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Hao, Y.

Publications and source records attributed to Hao, Y..

At least 145 records · Page 8Linked to original sources

Predicting the Toxicity of Druggable Proteins to Human Tissues

Assessing in vivo tissue toxicity of therapeutic targets remains a major challenge in drug development and drug safety research. We developed TissueTox, an algorithm that learns from multi-omic features of a target protein and predicts toxicity in human body systems and tissues. Predicted TissueTox scores accurately differentiate drugs that failed clinical trials from those that succeeded, and, importantly, can be used to identify the tissues where toxic events occurred.

bioinformatics↗

Gut-testis axis: microbiota-(n-3) PUFA improving semen quality in type 1 diabetes

Gut dysbiosis and type 1 diabetes (T1D) are closely related, and gut dysbiosis and male infertility are correlated, too. Moreover, most male T1D patients are of active reproductive age. Therefore, it is crucial to explore possible means for improving their semen quality. Here, we found that fecal microbiota transplantation (FMT) from alginate oligosaccharide (AOS) improved gut microbiota (A10-FMT) significantly decreased blood glucose and glycogen, and increased semen quality in streptozotocin-induced T1D subjects. A10-FMT improved T1D-disturbed gut microbiota, especially the increase in small intestinal lactobacillus, and blood and testicular metabolome to produce n-3 polyunsaturated fatty acid (PUFA) docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA) to ameliorate spermatogenesis and semen quality. Moreover, A10-FMT can improve spleen and liver function to strengthen the systemic environment for sperm development. FMT from gut microbiota of control animals (Con-FMT) produced some beneficial effects; however, to a smaller extent. Thus, AOS improved gut microbiota may be a useful protocol for improving semen quality and male fertility in T1D patients. ImportanceClinical data suggest that male reproductive dysfunction especially infertility is a critical issue for type 1 diabetic patient (T1D) because most of them are at the reproductive age. Gut dysbiosis is involved in T1D related male infertility. However, improved gut microbiota can be used to improve spermatogenesis and male fertility in T1D remains incompletely understood. We discovered that alginate oligosaccharide-improved gut microbiota (A10-FMT) significantly ameliorated spermatogenesis and semen quality. AOS-improved gut microbiota (specific microbes) may serve as a novel, promising therapeutic approach for the improvement of semen quality and male fertility in T1D patients.

developmental biology↗

Antigen presentation by type 3 innate lymphoid cells instructs the differentiation of gut microbiota-specific regulatory T cells

The mutualistic relationship of gut-resident microbiota and cells of the host immune system promotes homeostasis that ensures maintenance of the microbial community and of a poised, but largely non-aggressive, immune cell compartment1, 2. Consequences of disturbing this balance, by environmental or genetic factors, include proximal inflammatory conditions, like Crohns disease, and systemic illnesses, both metabolic and autoimmune. One of the means by which this equilibrium is achieved is through induction of both effector and suppressor or regulatory arms of the adaptive immune system. In mice, Helicobacter species induce regulatory (iTreg) and follicular helper (Tfh) T cells in the colon-draining mesenteric lymph nodes under homeostatic conditions, but can instead induce inflammatory Th17 cells when iTreg cells are compromised3, 4. How Helicobacter hepaticus and other gut bacteria direct T cells to adopt distinct functions remains poorly understood. Here, we investigated which cells and molecular components are required to convey the microbial instruction for the iTreg differentiation program. We found that antigen presentation by cells expressing ROR{gamma}t, rather than by classical dendritic cells, was both required and sufficient for iTreg induction. These ROR{gamma}t+ cells, likely to be type 3 innate lymphoid cells (ILC3) and/or a recently-described population of Aire+ cells termed Janus cells5, require the MHC class II antigen presentation machinery, the chemokine receptor CCR7, and v integrin, which activates TGF-{beta}, for iTreg cell differentiation. In the absence of any of these, instead of iTreg cells there was expansion of microbiota-specific pathogenic Th17 cells, which were induced by other antigen presenting cells (APCs) that did not require CCR7. Thus, intestinal commensal microbes and their products target multiple APCs with pre-determined features suited to directing appropriate T cell differentiation programs, rather than a common APC that they endow with appropriate functions. Our results illustrate the ability of microbiota to exploit specialized functions of distinct innate immune system cells, targeting them to achieve the desired composition of equipoised T cells, thus maintaining tolerance.

immunology↗

Trait Association and Prediction Through Integrative K-mer Analysis

Genome-wide association study (GWAS) with single nucleotide polymorphisms (SNPs) has been widely used to explore genetic controls of phenotypic traits. Here we employed an GWAS approach using k-mers, short substrings from sequencing reads. Using maize cob and kernel color traits, we demonstrated that k-mer GWAS can effectively identify associated k-mers. Co-expression analysis of kernel color k-mers and pathway genes directly found k-mers from causal genes. Analyzing complex traits of kernel oil and leaf angle resulted in k-mers from both known and candidate genes. Evolution analysis revealed most k-mers positively correlated with kernel oil were strongly selected against in maize populations, while most k-mers for upright leaf angle were positively selected. In addition, phenotypic prediction of kernel oil, leaf angle, and flowering time using k-mer data showed at least a similarly high prediction accuracy to the standard SNP-based method. Collectively, our results demonstrated the bridging role of k-mers for data integration and functional gene discovery.

genetics↗

Convergent evolution of polyploid genomes from across the eukaryotic tree of life

By modeling the homoeologous gene losses that occurred in fifty genomes deriving from ten distinct polyploidy events, we show that the evolutionary forces acting on polyploids are remarkably similar, regardless of whether they occur in flowering plants, ciliates, fishes or yeasts. The models suggest these events were nearly all allopolyploidies, with two distinct progenitors contributing to the modern species. We show that many of the events show a relative rate of duplicate gene loss prior to the first post-polyploidy speciation that is significantly higher than in later phases of their evolution. The relatively low selective constraint seen for the single-copy genes these losses produced lead us to suggest that most of the purely selectively neutral duplicate gene losses occur in the immediate post-polyploid period. We also find ongoing and extensive reciprocal gene losses (RGL; alternative losses of duplicated ancestral genes) between these genomes. With the exception of a handful of closely related taxa, all of these polyploid organisms are separated from each other by tens to thousands of reciprocal gene losses. As a result, it is very unlikely that viable diploid hybrid species could form between these taxa, since matings between such hybrids would tend to produce offspring lacking essential genes. It is therefore possible that the relatively high frequency of recurrent polyploidies in some lineages may be due to the ability of new polyploidies to bypass RGL barriers.

evolutionary biology↗

KEAP1 mutation in lung adenocarcinoma promotes immune evasion and immunotherapy resistance

Lung cancer treatment has benefited greatly from the development of effective immune-based therapies. However, these strategies still fail in a large subset of patients. Tumor-intrinsic mutations can drive immune evasion via recruiting immunosuppressive populations or suppressing anti-tumor immune responses. KEAP1 is one of the most frequently mutated genes in lung adenocarcinoma patients and is associated with poor prognosis and inferior response to all therapies, including checkpoint blockade. Here, we established a novel antigenic lung cancer model and showed that Keap1-mutant tumors promote dramatic remodeling of the tumor immune microenvironment. Combining single-cell technology and depletion studies, we demonstrate that Keap1-mutant tumors diminish dendritic cell and T cell responses driving immunotherapy resistance. Importantly, analysis of KEAP1 mutant patient tumors revealed analogous decrease in dendritic cell and T cell infiltration. Our study provides new insight into the role of KEAP1 mutations in promoting immune evasion and suggests a path to novel immune-based therapeutic strategies for KEAP1 mutant lung cancer. Statement of significanceThis study establishes that tumor-intrinsic KEAP1 mutations contribute to immune evasion through suppression of dendritic cell and T cell responses, explaining the observed resistance to immunotherapy of KEAP1 mutant tumors. These results highlight the importance of stratifying patients based on KEAP1 status and paves the way for novel therapeutic strategies.

cancer biology↗

Characterizing cellular heterogeneity in chromatin state with scCUT&Tag-pro

New technologies that profile chromatin modifications at single-cell resolution offer enormous promise for functional genomic characterization. However, the sparsity of these measurements and the challenge of integrating multiple binding maps represent significant challenges. Here we introduce scCUT&Tag-pro, a multimodal assay for profiling protein-DNA interactions coupled with the abundance of surface proteins in single cells. In addition, we introduce scChromHMM, which integrates data from multiple experiments to infer and annotate chromatin states based on combinatorial histone modification patterns. We apply these tools to perform an integrated analysis across nine different molecular modalities in circulating human immune cells. We demonstrate how these two approaches can characterize dynamic changes in the function of individual genomic elements across both discrete cell states and continuous developmental trajectories, nominate associated motifs and regulators that establish chromatin states, and identify extensive and cell type-specific regulatory priming. Finally, we demonstrate how our integrated reference can serve as a scaffold to map and improve the interpretation of additional scCUT&Tag datasets.

genomics↗

ZetaSuite, A Computational Method for Analyzing Multi-dimensional High-throughput Data, Reveals Genes with Opposite Roles in Cancer Dependency

The rapid advance of high-throughput technologies has enabled the generation of two-dimensional or even multi-dimensional high-throughput data, e.g., genome-wide siRNA screen (1st dimension) for multiple changes in gene expression (2nd dimension) in many different cell types or tissues or under different experimental conditions (3rd dimension). We show that the simple Z-based statistic and derivatives are no longer suitable for analyzing such data because of the accumulation of experimental noise and/or off-target effects. Here, we introduce ZetaSuite, a statistical package designed to score and rank hits from two-dimensional screens, construct regulatory networks based on response similarities, and eliminate off-targets. Applying this method to two large cancer dependency screen datasets, we identify not only genes critical for cell fitness, but also those required for constraining cell proliferation. Strikingly, most of those cancer constraining genes function in DNA replication/repair checkpoint, suggesting that cancer cells also need to protect their genomes for long-term survival.

cell biology↗

Improving QSAR Modeling for Predictive Toxicology using Publicly Aggregated Semantic Graph Data and Graph Neural Networks

Quantitative Structure-Activity Relationship (QSAR) modeling is the most common computational technique for predicting chemical toxicity, but a lack of methodological innovations in QSAR have led to underwhelming performance. We show that contemporary QSAR modeling for predictive toxicology can be substantially improved by incorporating semantic graph data aggregated from open-access public databases, and analyzing those data in the context of graph neural networks (GNNs). Furthermore, we introspect the GNNs to demonstrate how they can lead to more interpretable applications of QSAR, and use ablation analysis to explore the contribution of different data elements to the final models performance.

pharmacology and toxicology↗

Making Strides in Doctoral-Level Career Outcomes Reporting: Surveying the Landscape of Classification and Visualization Methodologies and Creating a Crosswalk Tool

The recent movement underscoring the importance of career taxonomies has helped usher in a new era of transparency in PhD career outcomes. The convergence of discipline-specific organizational movements, interdisciplinary collaborations, and federal initiatives have all helped to increase PhD career outcomes tracking and reporting. Transparent and publicly available PhD career outcomes are being used by institutions to attract top applicants, as prospective graduate students are factoring these in when deciding on the program and institution in which to enroll for their PhD studies. Given the increasing trend to track PhD career outcomes, the number of institutional efforts and supporting offices for these studies have increased, as has the variety of methods being used to classify and report/visualize outcomes. This report identifies and summarizes currently available PhD career taxonomy tools, resources, and visualization options to help catalyze and empower institutions to develop and publish their own PhD career outcomes. Similar fields between taxonomies were mapped to create a new crosswalk tool. This work serves as an empirical review of the career outcome tracking systems available and highlights organizations, consortia, and funding agencies that are impacting policy change toward greater transparency in PhD career outcomes reporting.

scientific communication and education↗

Double nicking by RNA-directed Cascade-nCas3 for high-efficiency large-scale genome engineering

New CRISPR-based genome editing technologies are developed to continuedly drive advances in life sciences, which, however, are predominantly derived from systems of Type II CRISPR-Cas9 and Type V CRISPR-Cas12a for eukaryotes. Here we report a novel CRISPR-n(nickase)Cas3 genome editing tool established upon an endogenous Type I system of Zymomonas mobilis. We demonstrate that nCas3 variants can be created by alanine-substituting any catalytic residue of the Cas3 helicase domain. While nCas3 overproduction via plasmid shows severe cytotoxicity; an in situ nCas3 introduces targeted double-strand breaks, facilitating genome editing, without visible cell killing. By harnessing this CRISPR-nCas3, deletion of genes or genomic DNA stretches can be consistently accomplished with near-100% efficiencies, including simultaneous removal of two large genomic fragments. Our work describes the first establishment of a CRISPR-nCas3-based genome editing technology, thereby offering a simple, easy, yet useful approach to convert many endogenous Type I systems into advanced genome editing tools. We envision that many CRISPR-nCas3-based toolkits would be soon available for various industrially important non-model bacteria that carry active Type I systems to facilitate high-throughput prokaryotic engineering.

microbiology↗

Antibiotics drive expansion of rare pathogens in a chronic infection microbiome model

Chronic (long-lasting) infections are globally a major and rising cause of morbidity and mortality. Unlike typical acute infections, chronic infections are ecologically diverse, characterized by the presence of a polymicrobial mix of opportunistic pathogens and human-associated commensals. To address the challenge of chronic infection microbiomes, we focus on a particularly well-characterized disease, cystic fibrosis (CF), where polymicrobial lung infections persist for decades despite frequent exposure to antibiotics. Epidemiological analyses point to conflicting results on the benefits of antibiotic treatment, and are confounded by the dependency of antibiotic exposures on prior pathogen presence, limiting their ability to draw causal inferences on the relationships between antibiotic exposure and pathogen dynamics. To address this limitation, we develop a synthetic infection microbiome model, and benchmark on clinical data. We show that, in the absence of antibiotics, the microbiome structure in a synthetic sputum medium is highly repeatable and dominated by oral commensals. In contrast, challenge with physiologically relevant antibiotic doses leads to substantial community perturbation characterized by multiple alternate pathogen-dominant states and enrichment of drug-resistant species. These results provide evidence that antibiotics can drive the expansion (via competitive release) of previously rare opportunistic pathogens and offer a path towards microbiome-informed conditional treatment strategies.

microbiology↗

VP-Detector: A 3D convolutional neural network for automated macromolecule localization and classification in cryo-electron tomograms

MotivationCryo-electron tomography (Cryo-ET) with sub-tomogram averaging (STA) is indispensable when studying macromolecule structures and functions in their native environments. However, current tomographic reconstructions suffer the low signal-to-noise (SNR) ratio and the missing wedge artifacts. Hence, automatic and accurate macromolecule localization and classification become the bottleneck problem for structural determination by STA. Here, we propose a 3D multi-scale dense convolutional neural network (MSDNet) for voxel-wise annotations of tomograms. Weighted focal loss is adopted as a loss function to solve the class imbalance. The proposed network combines 3D hybrid dilated convolutions (HDC) and dense connectivity to ensure an accurate performance with relatively few trainable parameters. 3D HDC expands the receptive field without losing resolution or learning extra parameters. Dense connectivity facilitates the re-use of feature maps to generate fewer intermediate feature maps and trainable parameters. Then, we design a 3D MSDNet based approach for fully automatic macromolecule localization and classification, called VP-Detector (Voxel-wise Particle Detector). VP-Detector is efficient because classification performs on the pre-calculated coordinates instead of a sliding window. ResultsWe evaluated the VP-Detector on simulated tomograms. Compared to the state-of-the-art methods, our method achieved a competitive performance on localization with the highest F1-score. We also demonstrated that the weighted focal loss improves the classification of hard classes. We trained the network on a part of training sets to prove the availability of training on relatively small datasets. Moreover, the experiment shows that VP-Detector has a fast particle detection speed, which costs less than 14 minutes on a test tomogram. Contactzsh@amss.ac.cn, xfcui@email.sdu.edu.cn, zhangfa@ict.ac.cn Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

Homogeneous environmental selection overturns distance-decay relationship of soil prokaryotic community

Though being fundamental to global diversity distribution, little is known about the geographic pattern of soil microorganisms across different biomes on a large scale. Here, we investigated soil prokaryotic communities from Chinese northern grasslands on a scale up to 4,000 km in both alpine and temperate biomes. Surprisingly, prokaryotic similarities increased with geographic distance after tipping points of 1,760 - 1,920 km, overturning the well-accepted distance-decay relationship and generating a significant U-shape pattern. Such U-shape pattern was likely due to decreased disparities in environmental heterogeneity along with geographic distance when across biomes, as homogeneous environmental selection dominated prokaryotic assembly based on {beta}NTI analysis. Consistently, short-term environmental heterogeneity also followed the U-shape pattern spatially, mainly attributed to dissolved nutrients. In sum, these results demonstrate that homogeneous environmental selection via dissolved nutrients overwhelmed the "distance" effect when across biomes, subverting the previously well-accepted geographic pattern for microbes on a large scale.

ecology↗

SmProt: a reliable repository with comprehensive annotation of small proteins identified from ribosome profiling

Small proteins specifically refer to proteins consisting of less than 100 amino acids translated from small open reading frames (sORFs), which were usually missed in previous genome annotation. The significance of small proteins has been revealed in current years, along with the discovery of their diverse functions. However, systematic annotation of small proteins is still insufficient. SmProt was specially developed to provide valuable information on small proteins for scientific community. Here we present the update of SmProt, which emphasizes reliability of translated sORFs, genetic variants in translated sORFs, disease-specific sORFs translation events or sequences, and significantly increased data volume. More components such as non-AUG translation initiation, function, and new sources are also included. SmProt incorporated 638,958 unique small proteins curated from 3,165,229 primary records, which were computationally predicted from 419 ribosome profiling (Ribo-seq) datasets and collected from the literature and other sources originating from 370 cell lines or tissues in 8 species (Homo sapiens, Mus musculus, Rattus norvegicus, Drosophila melanogaster, Danio rerio, Saccharomyces cerevisiae, Caenorhabditis elegans, and Escherichia coli). In addition, small protein families identified from human microbiomes were collected. All datasets in SmProt are free to access, and available for browse, search, and bulk downloads at http://bigdata.ibp.ac.cn/SmProt/.

bioinformatics↗

Surrogate production of genome edited sperm from a different subfamily by spermatogonial stem cell transplantation

The surrogate reproduction technique provides a powerful tool for production of allogenic or xenogeneic gametes derived from endangered species or those with valuable genetic traits. Production of functional donor-derived gametes through intra- or inter-specific spermatogonial stem cell transplantation (SSCT) has been achieved in many species. However, generation of functional gametes from a phylogenetically distant species such as from a different subfamily by SSCT has never been successful. Here, using two small cyprinid fishes, Chinese rare minnow (gobiocypris rarus, for brief: Gr) and zebrafish (danio rerio), which belong to different subfamilies, as donors and recipients for SSCT, we optimized the SSCT technique and successfully obtained Gr-derived sperm carrying targeted genome modifications in zebrafish. We revealed that the transplanted Gr spermatogonia supported the host gonadal development and underwent normal spermatogenesis, resulting in a reconstructed fertile testis containing Gr spermatids and zebrafish testicular somatic cells. Interestingly, the surrogate spermatozoa resembled those of host zebrafish but not donor Gr in morphology and swimming behavior. Finally, we showed that Gr-derived genome edited sperm was successfully produced in zebrafish by cross-subfamily SSCT, when the pou5f3 and chd gene knockout Gr SSCs were used as surrogate donors. This is the first report demonstrating the surrogate production of genome edited sperm from a phylogenetically distant species, and this method is feasible to be applied to future breeding of commercial fishes.

developmental biology↗

Ultrapotent SARS-CoV-2 neutralizing antibodies with protective efficacy against newly emerged mutational variants

Accumulating mutations in the SARS-CoV-2 Spike (S) protein can increase the possibility of immune escape, challenging the present COVID-19 prophylaxis and clinical interventions. Here, 3 receptor binding domain (RBD) specific monoclonal antibodies (mAbs), 58G6, 510A5 and 13G9, with high neutralizing potency blocking authentic SARS-CoV-2 virus displayed remarkable efficacy against authentic B.1.351 virus. Each of these 3 mAbs in combination with one neutralizing Ab recognizing non-competing epitope exhibited synergistic effect against authentic SARS-CoV-2 virus. Surprisingly, structural analysis revealed that 58G6 and 13G9, encoded by the IGHV1-58 and the IGKV3-20 germline genes, both recognized the steric region S470-495 on the RBD, overlapping the E484K mutation presented in B.1.351. Also, 58G6 directly bound to another region S450-458 in the RBD. Significantly, 58G6 and 510A5 both demonstrated prophylactic efficacy against authentic SARS-CoV-2 and B.1.351 viruses in the transgenic mice expressing human ACE2 (hACE2), protecting weight loss and reducing virus loads. These 2 ultrapotent neutralizing Abs can be promising candidates to fulfill the urgent needs for the prolonged COVID-19 pandemic.

immunology↗

Combined Inhibition of SHP2 and CXCR1/2 Promotes Anti-Tumor T Cell Response in NSCLC

Clinical trials of SHP2 inhibitors (SHP2i) alone and in various combinations are ongoing for multiple tumors with over-activation of the RAS/ERK pathway. SHP2 plays critical roles in normal cell signaling; hence, SHP2is could influence the tumor microenvironment. We found that SHP2i treatment depleted alveolar and M2-like macrophages and promoted B and T lymphocyte infiltration in Kras- and Egfr-mutant non-small cell lung cancer (NSCLC). However, treatment also increased intratumor gMDSCs via tumor-intrinsic, NF-kB-dependent production of CXCR2 ligands. Other RAS/ERK pathway inhibitors also induced CXCR2 ligands and gMDSC influx in mice, and CXCR2 ligands were induced in tumors from patients on KRASG12C-inhibitor trials. Combined SHP2(SHP099)/CXCR1/2(SX682) inhibition depleted a specific cluster of S100a8/9high gMDSCs, generated Klrg1+ CD8+ effector T cells with a strong cytotoxic phenotype but expressing the checkpoint receptor NKG2A, and enhanced survival in Kras-and Egfr-mutant models. Our results argue for testing RAS/ERK pathway/CXCR1/2/NKG2A inhibitor combinations in NSCLC patients. Statement of SignificanceOur study shows that inhibiting the SHP2/RAS/ERK pathway triggers NF-kB-dependent up-regulation of CXCR2 ligands and recruitment of S100A8high gMDSCs, which suppress T cells in NSCLC. Combining SHP2 and CXCR2 inhibitors blocks this gMDSC immigration, resulting in enhanced Th1 polarization, induction of CD8+ KLRG1+ effector T cells with high cytotoxic activity and improved survival in multiple NSCLC models.

cancer biology↗