XB130 Deficiency Affects Tracheal Epithelial Differentiation during Airway Repair

The repair and regeneration of airway epithelium is important for maintaining homeostasis of the respiratory system. XB130 is an adaptor protein involved in the regulation of cell proliferation, survival and migration. In the human trachea, XB130 is expressed on the apical site of ciliated epithelial cells. We hypothesize that XB130 may play a role in epithelial repair and regeneration after injury. Xb130 knockout (KO) mice were generated, and a mouse isogenic tracheal transplantation model was used. Adult Xb130 KO mice did not show any significant anatomical and physiological phenotypes in comparison with their wild type (WT) littermates. The tracheal epithelium in Xb130 KO mice, however, was significantly thicker than that in WT mice. Severe ischemic epithelial injury was observed immediately after the tracheal transplantation, which was followed by epithelial cell flattening, proliferation and differentiation. No significant differences were observed in terms of initial airway injury and apoptosis. However, at Day 10 after transplantation, the epithelial layer was significantly thicker in Xb130 KO mice, and associated with greater proliferative (Ki67+) and basal (CK5+) cells, as well as thickening of the connective tissue and fibroblast layer between the epithelium and tracheal cartilages. These results suggest that XB130 is involved in the regulation of airway epithelial differentiation, especially during airway repair after injury.     

Assessing the Microbial Community and Functional Genes in a Vertical Soil Profile with Long-Term Arsenic Contamination

Arsenic (As) contamination in soil and groundwater has become a serious problem to public health. To examine how microbial communities and functional genes respond to long-term arsenic contamination in vertical soil profile, soil samples were collected from the surface to the depth of 4 m (with an interval of 1 m) after 16-year arsenic downward infiltration. Integrating BioLog and functional gene microarray (GeoChip 3.0) technologies, we showed that microbial metabolic potential and diversity substantially decreased, and community structure was markedly distinct along the depth. Variations in microbial community functional genes, including genes responsible for As resistance, carbon and nitrogen cycling, phosphorus utilization and cytochrome c oxidases were detected. In particular, changes in community structures and activities were correlated with the biogeochemical features along the vertical soil profile when using the rbcL and nifH genes as biomarkers, evident for a gradual transition from aerobic to anaerobic lifestyles. The C/N showed marginally significant correlations with arsenic resistance (p = 0.069) and carbon cycling genes (p = 0.073), and significant correlation with nitrogen fixation genes (p = 0.024). The combination of C/N, NO₃ ⁻ and P showed the highest correlation (r = 0.779, p = 0.062) with the microbial community structure. Contradict to our hypotheses, a long-term arsenic downward infiltration was not the primary factor, while the spatial isolation and nutrient availability were the key forces in shaping the community structure. This study provides new insights about the heterogeneity of microbial community metabolic potential and future biodiversity preservation for arsenic bioremediation management.     

Inclusion of gene-gene and gene-environment interactions unlikely to dramatically improve risk prediction for complex diseases

Genome-wide association studies have identified hundreds of common genetic variants associated with the risk of multifactorial diseases. However, their impact on discrimination and risk prediction is limited. It has been suggested that the identification of gene-gene (G-G) and gene-environment (G-E) interactions would improve disease prediction and facilitate prevention. We conducted a simulation study to explore the potential improvement in discrimination if G-G and G-E interactions exist and are known. We used three diseases (breast cancer, type 2 diabetes, and rheumatoid arthritis) as motivating examples. We show that the inclusion of G-G and G-E interaction effects in risk-prediction models is unlikely to dramatically improve the discrimination ability of these models.     

Coev2Net: a computational framework for boosting confidence in high-throughput protein-protein interaction datasets

ABSTRACT: Improving the quality and coverage of the protein interactome is of tantamount importance for biomedical research, particularly given the various sources of uncertainty in high-throughput techniques. We introduce a structure-based framework, Coev2Net, for computing a single confidence score that addresses both false positive and false negative rates. Coev2Net is easily applied to thousands of binary protein interactions and has superior predictive performance over existing methods. We experimentally validate selected high-confidence predictions in the human MAPK network and show that predicted interfaces are enriched for cancer-related or damaging SNPs. Coev2Net can be downloaded at http://struct2net.csail.mit.edu/     

PKM2 coordinates glycolysis with mitochondrial fusion and oxidative phosphorylation

A change in the metabolic flux of glucose from mitochondrial oxidative phosphorylation (OXPHOS) to aerobic glycolysis is regarded as one hallmark of cancer. However, the mechanisms underlying the metabolic switch between aerobic glycolysis and OXPHOS are unclear. Here we show that the M2 isoform of pyruvate kinase (PKM2), one of the rate-limiting enzymes in glycolysis, interacts with mitofusin 2 (MFN2), a key regulator of mitochondrial fusion, to promote mitochondrial fusion and OXPHOS, and attenuate glycolysis. mTOR increases the PKM2:MFN2 interaction by phosphorylating MFN2 and thereby modulates the effect of PKM2: MFN2 on glycolysis, mitochondrial fusion and OXPHOS. Thus, an mTOR-MFN2-PKM2 signaling axis couples glycolysis and OXPHOS to modulate cancer cell growth.     

Fragment-HMM: a new approach to protein structure prediction

We designed a simple position-specific hidden Markov model to predict protein structure. Our new framework naturally repeats itself to converge to a final target, conglomerating fragment assembly, clustering, target selection, refinement, and consensus, all in one process. Our initial implementation of this theory converges to within 6 A of the native structures for 100% of decoys on all six standard benchmark proteins used in ROSETTA (discussed by Simons and colleagues in a recent paper), which achieved only 14%-94% for the same data. The qualities of the best decoys and the final decoys our theory converges to are also notably better.     

Design of peptide inhibitors for furin based on the C-terminal fragment of histone H1.2

The mammalian proprotein convertase furin has been found to play an important role in diverse physiological and pathological events, such as the activation of viral glycoproteins and bacterial exotoxins. Small, non-toxic and highly active, furin inhibitors are considered to be attractive drug candidates for diseases caused by virus and bacteria. In this study, a series of peptide inhibitors were designed and synthesized based on the C-terminal fragment of histone H1.2, which has an inhibitory effect on furin. Replacing the reactive site of inhibitors with the consensus substrate recognition sequence of furin has been found to increase inhibitory activity greatly. The most potent inhibitor, I4, with 14 amino acid residues has a Ki value of 17 nM for furin. Although most of the synthesized peptides were temporary inhibitors, the inhibitor I5, with nine amino acids, retained its full potency, even after a 3 h incubation period with furin at 37 degrees C. These inhibitors may potentially lead to the development of anti-viral and anti-bacterial drug compounds.