Ovaries of estrogen receptor 1-deficient mice show iron overload and signs of aging.

Introduction: Estrogens are crucial regulators of ovarian function, mediating their signaling through binding to estrogen receptors. The disruption of the estrogen receptor 1 (Esr1) provokes infertility associated with a hemorrhagic, cystic phenotype similar to that seen in diseased or aged ovaries. Our previous study indicated the possibility of altered iron metabolism in Esr1-deficient ovaries showing massive expression of lipocalin 2, a regulator of iron homeostasis. Methods: Therefore, we examined the consequences of depleting Esr1 in mouse ovaries, focusing on iron metabolism. For that reason, we compared ovaries of adult Esr1-deficient animals and age-matched wild type littermates. Results and discussion: We found increased iron accumulation in Esr1-deficient animals by using laser ablation inductively coupled plasma mass spectrometry. Western blot analysis and RT-qPCR confirmed that iron overload alters iron transport, storage and regulation. In addition, trivalent iron deposits in form of hemosiderin were detected in Esr1-deficient ovarian stroma. The depletion of Esr1 was further associated with an aberrant immune cell landscape characterized by the appearance of macrophage-derived multinucleated giant cells (MNGCs) and increased quantities of macrophages, particularly M2-like macrophages. Similar to reproductively aged animals, MNGCs in Esr1-deficient ovaries were characterized by iron accumulation and strong autofluorescence. Finally, deletion of Esr1 led to a significant increase in ovarian mast cells, involved in iron-mediated foam cell formation. Given that these findings are characteristics of ovarian aging, our data suggest that Esr1 deficiency triggers mechanisms similar to those associated with aging.

Coherent pathway enrichment estimation by modeling inter-pathway dependencies using regularized regression.

MOTIVATION: Gene set enrichment methods are a common tool to improve the interpretability of gene lists as obtained, for example, from differential gene expression analyses. They are based on computing whether dysregulated genes are located in certain biological pathways more often than expected by chance. Gene set enrichment tools rely on pre-existing pathway databases such as KEGG, Reactome, or the Gene Ontology. These databases are increasing in size and in the number of redundancies between pathways, which complicates the statistical enrichment computation. RESULTS: We address this problem and develop a novel gene set enrichment method, called pareg, which is based on a regularized generalized linear model and directly incorporates dependencies between gene sets related to certain biological functions, for example, due to shared genes, in the enrichment computation. We show that pareg is more robust to noise than competing methods. Additionally, we demonstrate the ability of our method to recover known pathways as well as to suggest novel treatment targets in an exploratory analysis using breast cancer samples from TCGA. AVAILABILITY AND IMPLEMENTATION: pareg is freely available as an R package on Bioconductor (https://bioconductor.org/packages/release/bioc/html/pareg.html) as well as on https://github.com/cbg-ethz/pareg. The GitHub repository also contains the Snakemake workflows needed to reproduce all results presented here.

Swiss public health measures associated with reduced SARS-CoV-2 transmission using genome data.

Genome sequences from evolving infectious pathogens allow quantification of case introductions and local transmission dynamics. We sequenced 11,357 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes from Switzerland in 2020-the sixth largest effort globally. Using a representative subset of these data, we estimated viral introductions to Switzerland and their persistence over the course of 2020. We contrasted these estimates with simple null models representing the absence of certain public health measures. We show that Switzerland's border closures decoupled case introductions from incidence in neighboring countries. Under a simple model, we estimate an 86 to 98% reduction in introductions during Switzerland's strictest border closures. Furthermore, the Swiss 2020 partial lockdown roughly halved the time for sampled introductions to die out. Last, we quantified local transmission dynamics once introductions into Switzerland occurred using a phylodynamic model. We found that transmission slowed 35 to 63% upon outbreak detection in summer 2020 but not in fall. This finding may indicate successful contact tracing over summer before overburdening in fall. The study highlights the added value of genome sequencing data for understanding transmission dynamics.

Early detection and surveillance of SARS-CoV-2 genomic variants in wastewater using COJAC.

The continuing emergence of SARS-CoV-2 variants of concern and variants of interest emphasizes the need for early detection and epidemiological surveillance of novel variants. We used genomic sequencing of 122 wastewater samples from three locations in Switzerland to monitor the local spread of B.1.1.7 (Alpha), B.1.351 (Beta) and P.1 (Gamma) variants of SARS-CoV-2 at a population level. We devised a bioinformatics method named COJAC (Co-Occurrence adJusted Analysis and Calling) that uses read pairs carrying multiple variant-specific signature mutations as a robust indicator of low-frequency variants. Application of COJAC revealed that a local outbreak of the Alpha variant in two Swiss cities was observable in wastewater up to 13 d before being first reported in clinical samples. We further confirmed the ability of COJAC to detect emerging variants early for the Delta variant by analysing an additional 1,339 wastewater samples. While sequencing data of single wastewater samples provide limited precision for the quantification of relative prevalence of a variant, we show that replicate and close-meshed longitudinal sequencing allow for robust estimation not only of the local prevalence but also of the transmission fitness advantage of any variant. We conclude that genomic sequencing and our computational analysis can provide population-level estimates of prevalence and fitness of emerging variants from wastewater samples earlier and on the basis of substantially fewer samples than from clinical samples. Our framework is being routinely used in large national projects in Switzerland and the UK.

Fractional 2'-O-methylation in the ribosomal RNA of Dictyostelium discoideum supports ribosome heterogeneity in Amoebozoa.

A hallmark of ribosomal RNA (rRNA) are 2'-O-methyl groups that are introduced sequence specifically by box C/D small nucleolar RNAs (snoRNAs) in ribonucleoprotein particles. Most data on this chemical modification and its impact on RNA folding and stability are derived from organisms of the Opisthokonta supergroup. Using bioinformatics and RNA-seq data, we identify 30 novel box C/D snoRNAs in Dictyostelium discoideum, many of which are differentially expressed during the multicellular development of the amoeba. By applying RiboMeth-seq, we find 49 positions in the 17S and 26S rRNA 2'-O-methylated. Several of these nucleotides are substoichiometrically modified, with one displaying dynamic modification levels during development. Using homology-based models for the D. discoideum rRNA secondary structures, we localize many modified nucleotides in the vicinity of the ribosomal A, P and E sites. For most modified positions, a guiding box C/D snoRNA could be identified, allowing to determine idiosyncratic features of the snoRNA/rRNA interactions in the amoeba. Our data from D. discoideum represents the first evidence for ribosome heterogeneity in the Amoebozoa supergroup, allowing to suggest that it is a common feature of all eukaryotes.

Contribution of 3D genome topological domains to genetic risk of cancers: a genome-wide computational study.

BACKGROUND: Genome-wide association studies have identified statistical associations between various diseases, including cancers, and a large number of single-nucleotide polymorphisms (SNPs). However, they provide no direct explanation of the mechanisms underlying the association. Based on the recent discovery that changes in three-dimensional genome organization may have functional consequences on gene regulation favoring diseases, we investigated systematically the genome-wide distribution of disease-associated SNPs with respect to a specific feature of 3D genome organization: topologically associating domains (TADs) and their borders. RESULTS: For each of 449 diseases, we tested whether the associated SNPs are present in TAD borders more often than observed by chance, where chance (i.e., the null model in statistical terms) corresponds to the same number of pointwise loci drawn at random either in the entire genome, or in the entire set of disease-associated SNPs listed in the GWAS catalog. Our analysis shows that a fraction of diseases displays such a preferential localization of their risk loci. Moreover, cancers are relatively more frequent among these diseases, and this predominance is generally enhanced when considering only intergenic SNPs. The structure of SNP-based diseasome networks confirms that localization of risk loci in TAD borders differs between cancers and non-cancer diseases. Furthermore, different TAD border enrichments are observed in embryonic stem cells and differentiated cells, consistent with changes in topological domains along embryogenesis and delineating their contribution to disease risk. CONCLUSIONS: Our results suggest that, for certain diseases, part of the genetic risk lies in a local genetic variation affecting the genome partitioning in topologically insulated domains. Investigating this possible contribution to genetic risk is particularly relevant in cancers. This study thus opens a way of interpreting genome-wide association studies, by distinguishing two types of disease-associated SNPs: one with an effect on an individual gene, the other acting in interplay with 3D genome organization.

Identifying cancer pathway dysregulations using differential causal effects.

MOTIVATION: Signaling pathways control cellular behavior. Dysregulated pathways, for example, due to mutations that cause genes and proteins to be expressed abnormally, can lead to diseases, such as cancer. RESULTS: We introduce a novel computational approach, called Differential Causal Effects (dce), which compares normal to cancerous cells using the statistical framework of causality. The method allows to detect individual edges in a signaling pathway that are dysregulated in cancer cells, while accounting for confounding. Hence, technical artifacts have less influence on the results and dce is more likely to detect the true biological signals. We extend the approach to handle unobserved dense confounding, where each latent variable, such as, for example, batch effects or cell cycle states, affects many covariates. We show that dce outperforms competing methods on synthetic datasets and on CRISPR knockout screens. We validate its latent confounding adjustment properties on a GTEx (Genotype-Tissue Expression) dataset. Finally, in an exploratory analysis on breast cancer data from TCGA (The Cancer Genome Atlas), we recover known and discover new genes involved in breast cancer progression. AVAILABILITY AND IMPLEMENTATION: The method dce is freely available as an R package on Bioconductor (https://bioconductor.org/packages/release/bioc/html/dce.html) as well as on https://github.com/cbg-ethz/dce. The GitHub repository also contains the Snakemake workflows needed to reproduce all results presented here. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Analyzing the Therapeutic Efficacy of Bis-Choline-Tetrathiomolybdate in the Atp7b-/- Copper Overload Mouse Model.

Bis-choline-tetrathiomolybdate, introduced as WTX101 (now known as ALXN1840), is a first-in-class copper-protein-binding agent for oral therapy of Wilson's disease. In contrast to other decoppering agents such as trientine or D-penicillamine it acts by forming a tripartite complex with copper and albumin, thereby detoxifying excess liver and blood copper through biliary excretion. Preclinical animal experimentation with this drug was typically done with the alternative ammonium salt of tetrathiomolybdate, which is expected to have identical properties in terms of copper binding. Here, we comparatively analyzed the therapeutic efficacy of ALXN1840, D-penicillamine and trientine in lowering hepatic copper content in Atp7b-/- mouse. Liver specimens were subjected to laser ablation inductively conductively plasma mass spectrometry and electron microscopic analysis. We found that ALXN1840 caused a massive increase of hepatic copper and molybdenum during early stages of therapy. Prolonged treatment with ALXN1840 reduced hepatic copper to an extent that was similar to that observed after administration of D-penicillamine and trientine. Electron microscopic analysis showed a significant increase of lysosomal electron-dense particles in the liver confirming the proposed excretory pathway of ALXN1840. Ultrastructural analysis of mice treated with dosages comparable to the bis-choline-tetrathiomolybdate dosage used in an ongoing phase III trial in Wilson's disease patients, as well as D-penicillamine and trientine, did not show relevant mitochondrial damage. In contrast, a high dose of ALXN1840 applied for four weeks triggered dramatic structural changes in mitochondria, which were notably characterized by the formation of holes with variable sizes. Although these experimental results may not be applicable to patients with Wilson's disease, the data suggests that ALXN1840 should be administered at low concentrations to prevent mitochondrial dysfunction and overload of hepatic excretory pathways.

Swiss public health measures associated with reduced SARS-CoV-2 transmission using genome data

Genome sequences from evolving infectious pathogens allow quantification of case introductions and local transmission dynamics. We sequenced 11,357 SARS-CoV-2 genomes from Switzerland in 2020 - the 6th largest effort globally. Using a representative subset of these data, we estimated viral introductions to Switzerland and their persistence over the course of 2020. We contrast these estimates with simple null models representing the absence of certain public health measures. We show that Switzerlands border closures de-coupled case introductions from incidence in neighboring countries. Under a simple model, we estimate an 86 - 98% reduction in introductions during Switzerlands strictest border closures. Furthermore, the Swiss 2020 partial lockdown roughly halved the time for sampled introductions to die out. Finally, we quantified local transmission dynamics once introductions into Switzerland occurred, using a novel phylodynamic model. We find that transmission slowed 35 - 63% upon outbreak detection in summer 2020, but not in fall. This finding may indicate successful contact tracing over summer before overburdening in fall. The study highlights the added value of genome sequencing data for understanding transmission dynamics. One Sentence SummaryPhylogenetic and phylodynamic methods quantify the drop in case introductions and local transmission with implementation of public health measures.

Quantification of the spread of SARS-CoV-2 variant B.1.1.7 in Switzerland.

BACKGROUND: In December 2020, the United Kingdom (UK) reported a SARS-CoV-2 Variant of Concern (VoC) which is now named B.1.1.7. Based on initial data from the UK and later data from other countries, this variant was estimated to have a transmission fitness advantage of around 40-80 % (Volz et al., 2021; Leung et al., 2021; Davies et al., 2021). AIM: This study aims to estimate the transmission fitness advantage and the effective reproductive number of B.1.1.7 through time based on data from Switzerland. METHODS: We generated whole genome sequences from 11.8 % of all confirmed SARS-CoV-2 cases in Switzerland between 14 December 2020 and 11 March 2021. Based on these data, we determine the daily frequency of the B.1.1.7 variant and quantify the variant's transmission fitness advantage on a national and a regional scale. RESULTS: We estimate B.1.1.7 had a transmission fitness advantage of 43-52 % compared to the other variants circulating in Switzerland during the study period. Further, we estimate B.1.1.7 had a reproductive number above 1 from 01 January 2021 until the end of the study period, compared to below 1 for the other variants. Specifically, we estimate the reproductive number for B.1.1.7 was 1.24 [1.07-1.41] from 01 January until 17 January 2021 and 1.18 [1.06-1.30] from 18 January until 01 March 2021 based on the whole genome sequencing data. From 10 March to 16 March 2021, once B.1.1.7 was dominant, we estimate the reproductive number was 1.14 [1.00-1.26] based on all confirmed cases. For reference, Switzerland applied more non-pharmaceutical interventions to combat SARS-CoV-2 on 18 January 2021 and lifted some measures again on 01 March 2021. CONCLUSION: The observed increase in B.1.1.7 frequency in Switzerland during the study period is as expected based on observations in the UK. In absolute numbers, B.1.1.7 increased exponentially with an estimated doubling time of around 2-3.5 weeks. To monitor the ongoing spread of B.1.1.7, our plots are available online.

Quantitative measures of within-host viral genetic diversity.

The genetic diversity of virus populations within their hosts is known to influence disease progression, treatment outcome, drug resistance, cell tropism, and transmission risk, and the study of dynamic changes of genetic heterogeneity can provide insights into the evolution of viruses. Several measures to quantify within-host genetic diversity capturing different aspects of diversity patterns in a sample or population are used, based on incidence, relative frequencies, pairwise distances, or phylogenetic trees. Here, we review and compare several of these measures.

Sustainable data analysis with Snakemake.

Data analysis often entails a multitude of heterogeneous steps, from the application of various command line tools to the usage of scripting languages like R or Python for the generation of plots and tables. It is widely recognized that data analyses should ideally be conducted in a reproducible way. Reproducibility enables technical validation and regeneration of results on the original or even new data. However, reproducibility alone is by no means sufficient to deliver an analysis that is of lasting impact (i.e., sustainable) for the field, or even just one research group. We postulate that it is equally important to ensure adaptability and transparency. The former describes the ability to modify the analysis to answer extended or slightly different research questions. The latter describes the ability to understand the analysis in order to judge whether it is not only technically, but methodologically valid. Here, we analyze the properties needed for a data analysis to become reproducible, adaptable, and transparent. We show how the popular workflow management system Snakemake can be used to guarantee this, and how it enables an ergonomic, combined, unified representation of all steps involved in data analysis, ranging from raw data processing, to quality control and fine-grained, interactive exploration and plotting of final results.

Quantification of the spread of SARS-CoV-2 variant B.1.1.7 in Switzerland

BackgroundIn December 2020, the United Kingdom (UK) reported a SARS-CoV-2 Variant of Concern (VoC) which is now named B.1.1.7. Based on initial data from the UK and later data from other countries, this variant was estimated to have a transmission fitness advantage of around 40-80% [1, 2, 3]. AimThis study aims to estimate the transmission fitness advantage and the effective reproductive number of B.1.1.7 through time based on data from Switzerland. MethodsWe generated whole genome sequences from 11.8% of all confirmed SARS-CoV-2 cases in Switzerland between 14 December 2020 and 11 March 2021. Based on these data, we determine the daily frequency of the B.1.1.7 variant and quantify the variants transmission fitness advantage on a national and a regional scale. ResultsWe estimate B.1.1.7 had a transmission fitness advantage of 43-52% compared to the other variants circulating in Switzerland during the study period. Further, we estimate B.1.1.7 had a reproductive number above 1 from 01 January 2021 until the end of the study period, compared to below 1 for the other variants. Specifically, we estimate the reproductive number for B.1.1.7 was 1.24 [1.07-1.41] from 01 January until 17 January 2021 and 1.18 [1.06-1.30] from 18 January until 01 March 2021 based on the whole genome sequencing data. From 10 March to 16 March 2021, once B.1.1.7 was dominant, we estimate the reproductive number was 1.14 [1.00-1.26] based on all confirmed cases. For reference, Switzerland applied more non-pharmaceutical interventions to combat SARS-CoV-2 on 18 January 2021 and lifted some measures again on 01 March 2021. ConclusionThe observed increase in B.1.1.7 frequency in Switzerland during the study period is as expected based on observations in the UK. In absolute numbers, B.1.1.7 increased exponentially with an estimated doubling time of around 2-3.5 weeks. To monitor the ongoing spread of B.1.1.7, our plots are available online.

V-pipe: a computational pipeline for assessing viral genetic diversity from high-throughput data.

MOTIVATION: High-throughput sequencing technologies are used increasingly not only in viral genomics research but also in clinical surveillance and diagnostics. These technologies facilitate the assessment of the genetic diversity in intra-host virus populations, which affects transmission, virulence and pathogenesis of viral infections. However, there are two major challenges in analysing viral diversity. First, amplification and sequencing errors confound the identification of true biological variants, and second, the large data volumes represent computational limitations. RESULTS: To support viral high-throughput sequencing studies, we developed V-pipe, a bioinformatics pipeline combining various state-of-the-art statistical models and computational tools for automated end-to-end analyses of raw sequencing reads. V-pipe supports quality control, read mapping and alignment, low-frequency mutation calling, and inference of viral haplotypes. For generating high-quality read alignments, we developed a novel method, called ngshmmalign, based on profile hidden Markov models and tailored to small and highly diverse viral genomes. V-pipe also includes benchmarking functionality providing a standardized environment for comparative evaluations of different pipeline configurations. We demonstrate this capability by assessing the impact of three different read aligners (Bowtie 2, BWA MEM, ngshmmalign) and two different variant callers (LoFreq, ShoRAH) on the performance of calling single-nucleotide variants in intra-host virus populations. V-pipe supports various pipeline configurations and is implemented in a modular fashion to facilitate adaptations to the continuously changing technology landscape. AVAILABILITYAND IMPLEMENTATION: V-pipe is freely available at https://github.com/cbg-ethz/V-pipe. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Detection of SARS-CoV-2 variants in Switzerland by genomic analysis of wastewater samples

The emergence of SARS-CoV-2 mutants with altered transmissibility, virulence, or immunogenicity emphasizes the need for early detection and epidemiological surveillance of genomic variants. Wastewater samples provide an opportunity to assess circulating viral lineages in the community. We performed genomic sequencing of 122 wastewater samples from three locations in Switzerland to analyze the B.1.1.7, B.1.351, and P.1 variants of SARS-CoV-2 on a population level. We called variant-specific signature mutations and monitored variant prevalence in the local population over time. To enable early detection of emerging variants, we developed a bioinformatics tool that uses read pairs carrying multiple signature mutations as a robust indicator of low-frequency variants. We further devised a statistical approach to estimate the transmission fitness advantage, a key epidemiological parameter indicating the speed at which a variant spreads through the population, and compared the wastewater-based findings to those derived from clinical samples. We found that the local outbreak of the B.1.1.7 variant in two Swiss cities was observable in wastewater up to 8 days before its first detection in clinical samples. We detected a high prevalence of the B.1.1.7 variant in an alpine ski resort popular among British tourists in December 2020, a time when the variant was still very rare in Switzerland. We found no evidence of local spread of the B.1.351 and P.1 variants at the monitored locations until the end of the study (mid February) which is consistent with clinical samples. Estimation of local variant prevalence performs equally well or better for wastewater samples as for a much larger number of clinical samples. We found that the transmission fitness advantage of B.1.1.7, i.e. the relative change of its reproductive number, can be estimated earlier and based on substantially fewer wastewater samples as compared to using clinical samples. Our results show that genomic sequencing of wastewater samples can detect, monitor, and evaluate genetic variants of SARS-CoV-2 on a population level. Our methodology provides a blueprint for rapid, unbiased, and cost-efficient genomic surveillance of SARS-CoV-2 variants.

Quantifying SARS-CoV-2 spread in Switzerland based on genomic sequencing data

Pathogen genomes provide insights into their evolution and epidemic spread. We sequenced 1,439 SARS-CoV-2 genomes from Switzerland, representing 3-7% of all confirmed cases per week. Using these data, we demonstrate that no one lineage became dominant, pointing against evolution towards general lower virulence. On an epidemiological level, we report no evidence of cryptic transmission before the first confirmed case. We find many early viral introductions from Germany, France, and Italy and many recent introductions from Germany and France. Over the summer, we quantify the number of non-traceable infections stemming from introductions, quantify the effective reproductive number, and estimate the degree of undersampling. Our framework can be applied to quantify evolution and epidemiology in other locations or for other pathogens based on genomic data. One Sentence SummaryWe quantify SARS-CoV-2 spread in Switzerland based on genome sequences from our nation-wide sequencing effort.

Within-patient genetic diversity of SARS-CoV-2

SARS-CoV-2, the virus responsible for the current COVID-19 pandemic, is evolving into different genetic variants by accumulating mutations as it spreads globally. In addition to this diversity of consensus genomes across patients, RNA viruses can also display genetic diversity within individual hosts, and co-existing viral variants may affect disease progression and the success of medical interventions. To systematically examine the intra-patient genetic diversity of SARS-CoV-2, we processed a large cohort of 3939 publicly-available deeply sequenced genomes with specialised bioinformatics software, along with 749 recently sequenced samples from Switzerland. We found that the distribution of diversity across patients and across genomic loci is very unbalanced with a minority of hosts and positions accounting for much of the diversity. For example, the D614G variant in the Spike gene, which is present in the consensus sequences of 67.4% of patients, is also highly diverse within hosts, with 29.7% of the public cohort being affected by this coexistence and exhibiting different variants. We also investigated the impact of several technical and epidemiological parameters on genetic heterogeneity and found that age, which is known to be correlated with poor disease outcomes, is a significant predictor of viral genetic diversity. Author SummarySince it arose in late 2019, the new coronavirus (SARS-CoV-2) behind the COVID-19 pandemic has mutated and evolved during its global spread. Individual patients may host different versions, or variants, of the virus, hallmarked by different mutations. We examine the diversity of genetic variants coexisting within patients across a cohort of 3939 publicly accessible samples and 749 recently sequenced samples from Switzerland. We find that a small number of patients carry most of the diversity, and that patients with more diversity tend to be older. We also find that most of the diversity is concentrated in certain regions and positions of the virus genome. In particular, we find that a variant reported to increase infectivity is among the most diverse positions. Our study provides a large-scale survey of within-patient diversity of the SARS-CoV-2 genome.

Accurate Measurement of Copper Overload in an Experimental Model of Wilson Disease by Laser Ablation Inductively Coupled Plasma Mass Spectrometry.

Wilson disease is a rare inherited autosomal recessive disorder. As a consequence of genetic alterations in the ATP7B gene, copper begins to accumulate in the body, particularly in the liver and brain. Affected persons are prone to develop liver cancer and severe psychiatric and neurological symptoms. Clinically, the development of corneal Kayser-Fleischer rings and low ceruloplasmin concentrations (<20 mg/dL) are indicative of Wilson disease. However, the detection of elevated hepatic copper content (>250 µg/g dry weight) alone is still considered as the best but not exclusive diagnostic test for Wilson disease. Presently, specific copper stains (e.g., rhodanine) or indirect staining for copper-associated proteins (e.g., orcein) are widely used to histochemically visualize hepatic copper deposits. However, these procedures only detect lysosomal copper, while cytosolic copper is not detectable. Similarly, elemental analysis in scanning electron microscope with energy dispersive X-ray analysis (EDX) often leads to false negative results and inconsistencies. Here, we tested the diagnostic potential of laser ablation inductively-coupled mass spectrometry (LA-ICP-MS) that allows quantitative analysis of multiple elements. Comparative studies were performed in wild type and the Atp7b null mouse model. We propose LA-ICP-MS as a versatile and powerful method for the accurate determination of hepatic copper in people with Wilson disease with high spatial resolution.

Eukaryotic life without tQCUG: the role of Elongator-dependent tRNA modifications in Dictyostelium discoideum.

In the Elongator-dependent modification pathway, chemical modifications are introduced at the wobble uridines at position 34 in transfer RNAs (tRNAs), which serve to optimize codon translation rates. Here, we show that this three-step modification pathway exists in Dictyostelium discoideum, model of the evolutionary superfamily Amoebozoa. Not only are previously established modifications observable by mass spectrometry in strains with the most conserved genes of each step deleted, but also additional modifications are detected, indicating a certain plasticity of the pathway in the amoeba. Unlike described for yeast, D. discoideum allows for an unconditional deletion of the single tQCUG gene, as long as the Elongator-dependent modification pathway is intact. In gene deletion strains of the modification pathway, protein amounts are significantly reduced as shown by flow cytometry and Western blotting, using strains expressing different glutamine leader constructs fused to GFP. Most dramatic are these effects, when the tQCUG gene is deleted, or Elp3, the catalytic component of the Elongator complex is missing. In addition, Elp3 is the most strongly conserved protein of the modification pathway, as our phylogenetic analysis reveals. The implications of this observation are discussed with respect to the evolutionary age of the components acting in the Elongator-dependent modification pathway.

Chromosomal origin of replication coordinates logically distinct types of bacterial genetic regulation.

For a long time it has been hypothesized that bacterial gene regulation involves an intricate interplay of the transcriptional regulatory network (TRN) and the spatial organization of genes in the chromosome. Here we explore this hypothesis both on a structural and on a functional level. On the structural level, we study the TRN as a spatially embedded network. On the functional level, we analyze gene expression patterns from a network perspective ("digital control"), as well as from the perspective of the spatial organization of the chromosome ("analog control"). Our structural analysis reveals the outstanding relevance of the symmetry axis defined by the origin (Ori) and terminus (Ter) of replication for the network embedding and, thus, suggests the co-evolution of two regulatory infrastructures, namely the transcriptional regulatory network and the spatial arrangement of genes on the chromosome, to optimize the cross-talk between two fundamental biological processes: genomic expression and replication. This observation is confirmed by the functional analysis based on the differential gene expression patterns of more than 4000 pairs of microarray and RNA-Seq datasets for E. coli from the Colombos Database using complex network and machine learning methods. This large-scale analysis supports the notion that two logically distinct types of genetic control are cooperating to regulate gene expression in a complementary manner. Moreover, we find that the position of the gene relative to the Ori is a feature of very high predictive value for gene expression, indicating that the Ori-Ter symmetry axis coordinates the action of distinct genetic control mechanisms.