A novel approach to full-mouth rehabilitation of dentinogenesis imperfecta type II: Case series with review of literature.

RATIONALE: Dentinogenesis imperfecta (DI) is an autosomal-dominant disorder. The most common clinical manifestations, including obliterated tooth tissues and severe tooth wear, usually lead to tooth extractions. It remains a great challenge for dentists to preserve the residual tooth tissue and establish the esthetics and occlusion of dentitions. PATIENTS CONCERNS: 25-year-old twin sisters, who had suffered from dentinogenesis imperfecta type II for more than 10 years, presented with continuous tooth wear and discomfort from wearing a removable partial denture for more than 3 years. DIAGNOSIS: Intraoral examination showed extensive tooth wear with enamel exfoliation and typical amber-brown color with an opalescent discoloration. Their panoramic radiographs revealed completely obliterated tooth tissues and severe tooth wear. INTERVENTIONS AND OUTCOMES: The dentitions were restored with post-and-core crowns and pin lays after preparing root post paths and pin holes guided by computer-aided design/computer-aided manufacturing (CAD/CAM) procedures, resulting in a successful repair. LESSONS: Severe tooth wear and tooth tissue obliteration are typical clinical manifestations in DI-affected dentitions, increasing the complexity and difficulty in dental restorations. Early diagnosis and appropriate treatments are essential to achieve a favorable prognosis. CAD/CAM procedures, permitting accurate and effective treatment, possess promising potential in the treatment of DI-affected dentitions.

Intermolecular Formal Cycloaddition of Indoles with Bicyclo[1.1.0]butanes by Lewis Acid Catalysis.

Herein, we develop a new approach to directly access architecturally complex polycyclic indolines from readily available indoles and bicyclo[1.1.0]butanes (BCBs) through formal cycloaddition promoted by commercially available Lewis acids. The reaction proceeded through a stepwise pathway involving a nucleophilic addition of indoles to BCBs followed by an intramolecular Mannich reaction to form rigid indoline-fused polycyclic structures, which resemble polycyclic indole alkaloids. This new reaction tolerated a wide range of indoles and BCBs, thereby allowing the one-step construction of various rigid indoline polycycles containing up to four contiguous quaternary carbon centers.

Relevant factors of posterior mandible lingual plate perforation during immediate implant placement: a virtual implant placement study using CBCT.

BACKGROUND: To explore the influence of cross-sectional type and morphological parameters at the mandibular molar sites on lingual plate perforation (LPP) during the immediate implant placement (IIP). METHODS: 181 implants were virtually placed in the mandibular molar sites on the cone beam computed tomography (CBCT). Each cross-section of the implantation site was divided into the Undercut (U)/Parallel (P)/Convex (C) types. Morphologically relevant parameters were measured on the cross-sections, including width of the upper end (Wb), width of the lower end (Wc), vertical height (V), angle between the natural crown axis and the alveolar bone axis (∠ß), LC depth (LCD), LC height, and angle between the horizontal line and the line connecting the most prominent point and the most concave point of lingual plate (∠α). Besides, the distance from the end of the virtual implant and the lingual bone plate of the cross-section (DIL) was calculated. Relationships between all the morphologically relevant parameters and the DIL were further analyzed. RESULTS: A total of 77 (42.5%) cross-sections were classified as U-type, which was the most common one, accounting for 63% of the second molar regions. All LPP cases and most of the nearly LPP (87.9%) cases occurred at the U-type cross-sections, and the relationship between the DIL and the morphological parameters can be expressed by a multivariate linear equation. CONCLUSIONS: The occurrence rate of U-type cross-sections in the second molar region was very high, and the risk of LPP should be considered during IIP. Except for the U-type, significant large LCD, small Wc, and large âˆ ß were the important relevant factors. CBCT and multivariate linear equations could help to assess the LPP risk and provide a reference for implant placement design pre-surgery.

Long-term LDR exposure may induce cognitive impairments: A possible association through targeting gut microbiota-gut-brain axis.

Environmental and occupational low-dose radiation (LDR) exposure may be harmful for health but the previous reports regarding effect of LDR on cognition are contradictory. Here we investigated the effect of long-term LDR exposure on cognition. In this study, male Balb/c mice' cognitive functions were tested at 15 weeks after being exposed to 0.5 Gy LDR in 10 fractions at each dose of 0.05 Gy. The results demonstrated that long-term LDR exposure increases escape latency and the time spent in finding exits in mice compared with non LDR exposure. Meanwhile, the inflammation-related proteins including NFκB and p38 also increased. Lipopolysaccharide (LPS) increased and short-chain fatty acid (SCFA) levels decreased following long term LDR exposure. Treatment with microbiota-derived LPS and SCFAs reversed these effects in mice. Furthermore, the gut barrier integrity was damaged in a time-dependent manner with the decreased expression of intestinal epithelial-related biomarkers such as ZO-1 and occludin. Mechanistically, long after exposure to LDR, increased LPS levels may cause cognitive impairment through the regulation of Akt/mTOR signaling in the mouse hippocampus. These findings provide new insight into the clinical applications of LDR and suggest that the gut microbiota-plasma LPS and SCFAs-brain axis may underlie long-term LDR-induced cognition effects.

Protein function annotation based on heterogeneous biological networks.

BACKGROUND: Accurate annotation of protein function is the key to understanding life at the molecular level and has great implications for biomedicine and pharmaceuticals. The rapid developments of high-throughput technologies have generated huge amounts of protein-protein interaction (PPI) data, which prompts the emergence of computational methods to determine protein function. Plagued by errors and noises hidden in PPI data, these computational methods have undertaken to focus on the prediction of functions by integrating the topology of protein interaction networks and multi-source biological data. Despite effective improvement of these computational methods, it is still challenging to build a suitable network model for integrating multiplex biological data. RESULTS: In this paper, we constructed a heterogeneous biological network by initially integrating original protein interaction networks, protein-domain association data and protein complexes. To prove the effectiveness of the heterogeneous biological network, we applied the propagation algorithm on this network, and proposed a novel iterative model, named Propagate on Heterogeneous Biological Networks (PHN) to score and rank functions in descending order from all functional partners, Finally, we picked out top L of these predicted functions as candidates to annotate the target protein. Our comprehensive experimental results demonstrated that PHN outperformed seven other competing approaches using cross-validation. Experimental results indicated that PHN performs significantly better than competing methods and improves the Area Under the Receiver-Operating Curve (AUROC) in Biological Process (BP), Molecular Function (MF) and Cellular Components (CC) by no less than 33%, 15% and 28%, respectively. CONCLUSIONS: We demonstrated that integrating multi-source data into a heterogeneous biological network can preserve the complex relationship among multiplex biological data and improve the prediction accuracy of protein function by getting rid of the constraints of errors in PPI networks effectively. PHN, our proposed method, is effective for protein function prediction.

Comparing the efficacy of different dexamethasone regimens for maintenance treatment of multiple myeloma in standard-risk patients non-eligible for transplantation.

BACKGROUND: Multiple myeloma (MM) is a plasma cell malignancy, while MM outcomes have significantly improved due to novel agents and combinations, MM remains an incurable disease. The key goal of treatment in MM is to achieve a maximal response and the subsequent consolidation of response after initial therapy. Many studies analyzed an improved progression-free survival (PFS) following lenalidomide alone maintenance versus placebo or observation after autologous stem cell transplant (ASCT) in patients with NDMM. In the SWOG S0777 clinical trial, patients newly diagnosed with MM (NDMM) without ASCT received lenalidomide plus low-dose dexamethasone (DXM) maintenance until progressive disease, where PFS and overall survival (OS) were significantly improved. In the present study, we assessed the efficacy and toxicity of the different doses of DXM combined with lenalidomide for maintenance treatment of NDMM for transplant noneligible patients in the standard-risk group. AIM: To investigate the efficacy and adverse effects of different administration modes of DXM combined with lenalidomide for maintenance treatment of MM in standard-risk patients ineligible for transplantation. METHODS: A total of 96 MM patients were enrolled in this study, among whom 48 patients received maintenance treatment that consisted of oral administration of 25 milligrams (mg) of lenalidomide from days 1-21 and 40 mg of DXM on days 1, 8, 15, and 22 (DXM 40 mg group), repeated every 4 wk. Another group was treated with oral administration of 25 mg of lenalidomide from days 1-21 and 20 mg of DXM on days 1-2, 8-9, 15-16, and 22-23 (DXM 20 mg group), which was also repeated every 4 wk. RESULTS: The median PFS was 37.25 mo in the DXM 40.00 mg group and 38.17 mo in the DXM 20 mg group (P = 0.171). The median OS was 50.78 mo in the DXM 40 mg group and 51.69 mo in the DXM 20 mg group (P = 0.171). Fourteen patients in the DXM 40 mg group and 6 patients in the DXM 20 mg group suffered from adverse gastrointestinal reactions after the oral administration of the DXM tablet (P = 0.044). Ten patients suffered from abnormal glucose tolerance (GTA), impaired fasting glucose (IFG), or diabetes mellitus in the DXM 40 mg group during our observation time compared to 19 patients with GTA, IFG, or DM in the DXM 20 mg group (P = 0.033). Abnormal ß-crosslaps or higher were found in 5 patients in the DXM 40 mg group and 12 patients in the DXM 20 mg group (P = 0.049). Insomnia or an increase in insomnia compared to the previous condition was evident in 2 patients in the DXM 40 mg group after maintenance treatment for more than 6 mo compared to 11 patients in the DXM 20 mg group (P = 0.017). CONCLUSION: The DXM 40 mg group exhibited efficacy similar to that of the DXM 20 mg group. However, the DXM 40 mg group had significantly decreased toxicity compared with the DXM 20 mg group in the long term.

The mitogenome of a medically important paper wasp, Polistes hebraeus (Hymenoptera, Vespidae).

Polistes hebraeus (Smith) is a common paper wasp species of subfamily Polistinae. The clinical potential allergic reaction after stings and valuable venom components of P. hebraeus make it a medical importance species. Here, the mitochondrial genome of P. hebraeus was analyzed. The mitogenome was 19,262 bp in length including 22 transfer RNAs (tRNA) and 2 ribosomal RNAs (rRNA). The protein coding gene (PCGs) number and order are consistent with existing records of Polistidae. The nucleotide composition is AT: 84.5% and CG: 15.5% which are largely similar with other complete Polistidae mitogenome (15.27 ∼ 19.16%). The available mitogenome resources of Polistidae species and P. hebraeus were used to construct the phylogenetic tree. It showed that the genus Polistes could clearly seperated from Parapolybia, and the relationship in Polistes was consistent with previous studies. This mitogenome resource can contribute to further phylogenetic and taxonomic study on paper wasp.

A tensor-based bi-random walks model for protein function prediction.

BACKGROUND: The accurate characterization of protein functions is critical to understanding life at the molecular level and has a huge impact on biomedicine and pharmaceuticals. Computationally predicting protein function has been studied in the past decades. Plagued by noise and errors in protein-protein interaction (PPI) networks, researchers have undertaken to focus on the fusion of multi-omics data in recent years. A data model that appropriately integrates network topologies with biological data and preserves their intrinsic characteristics is still a bottleneck and an aspirational goal for protein function prediction. RESULTS: In this paper, we propose the RWRT (Random Walks with Restart on Tensor) method to accomplish protein function prediction by applying bi-random walks on the tensor. RWRT firstly constructs a functional similarity tensor by combining protein interaction networks with multi-omics data derived from domain annotation and protein complex information. After this, RWRT extends the bi-random walks algorithm from a two-dimensional matrix to the tensor for scoring functional similarity between proteins. Finally, RWRT filters out possible pretenders based on the concept of cohesiveness coefficient and annotates target proteins with functions of the remaining functional partners. Experimental results indicate that RWRT performs significantly better than the state-of-the-art methods and improves the area under the receiver-operating curve (AUROC) by no less than 18%. CONCLUSIONS: The functional similarity tensor offers us an alternative, in that it is a collection of networks sharing the same nodes; however, the edges belong to different categories or represent interactions of different nature. We demonstrate that the tensor-based random walk model can not only discover more partners with similar functions but also free from the constraints of errors in protein interaction networks effectively. We believe that the performance of function prediction depends greatly on whether we can extract and exploit proper functional similarity information on protein correlations.

LINCS gene expression signature analysis revealed bosutinib as a radiosensitizer of breast cancer cells by targeting eIF4G1.

Radioresistance is the predominant cause for radiotherapy failure and disease progression, resulting in increased breast cancer­associated mortality. Using gene expression signature analysis of the Library of Integrated Network­Based Cellular Signatures (LINCS) and Gene Expression Omnibus (GEO), the aim of the present study was to systematically identify potential candidate radiosensitizers from known drugs. The similarity of integrated gene expression signatures between irradiated eukaryotic translation initiation factor 4 Î³ 1 (eIF4G1)­silenced breast cancer cells and known drugs was measured using enrichment scores (ES). Drugs with positive ES were selected as potential radiosensitizers. The radiosensitizing effects of the candidate drugs were analyzed in breast cancer cell lines (MCF­7, MX­1 and MDA­MB­231) using CCK­8 and colony formation assays following exposure to ionizing radiation. Cell apoptosis was measured using flow cytometry. The expression levels of eIF4G1 and DNA damage response (DDR) proteins were analyzed by western blotting. Bosutinib was identified as a promising radiosensitizer, as its administration markedly reduced the dosage required both for the drug and for ionizing radiation, which may be associated with fewer treatment­associated adverse reactions. Moreover, combined treatment of ionizing radiation and bosutinib significantly increased cell killing in all three cell lines, compared with ionizing radiation or bosutinib alone. Among the three cell lines, MX­1 cells were identified as the most sensitive to both ionizing radiation and bosutinib. Bosutinib markedly downregulated the expression of eIF4G1 in a dose­dependent manner and also reduced the expression of DDR proteins (including ATM, XRCC4, ATRIP, and GADD45A). Moreover, eIF4G1 was identified as a key target of bosutinib that may regulate DNA damage induced by ionizing radiation. Thus, bosutinib may serve as a potential candidate radiosensitizer for breast cancer therapy.

The p53/RMRP/miR122 signaling loop promotes epithelial-mesenchymal transition during the development of silica-induced lung fibrosis by activating the notch pathway.

BACKGROUND: Understanding the roles of long noncoding RNAs (lncRNAs) in EMT would help with establishing novel avenues for further uncovering the mechanisms of lung fibrosis and identifying preventative and therapeutic targets. This study aimed to identify silica-induced specific lncRNAs and investigate the feedback loop regulation among their upstream and downstream genes. METHODS AND MATERIALS: A microarray assay, quantitative real-time polymerase chain reaction and Western blot analysis dual-luciferase reporter gene activity and chromatin immunoprecipitation assays were used. Moreover, a silica-induced lung fibrosis mouse model was used to verify the roles of the lncRNAs. RESULTS: Following silica exposure, both RNA component of mitochondrial RNA processing endoribonuclease (RMRP) and p53 were significantly upregulated during the EMT. The upregulation of p53 upon silica exposure activated RMRP expression, which promoted the EMT. When RMRP is overexpressed, additional RMRP acts as a sponge to bind to miR122, thus decreasing miR122 levels. Using microarrays, miR122 was identified as a potential upstream regulator of p53. This relationship was also verified using the dual-luciferase reporter gene. Hence, decreased miR122 levels result in an increase in p53 activity. More importantly, RMRP promotes the transcription of Notch 1, which, in turn, results in Notch pathway activation. We show that the p53/RMRP/miR122 pathway creates a positive feedback loop that promotes EMT progress by activating the Notch signaling pathway. CONCLUSION: Our data indicated that p53/RMRP/miR122 feedback loop might contribute to the EMT development by activating Notch pathway, which provides new sight into understanding of the complex network regulating silica-induced lung fibrosis.

NPF:network propagation for protein function prediction.

BACKGROUND: The accurate annotation of protein functions is of great significance in elucidating the phenomena of life, treating disease and developing new medicines. Various methods have been developed to facilitate the prediction of these functions by combining protein interaction networks (PINs) with multi-omics data. However, it is still challenging to make full use of multiple biological to improve the performance of functions annotation. RESULTS: We presented NPF (Network Propagation for Functions prediction), an integrative protein function predicting framework assisted by network propagation and functional module detection, for discovering interacting partners with similar functions to target proteins. NPF leverages knowledge of the protein interaction network architecture and multi-omics data, such as domain annotation and protein complex information, to augment protein-protein functional similarity in a propagation manner. We have verified the great potential of NPF for accurately inferring protein functions. According to the comprehensive evaluation of NPF, it delivered a better performance than other competing methods in terms of leave-one-out cross-validation and ten-fold cross validation. CONCLUSIONS: We demonstrated that network propagation, together with multi-omics data, can both discover more partners with similar function, and is unconstricted by the "small-world" feature of protein interaction networks. We conclude that the performance of function prediction depends greatly on whether we can extract and exploit proper functional information of similarity from protein correlations.

TIP60 K430 SUMOylation attenuates its interaction with DNA-PKcs in S-phase cells: Facilitating homologous recombination and emerging target for cancer therapy.

Nonhomologous end joining (NHEJ) and homologous recombination (HR) are major repair pathways of DNA double-strand breaks (DSBs). The pathway choice of HR and NHEJ is tightly regulated in cellular response to DNA damage. Here, we demonstrate that the interaction of TIP60 with DNA-PKcs is attenuated specifically in S phase, which facilitates HR pathway activation. SUMO2 modification of TIP60 K430 mediated by PISA4 E3 ligase blocks its interaction with DNA-PKcs, whereas TIP60 K430R mutation recovers its interaction with DNA-PKcs, which results in abnormally increased phosphorylation of DNA-PKcs S2056 in S phase and marked inhibition of HR efficiency, but barely affects NHEJ activity. TIP60 K430R mutant cancer cells are more sensitive to radiation and PARP inhibitors in cancer cell killing and tumor growth inhibition. Collectively, coordinated regulation of TIP60 and DNA-PKcs facilitates HR pathway choice in S-phase cells. TIP60 K430R mutant is a potential target of radiation and PARPi cancer therapy.

Effects of low dose radiation on immune cells subsets and cytokines in mice.

Whole-body exposure to low-dose radiation due to diagnostic imaging procedures, occupational hazards and radiation accidents is a source of concern. In this study, we analyzed the effects of single and long-term low-dose irradiation on the immune system. Male Balb/c mice received a single whole-body dose of irradiation (0.01, 0.05, 0.2, 0.5 or 1 Gy). For long-term irradiation, mice were irradiated 10 times (total dose of 0.2, 0.5 or 1 Gy) over a period of 6 weeks. Two days after single or long-term irradiation, the numbers of splenic macrophages, natural killer cells and dendritic cells were reduced, and the spleen organ coefficient was decreased. At 2 Days after long-term low-dose irradiation, the number of white blood cells in the peripheral blood of the mice decreased. Between 7 and 14 Days after long-term low-dose irradiation, the number of immune cells in the thymus and spleen began to increase and then stabilized. Th1/Th2 cytokines and reactive oxygen species-related proteins first decreased and then increased to a plateau. Our results show a significant difference in the effects of single and long-term low-dose irradiation on the immune system.

Simulation of Land Use Change and Ecosystem Service Value Dynamics under Ecological Constraints in Anhui Province, China.

Land use change has a significant impact on the structure and function of ecosystems, and the transformation of ecosystems affects the mode and efficiency of land use, which reflects a mutual interaction relationship. The prediction and simulation of future land use change can enhance the foresight of land use planning, which is of great significance to regional sustainable development. In this study, future land use changes are characterized under an ecological optimization scenario based on the grey prediction (1,1) model (GM) and a future land use simulation (FLUS) model. In addition, the ecosystem service value (ESV) of Anhui Province from 1995 to 2030 were estimated based on the revised estimation model. The results indicate the following details: (1) the FLUS model was used to simulate the land use layout of Anhui Province in 2018, where the overall accuracy of the simulation results is high, indicating that the FLUS model is applicable for simulating future land use change; (2) the spatial layout of land use types in Anhui Province is stable and the cultivated land has the highest proportion. The most significant characteristic of future land use change is that the area of cultivated land continues to decrease while the area of built-up land continues to expand; and (3) the ESV of Anhui Province is predicted to increase in the future. The regulating service is the largest ESV contributor, and water area is the land use type with the highest proportion of ESV. These findings provide reference for the formulation of sustainable development policies of the regional ecological environment.

A Novel Computational Approach for Identifying Essential Proteins From Multiplex Biological Networks.

The identification of essential proteins can help in understanding the minimum requirements for cell survival and development. Ever-increasing amounts of high-throughput data provide us with opportunities to detect essential proteins from protein interaction networks (PINs). Existing network-based approaches are limited by the poor quality of the underlying PIN data, which exhibits high rates of false positive and false negative results. To overcome this problem, researchers have focused on the prediction of essential proteins by combining PINs with other biological data, which has led to the emergence of various interactions between proteins. It remains challenging, however, to use aggregated multiplex interactions within a single analysis framework to identify essential proteins. In this study, we created a multiplex biological network (MON) by initially integrating PINs, protein domains, and gene expression profiles. Next, we proposed a new approach to discover essential proteins by extending the random walk with restart algorithm to the tensor, which provides a data model representation of the MON. In contrast to existing approaches, the proposed MON approach considers for the importance of nodes and the different types of interactions between proteins during the iteration. MON was implemented to identify essential proteins within two yeast PINs. Our comprehensive experimental results demonstrated that MON outperformed 11 other state-of-the-art approaches in terms of precision-recall curve, jackknife curve, and other criteria.

HUWE1-dependent DNA-PKcs neddylation modulates its autophosphorylation in DNA damage response.

DNA-dependent protein kinase catalytic subunit (DNA-PKcs) is the core component of DNA-PK complex in the non-homologous end-joining (NHEJ) repair of DNA double-strand breaks, and its activity is strictly controlled by DNA-PKcs phosphorylation. The ubiquitin-like protein, NEDD8 is involved in regulation of DNA damage response, but it remains mysterious whether and how NEDD8-related neddylation affects DNA-PKcs and the NHEJ process. Here, we show that DNA-PKcs is poly-neddylated at its kinase domain. The neddylation E2-conjugating enzyme UBE2M and E3 ligase HUWE1 (HECT, UBA, and WWE domain containing E3 ubiquitin protein ligase 1) are responsible for the DNA-PKcs neddylation. Moreover, inhibition of HUWE1-dependent DNA-PKcs neddylation impairs DNA-PKcs autophosphorylation at Ser2056. Finally, depletion of HUWE1-dependent DNA-PKcs neddylation reduces the efficiency of NHEJ. These studies provide insights how neddylation modulates the activity of NHEJ core complex.

Self-Assembled Multivalent Aptamer Nanoparticles with Potential CAR-like Characteristics Could Activate T Cells and Inhibit Melanoma Growth.

In this study, the CAR-like multivalent aptamer nanoparticles (X-polymers) were assembled with the dimer of murine CD28 RNA aptamer (CD28Apt7), the tetramer of CTLA-4 (cytotoxic T-lymphocyte-associated protein 4) RNA aptamer (Del60), and a folic acid labeled ssDNA fragment in a stable nucleic acid three-way junction scaffold (3WJ). Results showed that the X-polymers could recognize both the mCD28 and mCTLA-4 molecules. Confocal imaging and flow cytometry assays showed that the X-polymers could target both T cells and B16 cells in vitro. With the first costimulatory signals provided by the CD3 antibodies, the X-polymers could increase T cell proliferation and reverse the inhibitory effect of interleukin-2 (IL-2) secreting caused by exogenous B7.1 molecules on T cells in vitro. Results of our study also showed that X-polymers could inhibit mouse melanoma B16 cell growth both in vitro and in vivo. Our study demonstrated for the first time that the multivalent aptamer nanoparticle-activated T cells could fulfill the function of CAR-T, which promised a novel approach to developing a multi-functional design of aptamer drugs with potential CAR-like characteristics to enhance the safety of CAR-T cell immunotherapy.

A novel method to predict essential proteins based on tensor and HITS algorithm.

BACKGROUND: Essential proteins are an important part of the cell and closely related to the life activities of the cell. Hitherto, Protein-Protein Interaction (PPI) networks have been adopted by many computational methods to predict essential proteins. Most of the current approaches focus mainly on the topological structure of PPI networks. However, those methods relying solely on the PPI network have low detection accuracy for essential proteins. Therefore, it is necessary to integrate the PPI network with other biological information to identify essential proteins. RESULTS: In this paper, we proposed a novel random walk method for identifying essential proteins, called HEPT. A three-dimensional tensor is constructed first by combining the PPI network of Saccharomyces cerevisiae with multiple biological data such as gene ontology annotations and protein domains. Then, based on the newly constructed tensor, we extended the Hyperlink-Induced Topic Search (HITS) algorithm from a two-dimensional to a three-dimensional tensor model that can be utilized to infer essential proteins. Different from existing state-of-the-art methods, the importance of proteins and the types of interactions will both contribute to the essential protein prediction. To evaluate the performance of our newly proposed HEPT method, proteins are ranked in the descending order based on their ranking scores computed by our method and other competitive methods. After that, a certain number of the ranked proteins are selected as candidates for essential proteins. According to the list of known essential proteins, the number of true essential proteins is used to judge the performance of each method. Experimental results show that our method can achieve better prediction performance in comparison with other nine state-of-the-art methods in identifying essential proteins. CONCLUSIONS: Through analysis and experimental results, it is obvious that HEPT can be used to effectively improve the prediction accuracy of essential proteins by the use of HITS algorithm and the combination of network topology with gene ontology annotations and protein domains, which provides a new insight into multi-data source fusion.