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Wolf, S.

Publications and source records attributed to Wolf, S..

At least 19 recordsLinked to original sources

Extracellular protein catabolism drives regulated nitrogen handling and ammonia buffering in acute myeloid leukemia

Acute myeloid leukemia (AML) cells exhibit pronounced metabolic plasticity, yet how amino acid supply is coordinated to sustain leukemic metabolism remains poorly understood. Here, we show that AML cells catabolize extracellular proteins as a major source of amino acids through lysosomal degradation of albumin. This proteocatabolic activity supports anabolic processes and mitochondrial energy production and establishes a regulated, high-throughput regime of primary nitrogen-containing metabolites (nitrogen regimen). Sustained proteocatabolism inevitably generates ammonia, and we find elevated ammonia concentrations in bone marrow plasma from newly diagnosed AML patients that decline with effective induction therapy. Using metabolomics, isotope tracing and targeted genetic and pharmacological manipulations, we identify glutamate-ammonia ligase (GS/GLUL) as a central enzyme that buffers proteocatabolism-derived ammonia by stabilizing intracellular nitrogen homeostasis. Loss of GS function limits sustainable nitrogen handling capacity, thereby impairing leukemic proliferation and delaying disease progression in vivo. Together, our findings define extracellular protein catabolism as a regulating nitrogen management strategy in AML and reveal GS as a capacity-defining vulnerability of proteocatabolic growth.

cancer biology↗

Human CEBPA-N AML exhibits enhanced engraftment and a C/EBPα-p30-driven leukemic stem cell program

Leukemic stem cells (LSCs) play a central role in disease progression, therapeutic resistance, and relapse in acute myeloid leukemia (AML). However, the identification and characterization of LSCs remain challenging because of their low abundance and their close phenotypic resemblance to normal hematopoietic stem and progenitor cells. Although patient-derived xenograft (PDX) models have provided important insights into AML biology and LSC heterogeneity, the relative engraftment potential of distinct CEBPA mutation subtypes and the immunophenotypic identity of LSCs in CEBPA N-terminal mutant AML (CEBPA-N-AML) remain poorly defined. To address these questions, we compared the engraftment characteristics of primary human CEBPA-mutated AML samples representing the major mutational subtypes using the highly permissive NSGS xenograft model. Primary CEBPA-N-AML samples exhibited markedly greater engraftment efficiency and leukemogenic potential than other CEBPA-mutated AML subtypes. Furthermore, we identified a CD366CD73CD123CD117CD371CD247 cell population that is highly enriched for functional LSCs in CEBPA-N-AML, demonstrating enhanced clonogenic activity, leukemia-initiating capacity, and long-term self-renewal. Collectively, our findings demonstrate that the leukemogenic potential of CEBPA-mutated AML is strongly influenced by mutation subtype, with CEBPA-N-AML exhibiting superior leukemia-propagating capacity in vivo. We further define a novel immunophenotypic LSC signature specific to CEBPA-N-AML, providing new insights into LSC heterogeneity in CEBPA-mutated AML and establishing a foundation for the development of LSC-directed therapeutic strategies.

cancer biology↗

Global Convergence of Plant Functional Trait Composition in the Anthropocene

Since the onset of European colonial expansion, humans have accelerated species migration across continents, reshaping plant functional composition and associated ecosystem processes. Plant functional traits-such as leaf area, plant height, or rooting depth-are structured along major axes of variation, including size and leaf economics, that reflect ecological strategies. While human-mediated changes in this trait space have been documented regionally or for specific taxa, there exists no global, grid cell-level quantification of past shifts across major axes of trait variation. Here, we link global citizen science plant occurrence data with data on 37 above- and below-ground traits, and information on native and introduced status for each occurrence. Using dimension-reduction on grid cell-level trait means and introduced species status as a proxy for anthropogenic change, we identify three major axes of functional variation: the size, leaf economics, and life-span axes. By comparing past (native-only) and present-day trait distributions in 3D trait space and geographically, we find prominent region-specific shifts along all three axes. Overall, functional composition converges toward (mostly) smaller, more acquisitive, and shorter-lived assemblages, with region-specific differences in which axis shifts are most pronounced. These results provide the first global estimate of how human-mediated plant introductions have altered ecosystem functional composition in the past centuries, highlighting the spatial patterns and trait dimensions most affected by anthropogenic pressures.

ecology↗

PACMON: Pathway-guided Multi-Omics data integration for interpreting large-scale perturbation screens

High-throughput perturbation screens coupled with single-cell molecular profiling enable systematic interrogation of gene function, yet interpreting the resulting data in terms of biological pathways remains challenging. Existing approaches either identify latent gene modules without linking them to perturbations, or model perturbation effects without incorporating prior biological knowledge, limiting interpretability and scalability. Here, we introduce PACMON (Pathwayguided Multi-Omics data integration for interpreting large-scale perturbation screens), a Bayesian latent factor model that jointly infers pathway-level programs and their modulation by experimental perturbations. PACMON decomposes multimodal molecular measurements into shared latent factors aligned with known biological pathways through structured sparsity priors, while simultaneously estimating how each perturbation activates or represses these pathway programs. The framework naturally accommodates multiple data modalities and employs stochastic variational inference for scalable application to large datasets. We evaluate PACMON in three settings of increasing complexity. On synthetic data with known ground truth, PACMON achieves near-perfect recovery of pathway structure and perturbation effects, outperforming existing methods in both accuracy and computational scalability. Applied to a multimodal Perturb-CITE-seq screen of melanoma cells, PACMON recovers coherent interferon-signaling and cell-cycle programs spanning RNA and surface-protein modalities and identifies interpretable perturbation-pathway associations consistent with known immune-evasion mechanisms. Finally, we apply PACMON to the Tahoe-100M perturbation atlas -- approximately 100 million cells and over 1,000 drug-dose combinations -- producing the first pathway-level latent factor analysis at this scale and revealing biologically meaningful drug-response landscapes across Hallmark pathway programs. PACMON provides a unified, scalable and interpretable framework for mapping perturbation effects onto biological pathways in modern large-scale perturbation experiments.

bioinformatics↗

IntegrateALL: an end-to-end RNA-seq analysis pipeline for multilevel data extraction and interpretable subtype classification in B-precursor ALL

Transcriptome sequencing (RNA-seq) is emerging as a diagnostic standard for B-cell precursor acute lymphoblastic leukemia (B-ALL). Expression-based classifiers reach [~]95% accuracy, but reproducible end-to-end solutions that also integrate transcript-derived genomic drivers and quantitative virtual karyotyping are lacking. We developed IntegrateALL, a Snakemake pipeline that standardizes RNA-seq analysis from FASTQ to rule-based subtype assignment across 26 WHO-HAEM5/ICC entities by integrating expression-based subtype prediction, gene fusion- / hotspot SNV calling and virtual karyotyping. We introduce KaryALL, a machine-learning classifier that uses normalized expression and minor-allele-frequency features (RNASeqCNV) to distinguish near haploid, hypodiploid and high hyperdiploid B-ALL and chromosome-21 gains/iAMP21 (accuracy: 0.98 / F1-score: 0.96 on 615 independent test samples). SNP-array concordance supported RNA-based karyotyping. Applied to 774 unselected B-ALL cases, IntegrateALL yielded unambiguous subtype assignments in 81.5%, based on concordance of gene expression class with a defining driver (75.3% of all cases) or, in selected cases, high-confidence expression-based classification alone (6.2%); the remainder (18.5%) were flagged for manual curation. Independent validation (3 cohorts; n=436, including pediatric cases) reproduced these distributions. Across all patients (n=1,210), 2.6% harbored two subtype defining drivers, including hyperdiploidy in fusion-driven subtypes where it was not expected or subtype-defining SNVs (e.g., PAX5 P80R / IKZF1 N159Y) co-occurring with BCR::ABL1-positive/-like, KMT2A- or DUX4-fusions. In most dual-driver cases, one subtype gene expression signature predominated, indicating a hierarchy of oncogenic control and the value of systematic driver screening alongside expression-based calls. IntegrateALL provides an adaptable fully reproducible workflow for molecular B-ALL characterization by systematically integrating genomic drivers and downstream gene regulation.

cancer biology↗

The interplay between biomechanics and cell kinetics explains the spatial pattern in liver fibrosis

The formation of liver fibrosis patterns, characterized by excess extracellular matrix (ECM), is a complex process that is difficult to investigate experimentally. To complement experimental approaches, we developed a digital twin (DT) model to simulate the pattern formation of septal and biliary fibrosis, the two common forms of liver fibrosis. This model is based on iterative calibration with experiments from animal models treated with the hepatotoxic substance CCl4 (septal form) and Abcb4-knockout mice (biliary form). Septal fibrosis is characterized by ECM accumulation along the connective line between the central veins of neighboring liver lobules, while biliary fibrosis is marked by a scattered ECM pattern within the portal fields. This mechanistic DT model includes the components of hepatocytes (Heps{spadesuit}), hepatic stellate cells (HSCs), macrophages (Mphs), bile duct (BD) cells, collagen fibers secreted by activated HSCs, blood vessels, and cell-cell communication. It allows for the integration and simultaneous modulation of multiple hypothesized mechanisms underlying fibrotic wall formation. The model simulates the formation of liver fibrosis pattern and demonstrates that ECM distribution results from the pattern of cell death zones and biomechanical compression due to cell proliferation. "Healthy" Heps proliferate to compensate for cell loss. In septal fibrosis, where the cell death zones are several cells thick, the proliferating Heps surrounding a zone mechanically compress the deposited collagen network. After a transient phase of collagen scattered between/around Heps, the ECM eventually adopts a sharp, "wall"-like structure. Whereas, in biliary fibrosis, the pattern of cell death is more scattered, leading to a corresponding scattered ECM pattern. In this case, a pattern of scattered distributed collagen forms without transitioning to a sharp wall. Notably, the failure of fibrotic wall formation in endothelial cell-specific GATA4LSEC-KO mice, due to the disrupted pattern of CYP2E1-expressing Heps, validates our DT model. In conclusion, the DT model provided a deeper understanding of liver fibrosis pattern formation. It enabled comparison between simulated outcomes of hypothesized mechanisms and experimental data. Additionally, it guided the design of validation experiments and enabled the identification of optimal strategies for drug testing and extrapolation to humans.

systems biology↗

The Cell Wall Controls Stem Cell Fate in the Arabidopsis Shoot Apical Meristem

At the basis of plant developmental plasticity are continuously active pluripotent stem cells, which fuel the life-long post-embryonic formation of new organs. Since plant cells are encased in cell walls and thus immotile, their individual fate-specification program is dependent on their relative position within the organism. It is assumed that the cell wall and its mechanical properties are under surveillance, linking cell wall state to intracellular gene-regulatory networks. However, the role of these cell wall signalling pathways in plant development and the contribution of cell wall properties to cell behaviour and identity are unclear. Here, we show that control of cell wall properties is essential for the perpetuation of stem cell populations and pattering in the shoot apical meristems. The expression of pectin methylesterases (PMEs), which modify homogalacturonan methylesterification and thereby modulate cell wall mechanics, is maintained at low levels in stem cells by the stem cell specifying transcription factor WUS. Low PME expression is required for stemness, auxin patterning and stem cell-specific mechanical properties. Conversely, WUS depletion reduces wall stiffness in the meristem centre, reinforcing its role in maintaining mechanical homeostasis. Together, our findings show that WUS-mediated control of cell wall-modifying enzymes is essential for sustaining stem cell identity and SAM organization. These results demonstrat that the plant cell wall is not only involved in cell differentiation, but also exerts feedback control on developmental transitions, contributing to our understanding how the immediate physical environment is able to guide cell fate decisions in plants.

plant biology↗

Exploring the Dynamics of Follicle Development and Hormone Synthesis: The Role of Oxygen Tension in Rhesus Macaque Follicle Culture

In vitro culture of cryopreserved ovarian follicles has the potential to extend fertility options for young women seeking to preserve ovarian tissue prior to undergoing cancer treatments. Successful implementation of this strategy has been elusive and likely requires a more complete understanding of the microenvironment of the developing ovarian follicles, including ovarian oxygen concentrations. The oxygen tension within the reproductive tract plays a crucial role in follicular development and oocyte maturation. While in vitro culture systems often use atmospheric oxygen (21%), the native environment in vivo is significantly lower, ranging from 1.5% to 8.7%. This study aimed to investigate the effects of reduced oxygen tensions (3% and 5%) on follicle survival, growth, antrum formation, and hormone production in cultured secondary follicles from rhesus macaques (macaca mulata). A total of 300 follicles were isolated from 7 animals and cultured under three oxygen conditions: 3%, 5%, and 21% O2. Follicle survival and antrum formation were assessed weekly by microscopy and Kaplan-Meier survival analysis, while growth dynamics and hormone levels (estradiol, progesterone, AMH, and inhibin B) were monitored throughout the culture period. Results demonstrated that follicles cultured at 3% and 5% oxygen exhibited significantly higher survival rates and antrum formation compared to those cultured at 21% O2. No significant differences in survival were observed between the 3% and 5% oxygen groups. Growth dynamics revealed distinct patterns, with both low oxygen groups promoting more robust and sustained follicle growth, while atmospheric oxygen led to rapid degeneration. Hormonal analysis showed that follicles in 21% O2 had elevated early hormone production but exhibited reduced long-term viability. In contrast, 3% and 5% oxygen delayed hormone production, reflecting a more stable and sustained follicular environment. These findings underscore the importance of low oxygen tensions in mimicking the physiological conditions of the reproductive tract, improving follicular development, and supporting optimal hormonal function in vitro. This study suggests that further reducing oxygen levels to 3% may offer additional advantages for long-term follicle viability and function in reproductive technologies. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=71 SRC="FIGDIR/small/652505v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@11fcb62org.highwire.dtl.DTLVardef@61c0beorg.highwire.dtl.DTLVardef@1b5328forg.highwire.dtl.DTLVardef@10a7538_HPS_FORMAT_FIGEXP M_FIG C_FIG

developmental biology↗

Prediction in trait-based ecology: global simulations of specific leaf area using a trait-based dynamic vegetation model

AbstractPredicting plant community functional traits is considered a Holy Grail of trait-based ecology because traits underpin ecosystem processes. Previous statistical, machine learning, and optimality approaches have produced global plant trait predictions. However, the utility of trait-based vegetation models, which include demographic processes and can represent trait diversity, remains unexplored at this scale. We use aDGVM2-LL, a trait- and individual-based dynamic global vegetation model (DGVM). aDGVM2-LL simulates community assembly, which is driven by natural selection, biotic, and abiotic conditions; simulated specific leaf area (SLA) is an emergent outcome of community assembly. We examine: 1) how well aDGVM2-LL can simulate global SLA by examining deviations from trait data, and 2) explore drivers of strong deviations. Compared to GBIF-derived SLA data, aDGVM2-LL displays mean SLA differences of -2.9 (m2/kg)(GBIF range ca. 4 - 35 m2/kg), a root mean square error (RMSE) of 7.25, and normalised mean absolute error (nMAE) of 26.54%. Published statistical, machine learning, and optimality approaches displayed differences with GBIF-derived trait data which range between (mean : -4.83 - 2.67, RMSE: 4.41 - 6.68, nMAE: 13.41% - 25.20%). Thus, aDGVM2-LL mean differences are comparable with published predictions while RMSEs and nMAEs are higher. Large aDGVM2-LL mismatches occur in areas where the model incorrectly simulates the relative abundances of deciduous vs. evergreen leaf phenologies. Correcting mismatches in leaf phenological abundances strongly reduces the range of mean SLA differences (-0.14 - 0.43), RMSEs (5.85 - 5.90), and nMAEs (15.44% - 20.61%). These results show that an eco-evolutionary, process-based approach can reasonably simulate global SLA values, particularly when leaf phenological abundances are accurate. Our results highlight the general importance of the global drivers of leaf phenology for leaf traits. The correct simulation of the relative abundances of deciduous and evergreen leaf phenologies is crucial to predict contemporary and future SLA.

ecology↗

From smartphones to satellites: Uniting crowdsourced biodiversity monitoring and Earth observation to fill the gaps in global plant trait mapping

Plant functional traits are fundamental to ecosystem dynamics and Earth system processes, but their global characterization is limited by the availability of field surveys and trait measurements. Recent expansions in biodiversity data aggregation, including large collections of vegetation surveys, citizen science observations, and trait measurements, offer new opportunities to overcome these constraints. Here we demonstrate that combining these diverse data sources with high-resolution Earth observation data enables accurate modeling of key plant traits at up to 1 km resolution. Our approach achieves high predictive power, reaching correlations up to 0.63 (15 of 31 traits exceeding 0.50) and improved spatial transferability, effectively bridging gaps in under-sampled regions. By capturing a broad range of traits with high spatial coverage, these maps can enhance our understanding of plant community properties and ecosystem functioning globally, and can serve as useful tools in modeling global biogeochemical processes and informing worldwide conservation efforts. Ultimately, our framework highlights the power and necessity of crowdsourced biodiversity data in high-resolution plant trait modeling. We anticipate that advancements in biodiversity data collection and remote sensing capabilities will further refine global trait mapping, fostering a dynamic trait-based understanding of the biosphere.

ecology↗

Pipeline Olympics: continuable benchmarking of computational workflows for DNA methylation sequencing data against an experimental gold-standard

DNA methylation is a widely studied epigenetic mark and a powerful biomarker of cell type, age, environmental exposures, and disease. Whole-genome sequencing following selective conversion of unmethylated cytosines into thymines via bisulfite treatment or enzymatic methods remains the reference method for DNA methylation profiling genome-wide. While numerous software tools facilitate processing of DNA methylation sequencing reads, a comprehensive benchmarking study has been lacking thus far. In this study, we systematically compared complete computational workflows for processing DNA methylation sequencing data using a dedicated benchmarking dataset generated with five genome-wide profiling protocols. As an evaluation reference, we employed highly quantitative locus-specific measurements from our preceding benchmark of targeted DNA methylation assays. Based on this experimental gold-standard assessment and several comprehensive metrics, we identified workflows that consistently demonstrated superior performance and revealed major workflow development trends. To facilitate the sustainability of our benchmark, we implemented an interactive workflow execution and data presentation platform, adaptable to user-defined criteria and seamlessly expandable to future software.

bioinformatics↗

Longitudinal sequencing reveals polygenic andepistatic nature of genomic response to selection

Evolutionary adaptation to new environments likely results from a combination of selective sweeps and polygenic shifts, depending on the genetic architecture of traits under selection. While selective sweeps have been widely studied, polygenic responses are considered more prevalent but challenging to quantify. The infinitesimal model makes explicit the hypothesis about the dynamics of changes in allele frequencies under selection, where only allelic effect sizes, frequencies, linkage, and gametic disequilibrium matter. Departures from this, like long-range correlations of allele frequency changes, could be a signal of epistasis in polygenic response. We performed an Evolve & Resequence experiment in Drosophila melanogaster exposing flies to a high-sugar diet as a source of environmental stress for over 100 generations. We tracked allele frequency changes in >3000 individually sequenced flies as well as population pools and searched for loci under selection by identifying sites with allele frequency trajectories that differentiated selection regimes consistently across replicates. We estimate that at least 4% of the genome was under positive selection, the result of a highly polygenic response. Most of this response was seen as small but consistent allele frequency changes over time, and there were only a few large allele-frequency changes (selective sweeps). We then searched for signatures of selection on pairwise combinations of alleles in the new environment and found several strong signals of putative epistatic interactions across unlinked loci that were consistent across selected populations. Finally, we measured differentially expressed genes (DEGs) across treatments and show that DEGs are enriched for selected SNPs, suggesting a regulatory basis for the selective response. Our results suggest that epistatic contributions to polygenic selective response are common and lead to detectable signatures.

evolutionary biology↗

GeneCOCOA: Detecting context-specific functions of individual genes using co-expression data

Extraction of meaningful biological insight from gene expression profiling often focuses on the identification of statistically enriched terms or pathways. These methods typically use gene sets as input data, and subsequently return overrepresented terms along with associated statistics describing their enrichment. This approach does not cater to analyses focused on a single gene-of-interest, particularly when the gene lacks prior functional characterization. To address this, we formulated GeneCOCOA, a method which utilizes context-specific gene co-expression and curated functional gene sets, but focuses on a user-supplied gene-of-interest. The co-expression between the gene-of-interest and subsets of genes from functional groups (e.g. pathways, GO terms) is derived using linear regression, and resulting root-mean-square error values are compared against background values obtained from randomly selected genes. The resulting p values provide a statistical ranking of functional gene sets from any collection, along with their associated terms, based on their co-expression with the gene of interest in a manner specific to the context and experiment. GeneCOCOA thereby provides biological insight into both gene function, and putative regulatory mechanisms by which the expression of the gene-of-interest is controlled. Despite its relative simplicity, GeneCOCOA outperforms similar methods in the accurate recall of known gene-disease associations. GeneCOCOA is formulated as an R package for ease-of-use, available at https://github.com/si-ze/geneCOCOA. Author summaryUnderstanding the biological functions of different genes and their respective products is a key element of modern biological research. While one can examine the relative abundance of a gene product in transcriptomics data, this alone does not provide any clue to the biological relevance of the gene. Using a type of analysis called co-expression, it is possible to identify other genes which have similar patterns of regulation to a gene-of-interest, but again, this cannot tell you what a gene does. Genes whose function has previously been studied are often assembled into groups (e.g. pathways, ontologies), which can be used to annotate gene sets of interest. However, if a gene has not yet been characterized, it will not appear in these gene set enrichment analyses. Here, we propose a new method - GeneCOCOA - which uses co-expression of a single gene with genes in functional groups to identify which functional group a gene is most similar too, resulting in a putative function for the gene, even if it has not been studied before. We tested GeneCOCOA by using it to find gene-disease links which have already been scientifically studied, and showed that GeneCOCOA can do this more effectively than other available methods.

bioinformatics↗

Molecular and functional profiling unravels targetable vulnerabilities in colorectal cancer

While colorectal cancer (CRC) patients with microsatellite instability (MSI) respond well to immunotherapy those with microsatellite stable (MSS) tumors rely on conventional chemotherapy, often with poor outcomes. Both types frequently carry mutations in KRAS or BRAF proto-oncogenes, rendering them more resistant to treatment. New therapeutic biomarkers and treatments remain a clinical need, especially for MSS tumors. We performed whole exome and RNA-Sequencing from 28 tumors of the Athens Comprehensive Cancer Center CRC cohort, and molecularly characterized colorectal cancer patients based on their MSI status, SNVs/CNAs, and pathway/transcription factor activities at the individual patient level. Variants were classified using a new computational score for integrative cancer variant annotation and prioritization. Complementing this molecular data with public multi-omics datasets, we identified activation of transforming growth factor beta (TGF{beta}) signaling to be stronger activated in the MSS patients whereas JAK-STAT and MAPK molecular cascades were activated specifically in MSI. We unraveled mechanisms consistently perturbed in the transcriptional and mutational circuits and identified RUNX transcription factors as putative novel targets. Assessing the immunogenicity of CRC tumors in the context of RAS/RAF mutations and MSI/MSS status revealed a critical impact KRAS mutations have on immunogenicity particularly in the MSS patient subgroup, with implications for diagnosis and treatment.

cancer biology↗

A RALF-Brassinosteroid morpho-signaling circuit regulates Arabidopsis hypocotyl cell shape

Plant cells survey and modulate their cell wall to control their shape and anisotropic growth. Signaling mediated by the plant steroid hormones brassinosteroids (BR) plays a central role in coordinating cell wall status and cell growth, and alterations in the cell wall - BR feedback loop leads to life-threatening defects in tissue and cellular integrity. How the status of the cell wall is relayed to BR signaling remains largely unclear. Increasing evidence shows that RAPID ALKALANIZATION FACTORs (RALFs), a class of secreted peptides, play structural and signaling roles at the cell surface. Here we show that perception of RALF23 promotes the formation and signaling of the main BR receptor complex formed by BRASSINOSTEROID INSENSITIVE 1 (BRI1) and BRI1-BRASSINOSTEROID INSENSITIVE1-BRASSINOSTEROID-ASSOCIATED KINASE 1 (BAK1). The loss of the plasma membrane-localized RALF receptor complex FERONIA (FER)-LORELEI LIKE GPI-anchor protein 1 (LLG1) leads to defects in cell expansion and anisotropy, as well as uncontrolled BRI1-BAK1 complex formation and signaling. RALF23 bioactivity relies on pectin status and its perception induces changes in pectin composition and the activity of pectin-modifying enzymes. Our observations suggest a model in which RALF23 functions as a cell wall-informed signaling cue initiating a feedback loop that solicits BR signaling, modifies the cell wall, and coordinates cell morphogenesis. Highlights-The RALF receptor complex FER-LLG1 regulates cell anisotropic growth -RALF23 promotes BRI1-BAK1 complex formation and signaling -RALF23 functions as a cell wall-informed and wall-modifying signaling cue

plant biology↗

Cortical high gamma power relates to movement speed and is disproportionately reduced after stroke

Motor cortical high-gamma oscillations (60 to 90 Hz) occur at the onset of movement and are spatially focused over the contralateral primary motor cortex. Although high-gamma oscillations are widely recognized for their significance in human motor control, their precise function on a cortical level remains elusive. Importantly, their relevance in human stroke pathophysiology is unknown. Understanding the neurophysiological processes of motor coding could be an important step in improving motor recovery after stroke. We recorded magnetoencephalography data during a thumb movement speed task in 14 chronic stroke survivors, 15 age-matched control participants and 29 healthy young participants. Motor cortical high-gamma oscillations showed a strong relation with movement speed as trials with higher movement speed were associated with greater high-gamma power. Stroke survivors showed reduced cortical high-gamma power, surpassing the effect attributable to decreased movement speed in these participants. Even though motor skill acquisition was evident in all groups, it was not linked to high-gamma power. Our study is, to our knowledge, the first to quantify high-gamma oscillations after stroke, revealing a reduction in movement-related high-gamma power. Moreover, we provide strong evidence for a pivotal role of motor cortical high-gamma oscillations in encoding movement speed.

neuroscience↗

Saturating the eQTL map in Drosophila melanogaster: genome-wide patterns of cis and trans regulation of transcriptional variation in outbred populations

Decades of genome-wide mapping have shown that most genetic polymorphisms associated with complex traits are found in non-coding regions of the genome. Characterizing the effect of such genetic variation presents a formidable challenge, and eQTL mapping has been a key approach to understand the non-coding genome. However, comprehensive eQTL maps are available only for a few species like yeast and humans. With the aim of understanding the genetic landscape that regulates transcriptional variation in Drosophila melanogaster, we developed an outbred mapping panel in this species, the Drosophila Outbred Synthetic Panel (Dros-OSP). Using this community resource, we collected transcriptomic and genomic data for 1800 individual flies and were able to map cis and trans eQTLs for 98% of the genes expressed in D. melanogaster, increasing by thousands the number of genes for which regulatory loci are known in this species. We described, for the first time in the context of an outbred population, the properties of local and distal regulation of gene expression in terms of genetic diversity, heritability, connectivity, and pleiotropy. We uncovered that, contrary to long-standing assumptions, a significant part of gene co-expression networks is organized in a non-modular fashion. These results bring the fruit fly to the level of understanding that was only available for a few other organisms, and offer a new mapping resource that will expand the possibilities currently available to the Drosophila community. This data is available at Drosophilaeqtl.org.

genomics↗

From GWAS to signal validation: An approach for estimating SNP genetic effects while preserving genomic context

Validating associations between genotypic and phenotypic variation remains a challenge, despite advancements in association studies. Common approaches for signal validation rely on gene-level perturbations, such as loss-of-function mutations or RNAi, which test the effect of genetic modifications usually not observed in nature. CRISPR-based methods can validate associations at the SNP level, but have significant drawbacks, including resulting off-target effects and being both time-consuming and expensive. Both approaches usually modify the genome of a single genetic background, limiting the generalizability of experiments. To address these challenges, we present a simple, low-cost experimental scheme for validating genetic associations at the SNP level in outbred populations. The approach involves genotyping live outbred individuals at a focal SNP, crossing homozygous individuals with the same genotype at that locus, and contrasting phenotypes across resulting synthetic outbred populations. We tested this method in Drosophila melanogaster, measuring the longevity effects of a polymorphism at a naturally-segregating cis-eQTL for the midway gene. Our results demonstrate the utility of this method in SNP-level validation of naturally occurring genetic variation regulating complex traits. This method provides a bridge between the statistical discovery of genotype-phenotype associations and their validation in the natural context of heterogeneous genomic contexts.

genetics↗