ALBERT

Description: Background: Vaccines have been one of the most successful public health interventions to date. The use of vaccination, however, sometimes comes with possible adverse events. The U.S. FDA/CDC Vaccine Adverse Event Reporting System (VAERS) currently contains more than 200,000 reports for post-vaccination events that occur after the administration of vaccines licensed in the United States. Although the data from the VAERS has been applied to many public health and vaccine safety studies, each individual report does not necessarily indicate a casuality relationship between the vaccine and the reported symptoms. Further statistical analysis and summarization needs to be done before this data can be leveraged. Methods: This paper introduces our efforts on representing the vaccine-symptom correlations and their corresponding meta-information extracted from the VAERS database using Resource Description Framework (RDF). Numbers of occurrences of vaccine-symptom pairs reported to the VAERS were summarized with corresponding proportional reporting ratios (PRR) calculated. All the data was stored in an RDF file. We then applied network analysis approaches to the RDF data to illustrate a use case of the data for longititual studies. We further dicussed our vision on integrating the data with vaccine information from other sources using RDF linked approach to facilitate more comprehensive analyses. Results: The 1990–2013 data from VAERS has been extracted from the VAERS database. There are 83,148 unique vaccine-symptom pairs with 75 vaccine types and 5,865 different reported symptoms. The yearly and over PRR values for each reported vaccine-symptom pair were calculated. The network properties of networks consisting of significant vaccine-symptom associations (i.e., PRR larger than 1) were then investigated. The results indicated that vaccine-symptom association network is a dense network, with any given node connected to all other nodes through an average of approximately two other nodes and a maximum of five nodes.

Description: Background: Vaccines have been one of the most successful public health interventions to date. The use of vaccination, however, sometimes comes with possible adverse events. The U.S. FDA/CDC Vaccine Adverse Event Reporting System (VAERS) currently contains more than 200,000 reports for post-vaccination events that occur after the administration of vaccines licensed in the United States. Although the data from the VAERS has been applied to many public health and vaccine safety studies, each individual report does not necessarily indicate a casuality relationship between the vaccine and the reported symptoms. Further statistical analysis and summarization needs to be done before this data can be leveraged. Methods: This paper introduces our efforts on representing the vaccine-symptom correlations and their corresponding meta-information extracted from the VAERS database using Resource Description Framework (RDF). Numbers of occurrences of vaccine-symptom pairs reported to the VAERS were summarized with corresponding proportional reporting ratios (PRR) calculated. All the data was stored in an RDF file. We then applied network analysis approaches to the RDF data to illustrate a use case of the data for longititual studies. We further dicussed our vision on integrating the data with vaccine information from other sources using RDF linked approach to facilitate more comprehensive analyses. Results: The 1990?2013 data from VAERS has been extracted from the VAERS database. There are 83,148 unique vaccine-symptom pairs with 75 vaccine types and 5,865 different reported symptoms. The yearly and over PRR values for each reported vaccine-symptom pair were calculated. The network properties of networks consisting of significant vaccine-symptom associations (i.e., PRR larger than 1) were then investigated. The results indicated that vaccine-symptom association network is a dense network, with any given node connected to all other nodes through an average of approximately two other nodes and a maximum of five nodes.

Description: Background: Identification of essential proteins plays a significant role in understanding minimal requirements for the cellular survival and development. Many computational methods have been proposed for predicting essential proteins by using the topological features of protein-protein interaction (PPI) networks. However, most of these methods ignored intrinsic biological meaning of proteins. Moreover, PPI data contains many false positives and false negatives. To overcome these limitations, recently many research groups have started to focus on identification of essential proteins by integrating PPI networks with other biological information. However, none of their methods has widely been acknowledged. Results: By considering the facts that essential proteins are more evolutionarily conserved than nonessential proteins and essential proteins frequently bind each other, we propose an iteration method for predicting essential proteins by integrating the orthology with PPI networks, named by ION. Differently from other methods, ION identifies essential proteins depending on not only the connections between proteins but also their orthologous properties and features of their neighbors. ION is implemented to predict essential proteins in S. cerevisiae. Experimental results show that ION can achieve higher identification accuracy than eight other existing centrality methods in terms of area under the curve (AUC). Moreover, ION identifies a large amount of essential proteins which have been ignored by eight other existing centrality methods because of their low-connectivity. Many proteins ranked in top 100 by ION are both essential and belong to the complexes with certain biological functions. Furthermore, no matter how many reference organisms were selected, ION outperforms all eight other existing centrality methods. While using as many as possible reference organisms can improve the performance of ION. Additionally, ION also shows good prediction performance in E.Coli K-12. Conclusions: The accuracy of predicting essential proteins can be improved by integrating the orthology with PPI networks.

Description: Background: Potassium (K+) is an important nutrient ion in plant cells and plays crucial roles in many plant physiological and developmental processes. In the natural environment, K+ deficiency is a common abiotic stress that inhibits plant growth and reduces crop productivity. Several microarray studies have been conducted on genome-wide gene expression profiles of rice during its responses to various stresses. However, little is known about the transcriptional changes in rice genes under low-K+ conditions. Results: We analyzed the transcriptomic profiles of rice roots in response to low-K+ stress. The roots of rice seedlings with or without low-K+ treatment were harvested after 6 h, and 3 and 5 d, and used for microarray analysis. The microarray data showed that many genes (2,896) were up-regulated or down-regulated more than 1.2-fold during low-K+ treatment. GO analysis indicated that the genes showing transcriptional changes were mainly in the following categories: metabolic process, membrane, cation binding, kinase activity, transport, and so on. We conducted a comparative analysis of transcriptomic changes between Arabidopsis and rice under low-K+ stress. Generally, the genes showing changes in transcription in rice and Arabidopsis in response to low-K+ stress displayed similar GO distribution patterns. However, there were more genes related to stress responses and development in Arabidopsis than in rice. Many auxin-related genes responded to K+ deficiency in rice, whereas jasmonic acid-related enzymes may play more important roles in K+ nutrient signaling in Arabidopsis. Conclusions: According to the microarray data, fewer rice genes showed transcriptional changes in response to K+ deficiency than to phosphorus (P) or nitrogen (N) deficiency. Thus, transcriptional regulation is probably more important in responses to low-P and -N stress than to low-K+ stress. However, many genes in some categories (protein kinase and ion transporter families) were markedly up-regulated, suggesting that they play important roles during K+ deficiency. Comparative analysis of transcriptomic changes between Arabidopsis and rice showed that monocots and dicots share many similar mechanisms in response to K+ deficiency, despite some differences. Further research is required to clarify the differences in transcriptional regulation between monocots and dicots.

Description: Background: We investigated whether polymorphisms in the toll-like receptor genes or gene–gene interactions are associated with susceptibility to latent tuberculosis infection (LTBI) or subsequent pulmonary tuberculosis (PTB) in a Chinese population. Methods: Two matched case–control studies were undertaken. Previously reported polymorphisms in the toll-like receptors (TLRs) were compared between 422 healthy controls (HC) and 205 LTBI patients and between 205 LTBI patients and 109 PTB patients, to assess whether these polymorphisms and their interactions are associated with LTBI or PTB. A PCR-based restriction fragment length polymorphism analysis was used to detect genetic polymorphisms in the TLR genes. Nonparametric multifactor dimensionality reduction (MDR) was used to analyze the effects of interactions between complex disease genes and other genes or environmental factors. Results: Sixteen markers in TLR1, TLR2, TLR4, TLR6, TLR8, TLR9, and TIRAP were detected. In TLR2, the frequencies of the CC genotype (OR = 2.262; 95% CI: 1.433–3.570) and C allele (OR = 1.566; 95% CI: 1.223–1.900) in single-nucleotide polymorphism (SNP) rs3804100 were significantly higher in the LTBI group than in the HC group, whereas the GA genotype of SNP rs5743708 was associated with PTB (OR = 6.087; 95% CI: 1.687–21.968). The frequencies of the GG genotype of SNP rs7873784 in TLR4 (OR = 2.136; 95% CI: 1.312–3.478) and the CC genotype of rs3764879 in TLR8 (OR = 1.982; 95% CI: 1.292-3.042) were also significantly higher in the PTB group than in the HC group. The TC genotype frequency of SNP rs5743836 in TLR9 was significantly higher in the LTBI group than in the HC group (OR = 1.664; 95% CI: 1.201–2.306). An MDR analysis of gene–gene and gene–environment interactions identified three SNPs (rs10759932, rs7873784, and rs10759931) that predicted LTBI with 84% accuracy (p = 0.0004) and three SNPs (rs3804100, rs1898830, and rs10759931) that predicted PTB with 80% accuracy (p = 0.0001). Conclusions: Our results suggest that genetic variation in TLR2, 4, 8 and 9, implicating TLR-related pathways affecting the innate immunity response, modulate LTBI and PTB susceptibility in Chinese.

Description: Recent advances in sequencing technology have opened a new era in RNA studies. Novel types of RNAs such as long non-coding RNAs (lncRNAs) have been discovered by transcriptomic sequencing and some lncRNAs have...

Description: Non-small cell lung cancer (NSCLC) is the most commonly diagnosed and fatal cancer worldwide. Sclerostin domain containing protein 1 (SOSTDC1) has been found to be tumor-suppressive in several types of cancers...

Description: Background: The rs17145738 single nucleotide polymorphism (SNP) near MLX interacting protein-like/transducin (beta)-like 2 (MLXIPL/TBL2) loci is associated with serum lipid levels, but the results are inconsistent in diverse ethnic/racial groups. The current study was to investigate the association of MLXIPL/TBL2 rs17145738 SNP and several environmental factors with serum lipid profiles in the Guangxi Mulao and Han populations. Methods: A total of 649 subjects of Mulao nationality and 712 participants of Han nationality aged 16--84 years were randomly selected from our previous stratified randomized samples. Genotyping was performed by polymerase chain reaction and restriction fragment length polymorphism combined with gel electrophoresis, and then confirmed by direct sequencing. Results: Serum apolipoprotein (Apo) B levels were higher in Mulao than in Han (P 〈 0.001). There were no significant differences in the genotypic and allelic frequencies of the MLXIPL/TBL2 rs17145738 SNP between the two ethnic groups or between males and females. The T allele carriers had higher triglyceride (TG) and ApoB levels in Mulao, and higher total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C) levels in Han than the T allele non-carriers (P 〈 0.05 for all). Subgroup analyses showed that the T allele carriers had higher ApoB levels in both Mulao and Han females than the T allele non-carriers, but the T allele carriers had lower ApoB levels in Han males than the T allele non-carriers (P 〈 0.05, respectively). The T allele carriers in Han had higher TC, high-density lipoprotein cholesterol (HDL-C) levels and ApoA1/ApoB ratio and lower TG levels in males, and higher LDL-C levels and lower ApoA1/ApoB ratio in females than the T allele non-carriers (P 〈 0.05 for all). Serum TC levels in the combined population of the two ethnic groups and in Han, and HDL-C levels in Han males were correlated with genotypes (P 〈 0.05 for all). Serum lipid parameters were also correlated with several environmental factors (P 〈 0.05-0.01). Conclusions: The association of MLXIPL/TBL2 rs17145738 SNP and serum lipid profiles is different between the Mulao and Han populations. There is a sex-specific association in the both ethnic groups.

Description: Background: The +294T/C polymorphism in the peroxisome proliferator-activated receptor delta (PPARD) gene is associated with hyperlipidemia in several younger populations, but results are still inconsistence across ethnic groups and its possible impact on the lipid profiles of long-lived individuals remains unexploited. Here, we aimed to evaluate the possible correlation between PPARD +294T/C and serum lipid levels in a long-lived population in Bama, a region known for longevity situated in Guangxi, China. Methods: Genotyping of PPARD +294T/C polymorphism was conducted in 505 long-lived inhabitants (aged 90 and above, long-lived group, LG) and 468 healthy controls (aged 60–75, non-long-lived group, non-LG) recruited from Bama area. Results: No difference in allelic and genotypic frequencies was found between the two groups (P 〉 0.05). However, C-allele and C-genotype (TC and CC) were significantly more frequent in the females of non-LG than were LG after sex stratification. CC carriers exhibited higher LDL-C level in LG (P