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Subcellular localization attempts to assign proteins to one of the cell compartments that performs specific biological functions. Finding the link between proteins, biological functions, and subcellular localization is an effective way to investigate the general organization of living cells in a systematic manner. However, determining the subcellular localization of proteins by traditional experimental approaches is difficult. Here, protein–protein interaction networks, functional enrichment on gene ontology and pathway, and a set of proteins having confirmed subcellular localization were applied to build prediction models for human protein subcellular localizations. To build an effective predictive model, we employed a variety of robust machine learning algorithms, including Boruta feature selection, minimum redundancy maximum relevance, Monte Carlo feature selection, and LightGBM. Then, the incremental feature selection method with random forest and support vector machine was used to discover the essential features. Furthermore, 38 key features were determined by integrating results of different feature selection methods, which may provide critical insights into the subcellular location of proteins. Their biological functions of subcellular localizations were discussed according to recent publications. In summary, our computational framework can help advance the understanding of subcellular localization prediction techniques and provide a new perspective to investigate the patterns of protein subcellular localization and their biological importance.

COVID-19 is hypothesized to be linked to the host’s excessive inflammatory immunological response to SARS-CoV-2 infection, which is regarded to be a major factor in disease severity and mortality. Numerous immune cells play a key role in immune response regulation, and gene expression analysis in these cells could be a useful method for studying disease states, assessing immunological responses, and detecting biomarkers. Here, we developed a machine learning procedure to find biomarkers that discriminate disease severity in individual immune cells (B cell, CD4+ cell, CD8+ cell, monocyte, and NK cell) using single-cell gene expression profiles of COVID-19. The gene features of each profile were first filtered and ranked using the Boruta feature selection method and mRMR, and the resulting ranked feature lists were then fed into the incremental feature selection method to determine the optimal number of features with decision tree and random forest algorithms. Meanwhile, we extracted the classification rules in each cell type from the optimal decision tree classifiers. The best gene sets discovered in this study were analyzed by GO and KEGG pathway enrichment, and some important biomarkers like TLR2, ITK, CX3CR1, IL1B, and PRDM1 were validated by recent literature. The findings reveal that the optimal gene sets for each cell type can accurately classify COVID-19 disease severity and provide insight into the molecular mechanisms involved in disease progression.

Due to the high homology of the ATP sites of the JAK family, the development of selective inhibitors for a certain JAK isoform is extremely challenging. Our strategy to achieve high selectivity for TYK2 relies on targeting the TYK2 pseudokinase (JH2) domain. Based on the clinical compound BMS-986165, through structure-activity relationship studies, a class of acyl compounds with excellent TYK2 inhibitory activity and selectivity to other subtypes of the JAK family was discovered.

Hepatocellular carcinoma (HCC) is the most common type among primary liver cancers (PLC). With its poor prognosis and survival rate, it is necessary for HCC patients to have a long-term follow-up. We believe that there are currently no relevant reports or literature about nomograms for predicting the cancer-specific mortality of HCC patients. Therefore, the primary goal of this study was to develop and evaluate nomograms to predict cancer-specific mortality and overall mortality. Data of 45,158 cases of HCC patients were collected from the Surveillance, Epidemiology, and End Results (SEER) program database between 2004 and 2013, which were then utilized to develop the nomograms. Finally, the performance of the nomograms was evaluated by the concordance index (C-index) and the area under the time-dependent receiver operating characteristic (ROC) curve (td-AUC). The categories selected to develop a nomogram for predicting cancer-specific mortality included marriage, insurance, radiotherapy, surgery, distant metastasis, lymphatic metastasis, tumor size, grade, sex, and the American Joint Committee on Cancer (AJCC) stage; while the marriage, radiotherapy, surgery, AJCC stage, grade, race, sex, and age were selected to develop a nomogram for predicting overall mortality. The C-indices for predicted 1-, 3-, and 5-year cancer-specific mortality were 0.792, 0.776, and 0.774; the AUC values for 1-, 3-, and 5-year cancer-specific mortality were 0.830, 0.830, and 0.830. The C-indices for predicted 1-, 3-, and 5-year overall mortality were 0.770, 0.755, and 0.752; AUC values for predicted 1-, 3-, and 5-year overall mortality were 0.820, 0.820, and 0.830. The results showed that the nomograms possessed good agreement compared with the observed outcomes. It could provide clinicians with a personalized predicted risk of death information to evaluate the potential changes of the disease-specific condition so that clinicians can adjust therapy options when combined with the actual condition of the patient, which is beneficial to patients.

COVID-19, a severe respiratory disease caused by a new type of coronavirus SARS-CoV-2, has been spreading all over the world. Patients infected with SARS-CoV-2 may have no pathogenic symptoms, i.e., presymptomatic patients and asymptomatic patients. Both patients could further spread the virus to other susceptible people, thereby making the control of COVID-19 difficult. The two major challenges for COVID-19 diagnosis at present are as follows: (1) patients could share similar symptoms with other respiratory infections, and (2) patients may not have any symptoms but could still spread the virus. Therefore, new biomarkers at different omics levels are required for the large-scale screening and diagnosis of COVID-19. Although some initial analyses could identify a group of candidate gene biomarkers for COVID-19, the previous work still could not identify biomarkers capable for clinical use in COVID-19, which requires disease-specific diagnosis compared with other multiple infectious diseases. As an extension of the previous study, optimized machine learning models were applied in the present study to identify some specific qualitative host biomarkers associated with COVID-19 infection on the basis of a publicly released transcriptomic dataset, which included healthy controls and patients with bacterial infection, influenza, COVID-19, and other kinds of coronavirus. This dataset was first analysed by Boruta, Max-Relevance and Min-Redundancy feature selection methods one by one, resulting in a feature list. This list was fed into the incremental feature selection method, incorporating one of the classification algorithms to extract essential biomarkers and build efficient classifiers and classification rules. The capacity of these findings to distinguish COVID-19 with other similar respiratory infectious diseases at the transcriptomic level was also validated, which may improve the efficacy and accuracy of COVID-19 diagnosis.

Transient receptor potential polycystic 2 (TRPP2) exerts vital roles in various types of cancer; however, its underlying mechanisms remain largely unknown. This study is aimed at investigating whether knockdown of TRPP2 affected the AMP-activated protein kinase (AMPK)/acetyl-CoA carboxylase (ACC) signaling pathway and the proliferation of HN-4, cell line originating from human oral and hypopharyngeal squamous cell carcinoma. In addition, the interactions among AMPK/ACC, AMPK/protein kinase RNA-like endoplasmic reticulum kinase (PERK)/eukaryotic initiation factor 2α (eIF2α) and TRPP2/PERK/eIF2α signaling pathways, and their association with cell proliferation were also explored. The results showed that the relative expression levels of phosphorylated (p)-ACC, p-PERK, and p-eIF2α in HN-4 cells were significantly increased following treatment with 5-aminoimidazole-4-carboxamide-1-β-D-ribofuranoside (AICAR) and significantly decreased in cells treated with compound C. Therefore, consistent with previous studies, the AMPK/ACC and AMPK/PERK/eIF2α signaling pathways were upregulated and downregulated following treatment with an AMPK agonist and inhibitor, respectively. Furthermore, TRPP2 knockdown decreased p-PERK and p-eIF2α expression levels and increased those of p-AMPK and p-ACC. Additionally, knockdown of TRPP2 increased HN-4 cell proliferation, while treatment with an AMPK inhibitor or agonist increased or inhibited TRPP2-specific siRNA-mediated cell proliferation, respectively. In conclusion, silencing of TRPP2 expression increased HN-4 cell proliferation via inhibiting the PERK/eIF2α signaling pathway, while the AMPK/ACC signaling pathway was possibly activated by a feedback mechanism to reduce enhanced cell proliferation.

Skeletal muscle is the most abundant and a highly plastic tissue of the mammals, especially when it comes to regenerate after trauma, but there is limited information about the mechanism of muscle repair and its regeneration. In the present study, we found that miR-204 is downregulated after skeletal muscle injury. In vitro experiments showed that over-expression of miR-204 by transfecting with miR-204 mimics suppressed C2C12 cell proliferation, migration, and blocked subsequent differentiation, whereas inhibition of miR-204 by transfecting with miR-204 inhibitor showed the converse effects. Furthermore, through the dual luciferase reporter system, we demonstrated that miR-204 can target the 3’UTR regions of Pax7, IGF1, and Mef2c and inhibit their expression. Taken together, our results suggest that Pax7, IGF1, and Mef2c are the target genes of miR-204 in the process of myoblasts proliferation, cell migration, and differentiation, respectively, and may contribute to mouse skeletal muscle regeneration. Our results may provide new ideas and references for the skeletal muscle study and may also provide therapeutic strategies of skeletal muscle injury.

Among various risk factors for the initiation and progression of cancer, alternative polyadenylation (APA) is a remarkable endogenous contributor that directly triggers the malignant phenotype of cancer cells. APA affects biological processes at a transcriptional level in various ways. As such, APA can be involved in tumorigenesis through gene expression, protein subcellular localization, or transcription splicing pattern. The APA sites and status of different cancer types may have diverse modification patterns and regulatory mechanisms on transcripts. Potential APA sites were screened by applying several machine learning algorithms on a TCGA-APA dataset. First, a powerful feature selection method, minimum redundancy maximum relevancy, was applied on the dataset, resulting in a feature list. Then, the feature list was fed into the incremental feature selection, which incorporated the support vector machine as the classification algorithm, to extract key APA features and build a classifier. The classifier can classify cancer patients into cancer types with perfect performance. The key APA-modified genes had a potential prognosis ability because of their significant power in the survival analysis of TCGA pan-cancer data.

Background. Intervertebral disc degeneration (IDD) at the cervicothoracic junction of spine is clinically relevant, however, little attention had been paid. T2 mapping and magnetic transfer ratio (MTR) are useful magnetic resonance imaging (MRI) techniques to quantitatively evaluate IDD, revealing the biochemical changes within the intervertebral disc. To compare T2 mapping with MTR imaging regarding their accuracy to quantitatively diagnose intervertebral disc degeneration at the cervicothoracic junction, influences of anatomical level, gender, age, and Pfirrmann grade of T2 relaxation time values and MTR values were evaluated. Methods. Sixty-seven patients with neck and upper back pain were included and examined with both T2 mapping and MTR imaging. The Pfirrmann grade, T2 relaxation time values, and MTR value of each disc between C7 and T3 were measured. Differences were investigated among different segmental levels, genders, age ranges, and Pfirrmann grades. The diagnostic accuracy of both MRI techniques was compared using the receiver operating characteristic (ROC) curves. Results. No significant difference was detected comparing T2 relaxation time values or MTR values among different anatomical levels, genders, and segmental levels. And we generally found that T2 relaxation time values decreased, while MTR value increased with increasing age. Importantly, we demonstrated the significant correlation between either T2 relaxation time values or MTR value and Pfirrmann grade. Conclusion. We proved the better accuracy of T2 mapping over MTR imaging to quantitatively evaluate the intervertebral disc degeneration of the cervicothoracic junction.

At present, the influence factors of posttraumatic growth (PTG) in colorectal cancer (CRC) patients and the relationship between PTG, self-perceived burden (SPB), and resilience are not completely clear. Thus, the present study examined whether resilience and SPB could predict PTG in CRC patients. The role of resilience as a potential mediator was also assessed. Using a cross-sectional design, a convenience sample of 157 CRC patients was selected as subjects, from July to December 2016 in a third-grade hospital. It was found that the main influencing factors for the total PTG score of CRC patients included work status, affordability for medical expenses, and duration of illness. Resilience was positively correlated with PTG, SPB was negatively correlated with PTG, and resilience played an intermediary role. Our findings remind clinicians to treat the psychosocial response of CRC patients from multiple perspectives, with a focus on their positive aspects. By increasing resilience and reducing the patient’s SPB, clinicians might enhance the patient’s PTG and quality of life.

Background. Hyposmia is one of the most common and best-characterized conditions that is also one of the first nonmotor features of Parkinson’s disease (PD). The association of hyposmia with PD is widely accepted; however the likelihood of developing PD is unclear. Our meta-analysis aimed to investigate the risk of PD in individuals with hyposmia.Methods. Prospective studies on humans published before December4th, 2018, were searched for in PubMed, Embase, Web of Science, and Cochrane Library databases. Two independent reviewers screened studies for inclusion and extracted data. We assessed the quality of studies using the Newcastle–Ottawa Scale and pooled data for analysis using random-effects models.Results. Of the 1774 studies retrieved, seven met the inclusion criteria for this review. A total of 3272 hyposmia and 176 PD events were reported over follow-up periods ranging from 3 to 17 years. Hyposmia was associated with a 3.84-fold risk of developing PD (pooled relative risk: 3.84, 95% CI 2.12−6.95). Subgroup analyses identified few differences between different hyposmia assessment methodologies and follow-up periods.Conclusions. Our findings suggest that deficiencies in olfaction are associated with an increased risk of developing PD. Future studies are needed to investigate whether hyposmia is a promising and feasible biomarker for the early diagnosis of PD.

The survival of individuals with tumors may be predicted by the peripheral blood lymphocyte-to-monocyte ratio (LMR) upon diagnosis in recent studies. For patients with multiple myeloma (MM) in the era of novel agents, the prognostic significance of LMR remains unclear. In this study, the prognostic impact of LMR is evaluated by 285 patients with MM who are treated with proteasome inhibitor and/or immunomodulatory drug. LMR is a proven predictor of survival using the receiver operating characteristic curve, with 4.2 as the cutoff point. Patients with LMR ≤ 4.2 at diagnosis had poorer overall survival (OS) and progression-free survival (PFS) than those with LMR > 4.2. In addition, multivariate analysis showed that LMR less than 4.2 is an independent predictor for the OS (hazard ratio [HR]: 1.703; 95% confidence interval [CI]: 1.020–2.842;P= 0.042) and PFS (HR: 1.831; 95% CI: 1.098–3.053;P= 0.021). According to the test, the LMR at diagnosis, which functions as a simple index reflecting host systemic immunity, can predict clinical outcomes in patients with MM who are treated with new agents.

Myocardial fibrosis (MF) can cause heart remodeling and it is an independent risk factor for malignant arrhythmias, sudden cardiac death, and other malignant cardiovascular events. It is often characterized by myocardial interstitial collagen deposition and hyperproliferation of cardiac fibroblasts (CFs). The transforming growth factor-β1 (TGF-β1) is the most influential profibrogenic factor. Resveratrol (RSV) is an active polyphenol substance that inhibits myocardial fibrosis. The mechanism of RSV-mediated inhibition of the proliferation of CFs at the microRNA level is not fully understood. We used TGF-β1 to induce CFs proliferation to simulate the pathogenesis of myocardial fibrosis. Neonatal rat CFs were treated with TGF-β1 in the presence or absence of resveratrol. Cell proliferation was measured using the CCK-8 and EdU assay. Collagen secretion was measured using hydroxyproline kit. Further, qPCR analysis was performed to determine microRNA levels after TGF-β1 or resveratrol treatment. To identify the target gene for miR-17, miR-17 was overexpressed or silenced, and the mRNA and protein levels of Smad7 were assessed. The effects of miR-17 silencing or Smad7 overexpression on cell proliferation and collagen secretion were also examined. Resveratrol treatment significantly decreased the TGF-β1-induced CF proliferation and collagen secretion. Resveratrol also decreased the levels of miR-17, miR-34a, and miR-181a in TGF-β1-treated CFs. Overexpression of miR-17 decreased the Smad7 mRNA and protein levels while silencing miR-17 increased them. Additionally, silencing miR-17 or overexpressing Smad7 decreased the TGF-β1-induced CFs proliferation and collagen secretion. In conclusion, resveratrol inhibits TGF-β1-induced CFs proliferation and collagen secretion. This inhibitory effect of resveratrol is orchestrated by the downregulation of miR-17 and the regulation of Smad7.

Isolation and identification of diverse porcine reproductive and respiratory syndrome viruses (PRRSVs) play a fundamental role in PRRSV research and disease management. However, PRRSV has a restricted cell tropism for infection. MARC-145 cells are routinely used for North American genotype PRRSV isolation and vaccine production. But MARC-145 cells have some limitations such as low virus yield. CD163 is a cellular receptor that mediates productive infection of PRRSV in various nonpermissive cell lines. In this study, we established a high and stable porcine CD163- (pCD163-) expressing MARC-145 cell line toward increasing its susceptibility to PRRSV infection. Indirect immunofluorescence assay (IFA) and Western blotting assays showed that pCD163 was expressed higher in pCD163-MARC cell line than MARC-145 cells. Furthermore, the ability of pCD163-MARC cell line to propagate PRRSV was significantly increased as compared with MARC-145 cells. Finally, we found that pCD163-MARC cell line had a higher isolation rate of clinical PRRSV samples and propagated live attenuated PRRS vaccine strains more efficiently than MARC-145 cells. This pCD163-MARC cell line will be a valuable tool for propagation and research of PRRSV.

Background. Our study aimed to assess the burden of depression and evaluate factors associated with depression and status of HIV risk behaviors among female sex workers (FSWs) in Guangdong, China. Method. We recruited FSWs from massage parlors, saunas, restaurants, hotels, hair salons, and streets in Guangdong, China, in 2014. Information on demographic characteristics, HIV testing history, and sexual behaviors was collected using a questionnaire. A blood sample was collected to test for HIV, syphilis, and HCV. A participant was defined as being depressed if she obtained 6 points or above using the 12-item General Health Questionnaire. Results. Among the 653 participants, 41.7% were 21–30 years old and 43.6% married. Overall, 52.4% were found to be depressed. FSWs who had correct syphilis related knowledge [aOR = 1.45; 95% CI: 1.04–2.03] and had primary sex partner (1.63, 1.14–2.33) were more likely to be depressed. FSWs who did not use a condom during their last sex with the primary sex partner were less likely to be depressed (0.47, 0.31–0.71). Conclusion. Our study observed high level of depression and HIV risk behaviors among Chinese FSWs. Future interventions should integrate mental health services in comprehensive interventions to prevent depression among Chinese FSWs.

In recent years, more and more omics data is generated. Even for the same samples, multiple levels of omics data can be measured in large scale. These multiscale and large-scale data could help in revealing the biological basis of complex diseases and optimizing the therapeutic strategies. Analysis of such data is very challenging since the data is inaccessible in the past and few methods are developed. In this special issue, we presented 17 novel studies about the analysis method of such complex data and their applications to interesting medical and biological questions.

With the explosively increasing high-throughput omics data, it is highly desired to develop effective computational methods and tools that can mine useful information to support the development of biochemistry, biomedicine, and drug design. Furthermore, in order to understand the protein-protein, protein-D/RNA, and other complex interactions, systems biology approaches are applied. In this collection, diverse topics were covered and there are many novel methods and intriguing findings. Y. Jiang et al. compared the gene expressions among the colorectal cancer patients in different stages and obtained the early and late stage biomarkers. Then, these two kinds of biomarkers were both mapped onto the protein interaction network, and the signal propagation path from the early stage biomarker to the late one was identified. Their findings may provide useful insights for revealing the mechanism of colorectal cancer progression at the cellular systems biology level. L. N. Lili et al. investigated the process of stroma activation in human ovarian cancer by molecular analysis of matched sets of cancer and surrounding stroma tissues. They found that functionally significant variability exists among ovarian cancer patients in the ability of the microenvironment to modulate cancer development. B. Yang et al. constructed a network-based inference framework for identifying cancer genes from gene expression data. Six identified genes (TSPYL5, CD55, CCNE2, DCK, BBC3, and MUC1) susceptible to breast cancer were verified through the literature mining, GO analysis, and pathway functional enrichment analysis. Lung cancer is one of the most malignant cancers. B. Q. Li et al. identified 25 NSCLC and 38 SCLC genes with the shortest path approach in PPI networks. These candidate genes contained more cancer genes and more functional similarity with cancer genes than those identified from the gene expression profiles. A. R. Iskandar et al. evaluated the perturbation of xenobiotic metabolism in response to cigarette smoke exposure in nasal and bronchial tissues. Their observation suggested that the effects of cigarette smoke exposure on the xenobiotic responses in the bronchial and nasal epithelium of smokers were similar to those observed in their respective organotypic models exposed to cigarette smoke, and nasal tissue could be a used as a reliable surrogate to measure the xenobiotic responses in the bronchial tissue. E. G. Maiorov et al. identified interconnected markers for T-cell acute lymphoblastic leukemia (T-ALL). Their identified genes may serve as biomarkers, alternative to the traditional ones used for the diagnosis of T-ALL, and help understand the pathogenesis of the disease. M. Kalita et al. used a multiplex gene expression profiling platform to investigate the perturbations of the innate pathways induced by TGF in a primary airway epithelial cell model of epithelial mesenchymal transition (EMT). Their results indicated that epigenetic changes produced by EMT induce dynamic state changes of the innate signaling pathway. C. Lu et al. studied the functions of microRNAs related to the liver regeneration of the whitespotted bamboo shark, Chiloscyllium plagiosum. Their work deepened the understanding of mechanisms of liver regeneration and resulted in the addition of a significant number of novel miRNAs sequences to GenBank. T. Alioto et al. presented a lightweight pipeline for first-pass gene prediction on newly sequenced genomes. The two main components are ASPic, a program that derives highly accurate, albeit not necessarily complete, EST-based transcript annotations from EST alignments. The other component is GeneID, a standard gene prediction program, which we have modified to take as evidence intron annotations. The pipeline was successfully tested on the entire C. elegans genome and the 44 ENCODE human pilot regions. J. Zou et al. reviewed advanced systems biology methods in drug discovery and translational biomedicine. Their review provided a framework for addressing disease mechanism and approaching drug discovery. L. Chen et al. proposed a computational method to predict the side effects of drugs, which integrated the information of chemical-chemical and protein-chemical interactions. Compared to most of the previous studies, the proposed method can provide the order information of the side effects for any query drug. K. Wang et al. proposed an accurate method for protein-ligand binding site on protein surface using SVM and statistical depth function. The accuracy, sensitivity, and specificity on training set are 77.55%, 56.15%, and 87.96%, respectively, and on the independent test set the accuracy, sensitivity, and specificity are 80.36%, 53.53%, and 92.38%, respectively. K. K. Tseng et al. presented a new system and novel approaches to classify different kinds of sperm images in order to assess their health. In their evaluation, the method reached accuracy of 87.5% and has better performance than the existing approaches to sperm classification. A rapid method is required to mitigate complexity and computation challenges on high throughput protein identification. In Method for Rapid Protein Identification in a Large Database, an accelerated open method is presented by W. Zhang et al. to satisfy this requirement to some extent. Q. Zou et al. proposed a novel method for distinguishing cytokine from other proteins. It is of vital importance of identifying cytokine in silicon. Ensemble classification strategy was employed for improving the prediction performance, and a friendly prediction web server was also developed. Du and Yu introduced a novel method, SubMito-PSPCP, which embeds the PSSM into the pseudoamino acid compositions, to predict protein submitochondrial locations. T. Gu et al. applied the Support Vector Regression and a two stage feature selection to developing the computational model which maps DPP-IV inhibitors to the activity. They also developed the online server. Based on nonlinear mapping and Coulomb function, X. Liu et al. applied 3D kernel approach to predict the four protein tertiary structural classes and five membrane protein types with satisfactory results. It has not escaped our notice that kernel approaches may hold a high potential for predicting the other protein features. T. H. Zhao et al. proposed a new method to predict protein disordered regions based on sequence features. The accuracy and MCC (Matthew's correlation coefficient) of their method are higher than three popular disordered region predictors: DISOPRED, DISOclust, and OnD-CRF. M. S. M. Ali et al. studied the structure and function of LipA8 which is able to adapt to extreme temperatures. Simulations show that it is most stable at 0°C and 5°C. In extreme temperature, the catalytic domain (N-terminus) maintained its stability than the noncatalytic domain (C-terminus), but the noncatalytic domain showed higher flexibility than the catalytic domain. A Boolean network (BN) is widely used as a model of gene regulatory networks. K. Kobayashi et al. proposed a BN model with two types of the control inputs and an optimal control method with duration of drug effectiveness. The optimal control problem is reduced to an integer programming problem. J. Zhang et al. studied the microRNA-mediated regulation in biological systems with oscillatory behavior. They started with two specific microRNA-mediated regulatory circuits which show their fine-tuning roles in the modulation of periodic behavior and then applied these results to study the effects of miR369-3 regulation of cell cycle. B. Yan et al. developed a mathematical model to study the mechanisms underlying the size checkpoint in fission yeast. They found that when the spatiotemporal regulation is coupled to the positive feedback loops, the mitosis-promoting factor (MPF) exhibits a bistable steady-state relationship with the cell size. The switch-like response from the positive feedback loops naturally generates the cell size checkpoint. Detection of potential siRNA off-targets is crucial for High Content Screening (HCS) using small interfering RNAs (siRNAs). S. Das et al. performed a detailed off-target analysis of three most commonly used kinome siRNA libraries based on latest RefSeq version and created SeedSeq database, a new unique format to store off-target information. L. Zhu et al. systematically investigated the characteristics and evolutionary pattern of actin gene family in primates. Phylogenetic analysis of 233 actin genes in human, chimpanzee, gorilla, orangutan, gibbon, rhesus monkey, and marmoset genomes showed that actin genes in the seven species could be divided into two major types of clades: orthologous group versus complex group. Codon usages and gene expression patterns of actin gene copies were highly consistent among the groups because of basic functions needed by the organisms but much diverged within species due to functional diversification. J. Ping et al. performed long time-scale molecular dynamics simulations on both open and closed states of Escherichia coli adenylate kinase (ADK); based on which a conformational selection mechanism was proposed to explain the large scale domain motion of this enzyme. Yudong Cai Tao Huang Lei Chen Bin Niu

Identifying disease genes is one of the most important topics in biomedicine and may facilitate studies on the mechanisms underlying disease. Age-related macular degeneration (AMD) is a serious eye disease; it typically affects older adults and results in a loss of vision due to retina damage. In this study, we attempt to develop an effective method for distinguishing AMD-related genes. Gene ontology and KEGG enrichment analyses of known AMD-related genes were performed, and a classification system was established. In detail, each gene was encoded into a vector by extracting enrichment scores of the gene set, including it and its direct neighbors in STRING, and gene ontology terms or KEGG pathways. Then certain feature-selection methods, including minimum redundancy maximum relevance and incremental feature selection, were adopted to extract key features for the classification system. As a result, 720 GO terms and 11 KEGG pathways were deemed the most important factors for predicting AMD-related genes.

Observing what phenotype the overexpression or knockdown of gene can cause is the basic method of investigating gene functions. Many advanced biotechnologies, such as RNAi, were developed to study the gene phenotype. But there are still many limitations. Besides the time and cost, the knockdown of some gene may be lethal which makes the observation of other phenotypes impossible. Due to ethical and technological reasons, the knockdown of genes in complex species, such as mammal, is extremely difficult. Thus, we proposed a new sequence-based computational method calledkNNA-based method for gene phenotypes prediction. Different to the traditional sequence-based computational method, our method regards the multiphenotype as a whole network which can rank the possible phenotypes associated with the query protein and shows a more comprehensive view of the protein's biological effects. According to the prediction result of yeast, we also find some more related features, including GO and KEGG information, which are making more contributions in identifying protein phenotypes. This method can be applied in gene phenotype prediction in other species.

One of the most important and challenging problems in biomedicine is how to predict the cancer related genes. Retinoblastoma (RB) is the most common primary intraocular malignancy usually occurring in childhood. Early detection of RB could reduce the morbidity and promote the probability of disease-free survival. Therefore, it is of great importance to identify RB genes. In this study, we developed a computational method to predict RB related genes based on Dagging, with the maximum relevance minimum redundancy (mRMR) method followed by incremental feature selection (IFS). 119 RB genes were compiled from two previous RB related studies, while 5,500 non-RB genes were randomly selected from Ensemble genes. Ten datasets were constructed based on all these RB and non-RB genes. Each gene was encoded with a 13,126-dimensional vector including 12,887 Gene Ontology enrichment scores and 239 KEGG enrichment scores. Finally, an optimal feature set including 1061 GO terms and 8 KEGG pathways was obtained. Analysis showed that these features were closely related to RB. It is anticipated that the method can be applied to predict the other cancer related genes as well.

Objective. To examine the biocompatibility of a novel nanohydroxyapatite/poly[lactic-co-glycolic acid] (nHA/PLGA) composite and evaluate its feasibility as a scaffold for cartilage tissue engineering.Methods. Chondrocytes of fetal rabbit were cultured with nHA/PLGA scaffoldin vitroand the cell viability was assessed by MTT assay first. Cells adhering to nHA/PLGA scaffold were then observed by inverted microscope and scanning electron microscope (SEM). The cell cycle profile was analyzed by flow cytometry.Results. The viability of the chondrocytes on the scaffold was not affected by nHA/PLGA comparing with the control group as it was shown by MTT assay. Cells on the surface and in the pores of the scaffold increased in a time-dependent manner. Results obtained from flow cytometry showed that there was no significant difference in cell cycle profiles between the coculture group and control (P>0.05).Conclusion. The porous nHA/PLGA composite scaffold is a biocompatible and good kind of scaffold for cartilage tissue engineering.

Most drugs have beneficial as well as adverse effects and exert their biological functions by adjusting and altering the functions of their target proteins. Thus, knowledge of drugs target proteins is essential for the improvement of therapeutic effects and mitigation of undesirable side effects. In the study, we proposed a novel prediction method based on drug/compound ontology information extracted from ChEBI to identify drugs target groups from which the kind of functions of a drug may be deduced. By collecting data in KEGG, a benchmark dataset consisting of 876 drugs, categorized into four target groups, was constructed. To evaluate the method more thoroughly, the benchmark dataset was divided into a training dataset and an independent test dataset. It is observed by jackknife test that the overall prediction accuracy on the training dataset was 83.12%, while it was 87.50% on the test dataset—the predictor exhibited an excellent generalization. The good performance of the method indicates that the ontology information of the drugs contains rich information about their target groups, and the study may become an inspiration to solve the problems of this sort and bridge the gap between ChEBI ontology and drugs target groups.

Lung cancer is one of the leading causes of cancer mortality worldwide. The main types of lung cancer are small cell lung cancer (SCLC) and nonsmall cell lung cancer (NSCLC). In this work, a computational method was proposed for identifying lung-cancer-related genes with a shortest path approach in a protein-protein interaction (PPI) network. Based on the PPI data from STRING, a weighted PPI network was constructed. 54 NSCLC- and 84 SCLC-related genes were retrieved from associated KEGG pathways. Then the shortest paths between each pair of these 54 NSCLC genes and 84 SCLC genes were obtained with Dijkstra’s algorithm. Finally, all the genes on the shortest paths were extracted, and 25 and 38 shortest genes with a permutationPvalue less than 0.05 for NSCLC and SCLC were selected for further analysis. Some of the shortest path genes have been reported to be related to lung cancer. Intriguingly, the candidate genes we identified from the PPI network contained more cancer genes than those identified from the gene expression profiles. Furthermore, these genes possessed more functional similarity with the known cancer genes than those identified from the gene expression profiles. This study proved the efficiency of the proposed method and showed promising results.