Potential Genes and Pathways of Neonatal Sepsis Based on Functional Gene Set Enrichment Analyses.

BACKGROUND: Neonatal sepsis (NS) is considered as the most common cause of neonatal deaths that newborns suffer from. Although numerous studies focus on gene biomarkers of NS, the predictive value of the gene biomarkers is low. NS pathogenesis is still needed to be investigated. METHODS: After data preprocessing, we used KEGG enrichment method to identify the differentially expressed pathways between NS and normal controls. Then, functional principal component analysis (FPCA) was adopted to calculate gene values in NS. In order to further study the key signaling pathway of the NS, elastic-net regression model, Mann-Whitney U test, and coexpression network were used to estimate the weights of signaling pathway and hub genes. RESULTS: A total of 115 different pathways between NS and controls were first identified. FPCA made full use of time-series gene expression information and estimated F values of genes in the different pathways. The top 1000 genes were considered as the different genes and were further analyzed by elastic-net regression and MWU test. There were 7 key signaling pathways between the NS and controls, according to different sources. Among those genes involved in key pathways, 7 hub genes, PIK3CA, TGFBR2, CDKN1B, KRAS, E2F3, TRAF6, and CHUK, were determined based on the coexpression network. Most of them were cancer-related genes. PIK3CA was considered as the common marker, which is highly expressed in the lymphocyte group. Little was known about the correlation of PIK3CA with NS, which gives us a new enlightenment for NS study. CONCLUSION: This research might provide the perspective information to explore the potential novel genes and pathways as NS therapy targets.

Pathway cross-talk network analysis identifies critical pathways in neonatal sepsis.

BACKGROUND: Despite advances in neonatal care, sepsis remains a major cause of morbidity and mortality in neonates worldwide. Pathway cross-talk analysis might contribute to the inference of the driving forces in bacterial sepsis and facilitate a better understanding of underlying pathogenesis of neonatal sepsis. OBJECTIVE: This study aimed to explore the critical pathways associated with the progression of neonatal sepsis by the pathway cross-talk analysis. METHODS: By integrating neonatal transcriptome data with known pathway data and protein-protein interaction data, we systematically uncovered the disease pathway cross-talks and constructed a disease pathway cross-talk network for neonatal sepsis. Then, attract method was employed to explore the dysregulated pathways associated with neonatal sepsis. To determine the critical pathways in neonatal sepsis, rank product (RP) algorithm, centrality analysis and impact factor (IF) were introduced sequentially, which synthetically considered the differential expression of genes and pathways, pathways cross-talks and pathway parameters in the network. The dysregulated pathways with the highest IF values as well as RP<0.01 were defined as critical pathways in neonatal sepsis. RESULTS: By integrating three kinds of data, only 6919 common genes were included to perform the pathway cross-talk analysis. By statistic analysis, a total of 1249 significant pathway cross-talks were selected to construct the pathway cross-talk network. Moreover, 47 dys-regulated pathways were identified via attract method, 20 pathways were identified under RP<0.01, and the top 10 pathways with the highest IF were also screened from the pathway cross-talk network. Among them, we selected 8 common pathways, i.e. critical pathways. CONCLUSIONS: In this study, we systematically tracked 8 critical pathways involved in neonatal sepsis by integrating attract method and pathway cross-talk network. These pathways might be responsible for the host response in infection, and of great value for advancing diagnosis and therapy of neonatal sepsis.

A rare variant of the posterior cord of the brachial plexus

In the dissection of a 67-year-old Chinese male cadaver, a variant of the posterior cord was observed. The posterior cord was consisted of two parts. The upper posterior cord was the continuation of the posterior division of the upper trunk. It gave off the suprascapular nerve, the subscapular nerve, a communicating branch and then continued as the axillary nerve. The lower posterior cord was formed by the posterior divisions of the middle and lower trunks. After giving off the thoracodorsal nerve, the lower posterior cord fused with the communicating branch and continued as the radial nerve

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Systematic Tracking of Disrupted Modules Identifies Altered Pathways Associated with Congenital Heart Defects in Down Syndrome.

BACKGROUND: This work aimed to identify altered pathways in congenital heart defects (CHD) in Down syndrome (DS) by systematically tracking the dysregulated modules of reweighted protein-protein interaction (PPI) networks. MATERIAL AND METHODS: We performed systematic identification and comparison of modules across normal and disease conditions by integrating PPI and gene-expression data. Based on Pearson correlation coefficient (PCC), normal and disease PPI networks were inferred and reweighted. Then, modules in the PPI network were explored by clique-merging algorithm; altered modules were identified via maximum weight bipartite matching and ranked in non-increasing order. Finally, pathways enrichment analysis of genes in altered modules was carried out based on Database for Annotation, Visualization, and Integrated Discovery (DAVID) to study the biological pathways in CHD in DS. RESULTS: Our analyses revealed that 348 altered modules were identified by comparing modules in normal and disease PPI networks. Pathway functional enrichment analysis of disrupted module genes showed that the 4 most significantly altered pathways were: ECM-receptor interaction, purine metabolism, focal adhesion, and dilated cardiomyopathy. CONCLUSIONS: We successfully identified 4 altered pathways and we predicted that these pathways would be good indicators for CHD in DS.

Distribution characteristics of rare earth elements in children's scalp hair from a rare earths mining area in southern China.

In order to demonstrate the validity of using scalp hair rare earth elements (REEs) content as a biomarker of human REEs exposure, data were collected on REEs exposure levels from children aged 11-15 years old and living in an ion-adsorptive type light REEs (LREEs) mining and surrounding areas in southern China. Sixty scalp hair samples were analyzed by ICP-MS for 16 REEs (La Lu, Y and Sc). Sixteen REEs contents in the samples from the mining area (e.g., range: La: 0.14-6.93 microg/g; Nd: 0.09-5.27 microg/g; Gd: 12.2-645.6ng/g; Lu: 0.2-13.3 ng/g; Y: 0.03-1.27 microg/g; Sc: 0.05-0.30 microg/g) were significantly higher than those from the reference area (range: La: 0.04-0.40 microg/g; Nd: 0.04-0.32 microg/g; Gd: 8.3-64.6 ng/g; Lu: 0.4-3.3ng/g; Y: 0.03-0.29 microg/g; Sc: 0.11-0.36 microg/g) and even much higher than those published in the literature. The distribution pattern of REEs in scalp hair from the mining area was very similar to that of REEs in the mine and the atmosphere shrouding that area. In conclusion, the scalp hair REEs contents may indicate not only quantitatively but also qualitatively (distribution pattern) the absorption of REEs from environmental exposure into human body. The children living in this mining area should be regarded as a high-risk group with REEs (especially LREEs) exposure, and their health status should be examined from a REEs health risk assessment perspective.