Biology subjects

Geng, C.

Publications and source records attributed to Geng, C..

At least 19 recordsLinked to original sources

bioRxiv2025

Effects of Inflammatory Factors on the Migration and Repair of Intestinal Mucosal Cells at Pancreaticojejunostomy Anastomosis: An Experimental Study in Beagle Dogs

ObjectiveTo investigate the effects of inflammatory factors on the migration and repair of intestinal mucosal cells at the site of pancreaticojejunostomy (PJ) anastomosis and to observe the safety and feasibility of PJ with mucosal-priority healing (PM-PJ). Materials and methodsThirty-six Beagle dogs were randomly divided into two groups: the PM-PJ group (n=18) and the control group (n=18), which included classic end-to-side invagination pancreaticojejunostomy (IPJ). Six Beagles were sacrificed on the 7th, 14th, and 21st days after surgery to obtain pancreaticojejunostomy anastomosis tissues. The primary outcomes were postoperative mortality, morbidity, and pathological changes in the anastomosis. The secondary outcomes were the expression levels of the cytokines TGF-{beta}1, IL-10, TNF-, IL-6, collagen, and -SMA. ResultsAll surgeries were successfully performed. In the study group, the incidences of anastomotic leakage and pancreatitis were 0% and 11.1% (2/18), respectively, whereas in the control group, the incidences were 5.6% (1/18) and 16.7% (3/18), respectively. The levels of serum TGF-{beta}1, IL-10, TNF-, and IL-6 were increased at all time points after surgery in both groups, and these increases were more significant at certain time points in the study group. Pathological observation revealed that the degree of anastomotic healing in the study group was greater than that in the control group at all time points. The protein expression levels of collagen I/III and -SMA in the anastomoses of both groups changed over time, and the changes were more significant at certain time points in the study group. ConclusionInflammatory factors play a positive role in the early stage of PJ anastomosis healing by stimulating collagen secretion and synthesis. PM-PJ is a safe and simple surgery with good anastomotic healing and a low incidence of complications.

animal behavior and cognition↗

bioRxiv2025

Epigenetic changes, neuronal dysregulation and metabolomic abnormalities in Zmym2 mutant mice, a genetic model of schizophrenia and neurodevelopmental disorders

Loss-of-function mutations in ZMYM2 are associated with an increased risk of schizophrenia (SCZ) and neurodevelopmental disorders (NDD). ZMYM2 interacts with proteins involved in histone modification and gene regulation, including LSD1 and ADNP; however, its specific roles in the brain remain poorly understood. In this multi-omics study, we demonstrate that heterozygous knockout of Zmym2 in mice results in widespread disturbances in gene expression affecting diverse molecular pathways, including those related to histone modifications and neuronal activity. Proteomic analysis of synapses reveals dysregulation of lipid metabolism and neurofilament-associated pathways, while metabolomic profiling identifies alterations in sphingomyelin and ceramide levels. Furthermore, Zmym2 mutant mice exhibit abnormal brain oscillation patterns on EEG and locomotor hyperactivity in the open field test. Collectively, these findings underscore the critical role of ZMYM2 in brain development and function and highlight Zmym2 mutant mice as a genetic animal model for SCZ and NDD.

neuroscience↗

bioRxiv2024

Deciphering the thiolactonization mechanism in thiolactomycin biosynthesis

Thiolactomycin (1), which features a unique{gamma} -thiolactone ring, is a promising antibiotic candidate that specifically targets bacterial type II fatty acid synthase. Despite extensive studies on its pharmacological activities, modes of action, and chemical synthesis, the enzymatic processes responsible for forming the activity-determining{gamma} -thiolactone ring have remained largely unknown. Here, we resolve this problem by revealing that the condensation and heterocyclization (Cy) domain of the nonribosomal peptide synthetase (NRPS) TlnC (TlnCCy), along with the cytochrome P450 enzyme TlnA, cooperatively enable the{gamma} -thiolactone assembly. TlnCCy mediates an unusual sulfurtransfer reaction to sulfurate the polyketide intermediate, generating a thiocarboxylate intermediate. Subsequently, TlnA acts as a{gamma} -thiolactone synthase, converting the linear thiocarboxylate intermediate into 1 via a distal radical-based cyclization mechanism. These findings not only expand the functional and catalytic repertoires of NRPS Cy domains and P450 enzymes, but also highlight a special enzymatic strategy for{gamma} -thiolactone biosynthesis in nature. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/629141v2_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@7e0ad2org.highwire.dtl.DTLVardef@f86b80org.highwire.dtl.DTLVardef@159d136org.highwire.dtl.DTLVardef@1b633ff_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2024

Reduction of SynGAP-γ, disrupted splicing of Agap3, and oligodendrocyte deficits in Srrm2 mice, a genetic model of schizophrenia and neurodevelopmental disorder

Rare loss-of-function variants in SRRM2, which encodes a nuclear speckle scaffold and splicing factor, are associated with schizophrenia and neurodevelopmental disorders. How SRRM2 haploinsufficiency disrupts brain function is unknown. We find that Srrm2+/- mice exhibit (i) large-scale changes in gene expression in neuronal and glial cells, affecting DNA-binding-, synapse-, translation-, mitochondria-related pathways across multiple brain regions; (ii) alterations in splicing and/or abundance of multiple postsynaptic proteins, including reduction of the gamma isoform of SynGAP and elevation of its interactor, Agap3; and (iii) reduced oligodendrocyte proportions, particularly in striatum, accompanied by decreased expression of myelin-related mRNAs and proteins. Human iPSC-derived neurons deficient in SRRM2 display conserved AGAP3 splicing defects. Behaviorally, Srrm2+/- mice have reduced locomotor activity and impaired startle responses, and EEG recordings reveal reduced sleep spindles resembling humans with schizophrenia. Our findings identify specific synaptic changes, splicing dysregulation, and impaired myelination as mechanisms linking SRRM2 haploinsufficiency to neuropsychiatric disease.

neuroscience↗

bioRxiv2024

Muti-omics characterization reveals brain-wide disruption of synapses and region- and age-specific changes in neurons and glia in Sp4 mutant mice, a genetic model of schizophrenia and bipolar disorder

Schizophrenia and bipolar disorder are highly heritable mental illnesses with unclear pathophysiology. Heterozygous loss-of-function mutations of Sp4, a zinc-finger transcription factor, greatly increase risk of schizophrenia and bipolar disorder. To investigate the molecular functions of Sp4 in an unbiased manner in vivo, we performed multi-omics analyses of Sp4 mutant mice. Bulk and single nucleus RNA-seq data showed prominent gene expression changes in all brain regions and most cell types, including neuronal and non-neuronal cells. Gene set enrichment analysis of transcriptomic changes revealed alterations in many molecular pathways, including synapse, oxidative phosphorylation, and ribosome. Synapse proteomics of Sp4 mutants pointed to impaired glutamatergic signaling and altered presynaptic function. In Sp4 heterozygous mutant mice, prefrontal cortex and striatum exhibited downregulation of synapse pathways and neuronal hypoactivity at 1 month, associated with reduced sterol biosynthesis in astrocytes, whereas at 3 months, there was a shift to neuronal hyperactivity, concurrent with suppressed immune pathways in the striatal microglia. Furthermore, our study found that much of the transcriptomic changes might be accounted for by a set of transcription regulators (Nr3c1, Creb1, and Kdm5b) under the control of Sp4. Overall, this study provides cellular and molecular features resulting from Sp4 LoF that may explain the pathophysiology of SCZ-BD psychotic disorder spectrum.

neuroscience↗

bioRxiv2024

Elevated synaptic PKA activity and abnormal striatal dopamine signaling in Akap11 mutant mice, a genetic model of schizophrenia and bipolar disorder

AbstractLoss-of-function mutations in AKAP11 (a protein kinase A (PKA)-binding protein) greatly increase the risk of bipolar disorder and schizophrenia. To determine the neurobiological functions of AKAP11, we conduct multi-omic and neurobiological analyses of Akap11 mutant mouse brains. We find that AKAP11 is a key regulator of PKA proteostasis in the brain whose loss leads to dramatically increased levels of PKA subunits and phosphorylated PKA substrates, especially in synapses. Akap11 mutant mice show extensive transcriptomic changes throughout the brain, including prominent decreases in synapse-related genes sets. Gene expression is highly impacted in spiny projection neurons of the striatum, a brain region implicated in motivation, cognition and psychotic disorders. Real-time measurements of PKA activity reveal elevated basal PKA activity in the striatum of Akap11-/- mice, with exaggerated additional response to dopamine receptor antagonists. Behaviorally, Akap11 mutant mice show abnormally prolonged locomotor response to amphetamine, deficits in associative learning and contextual discrimination, as well as depression-like behaviors. Our study connects molecular changes to circuit dysfunction and behavioral disturbance in a genetically valid animal model of psychotic disorder.

neuroscience↗

bioRxiv2024

Integrative modeling in the age of machine learning: a summary of HADDOCK strategies in CAPRI rounds 47-55

The HADDOCK team participated in CAPRI rounds 47-55 as both server, manual predictor, and scorers. Throughout these CAPRI rounds, we used a plethora of computational strategies to predict the structure of protein complexes. Of the 10 targets comprising 24 interfaces, we achieved acceptable or better models for 3 targets in the human category and 1 in the server category. Our performance in the scoring challenge was slightly better, with our simple scoring protocol being the only one capable of identifying an acceptable model for Target 234. This result highlights the robustness of the simple, fully physics-based HADDOCK scoring function, especially when applied to highly flexible antibody-antigen complexes. Inspired by the significant advances in machine learning for structural biology and the dramatic improvement in our success rates after the public release of Alphafold2, we identify the integration of classical approaches like HADDOCK with AI-driven structure prediction methods as a key strategy for improving the accuracy of model generation and scoring.

bioinformatics↗

bioRxiv2024

Parvalbumin interneuron ErbB4 controls ongoing network oscillations and olfactory behaviors in mice

Parvalbumin (PV)-positive interneurons modulate the processing of odour information. However, little is known about how PV interneurons dynamically remodel neural circuit responses in the olfactory bulb (OB) and their physiological significance. This study showed that a reinforced odour-discrimination task upregulated ErbB4 kinase activity in the mouse OB. ErbB4 knockout in the OB impaired the dishabituation of odour responses and discrimination of complex odours, whereas odour memory or adaptation was not altered in mice. ErbB4-positive neurones were localised throughout the OB, whereas ErbB4 was largely expressed in PV-positive interneurons within the internal and external plexiform layers. Similarly, ErbB4 ablation in PV interneurons disrupted olfactory discrimination and dishabituation in mice. ErbB4 knockout in PV interneurons disrupted the odour-evoked responses of mitral/tufted cells and increased the power of the ongoing local field potential in awake mice. We also observed a decrease in the frequency of miniature inhibitory postsynaptic currents and deficits in stimulus-evoked recurrent and lateral inhibition of mitral cells following PV-ErbB4 loss, suggesting a broad impairment in inhibitory microcircuits. These findings demonstrate that PV-ErbB4 signalling regulates inhibitory microcircuit activity, ongoing oscillations, and OB output, which underlie normal olfactory behaviour.

neuroscience↗

bioRxiv2021

Influence of sequencing depth on the fidelity and sensitivity of 1%-5% low-frequency mutation detection and recommendation for standardization of sequencing depth

Sequencing depth has always played an important role in the accurate detection of low-frequency mutations. The increase of sequencing depth and the reasonable setting of threshold can maximize the probability of true positive mutation, or sensitivity. Here, we found that when the threshold was set as a fixed number of positive mutated reads, the probability of both true and false-positive mutations increased with depth. However, When the number of positive mutated reads increased in an equal proportion with depth (the threshold was transformed from a fixed number to a fixed percentage of mutated reads), the true positive probability still increased while false positive probability decreased. Through binomial distribution simulation and experimental test, it is found that the "fidelity" of detected-VAFs is the cause of this phenomenon. Firstly, we used the binomial distribution to construct a model that can easily calculate the relationship between sequencing depth and probability of true positive (or false positive), which can standardize the minimum sequencing depth for different low-frequency mutation detection. Then, the effect of sequencing depth on the fidelity of NA12878 with 3% mutation frequency and circulating tumor DNA (ctDNA of 1%, 3% and 5%) showed that the increase of sequencing depth reduced the fluctuation range of detected-VAFs around the expected VAFs, that is, the fidelity was improved. Finally, based on our experiment result, the consistency of single-nucleotide variants (SNVs) between paired FF and FFPE samples of mice increased with increasing depth, suggesting that increasing depth can improve the precision and sensitivity of low-frequency mutations. HighlightsThe normalized relationship between sequencing depth and the probability of true positive mutation (sensitivity) is established based on binomial distribution. The probability of true positive increases and the probability of false positive decreases when the number of positive mutated reads increases (threshold) in an equal proportion with depth. Detected-VAFs fluctuates regularly around expected-VAFs. The amplitude of detected-VAFs fluctuation decreases with sequencing depth and the "fidelity" increases. The increase of "fidelity" leads to a higher degree of differentiation between true and false positive mutations, which ultimately increases the true positive probability and decreases the false positive probability.

genomics↗

bioRxiv2021

MetaScore: A novel machine-learning based approach to improve traditional scoring functions for scoring protein-protein docking conformations

Protein-protein interactions play a ubiquitous role in biological function. Knowledge of the three-dimensional (3D) structures of the complexes they form is essential for understanding the structural basis of those interactions and how they orchestrate key cellular processes. Computational docking has become an indispensable alternative to the expensive and timeconsuming experimental approaches for determining 3D structures of protein complexes. Despite recent progress, identifying near-native models from a large set of conformations sampled by docking - the so-called scoring problem - still has considerable room for improvement. We present here MetaScore, a new machine-learning based approach to improve the scoring of docked conformations. MetaScore utilizes a random forest (RF) classifier trained to distinguish near-native from non-native conformations using a rich set of features extracted from the respective protein-protein interfaces. These include physico-chemical properties, energy terms, interaction propensity-based features, geometric properties, interface topology features, evolutionary conservation and also scores produced by traditional scoring functions (SFs). MetaScore scores docked conformations by simply averaging of the score produced by the RF classifier with that produced by any traditional SF. We demonstrate that (i) MetaScore consistently outperforms each of nine traditional SFs included in this work in terms of success rate and hit rate evaluated over the top 10 predicted conformations; (ii) An ensemble method, MetaScore-Ensemble, that combines 10 variants of MetaScore obtained by combining the RF score with each of the traditional SFs outperforms each of the MetaScore variants. We conclude that the performance of traditional SFs can be improved upon by judiciously leveraging machine-learning.

bioinformatics↗

bioRxiv2021

Genetic characteristics of human papillomavirus type 16, 18, 52 and 58 in southern China

Persistent infections of high-risk human papillomaviruses (HPVs) are the leading cause of cervical cancers. We collected cervical exfoliated cell samples from females in Changsha city, Hunan Province and obtained 358 viral genomes of four major HPV types, including HPV 16 (n=82), 18 (n=35), 52 (n=121) and 58 (n=100). The lineage/sublineage distribution of the four HPVs confirmed previous epidemiological reports, with the predominant prevailing sublineage as A4 (50%), A1 (37%) and A3 (13%) for HPV16, A1 (83%) for HPV18, B2 (86%) for HPV52 and A1 (65%), A3 (19%) and A2 (12%) for HPV58. We also identified two potentially novel HPV18 sublineages, i.e. A6 and A7. Virus mutation analysis further revealed the presence of HPV16 and HPV58 strains associated with potentially high oncogenicity. These findings expanded our knowledge on the HPV genetic diversity in China, providing valuable evidence to facilitate HPV DNA screening, vaccine effectiveness evaluation and control strategy development.

genomics↗

bioRxiv2021

Activation of ACC synthase 2/6 increases stomatal density and cluster on the Arabidopsis leaf epidermis during drought

It is known that the transcription factor SPEECHLESS (SPCH) drives entry of epidermal cells into stomatal lineage, and that the activation of subtilisin-like protease SDD1 reduces stomatal density and cluster on the epidermis. However, there is still a big gap in our understanding of the relationship between stomatal development and the establishment of stomatal density and pattern, especially during drought. Interestingly, 1-aminocyclopropane-1-carboxylic acid (ACC) not only promotes stomatal development, but also is involved in the establishment of stomatal density and pattern. ACC generation comes from the activity of ACC synthase (ACS), while ACS activity could be mediated by drought. This work showed that the Arabidopsis SPCH activated ACS2/6 expression and ACC-dependent stomatal generation with an increase of stomatal density and cluster under drought conditions; and the possible mechanisms were that ACC-induced Ca2+ shortage in stomatal lineage reduced the inhibition of the transcription factor GT-2 Like 1 (GTL1) on SDD1 expression. These suggest that ACS2/6-dependent ACC accumulation integrated stomatal development with the establishment of stomatal density and pattern by mediating Ca2+ levels in stomatal lineage cells on the leaf epidermis, and this integration is directly related to the growth or survival of plants under escalated drought stress. HighlightACC synthase ACS2/6 activation integrated stomatal individual development with space setting between stomata by mediating Ca2+ levels in stomatal lineage on the leaf epidermis in response to drought.

cell biology↗

bioRxiv2021

DeepRank: A deep learning framework for data mining 3D protein-protein interfaces

Three-dimensional (3D) structures of protein complexes provide fundamental information to decipher biological processes at the molecular scale. The vast amount of experimentally and computationally resolved protein-protein interfaces (PPIs) offers the possibility of training deep learning models to aid the predictions of their biological relevance. We present here DeepRank, a general, configurable deep learning framework for data mining PPIs using 3D convolutional neural networks (CNNs). DeepRank maps features of PPIs onto 3D grids and trains a user-specified CNN on these 3D grids. DeepRank allows for efficient training of 3D CNNs with data sets containing millions of PPIs and supports both classification and regression. We demonstrate the performance of DeepRank on two distinct challenges: The classification of biological versus crystallographic PPIs, and the ranking of docking models. For both problems DeepRank is competitive or outperforms state-of-the-art methods, demonstrating the versatility of the framework for research in structural biology.

bioinformatics↗

bioRxiv2020

Gut microbiome couples gut and brain during calorie restriction in treating obesity

Calorie restriction (CR) has been widely recognized for its effect in reducing body weight and alleviating diabetes in humans, as well as prolonging life span in animal studies. Gut microbiome shifts contribute to part of the effects of CR, but little is known regarding their influences except on metabolism and immunity. Here we monitored gut microbiome using metagenomics and metatranscriptomics in obese individuals undergoing CR, and revealed microbial determinants that could contribute to successful weight loss. Microbiome changes are linked to changes in blood metabolome and hormones, which eventually correlate to brain functional changes as studied using functional magnetic resonance imaging (fMRI). Brain functional shifts indicate response of central neural system (CNS) to CR, and microbiome constitutes the keystone of gut-brain axis. Animal experiment further reaffirms the gut microbiome changes, metabolic and hormonal shifts of CR, while proteomic analysis of brain tissues suggest that epigenetic modifications of key proteins could explain responses of CNS to CR. Our study establishes linkage between CR, gut microbiome, metabolome/ hormones and CNS function, and demonstrates that CR has multi-facet, coordinated effects on the host, of which many could contribute to weight loss and other beneficial effects.

microbiology↗

bioRxiv2020

matchms - processing and similarity evaluation of mass spectrometry data

Mass spectrometry data is at the heart of numerable applications in the biomedical and life sciences. With growing use of high throughput techniques researchers need to analyse larger and more complex datasets. In particular through joint effort in the research community, fragmentation mass spectrometry datasets are growing in size and number. Platforms such as MassBank (Horai et al. 2010), GNPS (Wang et al. 2016) or MetaboLights (Haug et al. 2020) serve as an open-access hub for sharing of raw, processed, or annotated fragmentation mass spectrometry data (MS/MS). Without suitable tools, however, exploitation of such datasets remains overly challenging. In particular, large collected datasets contain data aquired using different instruments and measurement conditions, and can further contain a significant fraction of inconsistent, wrongly labeled, or incorrect metadata (annotations).

bioinformatics↗

bioRxiv2020

Genetic signatures for lineage/sublineage classification of HPV16, 18, 52 and 58 variants

Increasing evidences indicate that high-risk HPV variants are heterogeneous in carcinogenicity and ethnic dispersion. In this work, we identified genetic signatures for convenient determination of lineage/sublineage of HPV16, 18, 52 and 58 variants. Using publicly available genomes, we found that E2 of HPV16, L2 of HPV18, L1 and LCR of HPV52, and L2, LCR and E1 of HPV58 contain the proper genetic signature for lineage/sublineage classification. Sets of hierarchical signature nucleotide positions (SNPs) were further confirmed for high accuracy (>98%) by classifying HPV genomes obtained from Chinese females, which included 117 HPV16 variants, 48 HPV18 variants 117 HPV52 variants and 89 HPV58 variants. The circulation of HPV variants posing higher cancer risk in Eastern China, such as HPV16 A4 and HPV58 A3, calls for continuous surveillance in this region. The marker genes and signature nucleotide positions may facilitate cost-effective diagnostic detections of HPV variants in clinical settings.

evolutionary biology↗

bioRxiv2019

Detection and characterization of copy number variants based on whole-genome sequencing by MGI platforms

BackgroundNext-generation sequence (NGS) has rapidly developed in past years which makes whole-genome sequencing (WGS) becoming a more cost- and time-efficient choice in wide range of biological researches. We usually focus on some variant detection via WGS data, such as detection of single nucleotide polymorphism (SNP), insertion and deletion (Indel) and copy number variant (CNV), which playing an important role in many human diseases. However, the feasibility of CNV detection based on WGS by DNBSEQ platforms was unclear. We systematically analysed the genome-wide CNV detection power of DNBSEQ platforms and Illumina platforms on NA12878 with five commonly used tools, respectively.\n\nResultsDNBSEQ platforms showed stable ability to detect slighter more CNVs on genome-wide (average 1.24-fold than Illumina platforms). Then, CNVs based on DNBSEQ platforms and Illumina platforms were evaluated with two public benchmarks of NA12878, respectively. DNBSEQ and Illumina platforms showed similar sensitivities and precisions on both two benchmarks. Further, the difference between tools for CNV detection was analyzed, and indicated the selection of tool for CNV detection could affected the CNV performance, such as count, distribution, sensitivity and precision.\n\nConclusionThe major contribution of this paper is providing a comprehensive guide for CNV detection based on WGS by DNBSEQ platforms for the first time.

bioinformatics↗

bioRxiv2019

iScore: an MPI supported software for ranking protein-protein docking models based on a random walk graph kernel and support vector machines.

Computational docking is a promising tool to model three-dimensional (3D) structures of protein-protein complexes, which provides fundamental insights of protein functions in the cellular life. Singling out near-native models from the huge pool of generated docking models (referred to as the scoring problem) remains as a major challenge in computational docking. We recently published iScore, a novel graph kernel based scoring function. iScore ranks docking models based on their interface graph similarities to the training interface graph set. iScore uses a support vector machine approach with random-walk graph kernels to classify and rank protein-protein interfaces.\n\nHere, we present the software for iScore. The software provides executable scripts that fully automatize the computational workflow. In addition, the creation and analysis of the interface graph can be distributed across different processes using Message Passing interface (MPI) and can be offloaded to GPUs thanks to dedicated CUDA kernels.

bioinformatics↗