Biology subjects

Gong, H.

Publications and source records attributed to Gong, H..

10 recordsLinked to original sources

bioRxiv2018

Gut Microbiota in male patients with chronic traumatic complete spinal cord injury

This study examined the diversity and structure of gut microbiota in healthy adults and chronic traumatic complete spinal cord injury (SCI) patients, documented neurogenic bowel management of SCI patients. The V3-V4 region of 16S rRNA gene from DNA of 91 fecal samples of 48 healthy and 43 diseased subjects was amplified and sequenced. There was difference in gut microbiota between healthy adult males and females. Neurogenic bowel dysfunction (NBD) was common in patients with chronic traumatic complete SCI, patients with quadriplegia have longer time to defecate than paraplegic patients, with higher NBD scores and heavier neurogenic bowel symptoms. Gut microbiota dysbiosis existed in SCI patients. The abundance of Veillonellaceae and Prevotellaceae increased while Bacteroidaceae and Bacteroides decreased in SCI group. The abundance of Bacteroidaceae, Bacteroides in quadriplegia group and Acidaminococcaceae, Blautia in paraplegia group were significant high than the health male group. Serum biomarkers GLU, HDL, CR and NBD symptoms defecation time, COURSE had significant correlation with microbial community structure. This study presents a comprehensive landscape of gut microbiota in adult male patients with chronic traumatic complete SCI and documents their neurogenic bowel management. The gut microbiota dysbiosis of SCI patients was correlation with serum biomarkers and NBD symptoms.\n\nIMPORTANCENeurogenic bowel dysfunction is a major physical and psychological problem in patients with spinal cord injury, which can seriously affect the quality life of them. Gut dysbiosis are highly likely to occur in spinal cord injury patients There are few studies on intestinal microecology after spinal cord injury, and the clinical studies are fewer. It is importance to document their neurogenic bowel management and present a landscape of gut microbiota in them. We found the gut microbiota dysbiosis of spinal cord injury patients was correlation with serum biomarkers and neurogenic bowel dysfunction symptoms. These results may have implications in the next study about metagenomics and precision treatment of neurogenic bowel dysfunction in spinal cord injury patients.

neuroscience↗

bioRxiv2018

A robust image registration interface for large volume brain atlas

Mapping the brain structures in three-dimensional accurately is critical for an in-depth understanding of the brain functions. By using the brain atlas as a hub, mapping detected datasets into a standard brain space enables efficiently use of various datasets. However, because of the heterogeneous and non-uniform characteristics of the brain structures at cellular level brought with the recently developed high-resolution whole-brain microscopes, traditional registration methods are difficult to apply to the robust mapping of various large volume datasets. Here, we proposed a robust Brain Spatial Mapping Interface (BrainsMapi) to address the registration of large volume datasets at cellular level by introducing the extract regional features of the anatomically invariant method and a strategy of parameter acquisition and large volume transformation. By performing validation on model data and biological images, BrainsMapi can not only achieve robust registration on sample tearing and streak image datasets, different individual and modality datasets accurately, but also are able to complete the registration of large volume dataset at cellular level which dataset size reaches 20 TB. Besides, it can also complete the registration of historical vectorized dataset. BrainsMapi would facilitate the comparison, reuse and integration of a variety of brain datasets.

neuroscience↗

bioRxiv2018

Amyloid β oligomers constrict human capillaries in Alzheimer’s disease via signalling to pericytes

Vascular compromise occurs early in Alzheimers disease (AD) and other dementias1-3. Amyloid {beta} (A{beta}) reduces cerebral blood flow4-6 and, as most of the cerebral vasculature resistance is in capillaries7, A{beta} might mainly act on contractile pericytes on capillary walls8-10. Employing human tissue to establish disease-relevance, and rodent experiments to define mechanism, we now show that A{beta} constricts brain capillaries at pericyte locations in human subjects with cognitive decline. Applying soluble A{beta}1-42 oligomers to live human cortical tissue constricted capillaries. Using rat cortical slices, this was shown to reflect A{beta} evoking capillary pericyte contraction, with an EC50 of 4.7 nM, via the generation of reactive oxygen species and activation of endothelin ET-A receptors. In freshly-fixed diagnostic biopsies from human patients investigated for cognitive decline, mean capillary diameters were less in subjects showing A{beta} deposition than in subjects without A{beta} deposition. For patients with A{beta} deposition, the capillary diameter was 31% less at pericyte somata than away from somata, predicting a halving of blood flow. Constriction of capillaries by A{beta} will contribute to the energy lack1-3 occurring in AD, which promotes further A{beta} generation11,12. This mechanism reconciles the amyloid hypothesis13-15 with the earliest events in AD being vascular1.

neuroscience↗

bioRxiv2018

Contrasting patterns of coding and flanking region evolution in mammalian keratin associated protein-1 genes

DNA repeats are common elements in eukaryotic genomes, and their multi-copy nature provides the opportunity for genetic exchange. This exchange can produce altered evolutionary patterns, including concerted evolution where within genome repeat copies are more similar to each other than to orthologous repeats in related species. Here we investigated the genetic architecture of the keratin-associated protein (KAP) gene family, KRTAP1. This family encodes proteins that are important components of hair and wool in mammals, and the genes are present in tandem copies. Comparison of KRTAP1 gene repeats from species across the mammalian phylogeny shows strongly contrasting evolutionary patterns between the coding regions, which have a concerted evolution pattern, and the flanking regions, which have a normal, radiating pattern of evolution. This dichotomy in evolutionary pattern transitions abruptly at the start and stop codons, and we show it is not the result of purifying selection acting to maintain species-specific protein sequences, nor of codon adaptation or reverse transcription of KRTAP1-n mRNA. Instead, the results are consistent with short-tract gene conversion events coupled with selection for these events in the coding region driving the contrasting evolutionary patterns found in the KRTAP1 repeats. Our work shows the power that repeat recombination has to complement selection and finely tune the sequences of repetitive genes. Interplay between selection and recombination may be a more common mechanism than currently appreciated for achieving specific adaptive outcomes in the many eukaryotic multi-gene families, and our work argues for greater emphasis on exploring the sequence structures of these families.

genetics↗

bioRxiv2018

Mavacamten stabilizes a folded-back sequestered super-relaxed state of β-cardiac myosin

SummaryMutations in {beta}-cardiac myosin, the predominant motor protein for human heart contraction, can alter power output and cause cardiomyopathy. However, measurements of the intrinsic force, velocity and ATPase activityof myosin have not provided a consistent mechanism to link mutations to muscle pathology. An alternative modelpositsthat mutations in myosin affect the stability ofa sequestered, super-relaxed state (SRX) of the proteinwith very slow ATP hydrolysis and thereby change the number of myosin heads accessible to actin. Here, using a combination of biochemical and structural approaches, we show that purified myosin enters aSRX thatcorresponds to a folded-back conformation, which in muscle fibersresults insequestration of heads around the thick filament backbone. Mutations that cause HCM destabilize this state, while the small molecule mavacamtenpromotes it. These findings provide a biochemical and structural link between the genetics and physiology ofcardiomyopathywith implications for therapeutic strategies.

biochemistry↗

bioRxiv2017

Transfer RNA genes experience exceptionally elevated mutation rates

Transfer RNAs (tRNAs) are a central component for the biological synthesis of proteins, and they are among the most highly conserved and frequently transcribed genes in all living things. Despite their clear significance for fundamental cellular processes, the forces governing tRNA evolution are poorly understood. We present evidence that transcription-associated mutagenesis and strong purifying selection are key determinants of patterns of sequence variation within and surrounding tRNA genes in humans and diverse model organisms. Remarkably, the mutation rate at broadly expressed cytosolic tRNA loci is likely between seven and ten times greater than the nuclear genome average. Furthermore, evolutionary analyses provide strong evidence that tRNA genes, but not their flanking sequences, experience strong purifying selection, acting against this elevated mutation rate. We also find a strong correlation between tRNA expression levels and the mutation rates in their immediate flanking regions, suggesting a simple new method for estimating individual tRNA gene activity. Collectively, this study illuminates the extreme competing forces in tRNA gene evolution, and implies that mutations at tRNA loci contribute disproportionately to mutational load and have unexplored fitness consequences in human populations.\n\nSignificance StatementWhile transcription-associated mutagenesis (TAM) has been demonstrated for protein coding genes, its implications in shaping genome structure at transfer RNA (tRNA) loci in metazoans have not been fully appreciated. We show that cytosolic tRNAs are a striking example of TAM because of their variable rates of transcription, well-defined boundaries and internal promoter sequences. tRNA loci have a mutation rate approximately seven-to tenfold greater than the genome-wide average, and these mutations are consistent with signatures of TAM. These observations indicate that tRNA loci are disproportionately large contributors to mutational load in the human genome. Furthermore, the correlations between tRNA locus variation and transcription implicate that prediction of tRNA gene expression based on sequence variation data is possible.

bioinformatics↗

bioRxiv2017

Advanced NeuroGPS-Tree: dense reconstruction of brain-wideneuronal population close to ground truth

Recent progresses allow imaging specific neuronal populations at single-axon level across mouse brain. However, digital reconstruction of neurons in large dataset requires months of human labor. Here, we developed a tool to solve this problem. Our tool offers a special error-screening system for fast localization of submicron errors in densely packed neurites and along long projection across the whole brain, thus achieving reconstruction close to the ground-truth. Moreover, our tool equips algorithms that significantly reduce intensive manual interferences and achieve high-level automation, with speed 5 times faster compared to semi-automatic tools. We also demonstrated reconstruction of 35 long projection neurons around one injection site of a mouse brain at an affordable time cost. Our tool is applicable with datasets of 10 TB or higher from various light microscopy, and provides a starting point for the reconstruction of neuronal population for neuroscience studies at a single-cell level.

neuroscience↗

bioRxiv2017

Genetic Single Neuron Anatomy reveals fine granularity of cortical interneuron subtypes

Parsing diverse nerve cells into biological types is necessary for understanding neural circuit organization. Morphology is an intuitive criterion for neuronal classification and a proxy of connectivity, but morphological diversity and variability often preclude resolving the granularity of discrete cell groups from population continuum. Combining genetic labeling with high-resolution, large volume light microscopy, we established a platform of genetic single neuron anatomy that resolves, registers and quantifies complete neuron morphologies in the mouse brain. We discovered that cortical axo-axonic cells (AACs), a cardinal GABAergic interneuron type that controls pyramidal neuron (PyN) spiking at axon initial segment, consist of multiple subtypes distinguished by laminar position, dendritic and axonal arborization patterns. Whereas the laminar arrangements of AAC dendrites reflect differential recruitment by input streams, the laminar distribution and local geometry of AAC axons enable differential innervation of PyN ensembles. Therefore, interneuron types likely consist of fine-grained subtypes with distinct input-output connectivity patterns.

neuroscience↗

bioRxiv2017

Identification of residue pairing in interacting β-strands from a predicted residue contact map

Despite the rapid progress of protein residue contact prediction, predicted residue contact maps frequently contain many errors. However, information of residue pairing in {beta} strands could be extracted from a noisy contact map, due to the presence of characteristic contact patterns in {beta}-{beta} interactions. This information may benefit the tertiary structure prediction of mainly {beta} proteins. In this work, we introduce a novel ridge-detection-based {beta}-{beta} contact predictor, RDb2C, to identify residue pairing in {beta} strands from any predicted residue contact map. The algorithm adopts ridge detection, a well-developed technique in computer image processing, to capture consecutive residue contacts, and then utilizes a novel multi-stage random forest framework to integrate the ridge information and additional features for prediction. Starting from the predicted contact map of CCMpred, RDb2C remarkably outperforms all state-of-the-art methods on two conventional test sets of {beta} proteins (BetaSheet916 and BetaSheet1452), and achieves F1-scores of ~62% and ~76% at the residue level and strand level, respectively. Taking the prediction of the more advanced RaptorX-Contact as input, RDb2C achieves impressively higher performance, with F1-scores reaching ~76% and ~86% at the residue level and strand level, respectively. According to our tests on 61 mainly {beta} proteins, improvement in the {beta}-{beta} contact prediction can further ameliorate the structural prediction.\n\nAvailability: All source data and codes are available at http://166.111.152.91/Downloads.html or at the GitHub address of https://github.com/wzmao/RDb2C.\n\nAuthor summaryDue to the topological complexity, mainly {beta} proteins are challenging targets in protein structure prediction. Knowledge of the pairing between {beta} strands, especially the residue pairing pattern, can greatly facilitate the tertiary structure prediction of mainly {beta} proteins. In this work, we developed a novel algorithm to identify the residue pairing in {beta} strands from a predicted residue contact map. This method adopts the ridge detection technique to capture the characteristic pattern of {beta}-{beta} interactions from the map and then utilizes a multi-stage random forest framework to predict {beta}-{beta} contacts at the residue level. According to our tests, our method could effectively improve the prediction of {beta}-{beta} contacts even from a highly noisy contact map. Moreover, the refined {beta}-{beta} contact information could effectively improve the structural modeling of mainly {beta} proteins.

bioinformatics↗

bioRxiv2017

Identifying weak signals in inhomogeneous neuronal images for large-scale tracing of neurites

Reconstructing neuronal morphology across different regions or even the whole brain is important in many areas of neuroscience research. Large-scale tracing of neurites constitutes the core of this type of reconstruction and has many challenges. One key challenge is how to identify a weak signal from an inhomogeneous background. Here, we addressed this problem by constructing an identification model. In this model, empirical observations made from neuronal images are summarized into rules, which are used to design feature vectors that display the differences between the foreground and background, and a support vector machine is used to learn these feature vectors. We embedded this identification model into a tool that we previously developed, SparseTracer, and termed this integration SparseTracer-Learned Feature Vector (ST-LFV). ST-LFV can trace neurites with extremely weak signals (signal-to-background-noise ratio <1.1) against an inhomogeneous background. By testing 12 sub-blocks extracted from a whole imaging dataset, ST-LFV can achieve an average recall rate of 0.99 and precision rate of 0.97, which is superior to that of SparseTracer (which has an average recall rate of 0.93 and average precision rate of 0.86), indicating that this method is well suited to weak signal identification. We applied ST-LFV to trace neurites from large-scale images (approximately 105 GB). During the tracing process, obtaining results equivalent to the ground truth required only one round of manual editing for ST-LFV compared to 20 rounds of manual editing for SparseTracer. This improvement in the level of automatic reconstruction indicates that ST-LFV has the potential to rapidly reconstruct sparsely distributed neurons at the scale of an entire brain.

bioinformatics↗