Geometry of the Retinal Representation Manifold

The ability for the brain to discriminate among visual stimuli is constrained by their retinal representations. Previous studies of visual discriminability have been limited to either low-dimensional artificial stimuli or pure theoretical considerations without a realistic encoding model. Here we propose a novel framework for understanding stimulus discriminability achieved by retinal representations of naturalistic stimuli with the method of information geometry. To model the joint probability distribution of neural responses conditioned on the stimulus, we created a stochastic encoding model of a population of salamander retinal ganglion cells based on a three-layer convolutional neural network model. This model not only accurately captured the mean response to natural scenes but also a variety of second-order statistics. With the model and the proposed theory, we computed the Fisher information metric over stimuli to study the most discriminable stimulus directions. We found that the most discriminable stimulus varied substantially across stimuli, allowing an examination of the relationship between the most discriminable stimulus and the current stimulus. By examining responses generated by the most discriminable stimuli we further found that the most discriminative response mode is often aligned with the most stochastic mode. This finding carries the important implication that under natural scenes, retinal noise correlations are information-limiting rather than increasing information transmission as has been previously speculated. We additionally observed that sensitivity saturates less in the population than for single cells and that as a function of firing rate, Fisher information varies less than sensitivity. We conclude that under natural scenes, population coding benefits from complementary coding and helps to equalize the information carried by different firing rates, which may facilitate decoding of the stimulus under principles of information maximization.

neuroscience↗

bioRxiv2023

Sex similarities and dopaminergic differences in interval timing

Rodent behavioral studies have largely focused on male animals, which has limited the generalizability and conclusions of neuroscience research. Working with humans and rodents, we studied sex effects during interval timing that requires participants to estimate an interval of several seconds by making motor responses. Interval timing requires attention to the passage of time and working memory for temporal rules. We found no differences between human females and males in interval timing response times (timing accuracy) or the coefficient of variance of response times (timing precision). Consistent with prior work, we also found no differences between female and male rodents in timing accuracy or precision. In female rodents, there was no difference in interval timing between estrus and diestrus cycle stages. Because dopamine powerfully affects interval timing, we also examined sex differences with drugs targeting dopaminergic receptors. In both female and male rodents, interval timing was delayed after administration of sulpiride (D2-receptor antagonist), quinpirole (D2-receptor agonist), and SCH-23390 (D1-receptor antagonist). By contrast, after administration of SKF-81297 (D1-receptor agonist), interval timing shifted earlier only in male rodents. These data illuminate sex similarities and differences in interval timing. Our results have relevance for rodent models of both cognitive function and brain disease by increasing represenation in behavioral neuroscience.

neuroscience↗

bioRxiv2023

OpenABC Enables Flexible, Simplified, and Efficient GPU Accelerated Simulations of Biomolecular Condensates

Biomolecular condensates are important structures in various cellular processes but are challenging to study using traditional experimental techniques. In silico simulations with residue-level coarse-grained models strike a balance between computational efficiency and chemical accuracy. They could offer valuable insights by connecting the emergent properties of these complex systems with molecular sequences. However, existing coarse-grained models often lack easy-to-follow tutorials and are implemented in software that is not optimal for condensate simulations. To address these issues, we introduce OpenABC, a software package that greatly simplifies the setup and execution of coarse-grained condensate simulations with multiple force fields using Python scripting. OpenABC seamlessly integrates with the OpenMM molecular dynamics engine, enabling efficient simulations with performances on a single GPU that rival the speed achieved by hundreds of CPUs. We also provide tools that convert coarse-grained configurations to all-atom structures for atomistic simulations. We anticipate that Open-ABC will significantly facilitate the adoption of in silico simulations by a broader community to investigate the structural and dynamical properties of condensates. Open-ABC is available at https://github.com/ZhangGroup-MITChemistry/OpenABC

biophysics↗

bioRxiv2023

The BTB-ZF gene Bm-mamo regulate pigmentation in caterpillars

The color pattern of insects is one of the most diverse adaptive evolutionary phenotypes. However, the molecular regulation of this color pattern is not fully understood. In this study, we found that the transcription factor Bm-mamo is responsible for black dilute (bd) allele mutations in the silkworm. Bm-mamo belongs to the BTB zinc finger family and is orthologous to mamo in Drosophila melanogaster. This gene has a conserved function in gamete production in Drosophila and silkworms and has evolved a pleiotropic function in the regulation of color patterns in caterpillars. Using RNAi and clustered regularly interspaced short palindromic repeats (CRISPR) technology, we showed that Bm-mamo is a repressor or has dark melanin patterns in the larval epidermis. Using in vitro binding assays and gene expression profiling in wild-type and mutant larvae, we also showed that Bm-mamo likely regulates the expression of related pigment synthesis and cuticular protein genes in a coordinated manner to mediate its role in color pattern formation. This mechanism is consistent with the dual role of this transcription factor in regulating both the structure and shape of the cuticle and the pigments that are embedded within it. This study provides new insight into the regulation of color patterns as well as into the construction of more complex epidermis features in some insects.

genetics↗

bioRxiv2023

Genome-wide association study of occupational status and prestige identifies 106 genetic variants and defines their role for intergenerational status transmission and the life course

Socioeconomic status (SES) impacts health and the life course. This GWAS on sociologically informed occupational status measures (ISEI, SIOPS, and CAMSIS) using the UKBiobank (N=273,157) identified 106 genetic variants of which 8 are novel to the study of SES. Genetic correlation analyses point to a common genetic factor for SES. Within-family prediction and its reduction was attributable in equal parts to genetic nurture and assortative mating. Using polygenic scores from population predictions of 5-8%, we, firstly, showed that cognitive and non-cognitive traits - including scholastic and occupational motivation and aspiration - link genetic scores to occupational status. Second, 62% of the intergenerational transmission of occupational status can be ascribed to non-genetic inheritance (e.g., family environment). Third, the link between genetics, occupation, and health are interrelated with parental occupational status confounding the genetic prediction of general health. Finally, across careers, genetic prediction compresses during mid-career with divergence in status at later stages.

genetics↗

bioRxiv2023

Efficient Hi-C inversion facilitates chromatin folding mechanism discovery and structure prediction

Genome-wide chromosome conformation capture (Hi-C) experiments have revealed many structural features of chromatin across multiple length scales. Further understanding genome organization requires relating these discoveries to the mechanisms that establish chromatin structures and reconstructing these structures in three dimensions, but both objectives are difficult to achieve with existing algorithms that are often computationally expensive. To alleviate this challenge, we present an algorithm that efficiently converts Hi-C data into contact energies, which measure the interaction strength between genomic loci brought into proximity. Contact energies are local quantities unaffected by the topological constraints that correlate Hi-C contact probabilities. Thus, extracting contact energies from Hi-C contact probabilities distills the biologically unique information contained in the data. We show that contact energies reveal the location of chromatin loop anchors, support a phase separation mechanism for genome compartmentalization, and parameterize polymer simulations that predict three-dimensional chromatin structures. Therefore, we anticipate that contact energy extraction will unleash the full potential of Hi-C data and that our inversion algorithm will facilitate the widespread adoption of contact energy analysis. Significance StatementThe three-dimensional organization of the genome is essential to many DNA-templated processes, and numerous experimental techniques have been introduced to characterize its features. High-throughput chromosome conformation capture experiments, or Hi-C, have proven particularly useful, reporting the interaction frequency between pairs of DNA segments in vivo and genome-wide. However, the polymer topology of chromosomes complicates Hi-C data analysis, which often employs sophisticated algorithms without explicitly accounting for the disparate processes affecting each interaction frequency. In contrast, we introduce a computational framework based on polymer physics arguments that efficiently removes the correlation between Hi-C interaction frequencies and quantifies how each local interaction influences genome folding globally. This framework facilitates the identification of mechanistically important interactions and the prediction of three-dimensional genome structures.

biophysics↗

bioRxiv2023

Proteomic-based stratification of intermediate-risk prostate cancer patients

Gleason grading is an important prognostic indicator for prostate adenocarcinoma and is crucial for patient treatment decisions. However, intermediate-risk patients diagnosed in Gleason Grade Groups (GG) 2 and GG3 can harbour either aggressive or non-aggressive disease, resulting in under- or over-treatment of a significant number of patients. Here, we performed proteomic, differential expression, machine learning, and survival analyses for 1,348 matched tumour and benign sample runs from 278 patients. Three proteins (F5, TMEM126B and EARS2) were identified as candidate biomarkers in patients with biochemical recurrence. Multivariate Cox regression yielded 18 proteins, from which a risk score was constructed to dichotomise prostate cancer patients into low- and high-risk groups. This 18-protein signature is prognostic for the risk of biochemical recurrence and completely independent of the intermediate GG. Our results suggest that markers generated by computational proteomic profiling have the potential for clinical applications including integration into prostate cancer management.

cancer biology↗

bioRxiv2023

APPRAISE: Fast, accurate ranking of engineered proteins by receptor binding propensity using structural modeling

Deep learning-based methods for protein structure prediction have achieved unprecedented accuracy. However, the power of these tools to guide the engineering of protein-based therapeutics remains limited due to a gap between the ability to predict the structures of candidate proteins and the ability to assess which of those proteins are most likely to bind to a target receptor. Here we bridge this gap by introducing Automated Pairwise Peptide-Receptor AnalysIs for Screening Engineered proteins (APPRAISE), a method for predicting the receptor binding propensity of engineered proteins. After generating models of engineered proteins competing for binding to a target using an established structure-prediction tool such as AlphaFold-Multimer or ESM-Fold, APPRAISE performs a rapid (under 1 CPU second per model) scoring analysis that takes into account biophysical and geometrical constraints. As a proof-of-concept, we demonstrate that APPRAISE can accurately classify receptor-dependent vs. receptor-independent adeno-associated viral vectors and diverse classes of engineered proteins such as miniproteins targeting the SARS-CoV-2 spike, nanobodies targeting a G-protein-coupled receptor, and peptides that specifically bind to transferrin receptor or PD-L1. APPRAISE is accessible through a web-based notebook interface using Google Colaboratory (https://tiny.cc/APPRAISE). With its accuracy, interpretability, and generalizability, APPRAISE promises to expand the utility of protein structure prediction and accelerate protein engineering for biomedical applications.

bioengineering↗

bioRxiv2023

Primate-conserved Carbonic Anhydrase IV and murine-restricted Ly6c1 are new targets for crossing the blood-brain barrier

The blood-brain barrier (BBB) presents a major challenge to delivering large molecules to study and treat the central nervous system (CNS). This is due in part to the scarcity of effective targets for BBB crossing, the identification of which is the crucial first step of drug development. Here, we leveraged a panel of adeno-associated viruses (AAVs) previously identified through directed evolution for improved BBB transport to reverse engineer protein targets for enhanced BBB crossing. We identify both murine-restricted Ly6c1 and primate-conserved carbonic anhydrase IV (Car4; CA4) as novel receptors for crossing the BBB. We demonstrate how these receptors can unlock new experimental and computational target-focused engineering strategies by creating the enhanced Ly6c1-binding vector AAV-PHP.eC and by applying AlphaFold2-enabled in silico methods to rank capsids against identified receptors and generate capsid-receptor binding models. Here, with Car4, we add a completely new receptor to the very short list currently available for crossing the BBB in humans and, with Ly6c1, we validate a pipeline for receptor-targeted engineering. The identification of Car4/CA4 and structural insights from computational modeling provide new paths toward human brain-penetrant chemicals (drugs) and biologicals (including gene delivery).

bioengineering↗

bioRxiv2023

Functional gene delivery to and across brain vasculature of systemic AAVs with endothelial-specific tropism in rodents and broad tropism in primates

Delivering genes to and across the brain vasculature efficiently and specifically across species remains a critical challenge for addressing neurological diseases. We have evolved adeno-associated virus (AAV9) capsids into vectors that transduce brain endothelial cells specifically and efficiently following systemic administration in wild-type mice with diverse genetic backgrounds and rats. These AAVs also exhibit superior transduction of the CNS across non-human primates (marmosets and rhesus macaques), and ex vivo human brain slices although the endothelial tropism is not conserved across species. The capsid modifications translate from AAV9 to other serotypes such as AAV1 and AAV-DJ, enabling serotype switching for sequential AAV administration in mice. We demonstrate that the endothelial specific mouse capsids can be used to genetically engineer the blood-brain barrier by transforming the mouse brain vasculature into a functional biofactory. Vasculature-secreted Hevin (a synaptogenic protein) rescued synaptic deficits in a mouse model.

bioengineering↗

bioRxiv2022

Microbiome features differentiating unsupervised-stratification based clusters of patients with abnormal glycometabolism

The alteration of gut microbiota structure plays a pivotal role in the pathogenesis of abnormal glycometabolism. However, the microbiome features identified in patient groups stratified solely based on glucose levels remain controversial among different studies.. In this study, we stratified 258 participants (discovery cohort) into three clusters according to an unsupervised method based on 16 clinical parameters involving the levels of blood glucose, insulin, and lipid. We found 67 cluster-specific microbiome features (i.e., amplicon sequence variants, ASVs) based on 16S rRNA gene V3-V4 region sequencing. Specifically, ASVs belonging to Barnesville and Alistipes were enriched in Cluster 1, in which participants had the lowest blood glucose levels, high insulin sensitivity, and high-fecal short-chain fatty acid concentration. ASVs belonging to Prevotella copri and Ruminococcus gnavus were enriched in Cluster 2, which was characterized by a moderate level of blood glucose, serious insulin resistance, and high levels of cholesterol and triglyceride. Cluster 3 was characterized by a high level of blood glucose and insulin deficiency, enriched with ASVs in P. copri and Bacteroides vulgatus. In addition, machine learning classifiers using the 67 cluster-specific ASVs were used to distinguish individuals in one cluster from those in the other two clusters both in discovery and testing cohorts (N = 83). Therefore, microbiome features identified based on the unsupervised stratification of patients with more inclusive clinical parameters may better reflect microbiota alterations associated with the progression of abnormal glycometabolism. IMPORTANCEThe gut microbiota is altered in patients with type 2 diabetes (T2D) and prediabetes. The association of particular bacteria with T2D, however, varied among studies, which has made it challenging to develop precision medicine approaches for the prevention and alleviation of T2D. Blood glucose level is the only parameter in clustering patients when identifying the T2D-related bacteria in previous studies. This stratification ignores the fact that patients within the same blood glucose range differ in their insulin resistance and dyslipidemia, which also may be related to disordered gut microbiota. In addition to parameters of blood glucose levels, we also used additional parameters involving insulin and lipid levels to stratify participants into three clusters and further identified cluster-specific microbiome features. We further validated the association between these microbiome features and glycometabolism with an independent cohort. This work highlights the importance of stratification of patients with blood glucose, insulin, and lipid levels when identifying the microbiome features associated with the progression of abnormal glycometabolism.

microbiology↗

bioRxiv2022

Enhancing detectable fluorescence fluctuation for high-throughput and four-dimensional live-cell super-resolution imaging

Super-resolution (SR) imaging with high-throughput is invaluable to fast and high-precision profiling in a wide range of biomedical applications. However, prevalent SR methods require sophisticated acquisition devices and specific imaging control, and may cost a fairly long time on a single field-of-view. These essentially increase the construction difficulty, including challenges in imaging throughput, system establishment, and automation. Using the natural photophysics of fluorescence, fluctuation-based microscopy techniques can routinely break the diffraction limit with no need for additional optical components, but its long acquisition time still poses a challenge for high-throughput imaging or visualizing transient organelle dynamics. Here, we propose an SR method based on the Auto-Correlation with two-step Deconvolution (SACD) that reduces the number of frames required by maximizing the detectable fluorescence fluctuation behavior in each measurement, with further removal of tunable parameters by a Fourier ring correlation analysis. It only needs 20 frames for twofold lateral and axial resolution improvements, while the SR optical fluctuation imaging (SOFI) needs more than 1000 frames. By capturing raw images for [~]10 minutes, we record an SR image with [~]128 nm resolution that contains 2.4 gigapixels covering an area of [~]2.0 mm x 1.4 mm, including more than 2,000 cells. Beyond that, by applying continuity and sparsity joint constraint, the Sparse deconvolution-assisted SACD enables 4D live-cell SR imaging of events such as mitochondrial fission and fusion. Overall, as an open-sourced module, we anticipate SACD can offer direct access to SR, which may facilitate the biology studies of cells and organisms with high-throughput and low-cost.

biophysics↗

bioRxiv2022

Predicting distributed working memory activity in a large-scale mouse brain: the importance of the cell type-specific connectome

Recent advances in connectome and neurophysiology make it possible to probe whole-brain mechanisms of cognition and behavior. We developed a large-scale model of the mouse multiregional brain for a cardinal cognitive function called working memory, the brains ability to internally hold and process information without sensory input. The model is built on mesoscopic connectome data for inter-areal cortical connections and endowed with a macroscopic gradient of measured parvalbumin-expressing interneuron density. We found that working memory coding is distributed yet exhibits modularity; the spatial pattern of mnemonic representation is determined by long-range cell type-specific targeting and density of cell classes. Cell type-specific graph measures predict the activity patterns and a core subnetwork for memory maintenance. The model shows numerous self-sustained internal states (each engaging a distinct subset of areas). This work provides a framework to interpret large-scale recordings of brain activity during cognition, while highlighting the need for cell type-specific connectomics.

neuroscience↗

bioRxiv2022

Quantitatively mapping local quality of super-resolution microscopy by rolling Fourier ring correlation

In fluorescence microscopy, computational algorithms have been developed to suppress noise, enhance contrast, and even enable super-resolution (SR). However, the local quality of the images may vary on multiple scales, and these differences can lead to misconceptions, which is especially intractable in emerging deep-learning ones. Current mapping methods fail to finely estimate the local quality, challenging to associate the SR scale content. Here, we develop a rolling Fourier ring correlation (rFRC) framework to evaluate the reconstruction uncertainties down to SR scale. To visually pinpoint regions with low reliability, a filtered rFRC is combined with a modified resolution scaled error map (RSM), offering a comprehensive and concise map for further examination. We demonstrate their performances on various SR imaging modalities, and the resulting quantitative maps enable better SR images integrated from different reconstructions. Beyond that, we provide a strategy for learning-based restorations, allowing a direct detection of both data and model uncertainties, and expect the representative cases can inspire further advances in this rapidly developing field.

biophysics↗

bioRxiv2022

Searching across-cohort relatives via encrypted genotype regression

Explicitly sharing individual level data in genomics studies has many merits comparing to sharing summary statistics, including more strict QCs, common statistical analyses, relative identification and improved statistical power in GWAS, but it is hampered by privacy or ethical constraints. In this study, we developed encG-reg, a regression approach that can detect relatives of various degrees based on encrypted genomic data, which is immune of ethical constraints. The encryption properties of encG-reg are based on the random matrix theory by masking the original genotypic matrix without sacrificing precision of individual-level genotype data. We established a connection between the dimension of a random matrix, which masked genotype matrices, and the required precision of a study for encrypted genotype data. encG-reg has false positive and false negative rates equivalent to sharing original individual level data, and is computationally efficient when searching relatives. We split the UK Biobank into their respective centers, and then encrypted the genotype data. We observed that the relatives estimated using encG-reg was equivalently accurate with the estimation by KING, which is a widely used software but requires original genotype data. In a more complex application, we launched a finely devised multi-center collaboration across 5 research institutes in China, covering 9 cohorts of 54,092 GWAS samples. encG-reg again identified true relatives existing across the cohorts with even different ethnic backgrounds and genotypic qualities. Our study clearly demonstrates that encrypted genomic data can be used for data sharing without loss of information or data sharing barrier. Author SummaryEstimating pairwise genetic relatedness within a single cohort is straightforward. However, in practice, related samples are often distributed across different cohorts, making it challenging to estimate inter-cohort relatedness. In this study, we propose a method called encrypted genotype regression (encG-reg), which provides an unbiased estimation of inter-cohort relatedness using encrypted genotypes. The genotype matrix of each cohort is masked by a random matrix, which acts similarly to a private key in a cryptographic scheme. This masking process produces encrypted genotypes, which are a projection of the original genotype matrix. We derive the expectation and particularly the sampling variance for encG-reg, the latter involves eighth-order moments calculation. encG-reg allows us to accurately identify relatedness across cohorts, even for large-scale biobank data. To demonstrate the efficacy of encG-reg, we verified it in a multi-ethnicity UK Biobank dataset comprising 485,158 samples. For this case, we successfully tracked down to the 1st-degree relatedness (such as full sibs and parent-offspring). Furthermore, we used encG-reg in a collaboration involving 9 Chinese cohorts, encompassing a total of 54,092 samples from 5 genomic centers. It is worth noting that if the number of effective markers is sufficient encG-reg has the potential to detect even more distant degrees of relatedness beyond what we demonstrated.

genetics↗

bioRxiv2022

Surface Acoustic Wave Integrated Microfluidics for Repetitive and Reversible Temporary Immobilization of C. elegans

Caenorhabditis elegans is an important genetic model for neuroscience studies due to its unique combination of genetics, transparency, complete synaptic connectome, and well-characterized behaviors. These factors, in turn, enable analyses of how genes control connectivity, neuronal function, and behavior. To date, however, most studies of neuronal function in C. elegans are incapable of performing microscopy imaging with subcellular resolution and behavior analysis in the same set of animals. This constraint stems from the immobilization requirement for high-resolution imaging that is incompatible with behavioral analysis. In particular, conventional immobilization methods often lead to either irreversible, partial, or slowly reversible immobilization of animals preventing a multiplexed approach. Here, we present a novel microfluidic device that uses surface acoustic waves (SAW) as a non-contact method to temporarily immobilize worms for a short period (40 seconds). This device allows non-invasive analysis of swimming behavior and high-resolution synaptic imaging in the same animal. In addition, because of the low impact of this SAW approach, the device enables fast, repeated imaging of single neurons and behavior in the same animals for three to four days. We anticipate that this device will enable longitudinal analysis of animal motility and subcellular morphological changes during development and ageing in C. elegans.

neuroscience↗

bioRxiv2022

Sensitive bacterial Vm sensors revealed the excitability of bacterial Vm and its role in antibiotic tolerance

As an important free energy source, the membrane voltage (Vm) regulates many essential physiological processes in bacteria. However, in comparison with eukaryotic cells, knowledge of bacterial electrophysiology is very limited. Here, we developed a set of novel genetically encoded bacterial Vm sensors which allow single-cell recording of bacterial Vm dynamics in live cells with high temporal resolution. Using these new sensors, we reveal the electrically "excitable" and "resting" states of bacterial cells dependent on their metabolic status. In the electrically excitable state, frequent hyperpolarization spikes in bacterial Vm are observed, which facilitates increased antibiotic tolerance. In the electrically resting state, bacterial Vm displays significant cell-to-cell heterogeneity and is linked to the cell fate after antibiotic treatment.

microbiology↗