Biology subjects

Zhang, G.

Publications and source records attributed to Zhang, G..

At least 235 records · Page 13Linked to original sources

bioRxiv2023

Optimal Filters for ERP Research I: A General Approach for Selecting Filter Settings

Filtering plays an essential role in event-related potential (ERP) research, but filter settings are usually chosen on the basis of historical precedent, lab lore, or informal analyses. This reflects, in part, the lack of a well-reasoned, easily implemented method for identifying the optimal filter settings for a given type of ERP data. To fill this gap, we developed an approach that involves finding the filter settings that maximize the signal-to-noise ratio for a specific amplitude score (or minimizes the noise for a latency score) while minimizing waveform distortion. The signal is estimated by obtaining the amplitude score from the grand average ERP waveform (usually a difference waveform). The noise is estimated using the standardized measurement error of the single-subject scores. Waveform distortion is estimated by passing noise-free simulated data through the filters. This approach allows researchers to determine the most appropriate filter settings for their specific scoring methods, experimental designs, subject populations, recording setups, and scientific questions. We have provided a set of tools in ERPLAB Toolbox to make it easy for researchers to implement this approach with their own data.

neuroscience↗

bioRxiv2023

Synchrony of Bird Migration with Avian Influenza Global Spread; Implications for Vulnerable Bird Orders

Highly pathogenic avian influenza virus (HPAIV) A H5 clade 2.3.4.4 has caused worldwide outbreaks in domestic poultry, occasional spillover to humans, and increasing deaths of diverse species of wild birds since 2014. Wild bird migration is currently acknowledged as an important ecological process contributing to the global dispersal of HPAIV H5. However, it is unclear how seasonal bird migration facilitates global virus dispersal, and which avian species are exposed to HPAI H5 clade 2.3.4.4 viruses and where. To shed light on ongoing global outbreaks, we sought to explore these questions through phylodynamic analyses based on empirical data of bird movement tracking and virus genome sequences. First, based on viral phylogeography and bird migration networks, we demonstrate that seasonal bird migration can explain salient features of the global dispersal of clade 2.3.4.4. Second, we detect synchrony between the seasonality of bird annual cycle phases and virus lineage movements. We reveal the differing vulnerable bird orders at geographical origins and destinations of HPAIV H5 lineage movements. Notably, we highlight the potential importance of relatively under-discussed Suliformes and Ciconiiformes, in addition to Anseriformes and Charadriiformes, in virus lineage movements. Our study provides a phylodynamic framework that links the bird movement ecology and genomic epidemiology of avian influenza; it highlights the importance of integrating bird behaviour and life history in avian influenza studies.

ecology↗

bioRxiv2023

Genome-wide association and environmental suppression of the mortal germline phenotype of wild C. elegans

The animal germline lineage needs to be maintained along generations. However, some Caenorhabditis elegans wild isolates display a mortal germline phenotype, whereby the lineage becomes sterile after several generations at 25{degrees}C. We used a genome-wide association approach to study the genetic basis for this phenotype in C. elegans populations. We detected a significant peak on chromosome III around 5 Mb, which was confirmed using introgression lines. These results indicate that a seemingly deleterious genotype is maintained at intermediate frequency in the species. Environmental rescue is a likely explanation and we indeed find that naturally associated bacteria and microsporidia suppressed the phenotype. The tested bacteria also suppressed the temperature-sensitive mortal germline phenotype of mutants in small RNA inheritance (nrde-2) and histone modifications (set-2). Even Escherichia coli strains of the K-12 lineage suppressed the phenotype compared to B strains. By shifting a strain cultured on E. coli K-12 back to E. coli B, we found that C. elegans can keep over several generations the memory of the suppressing conditions. Thus, the mortal germline phenotype of wild C. elegans is lin part revealed by laboratory conditions and may represent variation in epigenetic inheritance and environmental interactions. This study also points to the importance of non-genetic memory in the face of environmental variation.

evolutionary biology↗

bioRxiv2023

Praziquantel inhibits Caenorhabditis elegans development and species-wide differences might be cct-8-dependent

Anthelmintic drugs are used to treat parasitic roundworm and flatworm infections in humans and other animals. Caenorhabditis elegans is an established model to investigate anthelmintics used to treat roundworms. In this study, we use C. elegans to examine the mode of action and the mechanisms of resistance against the flatworm anthelmintic drug praziquantel (PZQ), used to treat trematode and cestode infections. We found that PZQ inhibited development and that this developmental delay varies by genetic background. Interestingly, both enantiomers of PZQ are equally effective against C. elegans, but only the left-handed PZQ (S-PZQ) is effective against schistosome infections. We conducted a genome-wide association mapping with 74 wild C. elegans strains to identify a region on chromosome IV that is correlated with differential PZQ susceptibility. Five candidate genes in this region: cct-8, znf-782, Y104H12D.4, Y104H12D.2, and cox-18, might underlie this variation. The gene cct-8, a subunit of the protein folding complex TRiC, has variation that causes a putative protein coding change (G226V), which is correlated with reduced developmental delay. Gene expression analysis suggests that this variant correlates with slightly increased expression of both cct-8 and hsp-70. Acute exposure to PZQ caused increased expression of hsp-70, indicating that altered TRiC function might be involved in PZQ responses. To test if this variant affects development upon exposure to PZQ, we used CRISPR-Cas9 genome editing to introduce the V226 allele into the N2 genetic background (G226) and the G226 allele into the JU775 genetic background (V226). These experiments revealed that this variant was not sufficient to explain the effects of PZQ on development. Nevertheless, this study shows that C. elegans can be used to study responses to PZQ to identify mode of action and resistance mechanisms. Additionally, we show that the TRiC complex requires further evaluation for PZQ responses in C. elegans.

genetics↗

bioRxiv2023

GraphCPLMQA: Assessing protein model quality based on deep graph coupled networks using protein language model

Model quality evaluation is crucial part of protein structural biology. How to distinguish high-quality models from low-quality models, and to assess which high-quality models have relatively incorrect regions for improvement, are remain challenge. More importantly, the quality assessment of multimer models is a hot topic for structure predicton.In this work, we present GraphCPLMQA, a novel graph-coupled network that uses embeddings from protein language models to assess residue-level protein model quality. The GraphCPLMQA consists of a graph encoding module and a transform-based convolutional decoding module. In encoding module, the underlying relational representations of sequence and high-dimensional geometry structure are extracted by protein language models with Evolutionary Scale Modeling. In decoding module, the mapping connection between structure and quality are inferred by the representations and low-dimensional features. Specifically, the triangular location and residue level contact order features are designed to enhance the association between the local structure and the overall topology. Experimental results demonstrate that GraphCPLMQA using single-sequence embedding achieves the best performance compared to the CASP15 interface evaluation method in 9108 models of CASP15 multimer test set. In CAMEO blind test (2022-05-20[~]2022-08-13), GraphCPLMQA ranked first compared to other servers. GraphCPLMQA also outperforms state-of-the-art methods on 19,035 models in CASP13 and CASP14 monomer test set. Finally, on AlphaFold2 datasets, GraphCPLMQA was superior to self-assessment of AlphaFold2 in MAE metric, and it was able to screen out better models than AlphaFold2.

bioinformatics↗

bioRxiv2023

p300 Is an Obligate Integrator of Combinatorial Transcription Factor Inputs

Transcription coactivators are proteins or protein complexes that mediate transcription factor (TF) function. However, they lack DNA binding capacity, prompting the question of how they engage target loci. Three non-exclusive hypotheses have been posited: coactivators are recruited by complexing with TFs, by binding histones through epigenetic reader domains, or by partitioning into phase-separated compartments through their extensive intrinsically disordered regions (IDRs). Using p300 as a prototypical coactivator, we systematically mutated its annotated domains and show by single-molecule tracking in live cells that coactivator- chromatin binding depends entirely on combinatorial binding of multiple TF-interaction domains. Furthermore, we demonstrate that acetyltransferase activity negatively impacts p300-chromatin association and that the N-terminal TF-interaction domains regulate that activity. Single TF-interaction domains are insufficient for both chromatin binding and regulation of catalytic activity, implying a principle that could broadly inform eukaryotic gene regulation: a TF must act in coordination with other TFs to recruit coactivator activity.

biophysics↗

bioRxiv2023

Domain-based multi-domain protein and complex structure prediction using inter-domain interactions from deep learning

Accurately capturing domain-domain interactions is key to understanding protein function and designing structure-based drugs. Although AlphaFold2 has made a breakthrough on single domain, it should be noted that the structure modeling for multi-domain protein and complex remains a challenge. In this study, we developed a multi-domain and complex structure assembly protocol, named DeepAssembly, based on domain segmentation and single domain modeling algorithms. Firstly, DeepAssembly uses a population-based evolutionary algorithm to assemble multi-domain proteins by inter-domain interactions inferred from a developed deep learning network. Secondly, protein complexes are assembled by means of domains rather than chains using DeepAssembly. Experimental results show that on 219 multi-domain proteins, the average inter-domain distance precision by DeepAssembly is 22.7% higher than that of AlphaFold2. Moreover, DeepAssembly improves accuracy by 11.8% for 164 multi-domain structures with low confidence deposited in AlphaFold database. We apply DeepAssembly for the prediction of 247 heterodimers. We find that DeepAssembly generates models with acceptable quality (DockQ [≥] 0.23) for 32.4% of the dimers, suggesting a lighter way to assemble complex structures by treating domains as assembly units and using inter-domain interactions learned from monomer structures.

bioinformatics↗

bioRxiv2023

DeepUMQA3: a web server for model quality assessment of protein complexes

Model quality assessment is a crucial part of protein structure prediction and a gateway to proper usage of models in biomedical applications. Many methods have been proposed for assessing the quality of structural models of protein monomers, but few methods for evaluating protein complex models. As protein complex structure prediction becomes a new challenge, model quality assessment methods that can provide accurate evaluation of complex structures are urgently required. Here, we present DeepUMQA3, a web server for evaluating protein complex structures using deep neural network. For an input complex structure, features are extracted from three levels of overall complex, intra-monomer, and inter-monomer, and a improved deep residual neural network is used to predict per-residue lDDT and interface residue accuracy. DeepUMQA3 ranks first in the blind test of interface residue accuracy estimation in CASP15, with Pearson, Spearman and AUC of 0.564, 0.535 and 0.755 under the lDDT measurement, which are 18.5%, 23.6% and 10.9% higher than the second-best method, respectively. DeepUMQA3 can also accurately assess the accuracy of all residues in the entire complex and distinguish high- and low-precision residues/models. The websever of DeepUMQA3 are freely available at http://zhanglab-bioinf.com/DeepUMQA_server/.

bioinformatics↗

bioRxiv2023

Discovery of microRNA-derived RNAs and proteins in human cells

MicroRNAs (miRNAs) are short noncoding RNAs that can regulate gene expression through the binding of their 5-ends to mRNAs. However, the biological effects of miRNA binding via their 3-ends remain unclear. Here, we discover that the exact reverse pairing of the 3-ends of miRNAs or miRNA-like RNAs, collectively termed microsized RNAs (msRNAs), with template RNAs can initiate the production of msRNA-derived RNAs (msdRs), which can subsequently be translated into polypeptides (msdPs). Using 2,632 human msRNAs from miRBase, 11,121 msdRs and 1,239 msdPs were predicted based on a 15-nucleotide pairing threshold, and the presence of representative msdRs and msdPs was confirmed in human cells. Of clinical relevance, msdP0188 is highly expressed in human lung and breast cancers, and its corresponding msRNAs and msdRs represent novel anti-cancer targets. Notably, inhibiting telomerase reverse transcriptase, a putative RNA-dependent RNA polymerase identified by bioinformatic screening, led to reduced levels of msdP0188 in human cancer cells. Our findings reveal a novel "msRNA [->] msdR [->] msdP" axis, expanding the classical central dogma and predicting many previously unrecognized RNAs and polypeptides with potential biological functions in human cells and other systems.

cell biology↗

bioRxiv2023

Spatiotemporal transcriptome atlas of human embryos after gastrulation

The spatial and temporal atlas of gene expression in the human embryo at early gestation is critical in understanding embryo development, organogenesis, and disease origins. We obtained the spatiotemporal transcriptome from 90 sagittal sections of 16 whole human embryos from 3 to 8 post-conception weeks by Stereo-seq with high resolution and ultra-large field, establishing the development trajectory/regulatory profiling of 49 organs. We uncovered the organ-specific regulons as potential lineage-determining factors and identified the new regulatory networks during heart and brain development. The atlas refines the key organs/cell types vulnerable to virus infection and genetic disorders, and, reveals the dynamics of allelic gene expression in specific organs at different stages. These results present the first comprehensive delineation of the spatiotemporal transcriptomic dynamics of human organogenesis. One Sentence SummaryThe spatiotemporal transcriptome atlas presents a comprehensive delineation of human embryogenesis after gastrulation.

developmental biology↗

bioRxiv2023

Interplay between ATRX and IDH1 mutations governs innate immune responses in diffuse gliomas

Stimulating the innate immune system has been explored as a therapeutic option for the treatment of gliomas. Inactivating mutations in ATRX, defining molecular alterations in IDH-mutant astrocytomas, have been implicated in dysfunctional immune signaling. However, little is known about the interplay between ATRX loss and IDH mutation on innate immunity. To explore this, we generated ATRX knockout glioma models in the presence and absence of the IDH1R132H mutation. ATRX-deficient glioma cells were sensitive to dsRNA-based innate immune agonism and exhibited impaired lethality and increased T-cell infiltration in vivo. However, the presence of IDH1R132Hdampened baseline expression of key innate immune genes and cytokines in a manner restored by genetic and pharmacological IDH1R132H inhibition. IDH1R132H co-expression did not interfere with the ATRX KO-mediated sensitivity to dsRNA. Thus, ATRX loss primes cells for recognition of dsRNA, while IDH1R132H reversibly masks this priming. This work reveals innate immunity as a therapeutic vulnerability of astrocytoma.

cancer biology↗

bioRxiv2023

Pathfinder: protein folding pathway prediction based on conformational sampling

The study of protein folding mechanism is a challenge in molecular biology, which is of great significance for revealing the movement rules of biological macromolecules, understanding the pathogenic mechanism of folding diseases, and designing protein engineering materials. Based on the hypothesis that the conformational sampling trajectory contain the information of folding pathway, we propose a protein folding pathway prediction algorithm named Pathfinder. Firstly, Pathfinder performs large-scale sampling of the conformational space and clusters the decoys obtained in the sampling. The heterogeneous conformations obtained by clustering are named seed states. Then, a resampling algorithm that is not constrained by the local energy basin is designed to obtain the transition probabilities of seed states. Finally, protein folding pathways are inferred from the maximum transition probabilities of seed states. The proposed Pathfinder is tested on our developed test set (34 proteins). For 5 widely studied proteins, we correctly predicted their folding pathways. For 25 partial biological experiments proteins, we predicted folding pathways could be further verified. For the other 4 proteins without biological experiment results, potential folding pathways were predicted to provide new insights into protein folding mechanism. The results reveal that structural analogs may have different folding pathways to express different biological functions, homologous proteins may contain common folding pathways, and -helices may be more prone to early protein folding than {beta}-strands.

bioinformatics↗

bioRxiv2023

Characterization of Tryptophan Oxidation Affecting D1 Degradation by FtsH in the Photosystem II Quality Control of Chloroplasts

Photosynthesis is one of the most important reactions for sustaining our environment. Photosystem II (PSII) is the initial site of photosynthetic electron transfer by water oxidation. Light in excess, however, causes the simultaneous production of reactive oxygen species (ROS), leading to photo-oxidative damage in PSII. To maintain photosynthetic activity, the PSII reaction center protein D1, which is the primary target of unavoidable photo-oxidative damage, is efficiently degraded by FtsH protease. In PSII subunits, photo-oxidative modifications of several amino acids such as Trp have been indeed documented, whereas the linkage between such modifications and D1 degradation remains elusive. Here, we show that an oxidative post-translational modification of Trp residue at the N-terminal tail of D1 is correlated with D1 degradation by FtsH during high-light stress. We revealed that Arabidopsis mutant lacking FtsH2 had increased levels of oxidative Trp residues in D1, among which an N-terminal Trp-14 was distinctively localized in the stromal side. Further characterization of Trp-14 using chloroplast transformation in Chlamydomonas indicated that substitution of D1 Trp-14 to Phe, mimicking Trp oxidation enhanced FtsH-mediated D1 degradation under high light, although the substitution did not affect protein stability and PSII activity. Molecular dynamics simulation of PSII implies that both Trp-14 oxidation and Phe substitution cause fluctuation of D1 N-terminal tail. Furthermore, Trp-14 to Phe modification appeared to have an additive effect in the interaction between FtsH and PSII core in vivo. Together, our results suggest that the Trp oxidation at its N-terminus of D1 may be one of the key oxidations in the PSII repair, leading to processive degradation by FtsH. Competing Interest StatementThe authors have no conflict of interest, financial or otherwise, in relation to this study Impact StatementOxidative modification of Tryptophan residues in the reaction center protein D1 may be a key to drive the Photosystem II repair, likely enhancing accessibility of FtsH protease to D1.

plant biology↗

bioRxiv2023

Protein multiple conformations prediction using multi-objective evolution algorithm

MotivationWith the breakthrough of AlphaFold2 and the publication of AlphaFold DB, the protein structure prediction has made remarkable progress, which may further promote many potential applications of proteomics in all areas of life. However, it should be noted that AlphaFold2 models tend to represent only a single static structure, and accurately predicting multiple conformations remains a challenge. Therefore, it is essential to develop methods for predicting multiple conformations, which enable us to gain knowledge of multiple conformational states and the broader conformational landscape to better understand the mechanism of action. ResultsIn this work, we proposed a multiple conformational states folding method using the distance-based multi-objective evolutionary algorithm framework, named MultiSFold. First, a multi-objective energy landscape with multiple competing constraints generated by deep learning is constructed. Then, an iterative modal exploration and exploitation strategy based on multi-objective optimization, geometric optimization and structural similarity clustering is designed to perform conformational sampling. Finally, the final population is generated using a loop-specific perturbation strategy to adjust the spatial orientations. MultiSFold was compared with state-of-the-art methods on a developed benchmark testset containing 81 proteins with two representative conformational states. Based on the proposed metric, the success ratio of MultiSFold predicting multiple conformations was 70.4% while that of AlphaFold2 was 9.88%, which may indicate that conformational sampling combined with knowledge gained through deep learning has the potential to produce conformations spanned the range between two experimental structures. In addition, MultiSFold was tested on 244 human proteins with low structural accuracy in AlphaFold DB to test whether it could further improve the accuracy of static structures. The experimental results demonstrate that the TM-score of MultiSFold is 2.97% and 7.72% higher than that of AlphaFold2 and RoseTTAFold, respectively, supporting our hypothesis that multiple competing optimization objectives can further assist conformational search to improve prediction accuracy.

bioinformatics↗

bioRxiv2023

A transferrin receptor 1-targeted PNA-peptide conjugate inhibits microRNA-21 expression in cardiac and other mouse tissues

MicroRNAs (miRNAs) are implicated in the onset and progression of a variety of diseases. Modulating the expression of specific miRNAs is a possible option for therapeutic intervention. A promising strategy is the use of antisense oligonucleotides (ASOs) to inhibit miRNAs. Targeting ASOs to specific tissues can potentially lower the dosage and improve clinical outcomes by alleviating systemic toxicity. We leverage here automated peptide nucleic acid (PNA) synthesis technology to manufacture an anti-miRNA oligonucleotide (antagomir) covalently attached to a 12-mer peptide that binds to transferrin receptor 1. Our PNA-peptide conjugate is active in cells and animals, effectively inhibiting the expression of miRNA-21 both in cultured mouse cardiomyocytes and different mouse organs (heart, liver, kidney, lung, and spleen), while remaining well-tolerated in animals up to the highest tested dose of 30 mg/kg. Conjugating the targeting ligand to the PNA antagomir significantly improved inhibition of miRNA-21 in the heart by over 50% relative to the PNA alone. Given the modulation of biodistribution observed with our PNA-peptide conjugate, we anticipate this antagomir platform to serve as a starting point for pre-clinical development studies. Table of Contents Entry O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/536802v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@645857org.highwire.dtl.DTLVardef@1e3be1corg.highwire.dtl.DTLVardef@d6286aorg.highwire.dtl.DTLVardef@1f76032_HPS_FORMAT_FIGEXP M_FIG C_FIG SynopsisConjugating T12, a peptide targeting transferrin receptor 1 (TfR1), to a peptide nucleic acid (PNA) oligonucleotide targeting microRNA-21 increases delivery of the PNA-T12 conjugate to cardiac tissue relative to PNA alone.

molecular biology↗

bioRxiv2023

Targeting MCL1-driven anti-apoptotic pathways to overcome hypomethylating agent resistance in RAS-mutated chronic myelomonocytic leukemia

RAS pathway mutations, which are present in 30% of patients with chronic myelomonocytic leukemia (CMML) at diagnosis, confer a high risk of resistance to and progression after hypomethylating agent (HMA) therapy, the current standard of care for the disease. Using single-cell, multi-omics technologies, we sought to dissect the biological mechanisms underlying the initiation and progression of RAS pathway-mutated CMML. We found that RAS pathway mutations induced the transcriptional reprogramming of hematopoietic stem and progenitor cells (HSPCs), which underwent proliferation and monocytic differentiation in response to cell-intrinsic and -extrinsic inflammatory signaling that also impaired immune cells functions. HSPCs expanded at disease progression and relied on the NF-KB pathway effector MCL1 to maintain their survival, which explains why patients with RAS pathway- mutated CMML do not benefit from BCL2 inhibitors such as venetoclax. Our study has implications for developing therapies to improve the survival of patients with RAS pathway- mutated CMML.

cancer biology↗

bioRxiv2023

Self-supervised denoising for structured illumination microscopy enables long-term super-resolution live-cell imaging

Detection noise significantly degrades the quality of structured illumination microscopy (SIM) images, especially under low-light conditions. Although supervised learning based denoising methods have shown prominent advances in eliminating the noise-induced artifacts, the requirement of a large amount of high-quality training data severely limits their applications. Here we developed a pixel-realignment-based self-supervised denoising framework for SIM (PRS-SIM) that trains an SIM image denoiser with only noisy data and substantially removes the reconstruction artifacts. We demonstrated that PRS-SIM generates artifact-free images with 10-fold less fluorescence than ordinary imaging conditions while achieving comparable super-resolution capability to the ground truth (GT). Moreover, the proposed method is compatible with multiple SIM modalities such as total internal reflective fluorescence SIM (TIRF-SIM), three-dimensional SIM (3D-SIM), lattice light-sheet SIM (LLS-SIM), and non-linear SIM (NL-SIM). With PRS-SIM, we achieved long-term super-resolution live-cell imaging of various bioprocesses, revealing the clustered distribution of clathrin coated pits and detailed interaction dynamics of multiple organelles and the cytoskeleton.

cell biology↗

bioRxiv2023

Reversing myeloid-derived suppressor cells mediated immunosuppression via p38α inhibition enhances immunotherapy efficacy in triple negative breast cancer

Infiltration of myeloid-derived suppressor cells (MDSCs) leads to Immunosuppressive tumor microenvironment (TME), which is one of the major causes for low objective response rates of immune checkpoint blockade (ICB) therapy. Here, we report that chemical inhibition of p38 reverses this MDSC-induced immunosuppressive TME and improves the immunotherapy efficacy in triple negative breast cancer (TNBC). Firstly, by combining the tumor immunological phenotype (TIP) gene signature and high throughput sequencing based high throughput screening (HTS2), we identified that ponatinib significantly inhibits the expression of "cold" tumor associated chemokines CXCL1 and CXCL2 in cancer cells. This inhibition decreases the infiltration of MDSCs and consequently increased the accumulation of "hot" tumor associated T cells and NK cells and thus reverses the immunosuppressive TME. Then, by multiple preclinical models, we found that ponatinib significantly inhibits tumor growth in a TME-dependent manner and enhances the efficacy of anti-PD-L1 immunotherapy on TNBC in vivo. Notably, ponatinib exhibits no significant inhibition on immune cells in mouse spleens. Mechanistically, ponatinib directly inhibits the kinase activity of p38, which results in the reduction of the phosphorylation of STAT1 at Ser727, and thus the decreased expression of CXCL1 and CXCL2 in cancer cells. Our study provided the therapeutic potential of combining p38 inhibition with ICB for the treatment of TNBC.

cancer biology↗