Biology subjects

Gruber, M.

Publications and source records attributed to Gruber, M..

13 recordsLinked to original sources

bioRxiv2026

Tricuspid valve regurgitation accelerates heart failure via a cardio-intestinal innate immune circuit

Activation of the immune system impacts the progression of heart failure (HF), but the underlying mechanisms remain incompletely understood. Here, we identify a cardio-intestinal innate immune axis that links systemic venous congestion to myocardial inflammation, fibrosis, and functional decline. Using single-cell and single-nucleus transcriptomic profiling in patients and mice with tricuspid regurgitation (TR), we demonstrate that TR disrupts intestinal barrier integrity and elicits expansion of circulating monocytes which in turn orchestrate pathological crosstalk between the right and left heart. Monocyte-derived Interleukin-6 (IL-6) emerged as a key mediator of TR-driven myocardial fibrosis and dysfunction. Blockade of IL-6 attenuated cardiac fibrosis and improved cardiac function. In patients, catheter-based repair of TR resulted in reduced IL-6 levels. Together, these findings establish cardio-intestinal innate immunity as a mechanism linking altered hemodynamics to left ventricular remodeling and nominate TR patients as a selective target population for IL-6-directed therapy in HF. One Sentence SummaryThis work mechanistically resolves the heart-gut axis in tricuspid valve regurgitation, and its impact on heart failure progression as mediated by Interleukin-6.

immunology↗

bioRxiv2025

Fructose-1,6-bisphosphatase (FBPase) fine-tunes heterotrophic growth in cyanobacteria

Cyanobacteria switch their carbon metabolism between photoautotrophy and heterotrophy during diurnal cycles. In cyanobacteria, the classical glycolytic control point is characterized by two catabolic phosphofructokinases (PFKs) and a bifunctional anabolic fructose-1,6-biphosphatase/sedoheptulose-1,7-biphosphatase (F/SBPase; slr2094) catalyzing two key reactions in the Calvin-Benson-Bassham (CBB) cycle. In addition, Synechocystis possesses a fructose-1,6-bisphosphatase (FBPase; slr0952) with yet unknown physiological function and biochemical properties. Hence, our aim was to investigate the FBPase and the interplay of the four enzymes in photoautotrophic and heterotrophic carbon metabolism. We discovered that FBPase is specific for FBP, showing no SBPase activity, and unlike F/SBPase does not exhibit any biochemical regulatory properties. Growth studies with deletion mutants revealed that FBPase and PFKs play a major role under heterotrophic conditions. In contrast to F/SBPase, FBPase is not involved in the CBB cycle, but instead fine-tunes heterotrophic growth. Transaldolase cannot replace the function of SBPase in the CBB cycle. In conclusion, the classical Embden-Meyerhoff-Parnass pathway control point, which is known to be mediated by the antagonistic enzyme pair PFK and FBPase in heterotrophic bacteria and eukaryotes, is also present in Synechocystis. We found redox-insensitive FBPases from plant chloroplasts to be closely related to Synechocystis FBPase, indicating that they might serve a similar function. HighlightSynechocystis fructose-1,6-bisphosphatase (slr0952) is unlike fructose-1,6-biphosphatase/sedoheptulose-1,7-biphosphatase (slr2094) monofunctional, not redox-regulated, and supports heterotrophy in darkness, despite catalyzing an anabolic reaction. Thus, presumably Slr2094 alone drives two Calvin-Benson-Bassham cycle key reactions.

plant biology↗

bioRxiv2024

Is it me or the train moving? Humans resolve sensory conflicts with a nonlinear feedback mechanism in balance control

Humans use multiple sensory systems to estimate body orientation in space. Sensory contributions change depending on context. A predominant concept for the underlying multisensory integration (MSI) is the linear summation of weighted inputs from individual sensory systems. Changes of sensory contributions are typically attributed to some mechanism explicitly adjusting weighting factors. We provide evidence for a conceptually different mechanism that performs a multisensory correction if the reference of a sensory input moves in space without the need to explicitly change sensory weights. The correction is based on a reconstruction of the sensory reference frame motion (RFM) and automatically corrects erroneous inputs, e.g., when looking at a moving train. The proposed RFM estimator contains a nonlinear dead-zone that blocks corrections at slow velocities. We first demonstrate that this mechanism accounts for the apparent changes in sensory contributions. Secondly, using a balance control model, we show predictions of specific distortions in body sway responses to perturbations caused by this nonlinearity. Experiments measuring sway responses of 24 subjects (13 female, 11 male) to visual scene movements confirmed these predictions. The findings indicate that the central nervous system resolves sensory conflicts by an internal reconstruction of the cause of the conflict. Thus, the mechanism links the concept of causal inference to shifts in sensory contributions, providing a cohesive picture of MSI for the estimation of body orientation in space. Significance StatementHow the central nervous system (CNS) constructs body orientation in space from multiple sensory inputs is a fundamental question in neuroscience. It is a prerequisite to maintain balance, navigate and interact with the world. To estimate body orientation, the CNS dynamically changes the contribution of individual sensory inputs depending on context and reliability of the cues. However, it is not clear how the CNS achieves these dynamic changes. The findings in our study resolve major aspects of this question. Importantly, the proposed solution using nonlinear multisensory feedback contrasts with traditional approaches assuming context-dependent gain-scaling of individual inputs. Thus, our findings demonstrate how complex, intelligent, and unintuitive behavior can emerge from a comparably simple nonlinear feedback mechanism.

neuroscience↗

bioRxiv2024

Gray matter correlates of childhood maltreatment in the context of major depression: searching for replicability in a multi-cohort brain-wide association study of 3225 adults

Childhood maltreatment effects on cerebral gray matter have been frequently discussed as a neurobiological pathway for depression. However, localizations are highly heterogeneous, and recent reports have questioned the replicability of mental health neuroimaging findings. Here, we investigate the replicability of gray matter correlates of maltreatment (measured retrospectively via the Childhood Trauma Questionnaire) across three large adult cohorts (total N=3225). Pooling cohorts revealed maltreatment-related gray matter reductions, with most extensive effects when not controlling for depression diagnosis (maximum partial R2=.022). However, none of these effects significantly replicated across cohorts. Non-replicability was consistent across a variety of maltreatment subtypes and operationalizations, as well as subgroup analyses with and without depression, and stratified by sex. In this work we show that there is little evidence for the replicability of gray matter correlates of childhood maltreatment, when adequately controlling for psychopathology. This underscores the need to focus on replicability research in mental health neuroimaging.

neuroscience↗

bioRxiv2023

Quantifying brain connectivity signatures by means of polyconnectomic scoring

A broad range of neuropsychiatric disorders are associated with alterations in macroscale brain circuitry and connectivity. Identifying consistent brain patterns underlying these disorders by means of structural and functional MRI has proven challenging, partly due to the vast number of tests required to examine the entire brain, which can lead to an increase in missed findings. In this study, we propose polyconnectomic score (PCS) as a metric designed to quantify the presence of disease-related brain connectivity signatures in connectomes. PCS summarizes evidence of brain patterns related to a phenotype across the entire landscape of brain connectivity into a subject-level score. We evaluated PCS across four brain disorders (autism spectrum disorder, schizophrenia, attention deficit hyperactivity disorder, and Alzheimers disease) and 14 studies encompassing [~]35,000 individuals. Our findings consistently show that patients exhibit significantly higher PCS compared to controls, with effect sizes that go beyond other single MRI metrics ([min, max]: Cohens d = [0.30, 0.87], AUC = [0.58, 0.73]). We further demonstrate that PCS serves as a valuable tool for stratifying individuals, for example within the psychosis continuum, distinguishing patients with schizophrenia from their first-degree relatives (d = 0.42, p = 4 x 10-3, FDR-corrected), and first-degree relatives from healthy controls (d = 0.34, p = 0.034, FDR-corrected). We also show that PCS is useful to uncover associations between brain connectivity patterns related to neuropsychiatric disorders and mental health, psychosocial factors, and body measurements.

neuroscience↗

bioRxiv2023

Detecting Differential Alternative Splicing in Mass Spectrometry-based Proteomics Data

Alternative splicing can substantially diversify biological cell states and influence cellular function. The functional impact of splicing has to be estimated at protein level, typically by mass spectrometry (MS) -based proteomics. Although this technology measures increasingly large peptides sets, distinguishing isoform-specific peptides are rare, limiting detection and quantification of splicing. We introduce MS-EmpiReS, a quantification-based computational approach for differential alternative splicing detection in proteomics data. Its core principle is to differentially quantify peptides mapping to different regions of genes. This approach increased the number of testable peptides hundred-fold in a clinical cancer cohort, resulting in a large number of cancer-relevant splicing candidates. Splicing events detected by both MS-EmpiReS and deep RNA sequencing correlated well but also provided complementary information. The proteomics data allowed us to define a per-sample splicing score to separate cancer conditions. Finally, deep brain proteomes from different mice separated strongly by the lower abundance protein splicing isoform.

bioinformatics↗

bioRxiv2023

Cross-validation for the estimation of effect size generalizability in mass-univariate brain-wide association studies

IntroductionStatistical effect sizes are systematically overestimated in small samples, leading to poor generalizability and replicability of findings in all areas of research. Due to the large number of variables, this is particularly problematic in neuroimaging research. While cross-validation is frequently used in multivariate machine learning approaches to assess model generalizability and replicability, the benefits for mass-univariate brain analysis are yet unclear. We investigated the impact of cross-validation on effect size estimation in univariate voxel-based brain-wide associations, using body mass index (BMI) as an exemplary predictor. MethodsA total of n=3401 adults were pooled from three independent cohorts. Brain-wide associations between BMI and gray matter structure were tested using a standard linear mass-univariate voxel-based approach. First, a traditional non-cross-validated analysis was conducted to identify brain-wide effect sizes in the total sample (as an estimate of a realistic reference effect size). The impact of sample size (bootstrapped samples ranging from n=25 to n=3401) and cross-validation on effect size estimates was investigated across selected voxels with differing underlying effect sizes (including the brain-wide lowest effect size). Linear effects were estimated within training sets and then applied to unseen test set data, using 5-fold cross-validation. Resulting effect sizes (explained variance) were investigated. ResultsAnalysis in the total sample (n=3401) without cross-validation yielded mainly negative correlations between BMI and gray matter density with a maximum effect size of R2p=.036 (peak voxel in the cerebellum). Effects were overestimated exponentially with decreasing sample size, with effect sizes up to R2p=.535 in samples of n=25 for the voxel with the brain-wide largest effect and up to R2p=.429 for the voxel with the brain-wide smallest effect. When applying cross-validation, linear effects estimated in small samples did not generalize to an independent test set. For the largest brain-wide effect a minimum sample size of n=100 was required to start generalizing (explained variance >0 in unseen data), while n=400 were needed for smaller effects of R2p =.005 to generalize. For a voxel with an underlying null effect, linear effects found in non-cross-validated samples did not generalize to test sets even with the maximum sample size of n=3401. Effect size estimates obtained with and without cross-validation approached convergence in large samples. DiscussionCross-validation is a useful method to counteract the overestimation of effect size particularly in small samples and to assess the generalizability of effects. Train and test set effect sizes converge in large samples which likely reflects a good generalizability for models in such samples. While linear effects start generalizing to unseen data in samples of n>100 for large effect sizes, the generalization of smaller effects requires larger samples (n>400). Cross-validation should be applied in voxel-based mass-univariate analysis to foster accurate effect size estimation and improve replicability of neuroimaging findings. We provide open-source python code for this purpose (https://osf.io/cy7fp/?view_only=a10fd0ee7b914f50820b5265f65f0cdb).

neuroscience↗

bioRxiv2022

Spatial tumour gene signature discriminates neoplastic from non-neoplastic compartments in colon cancer: unravelling predictive biomarkers for relapse

BackgroundTherapeutic management of stage II colon cancer remains difficult regarding the decision whether adjuvant chemotherapy should be administered or not. Low rates of recurrence are opposed to chemotherapy induced toxicity and current clinical features are limited in predicting disease relapse. Predictive biomarkers are urgently needed and we hypothesise that the spatial tissue composition of relapsed and non-relapsed colon cancer stage II patients reveals relevant biomarkers. MethodsThe spatial tissue composition of stage II colon cancer patients was examined by in situ sequencing technology with sub-cellular resolution. A panel of 175 genes was designed investigating specific cancer-associated processes and components of the tumour microenvironment. We identified a tumour gene signature to subclassify tissue into neoplastic and non-neoplastic tissue compartments based on spatial expression patterns generated by in situ sequencing (GTC-tool - Genes-To-Count). ResultsThe GTC-tool automatically identified tissue compartments that were used to quantify gene expression of biological processes upregulated within the neoplastic tissue in comparison to non-neoplastic tissue and within relapsed versus non-relapsed stage II colon patients. Three differentially expressed genes (FGFR2, MMP11 and OTOP2) in the neoplastic tissue compartments of relapsed patients in comparison to non-relapsed patients were identified predicting recurrence in stage II colon cancer. ConclusionsIn depth spatial in situ sequencing revealed novel potential predictive biomarkers for disease relapse in colon cancer stage II patients. Our developed open-access GTC-tool allows to accurately capture the tumour compartment and quantify spatial gene expression in colon cancer tissue.

cancer biology↗

bioRxiv2022

Interrelated effects of age and parenthood on whole-brain controllability: protective effects of parenthood in mothers

BackgroundControllability is a measure of the brains ability to orchestrate neural activity which can be quantified in terms of properties of the brains network connectivity. Evidence from the literature suggests that aging can exert a general effect on whole-brain controllability. Mounting evidence, on the other hand, suggests that parenthood and motherhood in particular lead to long-lasting changes in brain architecture that effectively slow down brain aging. We hypothesize that parenthood might preserve brain controllability properties from aging. MethodsIn a sample of 814 healthy individuals (aged 33.9{+/-}12.7 years, 522 females), we estimate whole-brain controllability and compare the aging effects in subjects with vs. those without children. We use diffusion tensor imaging (DTI) to estimate the brain structural connectome. The level of brain control is then calculated from the connectomic properties of the brain structure. Specifically, we measure the network control over many low-energy state transitions (average controllability) and the network control over difficult-to-reach states (modal controllability). Results and conclusionIn nulliparous females, whole-brain average controllability increases, and modal controllability decreases with age, a trend that we do not observe in parous females. Statistical comparison of the controllability metrics shows that modal controllability is higher and average controllability is lower in parous females compared to nulliparous females. In men, we observed the same trend, but the difference between nulliparous and parous males do not reach statistical significance. Our results provide strong evidence that parenthood contradicts aging effects on brain controllability and the effect is stronger in mothers.

neuroscience↗

bioRxiv2021

Ventilation-induced epithelial injury drives biological onset of lung trauma in vitro and is mitigated with anti-inflammatory therapeutics

Mortality rates among patients suffering from acute respiratory failure remain perplexingly high despite maintenance of blood homeostasis. The biotrauma hypothesis advances that mechanical forces from invasive ventilation trigger immunological factors that spread systemically. Yet, how these forces elicit an immune response remains unclear. Here we show that flow-induced stresses under mechanical ventilation can injure the bronchial epithelium of ventilated in vitro upper airway models and directly modulate inflammatory cytokine secretion associated with pulmonary injury. We identify site-specific susceptibility to epithelial erosion in airways from jet-flow impaction and measure an increase in cell apoptosis and modulated secretions of cytokines IL-6, 8 and 10. We find that prophylactic pharmacological treatment with anti-inflammatory therapeutics reduces apoptosis and pro-inflammatory signaling during ventilation. Our 3D in vitro airway platform points to a previously overlooked origin of lung injury and showcases translational opportunities in preclinical pulmonary research towards protective therapies and improved protocols for patient care.

bioengineering↗

bioRxiv2020

EmpiReS: Differential Analysis of Gene Expression and Alternative Splicing

While absolute quantification is challenging in high-throughput measurements, changes of features between conditions can often be determined with high precision. Therefore, analysis of fold changes is the standard method, but often, a doubly differential analysis of changes of changes is required. Differential alternative splicing is an example of a doubly differential analysis, i.e. fold changes between conditions for different isoforms of a gene. EmpiRe is a quantitative approach for various kinds of omics data based on fold changes for appropriate features of biological objects. Empirical error distributions for these fold changes are estimated from Replicate measurements and used to quantify feature fold changes and their directions. We assess the performance of EmpiRe to detect differentially expressed genes applied to RNA-Seq using simulated data. It achieved higher precision than established tools at nearly the same recall level. Furthermore, we assess the detection of alternatively Spliced genes via changes of isoform fold changes (EmpiReS) on distribution-free simulations and experimentally validated splicing events. EmpiReS achieves the best precision-recall values for simulations based on different biological datasets. We propose EmpiRe(S) as a general, quantitative and fast approach with high reliability and an excellent trade-off between sensitivity and precision in (doubly) differential analyses.

bioinformatics↗

bioRxiv2019

MS-EmpiRe utilizes peptide-level noise distributions for ultra sensitive detection of differentially abundant proteins

1Mass spectrometry based proteomics is the method of choice for quantifying genome-wide differential changes of proteins in a wide range of biological and biomedical applications. Protein changes need to be reliably derived from a large number of measured peptide intensities and their corresponding fold changes. These fold changes vary considerably for a given protein.\n\nNumerous instrumental setups aim to reduce this variability, while current computational methods only implicitly account for this problem. We introduce a new method, MS-EmpiRe (github.com/zimmerlab/MS-EmpiRe), which explicitly accounts for the noise underlying peptide fold changes. We derive dataset-specific, intensity-dependent empirical error distributions, which are used for individual weighing of peptide fold changes to detect differentially abundant proteins. The method requires only peptide intensities mapped to proteins and, thus, can be applied to any common quantitative proteomics setup. In a recently published proteome-wide benchmarking dataset, MS-EmpiRe doubles the number of correctly identified changing proteins at a correctly estimated FDR cutoff in comparison to state-of-the-art tools. We confirm the superior performance of MS-EmpiRe on simulated data. MS-EmpiRe provides rapid processing (< 2min) and is an easy to use, general-purpose tool.

bioinformatics↗

bioRxiv2017

Alpha Oscillations During Incidental Encoding Predict Subsequent Memory For New "Foil" Information

People can employ adaptive strategies to increase the likelihood that previously encoded information will be successfully retrieved. One such strategy is to constrain retrieval towards relevant information by re-implementing the neurocognitive processes that were engaged during encoding. Using electroencephalography (EEG), we examined the temporal dynamics with which constraining retrieval towards semantic versus non-semantic information affects the processing of new \"foil\" information encountered during a memory test. Time-frequency analysis of EEG data acquired during an initial study phase revealed that semantic compared to non-semantic processing was associated with alpha decreases in a left frontal electrode cluster from around 600ms after stimulus onset. Successful encoding of semantic versus non-semantic foils during a subsequent memory test was related to decreases in alpha oscillatory activity in the same left frontal electrode cluster, which emerged relatively late in the trial at around 1000-1600ms after stimulus onset. Across subjects, left frontal alpha power elicited by semantic processing during the study phase correlated significantly with left frontal alpha power associated with semantic foil encoding during the memory test. Furthermore, larger left frontal alpha power decreases elicited by semantic foil encoding during the memory test predicted better subsequent semantic foil recognition in an additional surprise foil memory test. These findings indicate that constraining retrieval towards semantic information involves re-implementing semantic encoding operations that are mediated by alpha oscillations, and that such re-implementation occurs at a late stage of memory retrieval perhaps reflecting additional monitoring processes.

neuroscience↗