Quantitative comparison of impeller flowmeter and particle-size dsitribution techniques for the characterization of hydraulic conductivity variability

Basic univariate statistics and key geostatistical parameters of estimates of hydraulic conductivity obtained at the decimeter scale by two different methods are presented and compared. The two estimates are based on (1) the empirical Kozeny-Carman formulation, and (2) impeller flowmeter tests. The former provides values of conductivity, KGS, based on particle size distributions. Impeller flowmeter techniques allow inferring conductivities, KFM, from measurements of vertical flows within a borehole. Data obtained during an extensive monitoring campaign at an experimental site located near the city of Tübingen, Germany, are considered. Statistics of the natural logarithm of KGS and KFM at the site are similar in terms of mean values (with averages of ln KGS being slightly smaller than those of ln KFM) and differ in terms of variogram ranges and sample variances. The correlation between the two sets of estimates is virtually absent. Additional data from two different sites already presented in the literature allow comparing conductivity estimates from flowmeter and grain-size distributions (or permeameter measurements) taken at adjacent wells and support the finding that KGS and KFM lack correlation. The analysis highlights the difficulty in obtaining meaningful quantitatively comparable hydraulic conductivity data at the decimetric scale.Peer ReviewedPostprint (published version

RSR-2, the Caenorhabditis elegans Ortholog of Human Spliceosomal Component SRm300/SRRM2, Regulates Development by Influencing the Transcriptional Machinery

Protein components of the spliceosome are highly conserved in eukaryotes and can influence several steps of the gene expression process. RSR-2, the Caenorhabditis elegans ortholog of the human spliceosomal protein SRm300/SRRM2, is essential for viability, in contrast to the yeast ortholog Cwc21p. We took advantage of mutants and RNA interference (RNAi) to study rsr-2 functions in C. elegans, and through genetic epistasis analysis found that rsr-2 is within the germline sex determination pathway. Intriguingly, transcriptome analyses of rsr-2(RNAi) animals did not reveal appreciable splicing defects but instead a slight global decrease in transcript levels. We further investigated this effect in transcription and observed that RSR-2 colocalizes with DNA in germline nuclei and coprecipitates with chromatin, displaying a ChIP-Seq profile similar to that obtained for the RNA Polymerase II (RNAPII). Consistent with a novel transcription function we demonstrate that the recruitment of RSR-2 to chromatin is splicing-independent and that RSR-2 interacts with RNAPII and affects RNAPII phosphorylation states. Proteomic analyses identified proteins associated with RSR-2 that are involved in different gene expression steps, including RNA metabolism and transcription with PRP-8 and PRP-19 being the strongest interacting partners. PRP-8 is a core component of the spliceosome and PRP-19 is the core component of the PRP19 complex, which interacts with RNAPII and is necessary for full transcriptional activity. Taken together, our study proposes that RSR-2 is a multifunctional protein whose role in transcription influences C. elegans development

The LifeCycle Project-EU Child Cohort Network : a federated analysis infrastructure and harmonized data of more than 250,000 children and parents

Early life is an important window of opportunity to improve health across the full lifecycle. An accumulating body of evidence suggests that exposure to adverse stressors during early life leads to developmental adaptations, which subsequently affect disease risk in later life. Also, geographical, socio-economic, and ethnic differences are related to health inequalities from early life onwards. To address these important public health challenges, many European pregnancy and childhood cohorts have been established over the last 30 years. The enormous wealth of data of these cohorts has led to important new biological insights and important impact for health from early life onwards. The impact of these cohorts and their data could be further increased by combining data from different cohorts. Combining data will lead to the possibility of identifying smaller effect estimates, and the opportunity to better identify risk groups and risk factors leading to disease across the lifecycle across countries. Also, it enables research on better causal understanding and modelling of life course health trajectories. The EU Child Cohort Network, established by the Horizon2020-funded LifeCycle Project, brings together nineteen pregnancy and childhood cohorts, together including more than 250,000 children and their parents. A large set of variables has been harmonised and standardized across these cohorts. The harmonized data are kept within each institution and can be accessed by external researchers through a shared federated data analysis platform using the R-based platform DataSHIELD, which takes relevant national and international data regulations into account. The EU Child Cohort Network has an open character. All protocols for data harmonization and setting up the data analysis platform are available online. The EU Child Cohort Network creates great opportunities for researchers to use data from different cohorts, during and beyond the LifeCycle Project duration. It also provides a novel model for collaborative research in large research infrastructures with individual-level data. The LifeCycle Project will translate results from research using the EU Child Cohort Network into recommendations for targeted prevention strategies to improve health trajectories for current and future generations by optimizing their earliest phases of life.Peer reviewe

Quantitative comparison of impeller flowmeter and particle-size dsitribution techniques for the characterization of hydraulic conductivity variability

Basic univariate statistics and key geostatistical parameters of estimates of hydraulic conductivity obtained at the decimeter scale by two different methods are presented and compared. The two estimates are based on (1) the empirical Kozeny-Carman formulation, and (2) impeller flowmeter tests. The former provides values of conductivity, KGS, based on particle size distributions. Impeller flowmeter techniques allow inferring conductivities, KFM, from measurements of vertical flows within a borehole. Data obtained during an extensive monitoring campaign at an experimental site located near the city of Tübingen, Germany, are considered. Statistics of the natural logarithm of KGS and KFM at the site are similar in terms of mean values (with averages of ln KGS being slightly smaller than those of ln KFM) and differ in terms of variogram ranges and sample variances. The correlation between the two sets of estimates is virtually absent. Additional data from two different sites already presented in the literature allow comparing conductivity estimates from flowmeter and grain-size distributions (or permeameter measurements) taken at adjacent wells and support the finding that KGS and KFM lack correlation. The analysis highlights the difficulty in obtaining meaningful quantitatively comparable hydraulic conductivity data at the decimetric scale.Peer Reviewe

RSR-2, the Caenorhabditis elegans Ortholog of Human Spliceosomal Component SRm300/SRRM2, Regulates Development by Influencing the Transcriptional Machinery

Protein components of the spliceosome are highly conserved in eukaryotes and can influence several steps of the gene expression process. RSR-2, the Caenorhabditis elegans ortholog of the human spliceosomal protein SRm300/SRRM2, is essential for viability, in contrast to the yeast ortholog Cwc21p. We took advantage of mutants and RNA interference (RNAi) to study rsr-2 functions in C. elegans, and through genetic epistasis analysis found that rsr-2 is within the germline sex determination pathway. Intriguingly, transcriptome analyses of rsr-2(RNAi) animals did not reveal appreciable splicing defects but instead a slight global decrease in transcript levels. We further investigated this effect in transcription and observed that RSR-2 colocalizes with DNA in germline nuclei and coprecipitates with chromatin, displaying a ChIP-Seq profile similar to that obtained for the RNA Polymerase II (RNAPII). Consistent with a novel transcription function we demonstrate that the recruitment of RSR-2 to chromatin is splicing-independent and that RSR-2 interacts with RNAPII and affects RNAPII phosphorylation states. Proteomic analyses identified proteins associated with RSR-2 that are involved in different gene expression steps, including RNA metabolism and transcription with PRP-8 and PRP-19 being the strongest interacting partners. PRP-8 is a core component of the spliceosome and PRP-19 is the core component of the PRP19 complex, which interacts with RNAPII and is necessary for full transcriptional activity. Taken together, our study proposes that RSR-2 is a multifunctional protein whose role in transcription influences C. elegans development

RSR-2, the <i>Caenorhabditis elegans</i> Ortholog of Human Spliceosomal Component SRm300/SRRM2, Regulates Development by Influencing the Transcriptional Machinery

<div><p>Protein components of the spliceosome are highly conserved in eukaryotes and can influence several steps of the gene expression process. RSR-2, the <i>Caenorhabditis elegans</i> ortholog of the human spliceosomal protein SRm300/SRRM2, is essential for viability, in contrast to the yeast ortholog Cwc21p. We took advantage of mutants and RNA interference (RNAi) to study <i>rsr-2</i> functions in <i>C. elegans</i>, and through genetic epistasis analysis found that <i>rsr-2</i> is within the germline sex determination pathway. Intriguingly, transcriptome analyses of <i>rsr-2(RNAi)</i> animals did not reveal appreciable splicing defects but instead a slight global decrease in transcript levels. We further investigated this effect in transcription and observed that RSR-2 colocalizes with DNA in germline nuclei and coprecipitates with chromatin, displaying a ChIP-Seq profile similar to that obtained for the RNA Polymerase II (RNAPII). Consistent with a novel transcription function we demonstrate that the recruitment of RSR-2 to chromatin is splicing-independent and that RSR-2 interacts with RNAPII and affects RNAPII phosphorylation states. Proteomic analyses identified proteins associated with RSR-2 that are involved in different gene expression steps, including RNA metabolism and transcription with PRP-8 and PRP-19 being the strongest interacting partners. PRP-8 is a core component of the spliceosome and PRP-19 is the core component of the PRP19 complex, which interacts with RNAPII and is necessary for full transcriptional activity. Taken together, our study proposes that RSR-2 is a multifunctional protein whose role in transcription influences <i>C. elegans</i> development.</p></div

RSR-2 is associated with chromatin and modifies RNAPII distribution.

<p>(A) Snapshot of the genome browser (chromosome IV) showing ChIP-Seq data. Chromatin-binding profiles of RNAPII and RSR-2 are similar. RNAPII peaks are represented in black, RSR-2 peaks are represented in red, and input samples are represented in blue. Upon <i>rsr-2</i> RNAi, the RNAPII peak profile at <i>phy-2</i> locus shifts from the 3′ to the 5′end. RSR-2 peaks disappear upon <i>rsr-2</i> RNAi. Data was visualized with the Integrated Genome Browser (IGB) software. (B) ChIP-qPCR showing how RNAPII occupancy changes upon <i>rsr-2</i> RNAi at the <i>phy-2</i> locus. Black bars are a scaled representation of the regions covered by the primer pairs used in this experiment. (C) Distribution of RNAPII and RSR-2 ChIP peaks along five zones within an averaged gene. Statistically called peaks from a single ChIP-Seq experiment were classified with respect to their position relative to the nearest TSS. Y axis indicates the frequency of peaks within each zone. <i>rsr-2</i> RNAi slightly modifies the distribution of RNAPII along an averaged gene.</p

RSR-2 interacts with RNAPII and affects its phosphorylation state.

<p>RNAPII phosphoisoform detection by western blot in wild type and <i>rsr-2(RNAi)</i> worms. POL IIo is the abbreviation for the hyperphosphorylated form of the RNAPII whereas POL IIa represents the hypophosphorylated form of the RNAPII. Hyperphosphorylated RNAPII accumulates in <i>rsr-2(RNAi)</i> worms. The actin antibody C4 was used as a loading control. (A) RNAPII phosphoisoforms detected with the N-20 antibody. (B) RNAPII phosphoisoforms co-immunoprecipitate with RSR-2. IP was performed with the anti-RSR-2 antibody using an extract from a mixed-stage worm population. Eluted IP product was checked for the presence of RNAPII phosphorylated isoforms (Ser-2 and Ser-5 phosphorylation). Lane 1: Input (4% of IP), lane 2: IP with unspecific rabbit IgG antibody, lane 3: IP with RSR-2 antibody, lane 4: unspecific binding to beads. Pictures displayed are representative of a series of two experiments.</p

<i>rsr-2</i> expression in the soma and in the germline.

<p>(A) Expression of the transgene <i>cerEx01</i>[<i>rsr-2</i> promoter::GFP::H2B::<i>rsr-2</i> 3′ UTR] in the germline as complex array. Upper panel: detail of the distal part of the germline showing expression of GFP::H2B in the transition and meiotic zone (broken line) but not in the mitotic zone (continuous line). Bottom panel: corresponding Nomarski image. (B) Representative image of <i>rsr-2</i> mRNA in wild type germline detected by <i>in situ</i> hybridization (n = 56). Magnification of the distal part of the germline (left). The broken line labels the transition and meiotic areas whereas the continuous line marks the mitotic zone. (C) Expression of the transgene <i>cerEx04</i>[<i>rsr-2</i> promoter::GFP::<i>rsr-2</i> genomic fragment (exons, introns and 3′ UTR)]. <i>myo-2</i>::mcherry was used as a control of transformation. (D) GFP::RSR-2 forming nuclear speckles in a hypodermal cell nucleus (left) and a neuronal nucleus (right). (E) Confocal images of anti-RSR-2 immunofluorescence in the germline. Nuclei counterstained with DAPI. The three pictures on the right correspond to magnified images of the white boxes.</p

RSR-2 binds to intronless genes.

<p>(A) Chromatin-binding profiles of RNAPII and RSR-2, and RNA-Seq reads in intronless genes. RNA-Seq reads correspond to the N2 mid-L4 stage dataset from the modENCODE consortium <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003543#pgen.1003543-Gerstein1" target="_blank">[82]</a>. (B) ChIP-qPCR for intronless genes with mouse anti-RNAPII (8WG16), rabbit anti-RSR-2 (Q5092) and unspecific mouse and rabbit IgG antibodies (sc-2025 and sc-2027 respectively). All qPCR values are represented as the percentage of input immunoprecipitated.</p