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.     

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.     

Compatible Models of Carbon Content of Individual Trees on a Cunninghamia lanceolata Plantation in Fujian Province,China

We tried to establish compatible carbon content models of individual trees for a Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.) plantation from Fujian province in southeast China. In general, compatibility requires that the sum of components equal the whole tree, meaning that the sum of percentages calculated from component equations should equal 100%. Thus, we used multiple approaches to simulate carbon content in boles, branches, foliage leaves, roots and the whole individual trees. The approaches included (i) single optimal fitting (SOF), (ii) nonlinear adjustment in proportion (NAP) and (iii) nonlinear seemingly unrelated regression (NSUR). These approaches were used in combination with variables relating diameter at breast height (D) and tree height (H), such as D, D²H, DH and D&H (where D&H means two separate variables in bivariate model). Power, exponential and polynomial functions were tested as well as a new general function model was proposed by this study. Weighted least squares regression models were employed to eliminate heteroscedasticity. Model performances were evaluated by using mean residuals, residual variance, mean square error and the determination coefficient. The results indicated that models with two dimensional variables (DH, D²H and D&H) were always superior to those with a single variable (D). The D&H variable combination was found to be the most useful predictor. Of all the approaches, SOF could establish a single optimal model separately, but there were deviations in estimating results due to existing incompatibilities, while NAP and NSUR could ensure predictions compatibility. Simultaneously, we found that the new general model had better accuracy than others. In conclusion, we recommend that the new general model be used to estimate carbon content for Chinese fir and considered for other vegetation types as well.     

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.