BackgroundChronic disease patients often face multiple challenges from difficult comorbidities. Smartphone health technology can be used to help them manage their conditions only if they accept and use the technology.ObjectiveThe aim of this study was to develop and test a theoretical model to predict and explain the factors influencing patients’ acceptance of smartphone health technology for chronic disease management.MethodsMultiple theories and factors that may influence patients’ acceptance of smartphone health technology have been reviewed. A hybrid theoretical model was built based on the technology acceptance model, dual-factor model, health belief model, and the factors identified from interviews that might influence patients’ acceptance of smartphone health technology for chronic disease management. Data were collected from patient questionnaire surveys and computer log records about 157 hypertensive patients’ actual use of a smartphone health app. The partial least square method was used to test the theoretical model.ResultsThe model accounted for .412 of the variance in patients’ intention to adopt the smartphone health technology. Intention to use accounted for .111 of the variance in actual use and had a significant weak relationship with the latter. Perceived ease of use was affected by patients’ smartphone usage experience, relationship with doctor, and self-efficacy. Although without a significant effect on intention to use, perceived ease of use had a significant positive influence on perceived usefulness. Relationship with doctor and perceived health threat had significant positive effects on perceived usefulness, countering the negative influence of resistance to change. Perceived usefulness, perceived health threat, and resistance to change significantly predicted patients’ intentions to use the technology. Age and gender had no significant influence on patients’ acceptance of smartphone technology. The study also confirmed the positive relationship between intention to use and actual use of smartphone health apps for chronic disease management.ConclusionsThis study developed a theoretical model to predict patients’ acceptance of smartphone health technology for chronic disease management. Although resistance to change is a significant barrier to technology acceptance, careful management of doctor-patient relationship, and raising patients’ awareness of the negative effect of chronic disease can negate the effect of resistance and encourage acceptance and use of smartphone health technology to support chronic disease management for patients in the community.

As one of the most important and universal posttranslational modifications (PTMs) of proteins, S-nitrosylation (SNO) plays crucial roles in a variety of biological processes, including the regulation of cellular dynamics and many signaling events. Knowledge of SNO sites in proteins is very useful for drug development and basic research as well. Unfortunately, it is both time-consuming and costly to determine the SNO sites purely based on biological experiments. Facing the explosive protein sequence data generated in the post-genomic era, we are challenged to develop automated vehicles for timely and effectively determining the SNO sites for uncharacterized proteins. To address the challenge, a new predictor called iSNO-AAPair was developed by taking into account the coupling effects for all the pairs formed by the nearest residues and the pairs by the next nearest residues along protein chains. The cross-validation results on a state-of-the-art benchmark have shown that the new predictor outperformed the existing predictors. The same was true when tested by the independent proteins whose experimental SNO sites were known. A user-friendly web-server for iSNO-AAPair was established at , by which users can easily obtain their desired results without the need to follow the mathematical equations involved during its development.

Mitochondria play essential roles in cardiac pathophysiology and the murine model has been extensively used to investigate cardiovascular diseases. In the present study, we characterized murine cardiac mitochondria using an LC/MS/MS approach. We extracted and purified cardiac mitochondria; validated their functionality to ensure the final preparation contains necessary components to sustain their normal function; and subjected these validated organelles to LC/MS/MS-based protein identification. A total of 940 distinct proteins were identified from murine cardiac mitochondria, among which, 480 proteins were not previously identified by major proteomic profiling studies. The 940 proteins consist of functional clusters known to support oxidative phosphorylation, metabolism, and biogenesis. In addition, there are several other clusters, including proteolysis, protein folding, and reduction/oxidation signaling, which ostensibly represent previously under-appreciated tasks of cardiac mitochondria. Moreover, many identified proteins were found to occupy other subcellular locations, including cytoplasm, ER, and golgi, in addition to their presence in the mitochondria. These results provide a comprehensive picture of the murine cardiac mitochondrial proteome and underscore tissue- and species-specification. Moreover, the use of functionally intact mitochondria insures that the proteomic observations in this organelle are relevant to its normal biology and facilitates decoding the interplay between mitochondria and other organelles.

Mitochondrial functions are dynamically regulated in the heart. In particular, protein phosphorylation has been shown to be a key mechanism modulating mitochondrial function in diverse cardiovascular phenotypes. However, site-specific phosphorylation information remains scarce for this organ. Accordingly, we performed a comprehensive characterization of murine cardiac mitochondrial phosphoproteome in the context of mitochondrial functional pathways. A platform using the complementary fragmentation technologies of collision-induced dissociation (CID) and electron transfer dissociation (ETD) demonstrated successful identification of a total of 236 phosphorylation sites in the murine heart; 210 of these sites were novel. These 236 sites were mapped to 181 phosphoproteins and 203 phosphopeptides. Among those identified, 45 phosphorylation sites were captured only by CID, whereas 185 phosphorylation sites, including a novel modification on ubiquinol-cytochrome c reductase protein 1 (Ser-212), were identified only by ETD, underscoring the advantage of a combined CID and ETD approach. The biological significance of the cardiac mitochondrial phosphoproteome was evaluated. Our investigations illustrated key regulatory sites in murine cardiac mitochondrial pathways as targets of phosphorylation regulation, including components of the electron transport chain (ETC) complexes and enzymes involved in metabolic pathways (e.g. tricarboxylic acid cycle). Furthermore, calcium overload injured cardiac mitochondrial ETC function, whereas enhanced phosphorylation of ETC via application of phosphatase inhibitors restored calcium-attenuated ETC complex I and complex III activities, demonstrating positive regulation of ETC function by phosphorylation. Moreover, in silico analyses of the identified phosphopeptide motifs illuminated the molecular nature of participating kinases, which included several known mitochondrial kinases (e.g. pyruvate dehydrogenase kinase) as well as kinases whose mitochondrial location was not previously appreciated (e.g. Src). In conclusion, the phosphorylation events defined herein advance our understanding of cardiac mitochondrial biology, facilitating the integration of the still fragmentary knowledge about mitochondrial signaling networks, metabolic pathways, and intrinsic mechanisms of functional regulation in the heart. Molecular

Nitrotyrosine is one of the post-translational modifications (PTMs) in proteins that occurs when their tyrosine residue is nitrated. Compared with healthy people, a remarkably increased level of nitrotyrosine is detected in those suffering from rheumatoid arthritis, septic shock, and coeliac disease. Given an uncharacterized protein sequence that contains many tyrosine residues, which one of them can be nitrated and which one cannot? This is a challenging problem, not only directly related to in-depth understanding the PTM’s mechanism but also to the nitrotyrosine-based drug development. Particularly, with the avalanche of protein sequences generated in the postgenomic age, it is highly desired to develop a high throughput tool in this regard. Here, a new predictor called “iNitro-Tyr” was developed by incorporating the position-specific dipeptide propensity into the general pseudo amino acid composition for discriminating the nitrotyrosine sites from non-nitrotyrosine sites in proteins. It was demonstrated via the rigorous jackknife tests that the new predictor not only can yield higher success rate but also is much more stable and less noisy. A web-server for iNitro-Tyr is accessible to the public at http://app.aporc.org/iNitro-Tyr/. For the convenience of most experimental scientists, we have further provided a protocol of step-by-step guide, by which users can easily get their desired results without the need to follow the complicated mathematics that were presented in this paper just for the integrity of its development process. It has not escaped our notice that the approach presented here can be also used to deal with the other PTM sites in proteins.

Lysine succinylation in protein is one type of post-translational modifications (PTMs). Succinylation is associated with some diseases and succinylated sites data just has been found in recent years in experiments. It is highly desired to develop computational methods to identify the candidate proteins and their sites. In view of this, a new predictor called iSuc-PseAAC was proposed by incorporating the peptide position-specific propensity into the general form of pseudo amino acid composition. The accuracy is 79.94%, sensitivity 51.07%, specificity 89.42% and MCC 0.431 in leave-one-out cross validation with support vector machine algorithm. It demonstrated by rigorous leave-one-out on stringent benchmark dataset that the new predictor is quite promising and may become a useful high throughput tool in this area. Meanwhile a user-friendly web-server for iSuc-PseAAC is accessible at http://app.aporc.org/iSuc-PseAAC/ . Users can easily obtain their desired results without the need to understand the complicated mathematical equations presented in this paper just for its integrity.

Computational inference of novel therapeutic values for existing drugs, i.e., drug repositioning, offers the great prospect for faster and low-risk drug development. Previous researches have indicated that chemical structures, target proteins, and side-effects could provide rich information in drug similarity assessment and further disease similarity. However, each single data source is important in its own way and data integration holds the great promise to reposition drug more accurately. Here, we propose a new method for drug repositioning, PreDR (Predict Drug Repositioning), to integrate molecular structure, molecular activity, and phenotype data. Specifically, we characterize drug by profiling in chemical structure, target protein, and side-effects space, and define a kernel function to correlate drugs with diseases. Then we train a support vector machine (SVM) to computationally predict novel drug-disease interactions. PreDR is validated on a well-established drug-disease network with 1,933 interactions among 593 drugs and 313 diseases. By cross-validation, we find that chemical structure, drug target, and side-effects information are all predictive for drug-disease relationships. More experimentally observed drug-disease interactions can be revealed by integrating these three data sources. Comparison with existing methods demonstrates that PreDR is competitive both in accuracy and coverage. Follow-up database search and pathway analysis indicate that our new predictions are worthy of further experimental validation. Particularly several novel predictions are supported by clinical trials databases and this shows the significant prospects of PreDR in future drug treatment. In conclusion, our new method, PreDR, can serve as a useful tool in drug discovery to efficiently identify novel drug-disease interactions. In addition, our heterogeneous data integration framework can be applied to other problems.

BackgroundCervical cancer (CC) is one of the most common cancers among females worldwide. Spindle and kinetochore-associated complex subunit 3 (SKA3), located on chromosome 13q, was identified as a novel gene involved in promoting malignant transformation in cancers. However, the function and underlying mechanisms of SKA3 in CC remain unknown. Using the Oncomine database, we found that expression of SKA3 mRNA is higher in CC tissues than in normal tissues and is linked with poor prognosis.MethodsIn our study, immunohistochemistry showed increased expression of SKA3 in CC tissues. The effect of SKA3 on cell proliferation and migration was evaluated by CCK8, clone formation, Transwell and wound-healing assays in HeLa and SiHa cells with stable SKA3 overexpression and knockdown. In addition, we established a xenograft tumor model in vivo.ResultsSKA3 overexpression promoted cell proliferation and migration and accelerated tumor growth. We further identified that SKA3 is involved in regulating cell cycle progression and the PI3K/Akt signaling pathway via RNA-sequencing (RNA-Seq) and gene set enrichment analyses. Western blotting results revealed that SKA3 overexpression increased levels of p-Akt, cyclin E2, CDK2, cyclin D1, CDK4, E2F1 and p-Rb in HeLa cells. Additionally, the use of an Akt inhibitor (GSK690693) significantly reversed the cell proliferation capacity induced by SKA3 overexpression in HeLa cells.ConclusionsWe suggest that SKA3 overexpression contributes to CC cell growth and migration by promoting cell cycle progression and activating the PI3K–Akt signaling pathway, which may provide potential novel therapeutic targets for CC treatment.Electronic supplementary materialThe online version of this article (10.1186/s12935-018-0670-4) contains supplementary material, which is available to authorized users.