Biology subjects

Xia, Y.

Publications and source records attributed to Xia, Y..

At least 127 records · Page 7Linked to original sources

bioRxiv2023

Cell-type specific optogenetic fMRI on basal forebrain reveals functional network basis of behavioral preference

The basal forebrain (BF) is a complex structure that plays key roles in regulating various brain functions. However, it remains unclear how cholinergic and non-cholinergic BF neurons modulate large-scale functional networks and their relevance in intrinsic and extrinsic behaviors. With optimized awake mouse optogenetic fMRI approach, we revealed optogenetic stimulations of four BF neuron types evoked distinct cell-type specific whole-brain BOLD activations, which could attribute to BF-originated low dimensional structural networks. Additionally, optogenetic activation of VGLUT2, ChAT and PV neurons in BF modulated the preference of locomotion, exploration and grooming, respectively. Furthermore, we uncovered the functional network basis of the above BF-modulated behavioral preference, through a decoding model linking the BF-modulated BOLD activations, low dimensional structural networks, and behavioral preference. To summarize, we decoded functional network basis of differential behavioral preference with cell-type specific optogenetic fMRI on BF, and provided an avenue for investigating mouse behaviors from a whole-brain view.

neuroscience↗

bioRxiv2023

Pharmacological or genetic inhibition of Scn9a protects beta-cells while reducing insulin secretion in type 1 diabetes

Pancreatic {beta} cells are essential for glucose homeostasis and are progressively lost during the development of type 1 diabetes. We previously demonstrated that the use-dependent Na+ channel inhibitor, carbamazepine, protects mouse {beta} cells in vitro and in vivo. Here, we confirmed the protective effects of carbamazepine and other Na+ channel inhibitors in human {beta} cells and investigated the specific role of the Na+ channel {beta} subunit gene Scn9a (Nav1.7) in {beta} cell function and survival. We generated {beta} cell-specific knockout mice on the non-obese diabetic (NOD) background both Ins1Cre knock-in and AAV8-Ins1-Cre approaches resulting in significant reduction of {beta} cell Na+ currents. Ca2+ responses and insulin secretion were significantly reduced, but only under the highest glucose conditions. Notably, carbamazepine treatment did not further alter insulin secretion or {beta} cell survival in Scn9a-knockout islets, indicating that {beta} cell Scn9a primarily mediates this drug's measured effects. Consistent with this, {beta} cell-specific deletion of Scn9a using AAV8-Ins1-Cre significantly reduced diabetes incidence in NOD mice. scRNAseq showed that this protection was associated with reduced Ins2 and increased Cdk8 in {beta} cells. Collectively, our data show that Scn9a plays important roles in {beta} cell excitability and survival during type 1 diabetes, thereby supporting this ion channel as a potential therapeutic target for {beta} cell preservation.

physiology↗

bioRxiv2023

Genetic identification of novel medullary neurons underlying congenital central hypoventilation syndrome

Congenital Central Hypoventilation Syndrome (CCHS) is a rare, but life-threatening, respiratory disorder that is classically diagnosed in children. This disease is characterized by pronounced alveolar hypoventilation and diminished chemoreflexes, particularly to abnormally high levels of arterial pCO2. Mutations in the transcription factors PHOX2B and LBX1 have been identified in CCHS patients, but the dysfunctional circuit responsible for this disease remains unknown. Here, we show that distinct sets of medullary neurons co-expressing both transcription factors (dB2 neurons) account for specific respiratory functions and phenotypes seen in CCHS. By combining murine intersectional chemogenetics, intersectional labeling, and the selective targeting of the CCHS disease-causing Lbx1FS mutation to specific subgroups of dB2 neurons, we uncovered novel sets of these cells key for i) respiratory tidal volumes and the hypercarbic reflex, ii) neonatal respiratory stability and iii) neonatal survival. These data provide functional evidence for the essential role of dB2 neurons in neonatal respiratory physiology and will be instrumental for the development of therapeutic strategies for the management of CCHS. In summary, our work uncovers new neural components of the central circuit regulating breathing and establishes dB2 neuron dysfunction to be causative of CCHS.

neuroscience↗

bioRxiv2023

Diffusion MRI Harmonization via Personalized Template Mapping

One fundamental challenge in diffusion MRI (dMRI) harmonization is to disentangle the contributions of scanner-related effects from the variable brain anatomy for the observed imaging signals. Conventional harmonization methods rely on establishing an atlas space to resolve anatomical variability and generate a unified inter-site mapping function. However, this approach is limited in accounting for the misalignment of neuroanatomy that still widely persists even after registration, especially in regions close to cortical boundaries. To overcome this challenge, we propose a personalized framework in this paper to more effectively address the confounding from the misalignment of neuroanatomy in dMRI harmonization. Instead of using a common template representing site-effects for all subjects, the main novelty of our method is the adaptive computation of personalized templates for both source and target scanning sites to estimate the inter-site mapping function. We integrate our method with the rotation invariant spherical harmonics (RISH) features to achieve the harmonization of dMRI signals between sites. In our experiments, the proposed approach is applied to harmonize the dMRI data acquired from two scanning platforms: Siemens Prisma and GE MR750 from the Adolescent Brain Cognitive Development (ABCD) dataset and compared with a state-of-the-art method based on RISH features. Our results indicate that the proposed harmonization framework achieves superior performance not only in reducing inter-site variations due to scanner differences but also in preserving sex-related biological variability in original cohorts. Moreover, we assess the impact of harmonization on the estimation of fiber orientation distributions (FOD) and show the robustness of the personalized harmonization procedure in preserving the fiber orientation of original dMRI signals.

bioinformatics↗

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

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

The Role of MAP3K1 in the Development of the Female Reproductive Tract

Mitogen-Activated Protein 3 Kinase 1 (MAP3K1) is a dynamic signaling molecule with a plethora of cell-type specific functions, most of which are yet to be understood. Here we describe a role for MAP3K1 in the development of female reproductive tract (FRT). MAP3K1 kinase domain-deficient (Map3k1{Delta}KD) females exhibit imperforate vagina, labor failure, and infertility. These defects correspond to a shunted Mullerian duct (MD), the principle precursor of the FRT, in embryos, while they manifest as a contorted caudal vagina with abrogated vaginal-urogenital sinus fusion in neonates. In epithelial cells, MAP3K1 acts through JNK and ERK to activate WNT, yet in vivo MAP3K1 is crucial for WNT activity in mesenchyme associated with the caudal MD. Expression of Wnt7b is high in wild type, but low in Map3k1 knockout MD epithelium and MAP3K1-deficient keratinocytes. Correspondingly, conditioned media derived from MAP3K1-competent epithelial cells activate TCF/Lef-luciferase reporter in fibroblasts, suggesting that MAP3K1-induced factors released from epithelial cells trans-activate WNT signaling in fibroblasts. Our results reveal a temporal-spatial and paracrine MAP3K1-WNT crosstalk contributing to MD caudal elongation and FRT development. HighlightsO_LIMAP3K1 deficient female mice exhibit imperforate vagina and infertility C_LIO_LILoss of MAP3K1 kinase activity impedes Mullerian duct (MD) caudal elongation and fusion with urogenital sinus (UGS) in embryogenesis C_LIO_LIThe MAP3K1-MAPK pathway up-regulates WNT signaling in epithelial cells C_LIO_LIMAP3K1 deficiency down-regulates Wnt7b expression in the MD epithelium and prevents WNT activity in mesenchyme of the caudal MD C_LI

developmental biology↗

bioRxiv2023

Predicting depression risk in early adolescence via multimodal brain imaging

Depression is an incapacitating psychiatric disorder with high prevalence in adolescent populations that is influenced by many risk factors, including family history of depression. The ability to predict who may develop depression before adolescence, when rates of depression increase markedly, is important for early intervention and prevention. Using a large longitudinal sample from the Adolescent Brain Cognitive Development (ABCD) Study (2658 participants after imaging quality control, between 9-10 years at baseline), we applied machine learning methods on a set of comprehensive multimodal neuroimaging features to predict depression risk at the two-year follow-up from the baseline visit. Features include derivatives from structural MRI, diffusion tensor imaging, and task and rest functional MRI. A rigorous cross-validation method of leave-one-site-out was used. Additionally, we tested the prediction models in a high-risk group of participants with parental history of depression (N=625). The results showed all brain features had prediction scores significantly better than expected by chance. When predicting depression onset in the high-risk group, brain features from resting-state functional connectomes showed the best classification performance, outperforming other brain features based on structural MRI and task-based fMRI. Results demonstrate that the functional connectivity of the brain can predict the risk of depression in early adolescence better than other univariate neuroimaging derivatives, highlighting the key role of the interacting elements of the connectome capturing more individual variability in psychopathology compared to measures of single brain regions.

neuroscience↗

bioRxiv2023

Steroid Receptor Coactivator-3 is a Key Modulator of Regulatory T Cell-Mediated Tumor Evasion

AbstractsSteroid receptor coactivator 3 (SRC-3) is most strongly expressed in regulatory T cells (Tregs) and B cells, suggesting that it plays an important role in the regulation of Treg function. Using an aggressive E0771 mouse breast cell line syngeneic immune-intact murine model, we observed that breast tumors were permanently eradicated in a genetically engineered tamoxifen-inducible Treg-cell specific SRC-3 knockout (KO) female mouse that does not possess a systemic autoimmune pathological phenotype. A similar eradication of tumor was noted in a syngeneic model of prostate cancer. A subsequent injection of additional E0771 cancer cells into these mice showed continued resistance to tumor development without the need for tamoxifen induction to produce additional SRC-3 KO Tregs. SRC-3 KO Tregs were highly proliferative and preferentially infiltrated into breast tumors by activating the Chemokine (C-C motif) ligand (Ccl) 19/Ccl21/ Chemokine (C-C motif) Receptor (Ccr)7 signaling axis, generating antitumor immunity by enhancing the interferon-{gamma}/C-X-C Motif Chemokine Ligand (Cxcl) 9 signaling axis to facilitate the entrance and function of effector T cells and Natural Killer cells. SRC-3 KO Tregs also show a dominant effect by blocking the immune suppressive function of WT Tregs. Importantly, a single adoptive transfer of SRC-3 KO Tregs into wild-type E0771 tumor-bearing mice can completely abolish pre-established breast tumors by generating potent antitumor immunity with a durable effect that prevents tumor reoccurrence. Therefore, treatment with SRC-3 deleted Tregs represents a novel approach to completely block tumor growth and recurrence without the autoimmune side-effects that typically accompany immune checkpoint modulators. Significance statementTregs are essential in restraining immune responses for immune homeostasis. SRC-3 is a pleiotropic coactivator, the second-most highly expressed transcriptional coactivator in Tregs, and a suspect in Treg function. The disruption of SRC-3 expression in Tregs leads to a complete lifetime eradication of tumors in aggressive syngeneic breast cancer mouse models because deletion of SRC-3 alters the expression of a wide range of key genes involved in efferent and afferent Treg signaling. SRC-3KO Tregs confer this long-lasting protection against cancer recurrence in mice without an apparent systemic autoimmune pathological phenotype. Therefore, treatment with SRC-3 deleted Tregs could represent a novel and efficient future target for eliminating tumor growth and recurrence without the autoimmune side-effects that typically accompany immune checkpoint modulators.

cancer biology↗

bioRxiv2023

Host genetic variation guides hepacivirus clearance, chronicity, and liver fibrosis in mice

Background & AimsHuman genetic variation is thought to guide the outcome of hepatitis C virus (HCV) infection but model systems within which to dissect these host genetic mechanisms are limited. Norway rat hepacivirus (NrHV), closely related to HCV, causes chronic liver infection in rats but causes acute self-limiting hepatitis in typical strains of laboratory mice, which resolves in two weeks. The Collaborative Cross (CC) is a robust mouse genetics resource comprised of a panel of recombinant inbred strains, which model the complexity of the human genome and provide a system within which to understand diseases driven by complex allelic variation. Approach & ResultsWe infected a panel of CC strains with NrHV and identified several that failed to clear virus after 4 weeks. Strains displayed an array of virologic phenotypes ranging from delayed clearance (CC046) to chronicity (CC071, CC080) with viremia for at least 10 months. Body weight loss, hepatocyte infection frequency, viral evolution, T-cell recruitment to the liver, liver inflammation and the capacity to develop liver fibrosis varied among infected CC strains. ConclusionsThese models recapitulate many aspects of HCV infection in humans and demonstrate that host genetic variation affects a multitude of virus and host phenotypes. These models can be used to better understand the molecular mechanisms that drive hepacivirus clearance and chronicity, the virus and host interactions that promote chronic disease manifestations like liver fibrosis, therapeutic and vaccine performance, and how these factors are affected by host genetic variation.

microbiology↗

bioRxiv2023

Cross-species single-cell comparison of systemic and cardiac inflammatory responses after cardiac injury

The immune system coordinates the response to cardiac injury and is known to control regenerative and fibrotic scar outcomes in the heart and subsequent chronic low-grade inflammation associated with heart failure. Here we profiled the inflammatory response to heart injury using single cell transcriptomics to compare and contrast two experimental models with disparate outcomes. We used adult mice, which like humans lack the ability to fully recover and zebrafish which spontaneously regenerate after heart injury. The extracardiac reaction to cardiomyocyte necrosis was also interrogated to assess the specific peripheral tissue and immune cell reaction to chronic stress. Cardiac macrophages are known to play a critical role in determining tissue homeostasis by healing versus scarring. We identified distinct transcriptional clusters of monocytes/macrophages in each species and found analogous pairs in zebrafish and mice. However, the reaction to myocardial injury was largely disparate between mice and zebrafish. The dichotomous response to heart damage between the mammalian and zebrafish monocytes/macrophages may underlie the impaired regenerative process in mice, representing a future therapeutic target.

cell biology↗

bioRxiv2023

Targeted Phasing of 2-200 Kilobase DNA Fragments with a Short-Read Sequencer and a Single-Tube Linked-Read Library Method

In the human genome, heterozygous sites are genomic positions with different alleles inherited from each parent. On average, there is a heterozygous site every 1-2 kilobases (kb). Resolving whether two alleles in neighboring heterozygous positions are physically linked--that is, phased--is possible with a short-read sequencer if the sequencing library captures long-range information. TELL-Seq is a library preparation method based on millions of barcoded micro-sized beads that enables instrument-free phasing of a whole human genome in a single PCR tube. TELL-Seq incorporates a unique molecular identifier (barcode) to the short reads generated from the same high-molecular-weight (HMW) DNA fragment (known as linked-reads). However, genome-scale TELL-Seq is not cost-effective for applications focusing on a single locus or a few loci. Here, we present an optimized TELL-Seq protocol that enables the cost-effective phasing of enriched loci (targets) of varying sizes, purity levels, and heterozygosity. Targeted TELL-Seq maximizes linked-read efficiency and library yield while minimizing input requirements, fragment collisions on microbeads, and sequencing burden. To validate the targeted protocol, we phased seven 180-200 kb loci enriched by CRISPR/Cas9-mediated excision coupled with pulse-field electrophoresis, four 20 kb loci enriched by CRISPR/Cas9-mediated protection from exonuclease digestion, and six 2-13 kb loci amplified by PCR. The selected targets have clinical and research relevance (BRCA1, BRCA2, MLH1, MSH2, MSH6, APC, PMS2, SCN5A-SCN10A, and PKI3CA). These analyses reveal that targeted TELL-Seq provides a reliable way of phasing allelic variants within targets (2-200 kb in length) with the low cost and high accuracy of short-read sequencing.

genomics↗

bioRxiv2023

Confinement with Myosin-II suppression increases heritable loss of chromosomes, using live-cell ChReporters

Matrix around cells exerts many effects, some of which depend on the putative tumor suppressor Myosin-II, but whether such factors affect DNA sequences in a cell remains unclear. Here, live-cell monitoring of changes to chromosome copy numbers is developed and studied under diverse perturbations, including Myosin-II inhibition in confined mitosis. Squeezing of mitotic cells is seen in vivo and kills in vitro, but stem cells and cancer cells that survive show heritable loss of mono-allelic GFP/RFP-tagged constitutive genes that function as novel Chromosome-reporters (ChReporters). Myosin-II suppression increases such loss in 3D & 2D confinement but not in standard 2D, with "lethal" multipolar divisions proving myosin-dependent. Viable chromosome loss after confined mitosis associates more with mis-segregation than with multipolars or division number. Solid human tumors and teratomas in mice also show ChReporter loss and a confinement-signature of Myosin-II suppression, although losses are selected against in 2D culture. Heritable loss in rigid-confinement also appears independent of a spindle assembly checkpoint that functions in 2D. Confinement and myosin-II thus regulate pathways of heritable mechanogenetic change.

cell biology↗

bioRxiv2023

A maize lipid droplet-associated protein is modulated by a virus to promote viral multiplication and infection

Pathogen infection induces massive reprogramming of host primary metabolism. Lipid and fatty acid metabolism is generally disrupted by pathogens and co-opted for their proliferation. Lipid droplets (LDs) that play important roles in regulating cellular lipid metabolism are utilized by a variety of pathogens in mammalian cells. However, the function of LDs during pathogenic infection in plants remains unknown. We show here that infection by rice black streaked dwarf virus (RBSDV) affects the lipid metabolism of maize, which causes elevated accumulation of C18 polyunsaturated fatty acids (PUFAs) leading to viral proliferation and symptom development. Overexpression of one of the two novel LD-associated proteins (LDAPs) of maize (ZmLDAP1 and ZmLDAP2) induces LD clustering. The core capsid protein P8 of RBSDV interacts with ZmLDAP2 and prevents its degradation through the ubiquitin-proteasome system mediated by a UBX domain-containing protein, PUX10. In addition, silencing of the ZmLDAP2 down-regulates expression of fatty acid desaturase genes in maize, leading to a decrease in C18 PUFAs levels and suppression of RBSDV accumulation. Our findings reveal that the plant virus recruits LDAP to regulate cellular fatty acid metabolism to promote viral multiplication and infection. These results expand the knowledge of the LD functions and viral infection mechanism in plants. One Sentence SummaryRice black streaked dwarf virus recruits a lipid droplet-associated protein to regulate cellular fatty acid metabolism for promoting viral multiplication and infection.

pathology↗

bioRxiv2023

E2EDA: Protein domain assembly based on end-to-end deep learning

With the development of deep learning, almost all single-domain proteins can be predicted at experimental resolution. However, the structure prediction of multi-domain proteins remains a challenge. Achieving end-to-end protein domain assembly and further improving the accuracy of the full-chain modelling by accurately predicting inter-domain orientation while improving the assembly efficiency will provide significant insights into structure-based drug discovery. In addition, the available GPU memory limits the size of a full-chain protein which can be predicted. Based on the divide-and-conquer strategy, the single-domain structure is predicted by the state-of-the-art prediction method, such as AlphaFold2, and then assembled into a full-chain model through the domain assembly method, which can effectively reduce the demand for hardware resources. In this work, we propose an End-To-End Domain Assembly method based on deep learning, named E2EDA. We first develop an EffificientNetV2-based deep learning model (RMNet), which is specialised for predicting inter-domain orientations. The RMNet uses an attention mechanism to predict inter-domain rigid motion by fusing sequence features, multiple template features and single-domain features. Then, the predicted rigid motions are converted into inter-domain spatial transformations to assemble full-chain models of multi-domain proteins directly without time-consuming simulation processes. Finally, a scoring strategy, RMscore, is designed to select the best model from multiple assembled models to improve assembly accuracy. The experimental results show that the average TM-score of the model assembled by E2EDA on the benchmark set (356) is 0.84, which is better than other domain assembly methods SADA (0.80), DEMO (0.74) and AIDA (0.63). Meanwhile, on our constructed human protein dataset from AlphaFold DB, the model reassembled by E2EDA is 6.8% higher than the full-chain model predicted by AlphaFold2, indicating that E2EDA can capture more accurate inter-domain orientations to improve the quality of the model predicted by AlphaFold2. Furthermore, the average running time of E2EDA on the benchmark is reduced by 74.6% compared with the domain assembly simulation method SADA, which indicates that E2EDA can effectively improve assembly efficiency through an end-to-end manner.The online server is at http://zhanglab-bioinf.com/E2EDA/.

bioinformatics↗

bioRxiv2023

Rationally designed inhibitors of the Musashi protein-RNA interaction by hotspot mimicry

RNA-binding proteins (RBPs) are key post-transcriptional regulators of gene expression, and thus underlie many important biological processes. Here, we developed a strategy that entails extracting a "hotspot pharmacophore" from the structure of a protein-RNA complex, to create a template for designing small-molecule inhibitors and for exploring the selectivity of the resulting inhibitors. We demonstrate this approach by designing inhibitors of Musashi proteins MSI1 and MSI2, key regulators of mRNA stability and translation that are upregulated in many cancers. We report this novel series of MSI1/MSI2 inhibitors is specific and active in biochemical, biophysical, and cellular assays. This study extends the paradigm of "hotspots" from protein-protein complexes to protein-RNA complexes, supports the "druggability" of RNA-binding protein surfaces, and represents one of the first rationally-designed inhibitors of non-enzymatic RNA-binding proteins. Owing to its simplicity and generality, we anticipate that this approach may also be used to develop inhibitors of many other RNA-binding proteins; we also consider the prospects of identifying potential off-target interactions by searching for other RBPs that recognize their cognate RNAs using similar interaction geometries. Beyond inhibitors, we also expect that compounds designed using this approach can serve as warheads for new PROTACs that selectively degrade RNA-binding proteins.

biochemistry↗

bioRxiv2022

Inter-domain distance prediction based on deep learning for domain assembly

AlphaFold2 achieved a breakthrough in protein structure prediction through the end-to-end deep learning method, which can predict nearly all single-domain proteins at experimental resolution. However, the prediction accuracy of full-chain proteins is generally lower than that of single-domain proteins because of the incorrect interactions between domains. In this work, we develop an inter-domain distance prediction method, named DeepIDDP. In DeepIDDP, we design a neural network with attention mechanisms, where two new inter-domain features are used to enhance the ability to capture the interactions between domains. Furthermore, we propose a data enhancement strategy termed DPMSA, which is employed to deal with the absence of co-evolutionary information on targets. We integrate DeepIDDP into our previously developed domain assembly method SADA, termed SADA-DeepIDDP. Tested on a given multi-domain benchmark dataset, the accuracy of SADA-DeepIDDP inter-domain distance prediction is 11.3% and 21.6% higher than trRosettaX and trRosetta, respectively. The accuracy of the domain assembly model is 2.5% higher than that of SADA. Meanwhile, we reassemble 68 human multi-domain protein models with TM-score [≤]0.80 from the AlphaFold protein structure database, where the average TM-score is improved by 11.8% after the reassembly by our method. The online server is at http://zhanglab-bioinf.com/DeepIDDP/.

bioinformatics↗

bioRxiv2022

Heterotrophic bacteria drive sulfide oxidation in coastal sediments

Sulfate reduction and sulfur oxidation are very active in coastal sediments. They shape the biogeochemistry and microbial ecology at hot places of organic matter metabolism. Different from the well-studied sulfate reduction, sulfur oxidation in coastal sediments is still full of questions. Herein, we investigated the distribution of reduced sulfur compounds in differently layers of coastal sediments at the Yellow sea and found that sulfide (H2S), sulfane sulfur (S0), and thiosulfate mainly accumulated in anaerobic sediments and were mostly oxidized in anoxic and oxic interface in the sediments and the sea water. Bacterial community analysis indicated that heterotrophic bacteria are dominating species in surface sediments and sea water. Metagenome analysis showed that two sulfur-oxidizing genes encoding sulfide:quinone oxidoreductases (SQR) and persufide dioxygenases (PDO), were sharply more abundant than other sulfur-oxidizing genes in the coastal sediments. Since members of the marine Roseobacter clade were dominant in coastal waters and sediments, we studied the sulfur oxidation pathway in the Roseobacter Ruegeria pomeroyi DSS-3 and found that sulfide:quinone oxidoreductase, persulfide dioxygenase, and sulfite-oxidizing enzyme were the main enzymes for the oxidation of H2S, zerovalent sulfur, and sulfite/thiosulfate. This study, for the first time, clarified the dominating function of heterotrophic bacteria in sulfur oxidation in the coastal sediments and sea water. IMPORTANCECoastal sediments are the most productive ecosystems. We performed the microbial community diversity and metagenomic analysis of seawater and coastal sediments of the Yellow Sea and explored the sulfur oxidation process in them. We found that heterotrophic bacteria are dominating species in surface sediments and sea water, sulfide and sulfane sulfur were mostly oxidized in surface sediments, and the genes encoding SQR, PDO, and SOE are abundant. Using Ruegeria pomeroyi DSS-3 as the model strain, we studied how these enzymes cooperate to oxidize H2S to sulfate. Thus, this research revealed the critical role of heterotrophic bacteria in sulfur oxidation in coastal sediments and sea water.

microbiology↗