Biology subjects

Gerber, S.

Publications and source records attributed to Gerber, S..

47 records · Page 3Linked to original sources

bioRxiv2020

STK32A is a dual-specificity AGC kinase with a preference for acidic substrates

The STK32 kinases are a small subfamily of three uncharacterised serine/threonine kinases from the AGC kinase family whose functional role is so far unknown. Here, we analyse the consensus peptide sequence for STK32A phosphorylation, showing that STK32A is directed towards acidic substrate sequences and exhibits dual-specificity for serine/threonine and tyrosine residues. A crystal structure of STK32A reveals an overall structure typical of the AGC protein kinase family but with significant and unique features including an altered binding mode of the hydrophobic motif to the N-terminal lobe of the kinase domain, and a novel alpha-helix in between the turn motif and the hydrophobic motif. The crystal structure combined with phylogenetic analysis reveals the evolutionary conservation of the acidic substrate preference. In vitro binding assays demonstrated that the STK32 kinases bind significant numbers of clinically used kinase inhibitors.

biochemistry↗

bioRxiv2020

Septins disruption controls tumor growth and enhances efficacy of Herceptin

Septin expressions are altered in cancer cells and exhibit poor prognoses in malignancies. As the first approach to develop a septin filament targeting agent, we optimized the structure of Forchlorfenuron (FCF), a known plant cytokinin to generate UR214-9, which contrary to FCF, causes septin-2/9 filamental structural catastrophe in cancer cells without altering cellular septin protein levels. In-silico docking using septin-2/septin-2 dimer complex showed that UR214-9 displaced the guanine carbonyl oxygen from the GDP binding domain and showed increased binding energy than FCF(-8.59vs-7.21). UR214-9 reduced cancer cell growth, downregulated HER2/STAT-3 axis and controlled growth of HER2+ pancreatic, breast and ovarian cancer xenografts in NSG mice and enhanced response of Herceptin against HER2+breast cancer xenograft. Transcriptome analysis of UR214-9 exposed cells demonstrated significant perturbation of <20 genes compared to afatinib which impacted >1200 genes in JIMT-1 breast cancer cells indicating target specificity and non-transcriptional functions of UR214-9. In summary, disrupting septins via UR214-9 is a new approach to control the growth of HER2+ malignancies.

cancer biology↗

bioRxiv2020

Dynamic longitudinal behavior in animals exposed to chronic social defeat stress

Chronic social defeat (CSD) can lead to impairments in social interaction and other behaviors that are supposed to model features of major depressive disorder (MDD). Not all animals subjected to CSD, however, develop these impairments, and maintained social interaction in some animals is widely used as a model for resilience to stress-induced mental dysfunctions. So far, animals have mainly been studied shortly (24 hours and 7 days) after CSD exposure and longitudinal development of behavioral phenotypes in individual animals has been mostly neglected. We have analyzed social interaction and novel object recognition behavior of stressed mice at different time points after CSD and have found very dynamic courses of behavior of individual animals. Instead of the two groups, resilient or susceptible, that are found at early time points our data suggest four groups with (i, ii) animals behaving resilient or susceptible at early and late time points, respectively (iii) animals that start susceptible and recover with time or (iv) animals that are resilient at early time points but develop vulnerability later on.

animal behavior and cognition↗

bioRxiv2019

GEO2RNAseq: An easy-to-use R pipeline for complete pre-processing of RNA-seq data

In transcriptomics, the study of the total set of RNAs transcribed by the cell, RNA sequencing (RNA-seq) has become the standard tool for analysing gene expression. The primary goal is the detection of genes whose expression changes significantly between two or more conditions, either for a single species or for two or more interacting species at the same time (dual RNA-seq, triple RNA-seq and so forth). The analysis of RNA-seq can be simplified as many steps of the data pre-processing can be standardised in a pipeline.\n\nIn this publication we present the \"GEO2RNAseq\" pipeline for complete, quick and concurrent pre-processing of single, dual, and triple RNA-seq data. It covers all pre-processing steps starting from raw sequencing data to the analysis of differentially expressed genes, including various tables and figures to report intermediate and final results. Raw data may be provided in FASTQ format or can be downloaded automatically from the Gene Expression Omnibus repository. GEO2RNAseq strongly incorporates experimental as well as computational metadata. GEO2RNAseq is implemented in R, lightweight, easy to install via Conda and easy to use, but still very flexible through using modular programming and offering many extensions and alternative workflows.\n\nGEO2RNAseq is publicly available at https://anaconda.org/xentrics/r-geo2rnaseq and https://bitbucket.org/thomas_wolf/geo2rnaseq/overview, including source code, installation instruction, and comprehensive package documentation.

bioinformatics↗

bioRxiv2019

Low-cost scalable discretization, prediction and feature selection for complex systems

Finding reliable discrete approximations of complex systems is a key prerequisite when applying many of the most popular modeling tools. Common discretization approaches (for example, the very popular K-means clustering) are crucially limited in terms of quality and cost. We introduce a low-cost improved-quality Scalable Probabilistic Approximation (SPA) algorithm, allowing for simultaneous data-driven optimal discretization, feature selection and prediction. Cross-validated applications of SPA to a range of large realistic data classification and prediction problems reveal drastic cost and performance improvements. For example, SPA allows the unsupervised next-day surface temperature predictions for Europe with the mean crossvalidated one-day prediction error of 0.75{degrees}C on a common PC (being around 40% better in terms of errors and five to six orders-of-magnitude cheaper than the next-day surface temperature predictions calculated on supercomputers and provided by the weather services).\n\nOne Sentence SummaryIntroduced computational tool allows obtaining drastic cost and quality gains for a broad range of science applications.

bioinformatics↗

bioRxiv2018

Bracketing phenotypic limits of mammalian hybridization

An increasing number of mammalian species have been shown to have a history of hybridization and introgression based on genetic analyses. Only relatively few fossils, however, preserve genetic material and morphology must be used to identify the species and determine whether morphologically intermediate fossils could represent hybrids. Because dental and cranial fossils are typically the key body parts studied in mammalian paleontology, here we bracket the potential for phenotypically extreme hybridizations by examining uniquely preserved cranio-dental material of a captive hybrid between gray and ringed seals. We analyzed how distinct these species are genetically and morphologically, how easy it is to identify the hybrids using morphology, and whether comparable hybridizations happen in the wild. We show that the genetic distance between these species is more than twice the modern human-Neanderthal distance, but still within that of morphologically similar species-pairs known to hybridize. In contrast, morphological and developmental analyses show gray and ringed seals to be highly disparate, and that the hybrid is a predictable intermediate. Genetic analyses of the parent populations reveal introgression in the wild, suggesting that gray-ringed seal hybridization is not limited to captivity. Taken together, gray and ringed seals appear to be in an adaptive radiation phase of evolution, showing large morphological differences relative to their comparatively modest genetic distance. Because morphological similarity does not always correlate with genetic distance in nature, we postulate that there is considerable potential for mammalian hybridization between phenotypically disparate taxa.

evolutionary biology↗

bioRxiv2018

Geometric morphometrics of nested symmetries: Hierarchical inter- and intra-individual variation in biological shapes

Symmetry is a pervasive feature of organismal shape and the focus of a large body of research in Biology. Here, we consider complex patterns of symmetry where a phenotype exhibits a hierarchically structured combination of symmetries. We extend the Procrustes ANOVA for the analysis of nested symmetries and the decomposition of the overall morphological variation into components of symmetry (among-individual variation) and asymmetry (directional and fluctuating asymmetry). We illustrate its use with the Aristotles lantern, the masticatory apparatus of regular sea urchins, a complex organ displaying bilateral symmetry nested within five-fold rotational symmetry. Our results highlight the importance of characterising the full symmetry of a structure with nested symmetries. Higher order rotational symmetry appears strongly constrained and developmentally stable compared to lower level bilateral symmetry. This contrast between higher and lower levels of asymmetry is discussed in relation to the spatial pattern of the lantern morphogenesis. This extended framework is applicable to any biological object exhibiting nested symmetries, regardless of their type (e.g., bilateral, rotational, translational). Such cases are extremely widespread in animals and plants, from arthropod segmentation to angiosperm inflorescence and corolla shape. The method therefore widens the research scope on developmental instability, canalization, developmental modularity and morphological integration.

evolutionary biology↗

bioRxiv2018

Imputation of posterior linkage probability relations reveals a significant influence of structural 3D constraints on linkage disequilibrium

Genetic association studies have become increasingly important in unraveling the genetics of diseases or complex traits. Despite their value for modern genetics, conflicting conclusions often arise through the difficulty of confirming and replicating experimental results. We argue that this problem is largely based on the application of statistical relation measures that are not appropriate for genomic data analysis and demonstrate that the standard measures used for Genome-wide association studies or genomics linkage analysis bear a statistic bias. This may come from the violation of underlying assumptions (such as independence or stationarity) as well as from other conceptual limitations in the measures or relations, such as missing invariance with respect to coding or the inability to reflect latent factors. Attempts to introduce unbiased relation measures that avoid these limitations are usually computationally expensive and do not scale for large data sizes being typical for genomics applications.\n\nTo tackle these problems, we propose a straightforwardly computable relation measure called Linkage Probability (LP). This measure provides the posterior probability of a relation between two categorical data sets and considers potential biases from latent variables. We compare several aspects of popular relation measures through an illustrative example and human genomics data. We demonstrate that the application of LP to the analysis of Single Nucleotide Polymorphisms (SNP) reveals latent 3D steric effects within 1D SNP data, that approximate to chromatin loops captured by high resolution Hi-C maps.

bioinformatics↗

bioRxiv2017

Nutrient homeostasis and mechanisms related to nutrient retention by wetland macrophytes in a subtropical wetland.

Nutrient homeostasis relates ambient stoichiometric conditions in an environment to the stoichiometry of living entities of the ecosystem. In wetland ecosystems, vegetation can be a large, highly variable and dynamic sink of nutrients. This study investigated stoichiometric homeostasis of dominant emergent and submerged aquatic vegetation (EAV and SAV, respectively) within two treatment flow-ways (FW) of Everglades Stormwater Treatment Area 2 (STA-2). These FW encompass a large gradient in plant nutrient availability. The hypotheses of this study is that wetland vegetation is non-homeostatic relative to ambient nutrients and consequently nutrient resorption will not vary along the nutrient gradient. We developed a framework to investigate how vegetation uptake and resorption of nutrients contribute separately to homeostasis. Overall, the wetland vegetation in this study was non-homeostatic with respect to differential uptake of nitrogen (N) vs. phosphorus (P). Resorption evaluated for EAV was high for P and moderate for N, resorption efficiency did not significantly vary along the gradient and therefore did not affect overall homeostatic status. Nutrient addition experiments may help to compensate for some of the limitation of our study, especially with respect to resolving the primary nutrient source (organic vs. inorganic sources, water vs. soil compartment) and nutrient utilization rates.

ecology↗

bioRxiv2017

Stoichiometric relationships amongst ecosystem compartments of a subtropical treatment wetland

BackgroundEvaluation of carbon (C), nitrogen (N) and phosphorus (P) ratios in aquatic and terrestrial ecosystems can advance our understanding of biological processes, nutrient cycling and the fate of organic matter (OM) in aquatic ecosystems. Eutrophication of aquatic ecosystems can disrupt the accumulation and decomposition of OM which serves as the base of the aquatic food web, and is central to the effectiveness of a treatment wetland. This study investigated nutrient stoichiometry within and between wetland ecosystem compartments (i.e. water column, vegetation, flocculent and soil) of two treatment flow-ways (FWs) in the Everglades Stormwater Treatment Areas located in south Florida (USA). These FWs include an emergent aquatic vegetation cell dominated by Typha spp.(cattail) and a submerged aquatic vegetation cell composed of species such as Chara spp. (muskgrass) and Potamogeton spp. (pondweed). The primary objective of this evaluation was to determine if nutrient stoichiometry is consistent within and between ecosystems and compartments to understand biogeochemical cycling and controls of nutrient removal within a treatment wetland ecosystem.\n\nResultsThis study demonstrates that C, N, and P stoichiometry can be highly variable among ecosystem compartments and between differing wetland ecosystems. Generally, total P declined along the length of each treatment FW in all ecosystem compartments, whereas trends in total N and C trends were more variable. These changes in C and nutrient concentrations result in variable nutrient stoichiometry along treatment FWs signaling potential changes in absolute and relative nutrient availability and biogeochemical processes.\n\nConclusionsAssessment of wetland nutrient stoichiometry between and within ecosystem compartments suggest decoupling of C:N:P relationships likely as a consequence of differential external nutrient supply, differences in primary producer communities and differential decomposition of organic matter. However, stoichiometry varies often monotonous along the flow paths, likely exhibiting a response to nutrient loading. Differences in C:N:P ratios in primary producers, light availability, microbial immobilization in the early stage of decomposition as well as nutrient mining during decomposition of OM are likely feedback mechanisms that lead to deviations from fixed stoichiometry, which in turn may have considerable influence on nutrient removal rates. This information could be used to further understand water treatment performance with respect to stoichiometric processes and OM decomposition.

ecology↗

bioRxiv2017

Carbon pool trends and dynamics within a subtropical peatland during long-term restoration

BackgroundThe Florida Everglades has undergone significant ecological change spanning the continuum of disturbance to restoration. While the restoration effort is not complete and the ecosystem continues to experience short duration perturbations, a better understanding of long-term C dynamics of the Everglades is needed to facilitate new restoration efforts. This study evaluated temporal trends of different aquatic carbon (C) pools of the northern Everglades Protection Area over a 20-year period to gauge historic C cycling patterns. Dissolved inorganic C (DIC), dissolved organic C (DOC), particulate organic C (POC), and surface water carbon dioxide (pCO2(aq)) were investigated between May 1, 1994 and April 30, 2015.\n\nResultsAnnual mean concentrations of DIC, DOC, POC, and pCO2(aq) significantly decreased through time or remained constant across the Water Conservation Areas (WCAs). Overall, the magnitude of the different C pools in the water column significantly differed between regions. Outgassing of CO2 was dynamic across the Everglades ranging from 420 to 2001 kg CO2 yr-1. Overall the historic trend in CO2 flux from the marsh declined across our study area while pCO2(aq) largely remained somewhat constant with the exception of Water Conservation Area 2 which experienced significant declines in pCO2(aq). Particulate OC concentrations were consistent between WCAs, but a significantly decreasing trend in annual POC concentrations were observed.\n\nConclusionsHydrologic condition and nutrient inputs significantly influenced the balance, speciation, and flux of C pools across WCAs suggesting a subsidy-stress response in C dynamics relative to landscape scale responses in nutrient availability. The interplay between nutrient inputs and hydrologic condition exert a driving force on the balance between DIC and DOC production via the metabolism of organic matter which forms the base of the aquatic foodweb. Along the restoration trajectory as water quality and hydrology continues to improve it is expected that C pools will respond accordingly.

ecology↗