Red list and checklist of the leaf beetles (Chrysomelidae and Megalopodidae)

Aus Berlin sind bis heute 273 Blattkäferarten bekannt, darunter 11 Neobiota (4 %). Davon werden 93 Arten (34,1 %) in die Rote Liste aufgenommen. Bei der Gesamtartenzahl gibt es vier Neuzugänge gegenüber der letzten Checkliste von 1997. 32 Arten (11,7 %) gelten als ausgestorben oder verschollen. Bestandsgefährdet sind insgesamt 39 Arten (14,3 %), 90 % dieser Arten gelten als stenotop. Den höchsten prozentualen Anteil gefährdeter Arten weist die Unterfamilie Donaciinae der Chrysomelidae auf. In jüngerer Zeit zeigten 50 Arten (18,3 %) einen Populationsrückgang, aber nur 8 Arten (2,9 %) eine Zunahme. Die fortwährende Überwachung der Bestandsentwicklung ist notwendig, um eventuelle weitere Rückgänge rechtzeitig erkennen zu können und Gegenmaßnahmen einzuleiten. Ausgewählte Arten werden kommentiert.To date, 273 species of leaf beetles are recorded from Berlin, including 11 neobiota (4 %). The Red List contains 93 species (34.1 %). The total number of species increased by four compared to the last checklist from 1997. 32 species (11.7 %) are extinct or missing. A total of 39 species (14.3 %) are threatened, 90 % of which are stenotopic. The highest percentage of threatened species is in the subfamily Donaciinae of Chrysomelidae. Recently, the populations of 50 species (18.3 %) decreased, but only 8 species (2.9 %) increased. Constant monitoring is necessary in order to detect possible further decreases, and for conservation measures. Comments are provided for a number of selected species

Association of Human FOS Promoter Variants with the Occurrence of Knee-Osteoarthritis in a Case Control Association Study

Our aim was to analyse (i) the presence of single nucleotide polymorphisms (SNPs) in the JUN and FOS core promoters in patients with rheumatoid arthritis (RA), knee-osteoarthritis (OA), and normal controls (NC); (ii) their functional influence on JUN/FOS transcription levels; and (iii) their associations with the occurrence of RA or knee-OA. JUN and FOS promoter SNPs were identified in an initial screening population using the Non-Isotopic RNase Cleavage Assay (NIRCA); their functional influence was analysed using reporter gene assays. Genotyping was done in RA (n = 298), knee-OA (n = 277), and NC (n = 484) samples. For replication, significant associations were validated in a Finnish cohort (OA: n = 72, NC: n = 548). Initially, two SNPs were detected in the JUN promoter and two additional SNPs in the FOS promoter in perfect linkage disequilibrium (LD). JUN promoter SNP rs4647009 caused significant downregulation of reporter gene expression, whereas reporter gene expression was significantly upregulated in the presence of the FOS promoter SNPs. The homozygous genotype of FOS promoter SNPs showed an association with the susceptibility for knee-OA (odds ratio (OR) 2.12, 95% confidence interval (CI) 1.2–3.7, p = 0.0086). This association was successfully replicated in the Finnish Health 2000 study cohort (allelic OR 1.72, 95% CI 1.2–2.5, p = 0.006). FOS Promoter variants may represent relevant susceptibility markers for knee-OA

Association of Human FOS Promoter Variants with the Occurrence of Knee-Osteoarthritis in a Case Control Association Study

Our aim was to analyse (i) the presence of single nucleotide polymorphisms (SNPs) in the JUN and FOS core promoters in patients with rheumatoid arthritis (RA), knee-osteoarthritis (OA), and normal controls (NC); (ii) their functional influence on JUN/FOS transcription levels; and (iii) their associations with the occurrence of RA or knee-OA. JUN and FOS promoter SNPs were identified in an initial screening population using the Non-Isotopic RNase Cleavage Assay (NIRCA); their functional influence was analysed using reporter gene assays. Genotyping was done in RA (n = 298), knee-OA (n = 277), and NC (n = 484) samples. For replication, significant associations were validated in a Finnish cohort (OA: n = 72, NC: n = 548). Initially, two SNPs were detected in the JUN promoter and two additional SNPs in the FOS promoter in perfect linkage disequilibrium (LD). JUN promoter SNP rs4647009 caused significant downregulation of reporter gene expression, whereas reporter gene expression was significantly upregulated in the presence of the FOS promoter SNPs. The homozygous genotype of FOS promoter SNPs showed an association with the susceptibility for knee-OA (odds ratio (OR) 2.12, 95% confidence interval (CI) 1.2-3.7, p = 0.0086). This association was successfully replicated in the Finnish Health 2000 study cohort (allelic OR 1.72, 95% CI 1.2-2.5, p = 0.006). FOS Promoter variants may represent relevant susceptibility markers for knee-OA.Peer reviewe

Network reconstruction for trans acting genetic loci using multi-omics data and prior information

BACKGROUND: Molecular measurements of the genome, the transcriptome, and the epigenome, often termed multi-omics data, provide an in-depth view on biological systems and their integration is crucial for gaining insights in complex regulatory processes. These data can be used to explain disease related genetic variants by linking them to intermediate molecular traits (quantitative trait loci, QTL). Molecular networks regulating cellular processes leave footprints in QTL results as so-called trans-QTL hotspots. Reconstructing these networks is a complex endeavor and use of biological prior information can improve network inference. However, previous efforts were limited in the types of priors used or have only been applied to model systems. In this study, we reconstruct the regulatory networks underlying trans-QTL hotspots using human cohort data and data-driven prior information. METHODS: We devised a new strategy to integrate QTL with human population scale multi-omics data. State-of-the art network inference methods including BDgraph and glasso were applied to these data. Comprehensive prior information to guide network inference was manually curated from large-scale biological databases. The inference approach was extensively benchmarked using simulated data and cross-cohort replication analyses. Best performing methods were subsequently applied to real-world human cohort data. RESULTS: Our benchmarks showed that prior-based strategies outperform methods without prior information in simulated data and show better replication across datasets. Application of our approach to human cohort data highlighted two novel regulatory networks related to schizophrenia and lean body mass for which we generated novel functional hypotheses. CONCLUSIONS: We demonstrate that existing biological knowledge can improve the integrative analysis of networks underlying trans associations and generate novel hypotheses about regulatory mechanisms

histoneHMM:Differential analysis of histone modifications with broad genomic footprints

BACKGROUND: ChIP-seq has become a routine method for interrogating the genome-wide distribution of various histone modifications. An important experimental goal is to compare the ChIP-seq profiles between an experimental sample and a reference sample, and to identify regions that show differential enrichment. However, comparative analysis of samples remains challenging for histone modifications with broad domains, such as heterochromatin-associated H3K27me3, as most ChIP-seq algorithms are designed to detect well defined peak-like features. RESULTS: To address this limitation we introduce histoneHMM, a powerful bivariate Hidden Markov Model for the differential analysis of histone modifications with broad genomic footprints. histoneHMM aggregates short-reads over larger regions and takes the resulting bivariate read counts as inputs for an unsupervised classification procedure, requiring no further tuning parameters. histoneHMM outputs probabilistic classifications of genomic regions as being either modified in both samples, unmodified in both samples or differentially modified between samples. We extensively tested histoneHMM in the context of two broad repressive marks, H3K27me3 and H3K9me3, and evaluated region calls with follow up qPCR as well as RNA-seq data. Our results show that histoneHMM outperforms competing methods in detecting functionally relevant differentially modified regions. CONCLUSION: histoneHMM is a fast algorithm written in C++ and compiled as an R package. It runs in the popular R computing environment and thus seamlessly integrates with the extensive bioinformatic tool sets available through Bioconductor. This makeshistoneHMM an attractive choice for the differential analysis of ChIP-seq data. Software is available from http://histonehmm.molgen.mpg.de

EGFL7 loss correlates with increased VEGF-D expression, upregulating hippocampal adult neurogenesis and improving spatial learning and memory

Correction: Volume: 80 Issue: 8 DOI: 10.1007/s00018-023-04835-3 Article Number: 201 Published: AUG 2023Neural stem cells reside in the subgranular zone, a specialized neurogenic niche of the hippocampus. Throughout adulthood, these cells give rise to neurons in the dentate gyrus, playing an important role in learning and memory. Given that these core cognitive processes are disrupted in numerous disease states, understanding the underlying mechanisms of neural stem cell proliferation in the subgranular zone is of direct practical interest. Here, we report that mature neurons, neural stem cells and neural precursor cells each secrete the neurovascular protein epidermal growth factor-like protein 7 (EGFL7) to shape this hippocampal niche. We further demonstrate that EGFL7 knock-out in a Nestin-CreERT2-based mouse model produces a pronounced upregulation of neurogenesis within the subgranular zone. RNA sequencing identified that the increased expression of the cytokine VEGF-D correlates significantly with the ablation of EGFL7. We substantiate this finding with intraventricular infusion of VEGF-D upregulating neurogenesis in vivo and further show that VEGF-D knock-out produces a downregulation of neurogenesis. Finally, behavioral studies in EGFL7 knock-out mice demonstrate greater maintenance of spatial memory and improved memory consolidation in the hippocampus by modulation of pattern separation. Taken together, our findings demonstrate that both EGFL7 and VEGF-D affect neurogenesis in the adult hippocampus, with the ablation of EGFL7 upregulating neurogenesis, increasing spatial learning and memory, and correlating with increased VEGF-D expression.Peer reviewe

Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data

Background: Expression quantitative trait loci (eQTL) studies show how genetic variants affect downstream gene expression. Single-cell data allows reconstruction of personalized co-expression networks and therefore the identification of SNPs altering co-expression patterns (co-expression QTLs, co-eQTLs) and the affected upstream regulatory processes using a limited number of individuals. Results: We conduct a co-eQTL meta-analysis across four scRNA-seq peripheral blood mononuclear cell datasets using a novel filtering strategy followed by a permutation-based multiple testing approach. Before the analysis, we evaluate the co-expression patterns required for co-eQTL identification using different external resources. We identify a robust set of cell-type-specific co-eQTLs for 72 independent SNPs affecting 946 gene pairs. These co-eQTLs are replicated in a large bulk cohort and provide novel insights into how disease-associated variants alter regulatory networks. One co-eQTL SNP, rs1131017, that is associated with several autoimmune diseases, affects the co-expression of RPS26 with other ribosomal genes. Interestingly, specifically in T cells, the SNP additionally affects co-expression of RPS26 and a group of genes associated with T cell activation and autoimmune disease. Among these genes, we identify enrichment for targets of five T-cell-activation-related transcription factors whose binding sites harbor rs1131017. This reveals a previously overlooked process and pinpoints potential regulators that could explain the association of rs1131017 with autoimmune diseases. Conclusion: Our co-eQTL results highlight the importance of studying context-specific gene regulation to understand the biological implications of genetic variation. With the expected growth of sc-eQTL datasets, our strategy and technical guidelines will facilitate future co-eQTL identification, further elucidating unknown disease mechanisms.</p

Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data

Background: Expression quantitative trait loci (eQTL) studies show how genetic variants affect downstream gene expression. Single-cell data allows reconstruction of personalized co-expression networks and therefore the identification of SNPs altering co-expression patterns (co-expression QTLs, co-eQTLs) and the affected upstream regulatory processes using a limited number of individuals. Results: We conduct a co-eQTL meta-analysis across four scRNA-seq peripheral blood mononuclear cell datasets using a novel filtering strategy followed by a permutation-based multiple testing approach. Before the analysis, we evaluate the co-expression patterns required for co-eQTL identification using different external resources. We identify a robust set of cell-type-specific co-eQTLs for 72 independent SNPs affecting 946 gene pairs. These co-eQTLs are replicated in a large bulk cohort and provide novel insights into how disease-associated variants alter regulatory networks. One co-eQTL SNP, rs1131017, that is associated with several autoimmune diseases, affects the co-expression of RPS26 with other ribosomal genes. Interestingly, specifically in T cells, the SNP additionally affects co-expression of RPS26 and a group of genes associated with T cell activation and autoimmune disease. Among these genes, we identify enrichment for targets of five T-cell-activation-related transcription factors whose binding sites harbor rs1131017. This reveals a previously overlooked process and pinpoints potential regulators that could explain the association of rs1131017 with autoimmune diseases. Conclusion: Our co-eQTL results highlight the importance of studying context-specific gene regulation to understand the biological implications of genetic variation. With the expected growth of sc-eQTL datasets, our strategy and technical guidelines will facilitate future co-eQTL identification, further elucidating unknown disease mechanisms.</p