Biology subjects

Zhuo, C.

Publications and source records attributed to Zhuo, C..

11 recordsLinked to original sources

bioRxiv2025

Cerebellum-Targeted Transcranial Focused Ultrasound Stimulation Modulates Hippocampus Neural Activities

Low-intensity transcranial focused ultrasound stimulation (tFUS) has recently emerged as a promising neuromodulation technique due to its non-invasive nature, ability to penetrate deeply, and high spatial resolution. On the other hand, the cerebellum has attracted new interest as a target for neuromodulation. In this study, we investigated how cerebellum-targeted tFUS could modulate hippocampal neural activity. We found that tFUS at pulse repetition frequency (PRF) as high as 10kHz can effectively activate the cerebellum in vivo. Furthermore, PRF at 10kHz, rather than 1kHz, favored remote inhibition of hippocampus neurons. We found that the neuromodulation at hippocampus was mainly mediated by cerebellum cortex than deep cerebellar nucleus. Furthermore, by cFos expression mapping and phase-amplitude coupling analysis, we showed that the hippocampal response at different PRFs resulted from altered neurodynamic interaction rather than overall activation of engaged brain regions. Our results reveal new potential for cerebellum as a neuromodulation target for hippocampus-related functions.

bioengineering↗

bioRxiv2025

Mapping individualized multi-scale hierarchical brain functional networks from fMRI by self-supervised deep learning

The brains multi-scale hierarchical organization supports functional segregation and integration. Characterizing the hierarchy of individualized multi-scale functional networks (FNs) is crucial for understanding these fundamental brain processes. It provides promising opportunities for both basic neuroscience and translational research in neuropsychiatric illness. However, current methods typically compute individualized FNs at a single scale and are not equipped to quantify any possible hierarchical organization. To address this limitation, we present a self-supervised deep learning (DL) framework that simultaneously computes multi-scale FNs and characterizes their across-scale hierarchical structure at the individual level. Our method learns intrinsic representations of fMRI data in a low-dimensional latent space to effectively encode multi-scale FNs and their hierarchical structure by optimizing functional homogeneity of FNs across scales jointly in an end-to-end learning manner. A DL model trained on fMRI scans from the Human Connectome Project successfully identified individualized multi-scale hierarchical FNs for unseen individuals and generalized to two external cohorts. Furthermore, the individualized hierarchical structure of FNs was significantly associated with biological phenotypes, including sex, brain development, and brain health. Our framework provides an effective method to compute multi-scale FNs and to characterize the inter-scale hierarchy of FNs for individuals, facilitating a comprehensive understanding of brain functional organization and its inter-individual variation.

neuroscience↗

bioRxiv2022

Extended-spectrum beta-lactamase genes traverse the Escherichia coli populations of ICU patients, staff and environment.

Over a three-month period, we monitored the population of extended-spectrum {beta}-lactam-resistant Escherichia coli (ESBL-EC) associated with the patients, staff and environment of an intensive care unit (ICU) in Guangzhou, China. Thirty-four clinical isolates were obtained from the same hospital 12 months later. A total of 165 isolates were characterised and whole-genome sequenced, with 24 isolates subjected to long-read sequencing. The diverse population included representatives of 59 different sequence types (STs). ICU patient and environmental isolates were largely distinct from staff isolates and clinical isolates. We observed five instances of highly similar isolates (0-13 core-gene SNPs) being obtained from different patients or bed unit environments. ESBL resistance in this collection was largely conferred by blaCTX-M genes, which were found in 96.4% of all isolates. The contexts of blaCTX-M genes were diverse, situated in multiple chromosomal positions and in various plasmids. We identified blaCTX-M-bearing plasmid lineages that were present in multiple STs across the surveillance, staff and clinical collections. Closer examination of ISEcp1-blaCTX-M transposition units shed light on the dynamics of their transmission, with evidence for the acquisition of chromosomal copies of blaCTX-M genes from specific plasmid lineages, and for the movement of blaCTX-M-55 from a ST1193 chromosome to a small mobilisable plasmid. A carbapenem-resistant ST167 strain isolated from a patient that had been treated with meropenem and piperacillin-tazobactam contained seven copies of blaCMY-146, which appears to have been amplified by IS1. Our data revealed limited persistence and movement of ESBL-EC strains in the ICU environment, but we observed circulating plasmid lineages playing an essential and ongoing role in shaping the cephalosporin-resistance landscape in the population examined. Impact statementESBL resistance significantly impacts clinical management of E. coli infections in hospitals globally. It is important to understand the structures of ESBL-EC populations carried by hospital patients and staff, their capacity to persist in hospital environments, and the dynamics of mobile genes that drive the spread of ESBL resistance. In our three-month study, ESBL-EC strains found in the ICU environment were strongly associated with patient carriage, but distinct from strains found in staff. However, plasmid lineages carrying blaCTX-M genes were found across the ICU populations and in a collection of clinical isolates obtained one year later. By examining their content and contexts, we have traced the recent histories of chromosomal and plasmid-borne ISEcp1-blaCTX-M transposition units in the ICU population. This allowed us to implicate specific plasmid lineages in the acquisition of chromosomal blaCTX-M genes, even when the plasmids were no longer present, and to detect recent transposition of blaCTX-M-55 from a chromosome to a mobilisable plasmid. Similar high-resolution approaches to the study of mobile genetic elements will be essential if the transmission routes associated with the spread of ESBL resistance are to be understood and subjected to interventions. Data summarySequencing reads are available under NCBI BioProject accession PRJNA907549. The 91 complete plasmid sequences generated in this study are in a supplementary file called pDETEC_collection.fa.

microbiology↗

bioRxiv2022

Canonical and Replicable Multi-Scale Intrinsic Connectivity Networks in 100k+ Resting-State fMRI Datasets

Resting-state functional magnetic resonance imaging (rsfMRI) has shown considerable promise for improving our understanding of brain function and characterizing various mental and cognitive states in the healthy and disordered brain. However, the lack of accurate and precise estimations of comparable functional patterns across datasets, individuals, and ever-changing brain states in a way that captures both individual variation and inter-subject correspondence limits the clinical utility of rsfMRI and its application to single-subject analyses. We posit that using reliable network templates and advanced group-informed network estimation approaches to accurately and precisely obtain individualized (dynamic) networks that retain cross-subject correspondence while maintaining subject-specific information is one potential solution to overcome the aforementioned barrier when considering cross-study comparability, independence of subject-level estimates, the limited data available in single studies, and the low signal-to-noise ratio (SNR) of rsfMRI. Toward this goal, we first obtained a reliable and replicable network template. We combined rsfMRI data of over 100k individuals across private and public datasets and selected around 58k that meet quality control (QC) criteria. We then applied multi-model-order independent component analysis (ICA) and subsampling to obtain reliable canonical intrinsic connectivity networks (ICNs) across multiple spatial scales. The selected ICNs (i.e., network templates) were also successfully replicated by independently analyzing the data that did not pass the QC criteria, highlighting the robustness of our adaptive template to data quality. We next studied the feasibility of estimating the corresponding subject-specific ICNs using a multivariate-spatially constrained ICA as an example of group-informed network estimation approaches. The results highlight that several factors, including ICNs themselves, data length, and spatial resolution, play key roles in successfully estimating the ICNs at the subject level. Large-scale ICNs, in general, require less data to achieve a specific level of spatial similarity with their templates (as well as within- and between-subject spatial similarity). Moreover, increasing data length can reduce an ICNs subject-level specificity, suggesting longer scans might not always be desirable. We also show spatial smoothing can alter results, and the positive linear relationship we observed between data length and spatial smoothness (we posit that it is at least partially due to averaging over intrinsic dynamics or individual variation) indicates the importance of considering this factor in studies such as those focused on optimizing data length. Finally, the consistency in the spatial similarity between ICNs estimated using the full-length of data and subset of it across different data lengths may suggest that the lower within-subject spatial similarity in shorter data lengths is not necessarily only defined by lower reliability in ICN estimates; rather, it can also be an indication of brain dynamics (i.e., different subsets of data may reflect different ICN dynamics), and as we increase the data length, the result approaches the average (also known as static) ICN pattern, and therefore loses its distinctiveness.

neuroscience↗

bioRxiv2021

Computing personalized brain functional networks from fMRI using self-supervised deep learning

A novel self-supervised deep learning (DL) method is developed for computing bias-free, personalized brain functional networks (FNs) that provide unique opportunities to better understand brain function, behavior, and disease. Specifically, convolutional neural networks with an encoder-decoder architecture are employed to compute personalized FNs from resting-state fMRI data without utilizing any external supervision by optimizing functional homogeneity of personalized FNs in a self-supervised setting. We demonstrate that a DL model trained on fMRI scans from the Human Connectome Project can identify canonical FNs and generalizes well across four different datasets. We further demonstrate that the identified personalized FNs are informative for predicting individual differences in behavior, brain development, and schizophrenia status. Taken together, self-supervised DL allows for rapid, generalizable computation of personalized FNs.

neuroscience↗

bioRxiv2021

Vesicular stomatitis virus chimeras expressing the Oropouche virus glycoproteins elicit protective immune responses in mice.

Oropouche virus (OROV) infection of humans is associated with a debilitating febrile illness that can progress to meningitis or encephalitis. First isolated from a forest worker in Trinidad and Tobago in 1955, the arbovirus OROV has since been detected throughout the Amazon basin with an estimated 500,000 human infections. Like other members of the family Peribunyaviridae, the viral genome exists as 3 single-stranded negative-sense RNA segments. The medium sized segment encodes a viral glycoprotein complex (GPC) that is proteolytically processed into two viral envelope proteins Gn and Gc responsible for attachment and membrane fusion. There are no therapeutics or vaccines to combat OROV infection, and we have little understanding of protective immunity to infection. Here we generated a replication competent chimeric vesicular stomatitis virus (VSV), in which the endogenous glycoprotein was replaced by the GPC of OROV. Serum from mice immunized with VSV-OROV specifically neutralized wild type OROV, and using peptide arrays we mapped multiple epitopes within an N-terminal variable region of Gc recognized by the immune sera. VSV-OROV lacking this variable region of Gc was also immunogenic in mice producing neutralizing sera that recognize additional regions of Gc. Challenge of both sets of immunized mice with wild type OROV shows that the VSV-OROV chimeras reduce wild type viral infection and suggest that antibodies that recognize the variable N-terminus of Gc afford less protection than those that target more conserved regions of Gc. ImportanceOropouche virus (OROV), an orthobunyavirus found in Central and South America, is an emerging public health challenge that causes debilitating febrile illness. OROV is transmitted by arthropods, and increasing mobilization has the potential to significantly increase the spread of OROV globally. Despite this, no therapeutics or vaccines have been developed to combat infection. Using vesicular stomatitis (VSV) as a backbone, we developed a chimeric virus bearing the OROV glycoproteins (VSV-OROV) and tested its ability to elicit a neutralizing antibody response. Our results demonstrate that VSV-OROV produces a strong neutralizing antibody response that is at least partially targeted to the N-terminal region of Gc. Importantly, vaccination with VSV-OROV reduces viral loads in mice challenged with wildtype virus. This data provides the first evidence that targeting the OROV glycoproteins may be an effective vaccination strategy to combat OROV infection.

microbiology↗

bioRxiv2021

BnaA03.WRKY28, interacting with BnaA09.VQ12, acts as a brake factor of activated BnWRKY33-mediated resistance outburst against Sclerotinia sclerotiorum in Brassica napus

Sclerotinia sclerotiorum causes substantial damage to the growth of Brassica napus (rapeseed) and makes a significant loss of crop yield. The plant innate immune system may be the primary solution to defense against S. sclerotiorum for rapeseed. Here, we identify that BnWRKY33, a transcription factor in the innate immune pathway, can be rapidly phosphorylated and activated by the MAPK cascade after rapeseed is infected with S. sclerotiorum. In the MAPK cascade, activated BnaA03.MKK4 phosphorylates and activates BnaA06.MPK3 and BnaC03.MPK3. The activated BnMPK3 acts on the substrate BnWRKY33 to enhance its transcriptional activity and trigger a transcriptional burst of BnWRKY33, which helps plants effectively resist the pathogenic fungi by enhancing the expression of phytoalexin synthesis-related genes. With constant infection, BnaA03.WRKY28 and BnaA09.VQ12 are induced, and BnaA03.WRKY28 physically interacts with BnaA09.VQ12 to form a protein complex. BnaA03.WRKY28 preferentially binds to the promoter of BnWRKY33 with the help of BnaA09.VQ12. Compared with activated BnWRKY33, BnaA03.WRKY28 has a lower transcriptional activity on downstream BnWRKY33, which leads to weaker resistance against S. sclerotiorum for rapeseed in the later stage of infection. Furthermore, the induced BnaA03.WRKY28 may promote axillary bud activity and axillary meristem initiation by regulating the expression of branching-related genes (such as BnBRC1), thus promoting the formation of branches in the leaf axils. One-sentence summaryUnder constant infection by Sclerotinia sclerotiorum, BnaA03.WRKY28 interacts with BnaA09.VQ12 and takes precedence over phosphorylated BnWRKY33 to bind to the BnWRKY33 promoter, thereby weakening resistance but promoting branching.

plant biology↗

bioRxiv2021

Incidence and Characteristics of Co-infection and Secondary Infection in Patients with COVID-19

ObjectiveThe etiology and epidemiology of co-infection and secondary infection in COVID-19 patients remain unknown. The study aims to investigate the occurrence and characteristics of co-infection and secondary infection in COVID-19 patients, mainly focusing on Streptococcus pneumoniae co-infections. MethodsThis study was a prospective, observational cohort study of the inpatients diagnosed with COVID-19 in two designated hospitals in south China enrolled between Jan 11 and Feb 22, 2020. The urine specimen was collected on admission and applied for pneumococcal urinary antigen tests (PUATs). Demographic, clinical and microbiological data of patients were recorded simultaneously. ResultA total of 146 patients with a confirm diagnosis of COVID-19 at the median age of 50.0 years (IQR 36.0-61.0) were enrolled, in which, 16 (11.0%) were classified as severe cases and 130 (89.0%) as non-severe cases. Of the enrolled patients, only 3 (2.1%) were considered to present the co-infection, in which 1 was co-infected with S.pneumoniae, 1 with B. Ovatus infection and the other one with Influenza A virus infection. Secondary infection occurred in 16 patients, with S. maltophilia as the most commonly isolated pathogen (43.8%), followed by P. aeruginosa (25.0%), E. aerogenes (25.0%), C. parapsilosis (25.0%) and A. fumigates (18.8%). ConclusionPatients with confirmed COVID-19 were rarely co-infected with Streptococcus pneumoniae or other pathogens, indicating that the application of antibiotics against CAP on admission may not be necessary in the treatment of COVID-19 cases.

microbiology↗

bioRxiv2020

Persistent bacterial coinfection of a COVID-19 patient caused by a genetically adapted Pseudomonas aeruginosa chronic colonizer

This study characterized a genetically adapted Pseudomonas aeruginosa small colony variant isolated from a COVID-19 patient who suffered persistent bacterial coinfection and eventually recovered from critical illness. Specification and modification of the isolates discovered at genomic and transcriptomic levels with aligned phenotypic observations indicated that these isolates formed excessive biofilm with elevated quorum sensing systems.

microbiology↗

bioRxiv2019

Structural brain architectures match intrinsic functional networks and vary across domains: A study from 15000+ individuals

Brain structural networks have been shown to consistently organize in functionally meaningful architectures covering the entire brain. However, to what extent brain structural architectures match the intrinsic functional networks in different functional domains remains under explored. In this study, based on independent component analysis, we revealed 45 pairs of structural-functional (S-F) component maps, distributing across 9 functional domains, in both a discovery cohort (n=6005) and a replication cohort (UK Biobank, n=9214), providing a well-match multimodal spatial map template for public use. Further network module analysis suggested that unimodal cortical areas (e.g. somatomotor and visual networks) indicate higher S-F coherence, while heteromodal association cortices, especially the frontoparietal network (FPN), exhibit more S-F divergence. Collectively, these results suggest that the expanding and maturing brain association cortex demonstrates a higher degree of changes compared to unimodal cortex, which may lead to higher inter-individual variability and lower S-F coherence.

neuroscience↗

bioRxiv2019

Harmonization of large multi-site imaging datasets: Application to 10,232 MRIs for the analysis of imaging patterns of structural brain change throughout the lifespan

As medical imaging enters its information era and presents rapidly increasing needs for big data analytics, robust pooling and harmonization of imaging data across diverse cohorts with varying acquisition protocols have become critical. We describe a comprehensive effort that merges and harmonizes a large-scale dataset of 10,232 structural brain MRI scans from participants without known neuropsychiatric disorder from 18 different studies that represent geographic diversity. We use this dataset and multi-atlas-based image processing methods to obtain a hierarchical partition of the brain from larger anatomical regions to individual cortical and deep structures and derive normative age trends of brain structure through the lifespan (3 to 96 years old). Critically, we present and validate a methodology for harmonizing this pooled dataset in the presence of nonlinear age trends. We provide a web-based visualization interface to generate and present the resulting age trends, enabling future studies of brain structure to compare their data with this normative reference of brain development and aging, and to examine deviations from normative ranges, potentially related to disease.

bioinformatics↗