Deep soil microbial carbon metabolic function is important but often neglected: a study on the Songnen Plain reed wetland, Northeast China.

Soil microbial carbon metabolism is critical in wetland soil carbon cycling, and is also a research hotspot at present. However, most studies focus on the surface soil layer in the wetlands and the microorganisms associated with this layer. In this study, 0-75 cm soil profiles were collected from five widely separated reed wetlands in the Songnen Plain, which has a large number of middle-high latitude inland saline-sodic wetlands. The Biolog-ECO method was used to determine the carbon metabolic activity and functional diversity of soil microorganisms. The results showed that soil carbon metabolic activity decreased with increasing soil depth. The carbon metabolic activity of soil microorganisms in the 60-75 cm layer was approximately 57.41%-74.60% of that in the 0-15 cm layer. The soil microbial Shannon index and utilization rate of amines decreased with an increase in soil depth, while the Evenness index and utilization rate of polymers tended to increase with soil depth. Dissolved organic carbon (DOC) is the most important factor affecting microbial carbon source utilization preference, because microorganisms mainly obtain the carbon source from DOC. The result of the correlation analysis showed that the soil microbial carbon metabolic activity, Shannon index, and Evenness index significantly correlated with soil total carbon (TC), microbial biomass carbon (MBC), DOC, total nitrogen (TN), ammonium nitrogen (NH4 +-N), nitrate nitrogen (NO3 --N) contents, and electrical conductivity (EC). This study emphasized the important role of microbial carbon metabolic function in deep soil.

[Change in nitrogen availability of northern peatlands and its effect on carbon sink function]. / åŒ—æ–¹æ³¥ç‚­åœ°æ°®ç´ æœ‰æ•ˆæ€§çš„å˜åŒ–åŠå ¶å¯¹ç¢³æ±‡åŠŸèƒ½çš„å½±å“.

Northern peatlands are typical nitrogen-limited ecosystems, which are sensitive to global climate change and human activities. The increases of endogenous available nitrogen caused by climate warming and exogenous nitrogen input caused by human activities changed the nitrogen availability of northern peatlands, and would affect carbon and nitrogen cycling and carbon sink function of peatland. Here, we review the influence factors of carbon accumulation rate and carbon sink function in northern peatlands. The effects of nitrogen deposition, freezing and thawing, fire and other factors on nitrogen availability of northern peatlands were reviewed. The responses of plants and soil microorganisms to changes in nitrogen availability were elaborated from carbon fixation and carbon emission processes, respectively. The research related to carbon sink function of peat ecosystems under the influence of glo-bal change was prospected, aiming to help the implementation of the 'double carbon' goal.

Toward an improved discrimination of outer membrane proteins using a sequence-based approach.

This article offers a novel sequence-based approach to discriminate outer membrane proteins (OMPs). The first step is to use a new representation approach, factor analysis scales of generalized amino acid information (FASGAI) representing hydrophobicity, alpha and turn propensities, bulky properties, compositional characteristics, local flexibility and electronic properties, etc., to characterize sequences of OMPs and non-OMPs. The subsequent data is then transformed into a uniform matrix by the auto cross covariance (ACC). The second step is to develop discrimination predictors of OMPs from non-OMPs using a support vector machine (SVM). The SVM predictors thus successfully produce a high Matthews correlation coefficient (MCC) of 0.916 on 208 OMPs from non-OMPs including 206 α-helical membrane proteins and 673 globular proteins by a fivefold cross validation test. Meanwhile, overall MCC values of 0.923 and 0.930 are obtained for the discrimination OMPs from the α-helical membrane proteins and the globular proteins, respectively. The results demonstrate that the FASGAI-ACC-SVM combination approach shows great prospect of application in the field of bioinformatics or proteomics studies.