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Jia, Z.

Publications and source records attributed to Jia, Z..

At least 73 records · Page 4Linked to original sources

Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex

Mammalian cortex features a vast diversity of neuronal cell types, each with characteristic anatomical, molecular and functional properties. Synaptic connectivity powerfully shapes how each cell type participates in the cortical circuit, but mapping connectivity rules at the resolution of distinct cell types remains difficult. Here, we used millimeter-scale volumetric electron microscopy1 to investigate the connectivity of all inhibitory neurons across a densely-segmented neuronal population of 1352 cells spanning all layers of mouse visual cortex, producing a wiring diagram of inhibitory connections with more than 70,000 synapses. Taking a data-driven approach inspired by classical neuroanatomy, we classified inhibitory neurons based on the relative targeting of dendritic compartments and other inhibitory cells and developed a novel classification of excitatory neurons based on the morphological and synaptic input properties. The synaptic connectivity between inhibitory cells revealed a novel class of disinhibitory specialist targeting basket cells, in addition to familiar subclasses. Analysis of the inhibitory connectivity onto excitatory neurons found widespread specificity, with many interneurons exhibiting differential targeting of certain subpopulations spatially intermingled with other potential targets. Inhibitory targeting was organized into "motif groups," diverse sets of cells that collectively target both perisomatic and dendritic compartments of the same excitatory targets. Collectively, our analysis identified new organizing principles for cortical inhibition and will serve as a foundation for linking modern multimodal neuronal atlases with the cortical wiring diagram.

neuroscience↗

Large-scale unsupervised discovery of excitatory morphological cell types in mouse visual cortex

Neurons in the neocortex exhibit astonishing morphological diversity which is critical for properly wiring neural circuits and giving neurons their functional properties. However, the organizational principles underlying this morphological diversity remain an open question. Here, we took a data-driven approach using graph-based machine learning methods to obtain a low-dimensional morphological "bar code" describing more than 30,000 excitatory neurons in mouse visual areas V1, AL and RL that were reconstructed from the millimeter scale MICrONS serial-section electron microscopy volume. Contrary to previous classifications into discrete morphological types (m-types), our data-driven approach suggests that the morphological landscape of cortical excitatory neurons is better described as a continuum, with a few notable exceptions in layers 5 and 6. Dendritic morphologies in layers 2-3 exhibited a trend towards a decreasing width of the dendritic arbor and a smaller tuft with increasing cortical depth. Inter-area differences were most evident in layer 4, where V1 contained more atufted neurons than higher visual areas. Moreover, we discovered neurons in V1 on the border to layer 5 which avoided deeper layers with their dendrites. In summary, we suggest that excitatory neurons morphological diversity is better understood by considering axes of variation than using distinct m-types.

neuroscience↗

IIIandMe: An Algorithm for Chromosome-scale Haplotype Determination Using Genome-wide Variants of Three Haploid Reproductive Cells

Our recent algorithm, Hapi, infers chromosome-scale haplotypes using genomic data of a small number of single gametes. Its advanced version, IIIandMe, is proposed here to achieve comparable phasing accuracy with as few as three gametes, pushing the analysis to its limit. The new method is validated with simulation and a citrus gamete dataset. The rapid advances in genotyping technologies promise a broad application of IIIandMe in disclosing important genetic information.

bioinformatics↗

PRMT5 links lipid metabolism to contractile function of skeletal muscles

The skeletal muscle plays a key role in systemic energy homeostasis besides its canonical contractile function, but what couples these functions is poorly defined. Protein Arginine MethylTransferase 5 (PRMT5) is a well-known oncoprotein but also expressed in healthy tissues with unclear physiological functions. As adult muscles express high levels of Prmt5, we generated myocyte-specific Prmt5 knockout (Prmt5MKO) mice. We observed reduced muscle mass, oxidative capacity, force production and exercise performance in Prmt5MKO mice. The motor deficiency is associated with scarce lipid droplets in myofibers due to defects in lipid biosynthesis and degradation. First, Prmt5MKO reduced demethylation and stability of Sterol Regulatory Element-Binding Transcription Factor 1a (SREBP1a), a master regulator of de novo lipogenesis. Second, Prmt5MKO impaired the repressive H4R3Me2s (histone H4 arginine-3 symmetric demethylation) at the Pnpla2 gene, elevating the level of its encoded protein ATGL, the rate-limiting enzyme catalyzing lipolysis. Accordingly, myocyte-specific double knockout of Pnpla2 and Prmt5 normalized muscle mass and function. Together, our findings delineate a physiological function of PRMT5 in linking lipid metabolism to contractile function of myofibers.

molecular biology↗

Microbial diversity and community dynamics in an active, high CO2 subsurface rift ecosystem

The Eger Rift subsurface is characterized by frequent seismic activity and consistently high CO2 concentrations, making it a unique deep biosphere ecosystem and a suitable site to study the interactions between volcanism, tectonics, and microbiological activity. Pulses of geogenic H2 during earthquakes may provide substrates for methanogenic and chemolithotrophic processes, but very little is currently known about the role of subsurface microorganisms and their cellular processes in this type of environment. To assess the impact of geologic activity on microbial life, we analyzed the geological, geochemical, and microbiological composition of rock and sediment samples from a 240 m deep drill core, running across six lithostratigraphic zones. In addition, we evaluated diversity as well as metabolic attributes of bacterial and archaeal communities. Our investigation revealed a distinct low biomass community, with a surprisingly diverse Archaea population, providing strong support that methanogenic archaea reside in the Eger subsurface. Geochemical analysis revealed sulfate and sodium concentrations as high as 1000 mg L-1 in sediment samples from a depth between 50 and 100 m and in weathered rock samples collected below 200 m. Most microbial signatures could be assigned to common soil and water bacteria, which together with the occurrence of freshwater Cyanobacteria at specific depths, emphasize the heterogenous, groundwater movement driven nature of this terrestrial subsurface environment. Although not as frequently and abundantly as initially expected, our investigations also found evidence for anaerobic, autotrophic, and acidophilic communities in Eger Rift sediments, as sulfur cycling taxa like Thiohalophilus and Desulfosporosinus were specifically enriched at depths below 100 m. The detection of methanogenic, halophilic, and ammonia oxidizing archaeal populations demonstrate that the unique features of the Eger Rift subsurface environment provide the foundation for diverse types of microbial life, including the microbial utilization of geologically derived CO2 and when available H2, as a primary energy source.

microbiology↗

origamiFISH allows universal, label-free, single molecule visualization of DNA origami nanodevices across biological samples

Structural DNA nanotechnology enables user-prescribed design of DNA nanostructures (DNs) for biological applications, but how DN design determines their bio-distribution and cellular interactions remain poorly understood. One challenge is that current methods for tracking DN fates in situ, including fluorescent-dye labeling, suffer from low sensitivity and dye-induced artifacts. Here we present origamiFISH, a label-free and universal method for single-molecule fluorescence detection of DNA origami nanostructures in cells and tissues. origamiFISH targets pan-DN scaffold sequences with hybridization chain reaction (HCR) probes to achieve thousand-fold signal amplification. We identify cell-type and shape-specific spatiotemporal uptake patterns within 1 minute of uptake and at picomolar DN concentrations, 10,000x lower than field standards. We additionally optimized compatibility with immunofluorescence and tissue clearing to visualize DN distribution within tissue cryo/vibratome-sections, slice cultures, and whole-mount organoids. Together, origamiFISH enables faithful mapping of DN interactions across subcellular and tissue barriers for guiding the development of DN-based therapeutics.

synthetic biology↗

Quantitative Census of Local Somatic Features in Mouse Visual Cortex

Mammalian neocortex contains a highly diverse set of cell types. These types have been mapped systematically using a variety of molecular, electrophysiological and morphological approaches. Each modality offers new perspectives on the variation of biological processes underlying cell type specialization. Cellular scale electron microscopy (EM) provides dense ultrastructural examination and an unbiased perspective into the subcellular organization of brain cells, including their synaptic connectivity and nanometer scale morphology. It also presents a clear challenge for analysis to identify cell-types in data that contains tens of thousands of neurons, most of which have incomplete reconstructions. To address this challenge, we present the first systematic survey of the somatic region of all cells within a cubic millimeter of cortex using quantitative features obtained from EM. This analysis demonstrates a surprising sufficiency of the perisomatic region to identify cell-types, including types defined primarily based on their connectivity patterns. We then describe how this classification facilitates cell type specific connectivity characterization and locating cells with rare connectivity patterns in the dataset.

neuroscience↗

KLF7 Promotes Preadipocyte Proliferation via Activation of the Akt Signaling Pathway by Cis-regulating CDKN3

Kruppel-like transcription factor 7 (KLF7) promotes preadipocyte proliferation; however, its target gene in this process has not yet been identified. Using KLF7 ChIP-seq analysis, we previously showed that a KLF7-binding peak is present upstream of the cyclin-dependent kinase inhibitor 3 gene (CDKN3) in chicken preadipocytes. In the current study, we identified CDKN3 as a target gene of KLF7 that mediates the effects of KLF7 on preadipocyte proliferation. Furthermore, 5'-truncating mutation analysis showed that the minimal promoter was located between CDKN3 nt -160 and nt -7 (relative to the translation initiation codon ATG). KLF7 overexpression increased CDKN3 promoter activity in the DF-1 and immortalized chicken preadipocyte (ICP1) cell lines. Deletion of the putative binding site of KLF7 abolished the promotive effect of KLF7 overexpression on CDKN3 promoter activity. Moreover, CDKN3-knockdown and -overexpression assays revealed that CDKN3 enhanced ICP1 cell proliferation. Flow cytometry analysis showed that CDKN3 accelerated the G1/S transition. Further, we found that KLF7 promoted ICP1 cell proliferation via Akt phosphorylation by regulating CDKN3. Taken together, these results suggest that KLF7 promotes preadipocyte proliferation via activating the Akt signaling pathway by cis-regulating CDKN3, thus driving the G1/S transition.

molecular biology↗

Development and Application of a Novel Simple Sequence Repeat Mining Algorithm Based on Regular Expression

Simple sequence repeats (SSRs) are molecular genetic markers that are powerful tools in genomics studies; SSR markers are routinely mined as a part of genetic workflows. Here, we developed a novel SSR mining algorithm based on regular expression that can reduce the complexity of commonly used SSR mining software. We used the following SSR mining regular expression: ({i, j}?) (\1) {k}, where i and j denote the minimum and maximum lengths of the motifs of the SSR sequence, respectively, and k is the minimum number of repeat motifs. From this SSR mining algorithm, we developed an SSR sequence analysis software (named "regexSSRw") that is capable of mining eligible SSR loci from FASTA format sequences; regexSSRw can be accessed at https://github.com/renm79/rgxSSRw. This SSR mining algorithm can aid a range of applications, from being used by programmers in the development of SSR mining software to being implemented by scholars into their SSR marker workflow.

bioinformatics↗

ACSF2: A MEDIUM-CHAIN ACYL-CoA SYNTHETASE WITH A POTENTIAL ROLE IN NEURONAL DIFFERENTIATION

By activating fatty acids to their CoA derivatives, acyl-CoA synthetases (ACS) play an essential role in fatty acid metabolism. We previously identified ACSF2 as an ACS that was phylogenetically distinct from known families of short-chain, medium-chain, long-chain, very long-chain, and bubblegum ACSs. Functionally, ACSF2 preferentially activated medium-chain fatty acids. In this work, we provide further characterization of this unique ACS. ACSF2 mRNA expression was found in most tissues, although immunohistochemical analysis revealed differences in protein expression between various cell types in each tissue. Endogenous ACSF2 was found in the Golgi region in Neuro2a and P19 cells, and disruption of the Golgi in Neuro2a cells with nocodazole disrupted ACSF2 localization. In contrast, MA-10, HepG2, and skin fibroblasts had a mitochondrial ACSF2 localization. ACSF2 activated saturated fatty acids containing 6 to 10 carbons when overexpressed in COS-1 cells. The Kmapp for C10:0 was 24.4 M and Vmaxapp was 385 nmol/20min/mg COS cell protein. Knockdown by RNA interference revealed that ACSF2 was responsible for most of the medium-chain ACS activity in Neuro2a cells. A lysine residue critical for activity in bacterial short-chain ACSs was found in ACSF2, and mutation of this residue to alanine abolished enzyme activity. Neurite outgrowth results when Neuro2a cells are induced to differentiate with retinoic acid, and ACSF2 migrated to nodes and points of neurite-neurite contact along with the presynaptic marker, synaptophysin. ACSF2-deficient Neuro2a cells showed significantly blunted neurite outgrowth in response to retinoic acid. These results suggest that this medium-chain ACS may play an important role in neuronal development.

biochemistry↗

Petascale pipeline for precise alignment of images from serial section electron microscopy

The reconstruction of neural circuits from serial section electron microscopy (ssEM) images is being accelerated by automatic image segmentation methods. Segmentation accuracy is often limited by the preceding step of aligning 2D section images to create a 3D image stack. Precise and robust alignment in the presence of image artifacts is challenging, especially as datasets are attaining the petascale. We present a computational pipeline for aligning ssEM images with several key elements. Self-supervised convolutional nets are trained via metric learning to encode and align image pairs, and they are used to initialize iterative fine-tuning of alignment. A procedure called vector voting increases robustness to image artifacts or missing image data. For speedup the series is divided into blocks that are distributed to computational workers for alignment. The blocks are aligned to each other by composing transformations with decay, which achieves a global alignment without resorting to a time-consuming global optimization. We apply our pipeline to a whole fly brain dataset, and show improved accuracy relative to prior state of the art. We also demonstrate that our pipeline scales to a cubic millimeter of mouse visual cortex. Our pipeline is publicly available through two open source Python packages.

neuroscience↗

An allosteric agonist activates BK channels by perturbing coupling between Ca2+ binding and pore opening

BK type Ca2+-activated K+ channels activate in response to both the membrane voltage and intracellular Ca2+ with distinct mechanisms. Ca2+ binds to the cytosolic domain (CTD) to open the pore across the membrane, but the mechanism that couples Ca2+ binding to pore opening is not clear. Here we show that a compound, BC5, identified using in silico screening, interacts with BK channels at the interface between the CTD and the transmembrane voltage sensing domain (VSD) and enhances channel activity by specifically affecting the Ca2+ dependent mechanism. BC5 activates the channel in the absence of Ca2+ binding but Ca2+ binding inhibits BC5 effects. Thus, BC5 perturbs the pathway that couples Ca2+ binding to pore opening to allosterically affect both, which is supported by atomistic simulations and mutagensis. The results suggest that the CTD- VSD interaction makes a major contribution to the mechanism of Ca2+ dependent activation and is an important site for allosteric agonists to modulate BK channel activation.

biophysics↗

Structural and functional characterizations of altered infectivity and immune evasion of SARS-CoV-2 Omicron variant

The SARS-CoV-2 Omicron with increased fitness is spreading rapidly worldwide. Analysis of cryo-EM structures of the Spike (S) from Omicron reveals amino acid substitutions forging new interactions that stably maintain an "active" conformation for receptor recognition. The relatively more compact domain organization confers improved stability and enhances attachment but compromises the efficiency of viral fusion step. Alterations in local conformation, charge and hydrophobic microenvironments underpin the modulation of the epitopes such that they are not recognized by most NTD- and RBD-antibodies, facilitating viral immune escape. Apart from already existing mutations, we have identified three new immune escape sites: 1) Q493R, 2) G446S and 3) S371L/S373P/S375F that confers greater resistance to five of the six classes of RBD-antibodies. Structure of the Omicron S bound with human ACE2, together with analysis of sequence conservation in ACE2 binding region of 25 sarbecovirus members as well as heatmaps of the immunogenic sites and their corresponding mutational frequencies sheds light on conserved and structurally restrained regions that can be used for the development of broad-spectrum vaccines and therapeutics.

biophysics↗

A subset of Memory B-derived antibody repertoire from 3-dose vaccinees is ultrapotent against diverse and highly transmissible SARS-CoV-2 variants, including Omicron

Omicron, the most heavily mutated SARS-CoV-2 variant so far, is highly resistant to neutralizing antibodies, raising unprecedented concerns about the effectiveness of antibody therapies and vaccines. We examined whether sera from individuals who received two or three doses of inactivated vaccine, could neutralize authentic Omicron. The seroconversion rates of neutralizing antibodies were 3.3% (2/60) and 95% (57/60) for 2- and 3-dose vaccinees, respectively. For three-dose recipients, the geometric mean neutralization antibody titer (GMT) of Omicron was 15, 16.5-fold lower than that of the ancestral virus (254). We isolated 323 human monoclonal antibodies derived from memory B cells in 3-dose vaccinees, half of which recognize the receptor binding domain (RBD) and show that a subset of them (24/163) neutralize all SARS-CoV-2 variants of concern (VOCs), including Omicron, potently. Therapeutic treatments with representative broadly neutralizing mAbs individually or antibody cocktails were highly protective against SARS-CoV-2 Beta infection in mice. Atomic structures of the Omicron S in complex with three types of all five VOC-reactive antibodies defined the binding and neutralizing determinants and revealed a key antibody escape site, G446S, that confers greater resistance to one major class of antibodies bound at the right shoulder of RBD through altering local conformation at the binding interface. Our results rationalize the use of 3-dose immunization regimens and suggest that the fundamental epitopes revealed by these broadly ultrapotent antibodies are a rational target for a universal sarbecovirus vaccine. One sentence summaryA sub-set of antibodies derived from memory B cells of volunteers vaccinated with 3 doses of an inactivated SARS-CoV-2 vaccine work individually as well as synergistically to keep variants, including Omicron, at bay.

microbiology↗

Bioorthogonal Functionalization of Material Surfaces with Bioactive Molecules

The functionalization of material surfaces with biologically active molecules is crucial for enabling technologies in life sciences, biotechnology, and medicine. However, achieving biocompatibility and bioorthogonality with current synthetic methods remains a challenge. We report herein a novel surface functionalization method that proceeds chemoselectively and without a free transition metal catalyst. In this method, a coating is first formed via the tyrosinase-catalyzed putative polymerization of a tetrazine-containing catecholamine (DOPA-Tet). One or more types of molecule of interest containing trans-cyclooctene are then grafted onto the coating via tetrazine ligation. The entire process proceeds under physiological conditions and is suitable for grafting bioactive molecules with diverse functions and structural complexities. Utilizing this method, we functionalized material surfaces with enzymes (alkaline phosphatase, glucose oxidase, horseradish peroxidase), a cyclic peptide (cyclo[Arg-Gly-Asp-D-Phe-Lys], or c(RGDfK)), and an antibiotic (vancomycin). Colorimetric assays confirmed the maintenance of the biocatalytic activities of the grafted enzymes on the surface. We established the mammalian cytocompatibility of the functionalized materials with fibroblasts. Surface functionalization with c(RGDfK) showed improved fibroblast cell adhesion and cytoskeletal organization. Microbiological studies with Staphylococcus aureus indicated that surfaces coated using DOPA-Tet inhibit the formation of biofilms. Vancomycin-grafted surfaces additionally display significant inhibition of planktonic S. aureus growth.

biochemistry↗

Petascale neural circuit reconstruction: automated methods

3D electron microscopy (EM) has been successful at mapping invertebrate nervous systems, but the approach has been limited to small chunks of mammalian brains. To scale up to larger volumes, we have built a computational pipeline for processing petascale image datasets acquired by serial section EM, a popular form of 3D EM. The pipeline employs convolutional nets to compute the nonsmooth transformations required to align images of serial sections containing numerous cracks and folds, detect neuronal boundaries, label voxels as axon, dendrite, soma, and other semantic categories, and detect synapses and assign them to presynaptic and postsynaptic segments. The output of neuronal boundary detection is segmented by mean affinity agglomeration with semantic and size constraints. Pipeline operations are implemented by leveraging distributed and cloud computing. Intermediate results of the pipeline are held in cloud storage, and can be effortlessly viewed as images, which aids debugging. We applied the pipeline to create an automated reconstruction of an EM image volume spanning four visual cortical areas of a mouse brain. Code for the pipeline is publicly available, as is the reconstructed volume.

neuroscience↗

Comprehensive evaluation of machine learning models and gene expression signatures for prostate cancer prognosis using large population cohorts

Overtreatment remains the pervasive problem in prostate cancer (PCa) management due to the highly variable and often indolent course. Molecular signatures derived from gene expression profiling have played critical roles in PCa treatment decision-making. Many gene expression signatures have been developed to improve the risk stratification of PCa and some of them have already been translationally applied to clinical practice, however, no comprehensive evaluation was performed to compare the performances of the signatures. In this study, we conducted a systematic and unbiased evaluation of 15 machine learning (ML) algorithms and 30 published PCa gene expression-based prognostic signatures leveraging 10 transcriptomics datasets with 1,558 primary PCa patients from public data repositories. The results revealed that survival analysis models outperformed binary classification models for risk assessment, and the performances of the survival analysis methods - Cox model regularized with ridge penalty (Cox-Ridge) and partial least squares regression for Cox model (Cox-PLS) - were generally more robust than the other methods. Based on the Cox-Ridge algorithm, a few top prognostic signatures that have comparable or even better performances than the commercial panels have been identified. The findings from the study will greatly facilitate the identification of existing prognostic signatures that are promising for further validations in prospective studies and promote the development of robust prognostic models to guide clinical decision-making. Moreover, the study provided a valuable data resource from large primary PCa cohorts, which can be used to develop, validate, and evaluate novel statistical methodologies and molecular signatures to improve PCa management.

bioinformatics↗

PCaDB - a comprehensive and interactive database for transcriptomes from prostate cancer population cohorts

Prostate cancer (PCa) is a heterogeneous disease with highly variable clinical outcomes which presents enormous challenges in the clinical management. A vast amount of transcriptomics data from large PCa cohorts have been generated, providing extraordinary opportunities for the molecular characterization of the PCa disease and the development of diagnostic and prognostic signatures. The lack of an inclusive collection and harmonization of the scattered public datasets constrains the extensive use of the valuable resources. In this study, we present a user-friendly database, PCaDB, for a comprehensive and interactive analysis and visualization of gene expression profiles from 77 transcriptomics datasets with 9,068 patient samples. PCaDB also includes a single-cell RNA-sequencing (scRNAseq) dataset for normal human prostates and 30 published PCa prognostic signatures. The comprehensive data resources and advanced analytical methods equipped in PCaDB would greatly facilitate data mining to understand the heterogeneity of PCa and to develop machine learning models for accurate PCa diagnosis and prognosis to assist on clinical decision-making. PCaDB is publicly available at http://bioinfo.jialab-ucr.org/PCaDB/.

bioinformatics↗