Biology subjects

Gross, V.

Publications and source records attributed to Gross, V..

4 recordsLinked to original sources

bioRxiv2025

Methane sink function of grassland soil microbiomes - negative effects of intensive management persist three years after land-use extensification

Grassland soils are important methane (CH4) sinks through CH4 oxidation by methanotrophs, but intensive management with high nitrogen inputs and grazing densities reduces this potential. While long-term recovery of the CH4 sink after land-use change is generally established, little is known about the short-term effects of reducing land-use intensity index (LUI) through extensive management in grassland. We did not find an effect on potential CH4 oxidation rates (PMORs) and the abundances atmospheric CH4-consuming methanotrophs after three years of LUI reduction (no fertilization, no grazing, and one mowing per year) in 45 intensively managed grassland sites located in three different pedoclimatic regions of Germany. However, we observed a decline in the abundance of CH4 producing methanogens. Moreover, we found greater PMORs and higher abundance of Upland Soil Cluster {gamma} (USC{gamma}) methanotrophs on additional, historically low LUI sites. Soil bulk density decreased already after three years of LUI reduction and was even lower in historically low LUI sites. Strong correlations between the abundance canonical methanotrophs and methanogens highlight a CH4 filter function that was independent from LUI reduction across regions. Our study consistently shows that three years of LUI reduction are not sufficient to restore the CH4 sink function of temperate grasslands. However, the lower soil bulk density and the decreased abundance of methanogens indicate that LUI reduction will affect the habitat and living boundary conditions for CH4-cycling microorganisms in the long term. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=133 SRC="FIGDIR/small/646387v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@876847org.highwire.dtl.DTLVardef@1cf2e69org.highwire.dtl.DTLVardef@f02df9org.highwire.dtl.DTLVardef@5a3f1f_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2024

Pigment-dispersing factor neuropeptides act as multifunctional hormones and modulators in tardigrades

Pigment-dispersing factors (PDFs) are neuropeptides that play key roles in controlling the circadian rhythms in various insects, whereas their function remains elusive in other protostomes including tardigrades (water bears). Here we show that the three PDFs of the tardigrade Hypsibius exemplaris are co-localized in two pairs of inner lobe cells in the brain, whereas only one PDF occurs in four additional cerebral and two extracerebral cells. The axons of the inner lobe cells pass through the contralateral brain hemisphere, descend to the ventral nerve cord and terminate in two pairs of potential release sites in the posteriormost trunk ganglion. Using in vitro assays, we demonstrate that all three PDFs and their deorphanized receptor (PDFR) are functional. Widespread localization of PDFR suggests that tardigrade PDFs may act as multifunctional hormones and neuromodulators that control major functions including light detection, neural processing, locomotion, feeding, digestion, osmoregulation, growth, embryonic development, and oogenesis/reproduction.

zoology↗

bioRxiv2023

CebraEM: A practical workflow to segmentcellular organelles in volume SEM datasetsusing a transferable CNN-based membraneprediction

Segmentation of large-volume datasets obtained by volume SEM techniques is a challenging task that generally requires a considerable amount of human effort. Despite recent advances in deep learning leading to the successful segmentation of cellular organelles in a variety of datasets, it is still challenging and time-consuming to produce the necessary data for training a convolutional neural network as well as to set up targeted post-processing pipelines to obtain a good quality full-volume semantic instance segmentation. We present CebraEM, a software package that uses a novel workflow for the segmentation of organelles in volume EM datasets, which helps to minimize the annotation time for the generation of training data. It relies on a generic CNN-based membrane prediction, followed by a well-established machine-learning pipeline that includes over-segmentation before random forest classification and graph multi-cut grouping. The workflow was tested for the segmentation of organelles on different datasets originating from various sample preparations and imaging modalities in volume SEM, in each case resulting in state-of-the-art semantic instance segmentations without additional post-processing. Importantly, by considerably simplifying the segmentation problem, CebraEM empowers single users with the ability to efficiently segment hundreds of gigabytes of data.

cell biology↗

bioRxiv2021

Transcriptional dynamics of methane-cycling microbiomes are linked to seasonal CH4 fluxes in two hydromorphic and organic-rich grassland soils

Soil CH4 fluxes are driven by CH4-producing and -consuming microorganisms that determine whether soils are sources or sinks of this potent greenhouse gas. Using quantitative metatranscriptomics, we linked CH4-cycling microbiomes to net surface CH4 fluxes throughout a year in two drained peatland soils differing in grassland land-use intensity and physicochemical properties. CH4 fluxes were highly dynamic; both soils were net CH4 sources in autumn and winter and sinks in spring and summer. Despite similar net CH4 emissions, methanogen and methanotroph loads, as determined by small subunit rRNA transcripts per gram soil, differed strongly between sites. In contrast, mRNA transcript abundances were similar in both soils and correlated well with CH4 fluxes. The methane monooxygenase to methanogenesis mRNA ratio was higher in spring and summer, when the soils were net CH4 sinks. CH4 uptake was linked to an increased proportion of USC and {gamma} and pmoA2 pmoA transcripts. We assume that methanogen transcript abundance may be useful to approximate changes in net surface CH4 emissions from drained peat soils; high methanotroph to methanogen ratios would indicate CH4 sink properties. Our study shows the strength of quantitative metatranscriptomics; mRNA transcript abundance holds promising indicator to link soil microbiome functions to ecosystem-level processes.

microbiology↗