Biology subjects

Xu, C.

Publications and source records attributed to Xu, C..

At least 163 records · Page 9Linked to original sources

bioRxiv2024

scBSP: A fast and accurate tool for identifying spatially variable genes from spatial transcriptomic data

Spatially resolved transcriptomics have enabled the inference of gene expression patterns within two and three-dimensional space, while introducing computational challenges due to growing spatial resolutions and sparse expressions. Here, we introduce scBSP, an open-source, versatile, and user-friendly package designed for identifying spatially variable genes in large-scale spatial transcriptomics. scBSP implements sparse matrix operation to significantly increase the computational efficiency in both computational time and memory usage, processing the high-definition spatial transcriptomics data for 19,950 genes on 181,367 spots within 10 seconds. Applied to diverse sequencing data and simulations, scBSP efficiently identifies spatially variable genes, demonstrating fast computational speed and consistency across various sequencing techniques and spatial resolutions for both two and three-dimensional data with up to millions of cells. On a sample with hundreds of thousands of sports, scBSP identifies SVGs accurately in seconds to on a typical desktop computer.

bioinformatics↗

bioRxiv2024

SAMP: Identifying Antimicrobial Peptides by an Ensemble Learning Model Based on Proportionalized Split Amino Acid Composition

AbstractIt is projected that 10 million deaths could be attributed to drug-resistant bacteria infections in 2050. To address this concern, identifying new-generation antibiotics is an effective way. Antimicrobial peptides (AMPs), a class of innate immune effectors, have received significant attention for their capacity to eliminate drug-resistant pathogens, including viruses, bacteria, and fungi. Recent years have witnessed widespread applications of computational methods especially machine learning (ML) and deep learning (DL) for discovering AMPs. However, existing methods only use features including compositional, physiochemical, and structural properties of peptides, which cannot fully capture sequence information from AMPs. Here, we present SAMP, an ensemble random projection (RP) based computational model that leverages a new type of features called Proportionalized Split Amino Acid Composition (PSAAC) in addition to conventional sequence-based features for AMP prediction. With this new feature set, SAMP captures the residue patterns like sorting signals at around both the N-terminus and the C-terminus, while also retaining the sequence order information from the middle peptide fragments. Benchmarking tests on different balanced and imbalanced datasets demonstrate that SAMP consistently outperforms existing state-of-the-art methods, such as iAMPpred and AMPScanner V2, in terms of accuracy, MCC, G-measure and F1-score. In addition, by leveraging an ensemble RP architecture, SAMP is scalable to processing large-scale AMP identification with further performance improvement, compared to those models without RP. To facilitate the use of SAMP, we have developed a Python package freely available at https://github.com/wan-mlab/SAMP.

bioinformatics↗

bioRxiv2024

Three-photon excited fluorescence microscopy enables imaging of blood flow, neural structure and inflammatory response deep into mouse spinal cord in vivo

Nonlinear optical microscopy enables non-invasive imaging in scattering samples with cellular resolution. The spinal cord connects the brain with the periphery and governs fundamental behaviors such as locomotion and somatosensation. Because of dense myelination on the dorsal surface, imaging to the spinal grey matter is challenging, even with two-photon microscopy. Here we show that three-photon excited fluorescence (3PEF) microscopy enables multicolor imaging at depths of up to ~550 m into the mouse spinal cord, in vivo. We quantified blood flow across vessel types along the spinal vascular network. We then followed the response of neurites and microglia after occlusion of a surface venule, where we observed depth-dependent structural changes in neurites and interactions of perivascular microglia with vessel branches upstream from the clot. This work establishes that 3PEF imaging enables studies of functional dynamics and cell type interactions in the top 550 m of the murine spinal cord, in vivo.

neuroscience↗

bioRxiv2024

Human BioMolecular Atlas Program (HuBMAP): 3D Human Reference Atlas Construction and Usage

The Human BioMolecular Atlas Program (HuBMAP) aims to construct a reference 3D structural, cellular, and molecular atlas of the healthy adult human body. The HuBMAP Data Portal (https://portal.hubmapconsortium.org) serves experimental datasets and supports data processing, search, filtering, and visualization. The Human Reference Atlas (HRA) Portal (https://humanatlas.io) provides open access to atlas data, code, procedures, and instructional materials. Experts from more than 20 consortia are collaborating to construct the HRAs Common Coordinate Framework (CCF), knowledge graphs, and tools that describe the multiscale structure of the human body (from organs and tissues down to cells, genes, and biomarkers) and to use the HRA to understand changes that occur at each of these levels with aging, disease, and other perturbations. The 6th release of the HRA v2.0 covers 36 organs with 4,499 unique anatomical structures, 1,195 cell types, and 2,089 biomarkers (e.g., genes, proteins, lipids) linked to ontologies and 2D/3D reference objects. New experimental data can be mapped into the HRA using (1) three cell type annotation tools (e.g., Azimuth) or (2) validated antibody panels (OMAPs), or (3) by registering tissue data spatially. This paper describes the HRA user stories, terminology, data formats, ontology validation, unified analysis workflows, user interfaces, instructional materials, application programming interface (APIs), flexible hybrid cloud infrastructure, and previews atlas usage applications.

bioinformatics↗

bioRxiv2024

Reference-informed prediction of alternative splicing and splicing-altering mutations from sequences

Alternative splicing plays a crucial role in protein diversity and gene expression regulation in higher eukaryotes and mutations causing dysregulated splicing underlie a range of genetic diseases. Computational prediction of alternative splicing from genomic sequences not only provides insight into gene-regulatory mechanisms but also helps identify disease-causing mutations and drug targets. However, the current methods for the quantitative prediction of splice site usage still have limited accuracy. Here, we present DeltaSplice, a deep neural network model optimized to learn the impact of mutations on quantitative changes in alternative splicing from the comparative analysis of homologous genes. The model architecture enables DeltaSplice to perform "reference-informed prediction" by incorporating the known splice site usage of a reference gene sequence to improve its prediction on splicing-altering mutations. We benchmarked DeltaSplice and several other state-of-the-art methods on various prediction tasks, including evolutionary sequence divergence on lineage-specific splicing and splicing-altering mutations in human populations and neurodevelopmental disorders, and demonstrated that DeltaSplice outperformed consistently. DeltaSplice predicted [~]15% of splicing quantitative trait loci (sQTLs) in the human brain as causal splicing-altering variants. It also predicted splicing-altering de novo mutations outside the splice sites in a subset of patients affected by autism and other neurodevelopmental disorders, including 19 genes with recurrent splicing-altering mutations. Among the new candidate disease risk genes, MFN1 is involved in mitochondria fusion, which is frequently disrupted in autism patients. Our work expanded the capacity of in silico splicing models with potential applications in genetic diagnosis and the development of splicing-based precision medicine.

bioinformatics↗

bioRxiv2024

Engineering of highly active and diverse nuclease enzymes by combining machine learning and ultra-high-throughput screening

Optimizing enzymes to function in novel chemical environments is a central goal of synthetic biology, but optimization is often hindered by a rugged, expansive protein search space and costly experiments. In this work, we present TeleProt, an ML framework that blends evolutionary and experimental data to design diverse protein variant libraries, and employ it to improve the catalytic activity of a nuclease enzyme that degrades biofilms that accumulate on chronic wounds. After multiple rounds of high-throughput experiments using both TeleProt and standard directed evolution (DE) approaches in parallel, we find that our approach found a significantly better top-performing enzyme variant than DE, had a better hit rate at finding diverse, high-activity variants, and was even able to design a high-performance initial library using no prior experimental data. We have released a dataset of 55K nuclease variants, one of the most extensive genotype-phenotype enzyme activity landscapes to date, to drive further progress in ML-guided design.

bioinformatics↗

bioRxiv2024

Single-animal, single-tube RNA extraction for quantitative analysis of transcripts in the tardigrade Hypsibius exemplaris

The tardigrade Hypsibius exemplaris is an emerging model organism renowned for its ability to survive environmental extremes. To explore the molecular mechanisms and genetic basis of such extremotolerance, many studies rely on RNA-sequencing (RNA-seq), which can be performed on populations ranging from large cohorts to individual animals. Reverse Transcription Polymerase Chain Reaction (RT-PCR) and RNA interference (RNAi) are subsequently used to confirm RNA-seq findings and assess the genetic requirements for candidate genes, respectively. Such studies require an efficient, accurate, and affordable method for RNA extraction and measurement of relative transcript levels by quantitative RT-PCR (qRT-PCR). This work presents an efficient single-tardigrade, single-tube RNA extraction method (STST) that not only reliably isolates RNA from individual tardigrades but also reduces the required time and cost for each extraction. This RNA extraction method yields quantities of cDNA that can be used to amplify and detect multiple transcripts by quantitative PCR (qRT-PCR). The method is validated by analyzing dynamic changes in the expression of genes encoding two heat-shock-regulated proteins, Heat-Shock Protein 70 {beta}2 (HSP70 {beta}2) and Heat-Shock Protein 90 (HSP90), making it possible to assess their relative expression levels in heat-exposed individuals using qRT-PCR. STST effectively complements existing bulk and single tardigrade RNA extraction methods, permitting rapid and affordable examination of individual tardigrade transcriptional levels by qRT-PCR. SUMMARYThis work presents a rapid RNA extraction and transcript level comparison method for analyzing gene expression in the tardigrade Hypsibius exemplaris. Using physical lysis, this high-throughput method requires a single tardigrade as the starting material and results in robust production of cDNA for quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR).

molecular biology↗

bioRxiv2024

LRRK2 G2019S mutation suppresses differentiation of Th9 and Treg cells via JAK/STAT3

The Leucine-rich repeat kinase-2 (LRRK2) G2019S mutation, resulting in aberrantly enhanced kinase activity, is one of the well-recognized genetic risk factors in Parkinsons Disease (PD). Increased LRRK2 activity was also observed in immune cells from PD patients. Emerging results have also unveiled an upsurge in -synuclein (-syn)-specific CD4+ T cell responses in PD patients. Given that LRRK2 mutations in PD are germline mutations, there are unmet meets to explore whether LRRK2 G2019S mutation contributes to the pathogenesis of PD via altering CD4+ T-cell functions. To fill this knowledge gap, we generated a new T cell receptor (TCR) transgenic mouse strain bearing LRRK2 G2019S knock-in mutation, OT-II/LRRK2 (Refer to Mut). As CD4+ T cells from OT-II mice specifically recognize ovalbumin, this new strain enables us to explore the impact of LRRK2 G2019S mutation on T-cell functions in an antigen-specific manner. We found that the abundance and proliferation of major immune subsets in spleen tissue from Mut mice are comparable to wild-type (OT-II, Refer to WT) control. However, when we characterized T cell differentiation in these two strains, T cells derived from Mut mice displayed increased Th2 differentiation (IL-4) and decreased Th9 (IL-9) and Treg (Foxp3+ %) differentiation. LRRK2 G2019S mutation significantly altered the expression levels of master transcription factors (TFs) for T cell differentiation. Specifically, Mut T cells displayed an increase in mRNA expression of Gata3 (TF for Th2), a decrease in expression of Irf4 and Foxp3 (TFs for Th9 and Treg, respectively). Mechanistically, LRRK2 mutation decreased IL-9 production and Treg cell population through the JAK/STAT3 signaling. In conclusion, LRRK2 plays a critical role in regulating T cell differentiation, warranting further studies to evaluate the impacts of altered T cell differentiation led by LRRK2 mutation in dopaminergic neuron damages.

immunology↗

bioRxiv2024

Stabilization of Integrator/INTAC by the small but versatile DSS1 protein

Integrator-PP2A (INTAC) is a highly modular complex orchestrating the transition of paused RNA polymerase II into productive elongation or promoter-proximal premature termination, with its loss resulting in transcription dysregulation and genome instability. Here, we identify human DSS1--a flexible 70-residue protein found in multiple functionally diverse complexes including the 26S proteasome--as an integral subunit of the INTAC backbone. Structural analysis of DSS1-INTAC, both alone and in association with paused polymerase, demonstrates intimate interactions between DSS1 and the INTAC backbone subunit INTS7. We identify tryptophan 39 of DSS1 as being critical for interacting with INTAC and find that mutating this residue disrupts DSS1s interaction with INTAC, while maintaining DSS1s interaction with the proteasome. This substitution not only destabilizes INTAC and therefore INTAC-dependent transcriptional regulation, but also reveals that INTAC is DSS1s major chromatin-bound form. Together, our findings reveal the essential role of the versatile DSS1 protein in the structure and regulatory functions of INTAC.

molecular biology↗

bioRxiv2024

Molecular and Cellular Mechanisms of Teneurin Signaling in Synaptic Partner Matching

In developing brains, axons exhibit remarkable precision in selecting synaptic partners among many non-partner cells. Evolutionally conserved teneurins were the first identified transmembrane proteins that instruct synaptic partner matching. However, how intracellular signaling pathways execute teneurins functions is unclear. Here, we use in situ proximity labeling to obtain the intracellular interactome of teneurin (Ten-m) in the Drosophila brain. Genetic interaction studies using quantitative partner matching assays in both olfactory receptor neurons (ORNs) and projection neurons (PNs) reveal a common pathway: Ten-m binds to and negatively regulates a RhoGAP, thus activating the Rac1 small GTPases to promote synaptic partner matching. Developmental analyses with single-axon resolution identify the cellular mechanism of synaptic partner matching: Ten-m signaling promotes local F-actin levels and stabilizes ORN axon branches that contact partner PN dendrites. Combining spatial proteomics and high-resolution phenotypic analyses, this study advanced our understanding of both cellular and molecular mechanisms of synaptic partner matching. HIGHLIGHTSO_LIIn situ spatial proteomics reveal the first intracellular interactome of teneurins C_LIO_LITen-m signals via a RhoGAP and Rac1 GTPase to regulate synaptic partner matching C_LIO_LISingle-axon analyses reveal a stabilization-upon-contact model for partner matching C_LIO_LITen-m signaling promotes F-actin in axon branches contacting partner dendrites C_LI

neuroscience↗

bioRxiv2024

Stochastic regimes can hide the attractors in data, reconstruction algorithms can reveal them

Tipping points and alternative attractors have become an important focus of research and public discussions about the future of climate, ecosystems and societies. However, empirical evidence for the existence of alternative attractors remains scarce. For example, bimodal frequency distributions of state variables may suggest bistability, but can also be due to bimodality in external conditions. Here, we bring a new dimension to the classical arguments on alternative stable states and their resilience showing that the stochastic regime can distort the relationship between the probability distribution of states and the underlying attractors. Simple additive Gaussian white noise produces a one-to-one correspondence between the modes of frequency distributions and alternative stable states. However, for more realistic types of noise, the number and position of modes of the frequency distribution do not necessarily match the equilibria of the underlying deterministic system. We show that data must represent the stochastic regime as thoroughly as possible. When data are adequate then existing methods can be used to determine the nature of the underlying deterministic system and noise simultaneously. This may help resolve the question of whether there are tipping points, but also how realized states of a system are shaped by stochastic forcing vs internal stability properties. Open Research StatementData and MATLAB codes for results reported here are available in the Github repository https://github.com/mshoja/Reconst (Babak M. S. Arani 2023) The original data source is cited in the text.

ecology↗

bioRxiv2024

Charting the Spatial Transcriptome of the Human Cerebral Cortex at Single-Cell Resolution

In our pursuit of creating a comprehensive human cortical atlas to understand human intelligence, we examined the single-nuclei transcriptomes of 307,738 cells alongside spatial transcriptomics data from 46,948 VISIUM spots and 1,355,582 Stereo cells. Atlases reveal distinct expression patterns and spatial arrangements of cortical neural cell types. Glutamatergic neurons exhibit precise laminar patterns, often mirroring expression patterns in adjacent cortical regions. Overlaying our atlas with functional networks delineated substantial correlations between neural cell types and cortical region function. Notably, regions involved in processing sensory information (pain) display a pronounced accumulation of extratelencephalic neurons. Additionally, our atlas enabled precise localization of the thicker layer 4 of the visual cortex and an in-depth study of the stabilize the subplate structure, known as layer 6b, revealed specific marker genes and cellular compositions. Collectively, our research sheds light on the cellular foundations of the intricate and intelligent regions within the human cortex.

neuroscience↗

bioRxiv2024

Human vascular organoids with a mosaic AKT1 mutation recapitulate Proteus syndrome

Vascular malformation, a key clinical phenotype of Proteus syndrome, lacks effective models for pathophysiological study and drug development due to limited patient sample access. To bridge this gap, we built a human vascular organoid model replicating Proteus syndromes vasculature. Using CRISPR/Cas9 genome editing and gene overexpression, we created induced pluripotent stem cells (iPSCs) embodying the Proteus syndrome-specific AKTE17K point mutation for organoid generation. Our findings revealed that AKT overactivation in these organoids resulted in smaller sizes yet increased vascular connectivity, although with less stable connections. This could be due to the significant vasculogenesis induced by AKT overactivation. This phenomenon likely stems from boosted vasculogenesis triggered by AKT overactivation, leading to increased vascular sprouting. Additionally, a notable increase in dysfunctional PDGFR{beta} + mural cells, impaired in matrix secretion, was observed in these AKT-overactivated organoids. The application of AKT inhibitors (ARQ092, AZD5363, or GDC0068) reversed the vascular malformations; the inhibitors effectiveness was directly linked to reduced connectivity in the organoids. In summary, our study introduces an innovative in vitro model combining organoid technology and gene editing to explore vascular pathophysiology in Proteus syndrome. This model not only simulates Proteus syndrome vasculature but also holds potential for mimicking vasculatures of other genetically driven diseases. It represents an advance in drug development for rare diseases, historically plagued by slow progress.

bioengineering↗

bioRxiv2024

Structural basis of respiratory complexes adaptation to cold temperatures

In response to cold, mammals activate brown fat for respiratory-dependent thermogenesis reliant on the electron transport chain (1, 2). Yet, the structural basis of respiratory complex adaptation to cold remains elusive. Herein we combined thermoregulatory physiology and cryo-EM to study endogenous respiratory supercomplexes exposed to different temperatures. A cold-induced conformation of CI:III2 (termed type 2) was identified with a [~]25{degrees} rotation of CIII2 around its inter-dimer axis, shortening inter-complex Q exchange space, and exhibiting different catalytic states which favor electron transfer. Large-scale supercomplex simulations in lipid membrane reveal how unique lipid-protein arrangements stabilize type 2 complexes to enhance catalytic activity. Together, our cryo-EM studies, multiscale simulations and biochemical analyses unveil the mechanisms and dynamics of respiratory adaptation at the structural and energetic level.

cell biology↗

bioRxiv2024

Multi-omic characterization of allele-specific regulatory variation in hybrid pigs

Genetic variation in the regulation of gene expression contributes substantially to phenotypic variation. Understanding how variation in DNA sequences and epigentic modifications leads to gene expression variation remains a challenging task. In hybrid animals where cellular environments are homogeneous, differences between the expression of paternal and maternal alleles must be due to cis sequence or epigenic differences. Therefore, hybrid mapping is a powerful approach to efficiently identify and characterize genes under regulation through mechanisms in cis. In this study, using reciprocal crosses of the phenotypically divergent Duroc and Lulai pig breeds, we performed a comprehensive multi-omic characterization of regulatory variation across brain, liver, muscle, and placenta in four developmental stages. We produced one of the largest multi-omic datasets to date in pigs, including 16 whole genome sequenced genomes, 48 whole genome bisulfite sequencing, 168 ATAC-Seq and 168 RNA-Seq samples. We developed a novel read count-based method to reliably assess allele-specific methylation, chromatin accessibility, and RNA expression. We showed that tissue specificity was much stronger than developmental stage specificity in all of DNA methylation, chromatin accessibility, and gene expression. We identified 573 genes showing allele specific expression, including those influenced by parent-of-origin as well as allele genotype effects. By integrating methylation, chromatin accessibility, and gene expression data, many of these allele specific expression can be explained by allele specific methylation and/or chromatin accessibility. This study provides one of the most comprehensive characterizations of regulatory variation across multiple tissues and developmental stages in pigs and offers new opportunity of genetic improvement for this important food animal species.

developmental biology↗

bioRxiv2024

Multimodal profiling reveals tissue-directed signatures of human immune cells altered with age

The immune system comprises multiple cell lineages and heterogeneous subsets found in blood and tissues throughout the body. While human immune responses differ between sites and over age, the underlying sources of variation remain unclear as most studies are limited to peripheral blood. Here, we took a systems approach to comprehensively profile RNA and surface protein expression of over 1.25 million immune cells isolated from blood, lymphoid organs, and mucosal tissues of 24 organ donors aged 20-75 years. We applied a multimodal classifier to annotate the major immune cell lineages (T cells, B cells, innate lymphoid cells, and myeloid cells) and their corresponding subsets across the body, leveraging probabilistic modeling to define bases for immune variations across donors, tissue, and age. We identified dominant tissue-specific effects on immune cell composition and function across lineages for lymphoid sites, intestines, and blood-rich tissues. Age-associated effects were intrinsic to both lineage and site as manifested by macrophages in mucosal sites, B cells in lymphoid organs, and T and NK cells in blood-rich sites. Our results reveal tissue-specific signatures of immune homeostasis throughout the body and across different ages. This information provides a basis for defining the transcriptional underpinnings of immune variation and potential associations with disease-associated immune pathologies across the human lifespan.

immunology↗

bioRxiv2024

Polaris Neurosense: A General-Purpose Non-invasive Neurotechnology Research Platform

This article describes our initial work toward a general-purpose platform for non-invasive neurotechnology research. This platform consists of a multi-modal wireless recording device, an associated software API, and full integration into BCI2000 software. The device is placed on the forehead and features two electroencephalographic (EEG) sensors, an inertial movement sensor (IMU), a photoplethysmogram (PPG) sensor, a microphone, and vibration-based feedback. Herein, we demonstrate different technical characteristics of our platform and its use in the context of sleep monitoring/modulation, simultaneous and synchronized recordings from different hardware, and evoked potentials. With further development and widespread dissemination, our platform could become an important tool for research into new non-invasive neurotechnology protocols in humans.

bioengineering↗

bioRxiv2024

Control of ciliary transcriptional programs during spermatogenesis by antagonistic transcription factors

Existence of cilia in the last eukaryotic common ancestor (LECA) raises a fundamental question in biology: how the transcriptional regulation of ciliogenesis has evolved? One conceptual answer to this question is by an ancient transcription factor regulating ciliary gene expression in both unicellular and multicellular organisms, but examples of such transcription factors in eukaryotes are lacking. Previously, we showed that an ancient transcription factor XAP5 is required for flagellar assembly in Chlamydomonas. Here, we show that XAP5 and XAP5L are two conserved pairs of antagonistic transcription regulators that control ciliary transcriptional programs during spermatogenesis. Male mice lacking either XAP5 or XAP5L display infertility, as a result of meiotic prophase arrest and sperm flagella malformation, respectively. Mechanistically, XAP5 positively regulates the ciliary gene expression by activating the key regulators including FOXJ1 and RFX families during the early stage of spermatogenesis. In contrast, XAP5L negatively regulates the expression of ciliary genes via repressing these ciliary transcription factors during the spermiogenesis stage. Our results provide new insights into the mechanisms by which temporal and spatial transcription regulators are coordinated to control ciliary transcriptional programs during spermatogenesis.

cell biology↗