Identifying essential genes in bacterial metabolic networks with machine learning methods

Background  

Identifying essential genes in bacteria supports to identify potential drug targets and an understanding of minimal requirements for a synthetic cell. However, experimentally assaying the essentiality of their coding genes is resource intensive and not feasible for all bacterial organisms, in particular if they are infective.     

Trees on networks: resolving statistical patterns of phylogenetic similarities among interacting proteins

Background  

Phylogenies capture the evolutionary ancestry linking extant species. Correlations and similarities among a set of species are mediated by and need to be understood in terms of the phylogenic tree. In a similar way it has been argued that biological networks also induce correlations among sets of interacting genes or their protein products.     

The alkene monooxygenase from Xanthobacter Py2 is a binuclear non-haem iron protein closely related to toluene 4-monooxygenase

The genes encoding the six polypeptide components of the alkene monooxygenase from Xanthobacter Py2 have been sequenced. The predicted amino acid sequence of the first ORF shows homology with the iron binding subunits of binuclear non-haem iron containing monooxygenases including benzene monooxygenase, toluene 4-monooxygenase (>60% sequence similarity) and methane monooxygenase (>40% sequence similarity) and that the necessary sequence motifs associated with iron co-ordination are also present. Secondary structure prediction based on the amino acid sequence showed that the predominantly α-helical structure that surrounds the binuclear iron binding site was conserved allowing the sequence to be modelled on the co-ordinates of the methane monooxygenase α-subunit. Significant differences in the residues forming the hydrophobic cavity which forms the substrate binding site are discussed with reference to the differences in reaction specificity and stereospecificity of binuclear non-haem iron monooxygenases.     

Computational and Experimental Investigation of the Antimicrobial Peptide Cecropin XJ and its Ligands as the Impact Factors of Antibacterial Activity

Cecropin XJ, as a heat stable antimicrobial peptide (AMP), displayed broad bacteriostatic activities, effectively inhibited proliferation of cancer cells and induced cell apoptosis in vitro. However, it exhibited little hemolytic activity and very low cytotoxicity to erythrocytes and normal cells. Although exerts multiple remarkable bioactivities, the refined molecular conformation of native Cecropin XJ remains unsolved. The aim of the present study is to comprehensively investigate the physicochemical characteristics and structure-function relationship of this antimicrobial peptide by using a series of bioinformatics and experimental approaches. In this study, we revealed that the mature Cecropin XJ consists of 41 amino acids, containing two α-helical structures from Lys⁷ to Lys²⁵ and from Ala²⁹ to Ile³⁹. The phylogenetic tree indicated that Cecropin XJ belongs to the Class I AMPs of cecropin family. Hydrophobic analysis showed Cecropin XJ is a typical amphiphilic molecule. The surface of Cecropin XJ was found to have a much wide range of electrostatic potential from ?83.243 to +83.243. The amphipathicity and surface potential of Cecropin XJ partially supported the AMP pore-forming hypothesis. Scanning electron microscopy experimentally confirmed the damages of Cecropin XJ to microbial membrane. Four predicted docking sites respectively for magnesium ion (Mg²⁺), adenosine diphosphate (ADP), bacteriopheophytin (BPH), and guanosine triphosphate (GTP) were found on the surface of Cecropin XJ. Thereinto, Mg²⁺ was experimentally proved to suppress the antibacterial activity of Cecropin XJ; both GTP and ADP enhanced the bactericidal activities to varying degrees. The present study provides a foundation for further investigation of molecular evolution, structural modification, and functional mechanisms of Cecropin XJ.     

Distinguishing Drift and Selection Empirically: “The Great Snail Debate” of the 1950s

Biologists and philosophers have been extremely pessimistic about the possibility of demonstrating random drift in nature, particularly when it comes to distinguishing random drift from natural selection. However, examination of a historical case – Maxime Lamotte’s study of natural populations of the land snail, Cepaea nemoralis in the 1950s – shows that while some pessimism is warranted, it has been overstated. Indeed, by describing a unique signature for drift and showing that this signature obtained in the populations under study, Lamotte was able to make a good case for a significant role for␣drift. It may be difficult to disentangle the causes of drift and selection acting in a population, but it is not (always) impossible.     

Interactions Among Fuel Management,Species Composition,Bark Beetles,and Climate Change and the Potential Effects on Forests of the Lake Tahoe Basin

Climate-driven increases in wildfires, drought conditions, and insect outbreaks are critical threats to forest carbon stores. In particular, bark beetles are important disturbance agents although their long-term interactions with future climate change are poorly understood. Droughts and the associated moisture deficit contribute to the onset of bark beetle outbreaks although outbreak extent and severity is dependent upon the density of host trees, wildfire, and forest management. Our objective was to estimate the effects of climate change and bark beetle outbreaks on ecosystem carbon dynamics over the next century in a western US forest. Specifically, we hypothesized that (a) bark beetle outbreaks under climate change would reduce net ecosystem carbon balance (NECB) and increase uncertainty and (b) these effects could be ameliorated by fuels management. We also examined the specific tree species dynamics—competition and release—that determined NECB response to bark beetle outbreaks. Our study area was the Lake Tahoe Basin (LTB), CA and NV, USA, an area of diverse forest types encompassing steep elevation and climatic gradients and representative of mixed-conifer forests throughout the western United States. We simulated climate change, bark beetles, wildfire, and fuels management using a landscape-scale stochastic model of disturbance and succession. We simulated the period 2010–2100 using downscaled climate projections. Recurring droughts generated conditions conducive to large-scale outbreaks; the resulting large and sustained outbreaks significantly increased the probability of LTB forests becoming C sources over decadal time scales, with slower-than-anticipated landscape-scale recovery. Tree species composition was substantially altered with a reduction in functional redundancy and productivity. Results indicate heightened uncertainty due to the synergistic influences of climate change and interacting disturbances. Our results further indicate that current fuel management practices will not be effective at reducing landscape-scale outbreak mortality. Our results provide critical insights into the interaction of drivers (bark beetles, wildfire, fuel management) that increase the risk of C loss and shifting community composition if bark beetle outbreaks become more frequent.