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Fungal species have the capability of producing an overwhelming diversity of bioactive substances that can have beneficial but also detrimental effects on human health. These so-called secondary metabolites naturally serve as antimicrobial “weapon systems”, signaling molecules or developmental effectors for fungi and hence are produced only under very specific environmental conditions or stages in their life cycle. However, as these complex conditions are difficult or even impossible to mimic in laboratory settings, only a small fraction of the true chemical diversity of fungi is known so far. This also implies that a large space for potentially new pharmaceuticals remains unexplored. We here present an overview on current developments in advanced methods that can be used to explore this chemical space. We focus on genetic and genomic methods, how to detect genes that harbor the blueprints for the production of these compounds (i.e., biosynthetic gene clusters, BGCs), and ways to activate these silent chromosomal regions. We provide an in-depth view of the chromatin-level regulation of BGCs and of the potential to use the CRISPR/Cas technology as an activation tool.

The biogeochemical model, PnET-BGC, was modified and parameterized using field data from an experimental whole-tree harvest of watershed (W5) in 1983–1984 at the Hubbard Brook Experimental Forest (HBEF), New Hampshire, USA. The model simulated the hydrology, biomass accumulation, and soil solution and stream water chemistry responses to forest cutting. The parameterized model was then applied to other experimentally cut watersheds at the HBEF; including a devegetation experiment (W2; devegetation and herbicide treatment) and a commercial strip-cut (W4) to evaluate the ability of the model to depict ecosystem responses to a range of cutting regimes. Revisions of algorithms of PnET-BGC improved model performance in predicting shortand long-term dynamics of major elements following various approaches to forest cutting. Despite some initial differences in species composition and biomass accumulation rates among the cut watersheds, simulations of total forest biomass for all three treated watersheds (W2, W4 and W5) were consistent with expectations based on the growth trajectory of a second-growth, reference watershed (W6) at the HBEF. The modified two-soil-layer PnET-BGC captured the immediate increase in stream concentrations of NO₃⁻, Ca²⁺, Mg²⁺ and Na⁺ as well as enhanced adsorption of SO₄²⁻ following cuttings and indicated a greater response for the devegetation and the wholetree harvest treatments than the sequential strip-cut of W4. Simulations indicated intense NO₃⁻ leaching with the devegetation and herbicide treatment and consequent accelerated decline in soil base saturation and a slower recovery pattern during forest regrowth by the end of the simulation period (2100) compared to the other treatments.

The one strain many compounds (OSMAC) strategy is an effective method for activating silent gene clusters by cultivating microorganisms under various conditions. The whole genome sequence of the marine-derived strain Streptomyces globisporus SCSIO LCY30 revealed that it contains 30 biosynthetic gene clusters (BGCs). By using the OSMAC strategy, three types of secondary metabolites were activated and identified, including three angucyclines, mayamycin A (1), mayamycin B (2), and rabolemycin (3); two streptophenazines (streptophenazin O (4) and M (5)); and a macrolide dimeric dinactin (6), respectively. The biosynthetic pathways of the secondary metabolites in these three families were proposed based on the gene function prediction and structural information. The bioactivity assays showed that angucycline compounds 1–3 exhibited potent antitumor activities against 11 human cancer cell lines and antibacterial activities against a series of Gram-positive bacteria. Mayamycin (1) selectively exhibited potent cytotoxicity activity against triple-negative breast cancer (TNBC) cell lines such as MDA-MB-231, MDA-MB-468, and Bt-549, with IC50 values of 0.60–2.22 μM.

Our research group has recently proposed a strategy to simulate net forest carbon fluxes based on the coupling of a NDVI-driven parametric model, Modified C-Fix, and of a biogeochemical model, BIOME-BGC. The outputs of the two models are combined through the use of a proxy of ecosystem distance from equilibrium condition which accounts for the occurred disturbances. This modeling strategy is currently applied to all Italian forest areas using an available set of NDVI images and ancillary data descriptive of an 8-year period (1999–2006). The obtained estimates of forest net primary production (NPP) are first analyzed in order to assess the importance of the main model drivers on relevant spatial variability. This analysis indicates that growing stock is the most influential model driver, followed by forest type and meteorological variables. In particular, the positive influence of growing stock on NPP can be constrained by thermal and water limitations, which are most evident in the upper mountain and most southern zones, respectively. Next, the NPP estimates, aggregated over seven main forest types and twenty administrative regions in Italy, are converted into current annual increment of standing volume (CAI) by specific coefficients. The accuracy of these CAI estimates is finally assessed by comparison with the ground data collected during a recent national forest inventory. The results obtained indicate that the modeling approach tends to overestimate the ground CAI for most forest types. In particular, the overestimation is notable for forest types which are mostly managed as coppice, while it is negligible for high forests. The possible origins of these phenomena are investigated by examining the main model drivers together with the results of previous studies and of older forest inventories. The implications of using different NPP estimation methods are finally discussed in view of assessing the forest carbon budget on a national basis.

Climate change increasingly affects primary productivity and biogeochemical cycles in forest ecosystems at local and global scales. To predict change in vegetation, soil, and hydrologic processes, we applied an integrated biogeochemical model Photosynthesis-EvapoTranspration and BioGeoChemistry (PnET-BGC) to two high-elevation forested watersheds in the southern Appalachians in the US under representative (or radiative) concentration pathway (RCP)4.5 and RCP8.5 scenarios. We investigated seasonal variability of the changes from current (1986–2015) to future climate scenarios (2071–2100) for important biogeochemical processes/states; identified change points for biogeochemical variables from 1931 to 2100 that indicate potential regime shifts; and compared the climate change impacts of a lower-elevation watershed (WS18) with a higher-elevation watershed (WS27) at the Coweeta Hydrologic Laboratory, North Carolina, United States. We find that gross primary productivity (GPP), net primary productivity (NPP), transpiration, nitrogen mineralization, and streamflow are projected to increase, while soil base saturation, and base cation concentration and ANC of streamwater are projected to decrease at the annual scale but with strong seasonal variability under a changing climate, showing the general trend of acidification of soil and streamwater despite an increase in primary productivity. The predicted changes show distinct contrasts between lower and higher elevations. Climate change is predicted to have larger impact on soil processes at the lower elevation watershed and on vegetation processes at the higher elevation watershed. We also detect five change points of the first principal component of 17 key biogeochemical variables simulated with PnET-BGC between 1931 and 2100, with the last change point projected to occur 20 years earlier under RCP8.5 (2059 at WS18 and WS27) than under RCP4.5 (2079 at WS18 and 2074 at WS27) at both watersheds. The change points occurred earlier at WS18 than at WS27 in the 1980s and 2010s but in the future are projected to occur earlier in WS27 (2074) than WS18 (2079) under RCP4.5, implying that changes in biogeochemical cycles in vegetation, soil, and streams may be accelerating at higher-elevation WS27.

Natural products produced by microorganisms are considered an important resource of bioactive secondary metabolites, such as anticancer, antifungal, antibiotic, and immunosuppressive molecules. Streptomyces are the richest source of bioactive natural products via possessing a wide number of secondary metabolite biosynthetic gene clusters (SM-BGCs). Based on rapid development in sequencing technologies with advances in genome mining, exploring the newly isolated Streptomyces species for possible new secondary metabolites is mandatory to find novel natural products. The isolated Streptomyces thinghirensis strain HM3 from arid and sandy texture soil in Qassim, SA, exerted inhibition activity against tested animal pathogenic Gram-positive bacteria and pathogenic fungal species. In this study, we report the draft genome of S. thinghirensis strain HM3, which consists of 7,139,324 base pairs (bp), with an average G+C content of 71.49%, predicting 7949 open reading frames, 12 rRNA operons (5S, 16S, 23S) and 60 tRNAs. An in silico analysis of strain HM3 genome by the antiSMASH and PRISM 4 online software for SM-BGCs predicted 16 clusters, including four terpene, one lantipeptide, one siderophore, two polyketide synthase (PKS), two non-ribosomal peptide synthetase (NRPS) cluster)/NRPS-like fragment, two RiPP/RiPP-like (ribosomally synthesised and post-translationally modified peptide product), two butyrolactone, one CDPS (tRNA-dependent cyclodipeptide synthases), and one other (cluster containing a secondary metabolite-related protein that does not fit into any other category) BGC. The presented BGCs inside the genome, along with antibacterial and antifungal activity, indicate that HM3 may represent an invaluable source for new secondary metabolites.

The assessment of net forest production is important for both scientific and practical purposes. The current paper presents the application of a recently developed strategy to estimate the net primary productivity of an even-aged Mediterranean pine forest (Natural Park of San Rossore, Central Italy). The strategy is based on the use of two models, C-Fix and BIOME-BGC, whose outputs are combined with forest volume data in order to describe the actual status of the ecosystems examined. The accuracy of the simulation is tested against measurements of current annual increment (CAI) derived from a recent forest inventory of the Park. The results of this test indicate that the methodology must be modified to account for the even-aged nature of the study stands. The modification is performed by the analysis of dendrochronological and tree density data, which characterize the temporal volume and CAI evolution of representative forest stands. The modified methodology is capable of taking into account the effect of stand ageing and yielding nearly unbiased estimates of forest CAI.