Ecotubes
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PROVIDER: PRJEB22310 | ENA |
REPOSITORIES: ENA
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Project description:Soil microbial community is a complex blackbox that requires a multi-conceptual approach (Hultman et al., 2015; Bastida et al., 2016). Most methods focus on evaluating total microbial community and fail to determine its active fraction (Blagodatskaya & Kuzyakov 2013). This issue has ecological consequences since the behavior of the active community is more important (or even essential) and can be different to that of the total community. The sensitivity of the active microbial community can be considered as a biological mechanism that regulates the functional responses of soil against direct (i.e. forest management) and indirect (i.e. climate change) human-induced alterations. Indeed, it has been highglihted that the diversity of the active community (analyzed by metaproteomics) is more connected to soil functionality than the that of the total community (analyzed by 16S rRNA gene and ITS sequencing) (Bastida et al., 2016). Recently, the increasing application of soil metaproteomics is providing unprecedented, in-depth characterisation of the composition and functionality of active microbial communities and overall, allowing deeper insights into terrestrial microbial ecology (Chourey et al., 2012; Bastida et al., 2015, 2016; Keiblinger et al., 2016). Here, we predict the responsiveness of the soil microbial community to forest management in a climate change scenario. Particularly, we aim: i) to evaluate the impacts of 6-years of induced drought on the diversity, biomass and activity of the microbial community in a semiarid forest ecocosystem; and ii) to discriminate if forest management (thinning) influences the resistance of the microbial community against induced drought. Furthermore, we aim to ascertain if the functional diversity of each phylum is a trait that can be used to predict changes in microbial abundance and ecosystem functioning.
2021-02-02 | PXD005447 | Pride
Project description:Can we predict ecosystem functioning using tightly linked functional gene diversity?
| PRJEB21617 | ENA
Project description:Feng et al 2019 - Microarray Raw Data
| PRJNA562147 | ENA
Project description:Mammalian embryogenesis is characterized by rapid cellular proliferation and diversification. Within a few weeks, a single cell zygote gives rise to millions of cells expressing a panoply of molecular programs, including much of the diversity that will subsequently be present in adult tissues. Although intensively studied, a comprehensive delineation of the major cellular trajectories that comprise mammalian development in vivo remains elusive. For mouse embryogenesis in particular, we and others have performed single cell or single nucleus RNA-seq data (scRNA-seq) during implantation, gastrulation and organogenesis. Here we set out to integrate several single cell RNA-seq datasets (scRNA-seq) that collectively span mouse gastrulation and organogenesis. However, a technical challenge that we faced is that the datasets that we sought to integrate were generated by different groups at different times using different scRNA-seq technologies. In particular, probably because there was no overlapping timepoint, the integration of scRNA-seq data generated at E8.5 (cells, 10X Genomics) and E9.5 (nuclei, sci-RNA-seq3) was challenging (Cao et al. 2019; Pijuan-Sala et al. 2019). To address this, we set out to generate new data at E8.5 that might serve to “bridge” these two datasets. Because of how quickly changes are occurring during this window of development, we focused on individual, somite-resolved E8.5 embryos using a simplified, optimized version of sci-RNA-seq3. At the same time, to obtain higher quality data across E9.5-E13.5, we performed a deeper sequencing (specifically, three additional Novaseq runs) of previously reported libraries (Cao et al. 2019). Compared to the previous data (Cao et al. 2019), the median UMI count per cell improved from 671 to 1,434, while the median genes detected per cell improved from 518 to 735. Overall, we collected published data (Cheng et al. 2019; Mohammed et al. 2017; Pijuan-Sala et al. 2019), the new E8.5 data, and the deeper sequencing of previous libraries (Cao et al. 2019). Altogether, we define cell states at each of 19 successive stages spanning E3.5 to E13.5, heuristically connect them to their pseudo-ancestors and pseudo-descendants. Despite being constructed through automated procedures, the resulting trajectories of mammalian embryogenesis (TOME) are largely consistent with our contemporary understanding of mammalian development. In addition, the data of deeper sequencing of previously reported libraries itself comprises a foundational resource for mammalian developmental biology, and are made available in a way that will facilitate their ongoing annotation by the research community.
2022-03-11 | GSE186068 | GEO
Project description:While DNA methylation is an important gene regulatory mechanism in mammals (Razin and Riggs 1980; Moore, Le, and Fan 2013), its function in arthropods remains poorly understood. Studies in eusocial insects have argued for its role in caste development by regulating gene expression and splicing (Elango et al. 2009; Lyko et al. 2010; Bonasio et al. 2012; Flores et al. 2012; Foret et al. 2012; Li-Byarlay et al. 2013; Marshall, Lonsdale, and Mallon 2019; Shi et al. 2013)(Alvarado et al. 2015; Kucharski et al. 2008). However, such findings are not always consistent across studies, and have therefore remained controversial (Arsenault, Hunt, and Rehan 2018; Cardoso-Junior et al. 2021; Harris et al. 2019; Herb et al. 2012; Libbrecht et al. 2016; Oldroyd and Yagound 2021b; Patalano et al. 2015). Here we use CRISPR/Cas9 to mutate the maintenance DNA methyltransferase DNMT1 in the clonal raider ant, Ooceraea biroi. Mutants have greatly reduced DNA methylation but no obvious developmental phenotypes, demonstrating that, unlike mammals (Brown and Robertson 2007; En Li, Bestor, and Jaenisch 1992; Jackson-Grusby et al. 2001; Panning and Jaenisch 1996), ants can undergo normal development without DNMT1 or DNA methylation. Additionally, we find no evidence of DNA methylation regulating caste development. However, mutants are sterile, while in wildtypes, DNMT1 is localized to the ovaries and maternally provisioned into nascent oocytes. This supports the idea that DNMT1 plays a crucial but unknown role in the insect germline (Amukamara et al. 2020; Arsala et al. 2021; Bewick et al. 2019; Schulz et al. 2018; Ventós-Alfonso et al. 2020; Washington et al. 2020).
2023-03-23 | GSE182212 | GEO
Project description:Genomic DNA from 191 asy1/+ Col x Ler F2 individuals was extracted using CTAB and used to generate sequencing libraries as described (Lawrence et al, 2019 Current Biology). Sequencing data was analysed to identify crossovers using the TIGER pipeline as previously described (Rowan et al, 2015 G3 (Bethesda); Yelina et al, 2015 Genes & Dev; Lawrence et al, 2019 Current Biology).
2020-05-18 | E-MTAB-8725 | biostudies-arrayexpress
Project description:CD47 is a transmembrane glycoprotein that is ubiquitously expressed in different organs and tissues (Barclay and Van den Berg 2014; Liu, et al. 2017). In the human immune system, CD47 interacts with some integrins, two counter-receptor signal regulator protein (SIRP) family members, and the secreted thrombospondin-1 (TSP1) (Barclay and Van den Berg 2014; Gao, et al. 2016; Kaur, et al. 2013; Oldenborg, et al. 2000). CD47 has two established roles in the immune system. The CD47-SIRPα interaction was identified as a critical innate immune checkpoint, which delivers an antiphagocytic signal to macrophages and inhibits neutrophil cytotoxicity (Martínez- Sanz, et al. 2021). Its interaction with inhibitory SIRPα is a physiological anti-phagocytic “don’t eat me” signal on circulating red blood cells that is co-opted by cancer cells (Matlung, et al. 2017). Many malignant cells overexpress CD47 (Betancur, et al. 2017; Chao, et al. 2011; Jaiswal, et al. 2009; Majeti, et al. 2009; Oronsky, et al. 2020; Petrova, et al. 2017). CD47/SIRPα-targeted therapeutics have been developed to overcome this immune checkpoint for cancer treatment (Kaur, et al. 2020; Matlung, et al. 2017). Secondly, engagement of CD47 on T cells by TSP1 regulates their differentiation and survival (Grimbert, et al. 2006; Lamy, et al. 2007) and inhibits T cell receptor signaling and antigen presentation by dendritic cells (DCs) (Kaur, et al. 2014; Li, et al. 2002; Liu, et al. 2015; Miller, et al. 2013; Soto-Pantoja, et al. 2014; Weng, et al. 2014). TSP1/CD47 signaling has similar inhibitory functions to limit NK cell activation (Kim, et al. 2008; Nath, et al. 2018; Nath, et al. 2019; Schwartz, et al. 2019) and IL1β production by macrophages (Stein, et al. 2016). CD47 is therefore a checkpoint that regulates both innate and adaptive immunity. The recent understanding of CD47 antagonism associated with increased antigen presentation by DCs (Liu, et al. 2016) and natural killer cell cytotoxicity (Nath, et al. 2019) contributes to the heightened interest in CD47 as a therapeutic target (Kaur, et al. 2020).
2022-09-21 | GSE198820 | GEO
Project description:Genomic DNA from 187 wild type and 169 asy1 Col-0 x Ws-4 F2 individuals was extracted using CTAB and used to generate sequencing libraries as described (Lawrence et al, 2019 Current Biology). Sequencing data was analysed to identify crossovers using the TIGER pipeline as previously described (Rowan et al, 2015 G3 (Bethesda); Yelina et al, 2015 Genes & Dev; Lawrence et al, 2019 Current Biology).
2020-05-18 | E-MTAB-8715 | biostudies-arrayexpress