Biology subjects

Hao, T.

Publications and source records attributed to Hao, T..

25 records · Page 2Linked to original sources

bioRxiv2020

Comprehensive characterization of protein-protein interaction network perturbations by human disease mutations

Technological and computational advances in genomics and interactomics have made it possible to identify rapidly how disease mutations perturb interaction networks within human cells. In this study, we investigate at large-scale the effects of network perturbations caused by disease mutations within the human three-dimensional (3D), structurally-resolved macromolecular interactome. We show that disease-associated germline mutations are significantly enriched in sequences encoding protein-protein interfaces compared to mutations identified in healthy subjects from the 1000 Genomes and ExAC projects; these interface mutations correspond to protein-protein interaction (PPI)-perturbing alleles including p.Ser127Arg in PCSK9 at the PCSK9-LDLR interface. In addition, somatic missense mutations are significantly enriched in PPI interfaces compared to non-interfaces in 10,861 human exomes across 33 cancer subtypes/types from The Cancer Genome Atlas. Using a binomial statistical model, we computationally identified 470 PPIs harboring a statistically significant excess number of missense mutations at protein-protein interfaces (termed putative oncoPPIs) in pan-cancer analysis. We demonstrate that the oncoPPIs, including histone H4 complex in individual cancer types, are highly correlated with patient survival and drug resistance/sensitivity in human cancer cell lines and patient-derived xenografts. We experimentally validate the network effects of 13 oncoPPIs using a systematic binary interaction assay. We further showed that ALOX5 p.Met146Lys at the ALOX5-MAD1L1 interface and RXRA p.Ser427Phe at the RXRA-PPARG interface promote significant tumor cell growth using cell line-based functional assays, providing a functional proof-of-concept. In summary, if broadly applied, this human 3D interactome network analysis offers a powerful tool for prioritizing alleles with mutations altering PPIs that may contribute to the pathobiology of human diseases, and may offer disease-specific targets for genotype-informed therapeutic discovery.

systems biology↗

bioRxiv2020

Integrative omics data analysis uncovers biomarker genes and potential candidate drugs for G3 medulloblastoma

Medulloblastoma (MB) is the most common malignant brain tumor in infants and children. Four molecular subtypes of MB are recognized: WNT, SHH, Group 3 (G3), and Group 4 (G4). Compared with WNT and SHH subtypes, G3 MBs exhibit significantly worse outcomes and higher metastatic rates, and there is no effective treatment yet. Moreover, G3 and G4 MBs are much more common in boys than girls, i.e., sex bias, which also plays important roles in cancer prognosis and drug response. However, the molecular mechanism of G3 remains unclear, and there are no well-identified biomarker genes associated with these phenotypes, i.e., worse survival rate, higher metastasis rate, and sex bias. In this exploratory study, we aim to identify potential biomarkers associated with the three phenotypes using integrative analysis of gene expression, methylation and copy number variation datasets. In the results, we identified a set of biomarker genes and linked them into a network signature. The network signature showed better performance in the separation of G3 MB patients into subtypes with a significant difference in terms of the three phenotypes. To identify potentially effective drugs for G3 MBs, a set of drugs with diverse targets were prioritized, which can potentially inhibit the network signature. These drugs or combinations thereof might be effective for G3 treatment.

bioinformatics↗

bioRxiv2020

The construction and application of a new 17-plex Y-STR system using universal fluorescent PCR

Y-chromosomal short tandem repeat (Y-STR) polymorphisms are useful in forensic identification, population genetics and human structures. However, the current Y-STR systems are limited in discriminating distant relatives in a family with a low discrimination power. Increasing the capacity of detecting Y chromosomal polymorphisms will drastically narrow down the matching number of genealogy populations or pedigrees. In this study, we developed a system containing 17 Y-STRs that are complementary to the current commercially available Y-STR kits. This system was constructed by multiplex PCR with expected sizes of 126-400 bp labeled by different fluorescence molecules (DYS715, DYS709, DYS716, DYS713 and DYS607 labeled by FAM; DYS718, DYS723, DYS708 and DYS714 labeled by JOE; DYS712, DYS717, DYS721 and DYS605 labeled by TAMRA; and DYS719, DYS726, DYS598 and DYS722 labeled by ROX). The system was extensively tested for sensitivity, male specificity, species specificity, mixture, population genetics and mutation rates following the Scientific Working Group on DNA Analysis Methods (SWGDAM) guidelines. The genetic data were obtained from eight populations with a total of 1260 individuals. Our results showed that all the 17 Y-STRs are human- and male-specific and include only one copy of the Y-chromosome. The 17 Y-STR system detects 143 alleles and has a high discrimination power (0.996031746). Mutation rates were different among the 17 Y-STRs, ranging from 0.30% to 3.03%. In conclusion, our study provides a robust, sensitive and cost-effective genotyping method for human identification, which will be beneficial for narrowing the search scope when applied to genealogy searching with the Y-STR DNA databank.

genetics↗

bioRxiv2020

The role of Fnr paralogs for controlling anaerobic metabolism in diazotroph Paenibacillus polymyxa WLY78

Fnr is a transcriptional regulator that controls the expression of a variety of genes in response to oxygen limitation in bacteria. Genome sequencing revealed four genes (fnr1, fnr3, fnr5 and fnr7) coding for Fnr proteins in Paenibacillus polymyxa WLY78. Fnr1 and Fnr3 showed more similarity to each other than to Fnr5 and Fnr7. Also, Fnr1 and Fnr3 exhibited high similarity with Bacillus cereus Fnr and Bacillus subtilis Fnr in sequence and structures. Deletion analysis showed that the four fnr genes, especially fnr1 and fnr3, have significant impacts on the growth and nitrogenase activity. Single deletion of fnr1 or fnr3 led to 50% reduction in nitrogenase activity and double deletion of fnr1 and fnr3 resulted to 90% reduction in activity. Both of the aerobically purified His-tagged Fnr1 and His-tagged Fnr3 in Escherichia coli could bind to the specific DNA promoter. Genome-wide transcription analysis showed that Fnr1 and Fnr3 indirectly activated expression of nif (nitrogen fixation) genes and Fe transport genes under anaerobic condition. Fnr1 and Fnr3 inhibited expression of the genes involved in aerobic respiratory chain and activated expression of genes responsible for anaerobic electron acceptor genes. IMPORTANCEPaenibacillus is a genus of Gram-positive, facultative anaerobic and endospore-forming bacteria. The members of nitrogen-fixing Paenibacillus have great potential use as a bacterial fertilizer in agriculture. However, the functions of fnr gene(s) in nitrogen fixation and other metabolisms in Paenibacillus spp. are not known. Here, we revealed that copy numbers vary largely among different Paenibacillus species and strains. Deletion and complementation analysis demonstrated that fnr1 and fnr3 have significant impacts on the growth and nitrogenase activity. Both of the aerobically purified His-tagged Fnr1 and His-tagged Fnr3 purified in Escherichia coli could bind to the specific DNA promoter as Bacillus cereus Fnr did. Fnr1 and Fnr3 indirectly activated nif expression under anaerobic condition. Fnr1 and Fnr3 directly or indirectly activated or inhibited expression of many important genes involved in respiration, energy metabolism, Fe uptake and potentially specific electron transport for nitrogenase under anaerobic condition. This study not only reveals the roles of fnr genes in nitrogen fixation and anaerobic metabolism, but also provides insight into the evolution and regulatory mechanisms of fnr in Paenibacillus.

microbiology↗

bioRxiv2019

ORF Capture-Seq: a versatile method for targeted identification of full-length isoforms

Most human protein-coding genes are expressed as multiple isoforms. This in turn greatly expands the functional repertoire of the encoded proteome. While at least one reliable open reading frame (ORF) model has been assigned for every gene, the majority of alternative isoforms remains uncharacterized experimentally. This is primarily due to: i) vast differences of overall levels between different isoforms expressed from common genes, and ii) the difficulty of obtaining contiguous full-length ORF sequences. Here, we present ORF Capture-Seq (OCS), a flexible and cost-effective method that addresses both challenges for targeted full-length isoform sequencing applications using collections of cloned ORFs as probes. As proof-of-concept, we show that an OCS pipeline focused on genes coding for transcription factors increases isoform detection by an order of magnitude, compared to unenriched sample. In short, OCS enables rapid discovery of isoforms from custom-selected genes and will allow mapping of the full set of human isoforms at reasonable cost.

genomics↗

bioRxiv2019

Towards an ″assayome″ for binary interactome mapping

Complementary assays are required to comprehensively map complex biological entities such as genomes, proteomes and interactome networks. However, how various assays can be optimally combined to approach completeness while maintaining high precision often remains unclear. Here, we propose the concept of an "assayome" for binary protein-protein interaction (PPI) mapping as an optimal combination of assays and/or assay versions that maximizes detection of true positive interactions, while avoiding detection of random protein pairs. We engineered a novel NanoLuc two-hybrid (N2H) system that integrates 12 different versions differing by protein expression systems and tagging configurations. The resulting N2H assayome recovers as many PPIs as 10 distinct assays combined. Thus, to further improve PPI mapping, developing alternative versions of existing assays might be as productive as designing completely new assays. Our assayome concept should be applicable to systematic mapping of other biological landscapes.

systems biology↗

bioRxiv2018

Network-based prediction of protein interactions

As biological function emerges through interactions between a cells molecular constituents, understanding cellular mechanisms requires us to catalogue all physical interactions between proteins [1-4]. Despite spectacular advances in high-throughput mapping, the number of missing human protein-protein interactions (PPIs) continues to exceed the experimentally documented interactions [5, 6]. Computational tools that exploit structural, sequence or network topology information are increasingly used to fill in the gap, using the patterns of the already known interactome to predict undetected, yet biologically relevant interactions [7-9]. Such network-based link prediction tools rely on the Triadic Closure Principle (TCP) [10-12], stating that two proteins likely interact if they share multiple interaction partners. TCP is rooted in social network analysis, namely the observation that the more common friends two individuals have, the more likely that they know each other [13, 14]. Here, we offer direct empirical evidence across multiple datasets and organisms that, despite its dominant use in biological link prediction, TCP is not valid for most protein pairs. We show that this failure is fundamental - TCP violates both structural constraints and evolutionary processes. This understanding allows us to propose a link prediction principle, consistent with both structural and evo-lutionary arguments, that predicts yet uncovered protein interactions based on paths of length three (L3). A systematic computational cross-validation shows that the L3 principle significantly outperforms existing link prediction methods. To experimentally test the L3 predictions, we perform both large-scale high-throughput and pairwise tests, finding that the predicted links test positively at the same rate as previously known interactions, suggesting that most (if not all) predicted interactions are real. Combining L3 predictions with experimen-tal tests provided new interaction partners of FAM161A, a protein linked to retinitis pigmentosa, offering novel insights into the molecular mechanisms that lead to the disease. Because L3 is rooted in a fundamental biological principle, we expect it to have a broad applicability, enabling us to better understand the emergence of biological function under both healthy and pathological conditions.\n\nSummaryWe unveil a fundamental organizing principle of biological networks and demonstrate its predictive power for uncovering novel protein interactions.

systems biology↗