Biology subjects

Meier, J.

Publications and source records attributed to Meier, J..

34 records · Page 2Linked to original sources

bioRxiv2021

Loss of Prm1 leads to defective chromatin protamination, impaired PRM2 processing, reduced sperm motility and subfertility in male mice

One of the key events during spermiogenesis is the hypercondensation of chromatin by substitution of the majority of histones by protamines. In humans and mice, protamine 1 (PRM1/Prm1) and protamine 2 (PRM2/Prm2), are expressed in a species-specific ratio. Using CRISPR-Cas9-mediated gene editing we generated Prm1-deficient mice and demonstrate, that Prm1+/- mice are subfertile while Prm1-/- are infertile. Prm1-deficiency was associated with higher levels of 8-OHdG, an indicator for reactive oxygen mediated DNA-damage. While Prm1+/- males displayed moderate increased levels of 8-OHdG virtually all sperm of Prm1-/- males displayed ROS mediated DNA damage. Consequently, DNA integrity was slightly hampered in Prm1+/-, while DNA was completely fragmented in Prm1-/- animals. Interestingly CMA3 staining which indicates protamine-free DNA revealed, that Prm1+/- sperm displayed high levels (93%), compared to Prm2+/- (29%) and WT (2%) sperm. This is not due to increased histone retention as demonstrated by mass spectrometry (MassSpec) of nuclear proteins in Prm1+/- sperm. Further analysis of the MassSpec data from sperm nuclear proteome revealed, that only one protein (RPL31) is significantly higher abundant in Prm1+/- compared to WT sperm. Comparison of the proteome from Prm1-/- and Prm2-/- to WT suggested, that there are a small number of proteins which differ in abundance. However, their function was not linked mechanistically to primary defects seen in Prm1-/- mice and rather represent a general stress response. Interestingly, using acid urea gels we found that sperm from Prm1+/- and Prm1-/- mice contain a high level of unprocessed, full-length PRM2. Prm2 is transcribed as a precursor protein which, upon binding to DNA is successively processed. Further, the overall ratio of PRM1:PRM2 is skewed from 1:2 in WT to 1:5 in Prm1+/- animals. Our results reveal that Prm1 is required for proper processing of PRM2 to produce the mature PRM2 which, together with Prm1 is able to hypercondense DNA. Hence, the species specific PRM1:PRM2 ratio has to be precisely controlled in order to retain full fertility.

developmental biology↗

bioRxiv2021

Language models enable zero-shot prediction of the effects of mutations on protein function

Modeling the effect of sequence variation on function is a fundamental problem for understanding and designing proteins. Since evolution encodes information about function into patterns in protein sequences, unsupervised models of variant effects can be learned from sequence data. The approach to date has been to fit a model to a family of related sequences. The conventional setting is limited, since a new model must be trained for each prediction task. We show that using only zero-shot inference, without any supervision from experimental data or additional training, protein language models capture the functional effects of sequence variation, performing at state-of-the-art.

synthetic biology↗

bioRxiv2021

Virtual deep brain stimulation: Multiscale co-simulation of a spiking basal ganglia model and a whole-brain mean-field model with The Virtual Brain

Deep brain stimulation (DBS) has been successfully applied in various neurodegenerative diseases as an effective symptomatic treatment. However, its mechanisms of action within the brain network are still poorly understood. Many virtual DBS models analyze a subnetwork around the basal ganglia and its dynamics as a spiking network with their details validated by experimental data. However, connectomic evidence shows widespread effects of DBS affecting many different cortical and subcortical areas. From a clinical perspective, various effects of DBS besides the motoric impact have been demonstrated. The neuroinformatics platform The Virtual Brain (TVB) offers a modeling framework allowing us to virtually perform stimulation, including DBS, and forecast the outcome from a dynamic systems perspective prior to invasive surgery with DBS lead placement. For an accurate prediction of the effects of DBS, we implement a detailed spiking model of the basal ganglia, which we combine with TVB via our previously developed co-simulation environment. This multiscale co-simulation approach builds on the extensive previous literature of spiking models of the basal ganglia while simultaneously offering a whole-brain perspective on widespread effects of the stimulation going beyond the motor circuit. In the first demonstration of our model, we show that virtual DBS can move the firing rates of a Parkinsons disease patients thalamus - basal ganglia network towards the healthy regime while, at the same time, altering the activity in distributed cortical regions with a pronounced effect in frontal regions. Thus, we provide proof of concept for virtual DBS in a co-simulation environment with TVB. The developed modeling approach has the potential to optimize DBS lead placement and configuration and forecast the success of DBS treatment for individual patients. Highlights- We implement and validate a co-simulation approach of a spiking network model for subcortical regions in and around the basal ganglia and interface it with mean-field network models for each cortical region. - Our simulations are based on a normative connectome including detailed tracts between the cortex and the basal ganglia regions combined with subject-specific optimized weights for a healthy control and a patient with Parkinsons disease. - We provide proof of concept by demonstrating that the implemented model shows biologically plausible dynamics during resting state including decreased thalamic activity in the virtual patient and during virtual deep brain stimulation including normalized thalamic activity and distributed altered cortical activity predominantly in frontal regions. - The presented co-simulation model can be used to tailor deep brain stimulation for individual patients.

neuroscience↗

bioRxiv2021

Neural Potts Model

AO_SCPLOWBSTRACTC_SCPLOWWe propose the Neural Potts Model objective as an amortized optimization problem. The objective enables training a single model with shared parameters to explicitly model energy landscapes across multiple protein families. Given a protein sequence as input, the model is trained to predict a pairwise coupling matrix for a Potts model energy function describing the local evolutionary landscape of the sequence. Couplings can be predicted for novel sequences. A controlled ablation experiment assessing unsupervised contact prediction on sets of related protein families finds a gain from amortization for low-depth multiple sequence alignments; the result is then confirmed on a database with broad coverage of protein sequences.

bioinformatics↗

bioRxiv2021

Perivascular tenascin C triggers sequential activation of macrophages and endothelial cells to generate a pro-metastatic vascular niche in the lungs

When cancers progress to metastasis, disseminated cancer cells frequently lodge near vasculature in secondary organs. However, our understanding of the cellular crosstalk evoked at perivascular sites is still rudimentary. In this study, we identify intercellular machinery governing formation of a pro-metastatic vascular niche during breast cancer colonization in lungs. We show that four secreted factors, INHBB, SCGB3A1, OPG and LAMA1, induced in metastasis-associated endothelial cells (ECs), are essential components of the vascular niche and promote metastasis in mice by enhancing stem cell properties and survival ability of cancer cells. Notably, blocking VEGF, a key regulator of EC behavior, dramatically suppressed EC proliferation, whereas no impact was observed on the expression of the four vascular niche factors in lung ECs. However, perivascular macrophages, activated via the TNC-TLR4 axis, were shown to be crucial for EC-mediated production of niche components. Together, our findings provide mechanistic insights into the formation of vascular niches in metastasis.

cancer biology↗

bioRxiv2021

Don't Stress, It's Under Control: Neural Correlates of Stressor Controllability in Humans

Animal research has repeatedly shown that experience of control over an aversive event can protect against the negative consequences of later uncontrollable stress. Neurobiologically, this effect is assumed to correspond to persistent changes in the pathway linking the ventromedial prefrontal cortex (vmPFC) and the dorsal raphe nucleus. However, it remains unclear to what extent these findings translate to humans. During functional magnetic resonance imaging, we subjected participants to controllable and uncontrollable aversive but non-painful electric stimuli, as well as to a control condition without aversive stimulation. In each trial, a symbol signalled whether participants could terminate the stressor through correct performance in a button-matching task or whether the stressor would be randomly terminated, i.e., uncontrollable. Along with neural responses, we assessed participants accuracy, reaction times, and heart rate. To relate neural activations and subjective experience, we asked participants to rate perceived control, helplessness, and stress. Results were largely in line with our hypotheses. The vmPFC was generally deactivated by stress, but this effect was attenuated when participants could terminate the stressor compared to when their responses had no effect. Furthermore, activation in stress-responsive regions, including the bilateral insula, was reduced during controllable trials. Under uncontrollable stress, greater vmPFC recruitment was linked to reduced feelings of helplessness. An investigation of condition-dependent differences in vmPFC connectivity yielded no significant results. Our findings further corroborate animal research and emphasise the role of the vmPFC in controllability-dependent regulation of stress responses. Based on the results, we discuss future directions in the context of resilience research and mental health promotion.

neuroscience↗

bioRxiv2021

Glycine receptor α3K governs mobility and conductance of L/K splice variant heteropentamers

Glycine receptors (GlyRs) are ligand-gated pentameric chloride channels in the central nervous system. GlyR-3 is a possible target for chronic pain treatment and temporal lobe epilepsy. Alternative splicing into K or L variants determines the subcellular fate and function of GlyR-3, yet it remains to be shown whether its different splice variants can functionally co-assemble, and what the properties of such heteropentamers would be. Here, we subjected GlyR-3 to a combined fluorescence microscopy and electrophysiology analysis. We employ masked Pearsons and dual-color spatiotemporal correlation analysis to prove that GlyR-3 splice variants heteropentamerize, adopting the mobility of the K variant. Fluorescence-based single-subunit counting experiments revealed a variable and concentration ratio dependent hetero-stoichiometry. Via single-channel on-cell patch clamp we show heteropentameric conductances resemble those of the 3K splice variant. Our data are compatible with a model where 3 heteropentamerization fine-tunes mobility and activity of GlyR 3 channels, which is important to understand and tackle 3 related diseases. SummaryThe glycine receptor 3 is key to the central nervous systems physiology and involved in chronic pain and epilepsy. In this paper, Lemmens et al. reveal and functionally characterize 3 splice variant heteropentamerization via advanced single-molecule fluorescence image analysis. DeclarationsO_ST_ABSFundingC_ST_ABSWe acknowledge the UHasselt Advanced Optical Microscopy Centre (AOMC). Prof. Em. Marcel Ameloot, the Research Foundation Flanders (FWO, project G0H3716N) and the province of Limburg (Belgium) (tUL Impuls II) are acknowledged for funding the microscopy hardware. V. Lemmens is grateful for a doctoral scholarship from the UHasselt (17DOC11BOF) and KU Leuven (C14/16/053) Special Research Funds (BOF). Conflicts of interest / competing interestsNo conflicts of interest apply. Ethics approvalNot applicable Availability of data and materialAll data and material are available upon request. Code availabilityFluctuation imaging and co-localization analyses were performed in the software package PAM [71]. The software is available as source code, requiring MATLAB to run, or as pre-compiled standalone distributions for Windows or MacOS at http://www.cup.uni-muenchen.de/pc/lamb/software/pam.html or hosted in Git repositories under http://www.gitlab.com/PAM-PIE/PAM and http://www.gitlab.com/PAM-PIE/PAMcompiled. A detailed user manual is available at http://pam.readthedocs.io. Author contributionsConceptualization Meier J.C., Brone B. and Hendrix J.; Investigation and formal analysis Lemmens V. and Thevelein B.; Software development Hendrix J.; Writing the original draft Lemmens V., Thevelein B and Hendrix, J.; Review and editing by all authors.

biochemistry↗

bioRxiv2021

MSA Transformer

Unsupervised protein language models trained across millions of diverse sequences learn structure and function of proteins. Protein language models studied to date have been trained to perform inference from individual sequences. The longstanding approach in computational biology has been to make inferences from a family of evo lutionarily related sequences by fitting a model to each family independently. In this work we combine the two paradigms. We introduce a protein language model which takes as input a set of sequences in the form of a multiple sequence alignment. The model interleaves row and column attention across the input sequences and is trained with a variant of the masked language modeling objective across many protein families. The performance of the model surpasses current state-of-the-art unsupervised structure learning methods by a wide margin, with far greater parameter efficiency than prior state-of-the-art protein language models.

synthetic biology↗

bioRxiv2020

Transformer protein language models are unsupervised structure learners

AO_SCPLOWBSTRACTC_SCPLOWUnsupervised contact prediction is central to uncovering physical, structural, and functional constraints for protein structure determination and design. For decades, the predominant approach has been to infer evolutionary constraints from a set of related sequences. In the past year, protein language models have emerged as a potential alternative, but performance has fallen short of state-of-the-art approaches in bioinformatics. In this paper we demonstrate that Transformer attention maps learn contacts from the unsupervised language modeling objective. We find the highest capacity models that have been trained to date already outperform a state-of-the-art unsupervised contact prediction pipeline, suggesting these pipelines can be replaced with a single forward pass of an end-to-end model.1

synthetic biology↗

bioRxiv2019

Altitude and life-history shape the evolution of Heliconius wings

Phenotypic divergence between closely related species has long interested biologists. Taxa that inhabit a range of environments and have known and diverse, natural histories, can help understand how different selection pressures shape diverging traits. In butterflies, wing colour patterns have been extensively studied, whereas wing shape diversity is less well understood despite its importance for flight. Here we study a measure of wing shape, aspect ratio, and wing size in a large dataset of over 3200 individuals, representing 13 Heliconius species, from across the Neotropics. We assess the relative importance of phylogenetic relatedness, natural history and habitat in determining wing shape and size. We find that both larval and adult behavioural ecology affect patterns of adult size dimorphisms. On one hand, species with solitary larvae have larger adult males, in contrast to gregarious Heliconius species, and indeed most Lepidoptera. On the other hand, species in the pupal-mating clade are smaller overall than those in the adult-mating clade. Interestingly, while controlling for phylogeny, sex ratios and allometry, we find that species inhabiting higher altitudes have rounder wings and, in one of the two major Heliconius clades, are also bigger in size than their lowland relatives. Thus, we reveal novel adaptive wing morphological divergence among Heliconius species beyond that imposed by natural selection on aposematic wing colouration. Our study highlights the value of phylogenetic comparative methods in study systems that have diverse and well-studied natural histories to disentangle the selection pressures shaping adaptive phenotypes.

evolutionary biology↗

bioRxiv2019

Biological Structure and Function Emerge from Scaling Unsupervised Learning to 250 Million Protein Sequences

In the field of artificial intelligence, a combination of scale in data and model capacity enabled by un-supervised learning has led to major advances in representation learning and statistical generation. In the life sciences, the anticipated growth of sequencing promises unprecedented data on natural sequence diversity. Protein language modeling at the scale of evolution is a logical step toward predictive and generative artificial intelligence for biology. To this end we use unsupervised learning to train a deep contextual language model on 86 billion amino acids across 250 million protein sequences spanning evolutionary diversity. The resulting model contains information about biological properties in its representations. The representations are learned from sequence data alone. The learned representation space has a multi-scale organization reflecting structure from the level of biochemical properties of amino acids to remote homology of proteins. Information about secondary and tertiary structure is encoded in the representations and can be identified by linear projections. Representation learning produces features that generalize across a range of applications, enabling state-of-the-art supervised prediction of mutational effect and secondary structure, and improving state-of-the-art features for long-range contact prediction.

synthetic biology↗

bioRxiv2019

CXCR3-expressing metastasis-initiating cells induce and exploit a fibroblast niche in the lungs to fuel metastatic colonization

Metastatic colonization relies on interactions between disseminated cancer cells and the microenvironment in secondary organs. Here, we show that disseminated breast cancer cells evoke major phenotypic changes in lung fibroblasts to form a metastatic niche that supports malignant growth. Colonization of the lungs by cancer cells confers an inflammatory phenotype in associated fibroblasts, where IL-1 and IL-1{beta}, secreted by breast cancer cells, induce CXCL9 and CXCL10 production in metastasis-associated fibroblasts via NF-{kappa}B signaling. These paracrine interactions fuel the growth of lung metastases. Notably, we find that the chemokine receptor CXCR3, that binds CXCL9/10, is specifically expressed in a small subset of breast cancer cells with stem/progenitor cell properties and high tumor-initiating ability when co-transplanted with fibroblasts. CXCR3-expressing cancer cells show high JNK signaling that drives IL-1/{beta} expression. Thus, CXCR3 marks a population of breast cancer cells that induces CXCL9/10 production in fibroblast, but can also respond to and benefit from these chemokines. Importantly, disruption of this intercellular JNK-IL-1-CXCL9/10-CXCR3 axis significantly reduces metastatic colonization in xenograft and syngeneic mouse models. These data mechanistically demonstrate an essential role for this molecular crosstalk between breast cancer cells and their fibroblast niche in the progression of metastasis.

cancer biology↗

bioRxiv2018

Dynamic changes in cerebral and peripheral markers of glutamatergic signaling across the human sleep-wake cycle

Both sleep and glutamatergic signaling in the brain are tightly controlled and homeostatically regulated. Sleep homeostasis is reliably reflected by predictable changes in brain electrical activity in waking and sleep, yet the underlying molecular mechanisms remain elusive. Current hypotheses posit that recovery sleep following prolonged waking restores efficient functioning of the brain, for example by keeping glutamatergic signaling in a homeostatic range. We recently provided evidence in humans and mice that metabotropic glutamate receptors of subtype-5 (mGluR5) contribute to the brains coping mechanisms with sleep deprivation. Here we combined in 31 healthy men, proton magnetic resonance spectroscopy to measure the levels of glutamate (Glu), GLX (glutamate-to-glutamine ratio) and GABA ({gamma}-amino-butyric-acid) in basal ganglia (BG) and dorsolateral prefrontal cortex, simultaneous positron emission tomography to quantify mGluR5 availability with the novel radioligand, [18F]PSS232, and quantification in blood plasma of the mGluR5-regulated proteins, fragile-X mental retardation protein (FMRP) and brain-derived neurotrophic factor (BDNF). All measurements were conducted at the same circadian time in baseline, following sleep deprivation and after recovery sleep. We found that Glu and GLX in BG (pall < 0.01), but not in prefrontal cortex, and the plasma concentration of FMRP (p < 0.02), were increased after sleep loss and tended to normalize following recovery sleep (pall < 0.1). Furthermore, a night without sleep enhanced whole-brain and striatal mGluR5 availability and was normalized by recovery sleep (pall < 0.05). By contrast, other brain metabolites and plasma BDNF levels were not altered. The findings demonstrate convergent changes in distinct markers of glutamatergic signaling across prolonged wakefulness and recovery sleep in humans. They warrant further studies to elucidate the underlying mechanisms that link the homeostatic regulation of sleep and glutamatergic system activity in health and disease.\n\nOne-sentence summarySleep-dependent recovery of wakefulness-induced changes in, cerebral glutamatergic signaling\n\nMajor subject areaNeuroscience; Human Biology & Medicine

neuroscience↗

bioRxiv2018

CRISPR-Cas9 Screens Reveal Genes Regulating a G0-like State in Human Neural Progenitors

Single cell RNA-seq has emerged as a powerful tool for resolving cellular states associated with normal and maligned developmental processes. Here, we used scRNA-seq to examine the cell cycle states of expanding human neural stem cells (hNSCs). From this data, we created a cell cycle classifier, which, in addition to traditional cell cycle phases, also identifies a putative quiescent-like state in neuroepithelial-derived cell types during mammalian neurogenesis and in gliomas. This state, Neural G0, is enriched for expression of quiescent NSC genes and other neurodevelopmental markers found in non-dividing neural progenitors. For gliomas, Neural G0 cell populations and gene expression is significantly associated with less aggressive tumors and extended patient survival. Genetic screens to identify modulators of Neural G0 revealed that knockout of genes associated with the Hippo/Yap and p53 pathways diminished Neural G0 in vitro, resulting in faster G1 transit, down regulation of quiescence-associated markers, and loss of Neural G0 gene expression. Thus, Neural G0 represents a dynamic quiescent-like state found in neuro-epithelial derived cells and gliomas.

cell biology↗

bioRxiv2018

T Cell Repertoire Evolution After Allogeneic Bone Marrow Transplantation: An Organizational Perspective

High throughput sequencing (HTS) of human T cell receptors has revealed a high level of complexity in the T cell repertoire. In an attempt to correlate T cell reconstitution with clinical outcomes several measures of T cell repertoire complexity have emerged. However, the associations identified are of a broadly statistical nature, not allowing precise modeling of outcomes based on T cell repertoire development in clinical contexts such as following bone marrow transplantation (BMT). Previous work demonstrated that there is an inherent, mathematically definable order observed in the T cell population that is conserved in a diverse group of donors, and which is perturbed in recipients following BMT. Herein, we use a public database of human leukocyte antigen matched related-donor and recipient T cell receptor (TCR) {beta} sequences to further develop this methodology. TCR {beta} sequencing from unsorted T cells and sorted T cell subsets isolated from peripheral blood samples from BMT donors and recipients show remarkable conservation and symmetry of VJ segment usage in the clonal frequencies, linked to the organization of the gene segments along the TCR locus. This TCR {beta} VJ segment translational symmetry is preserved post-transplant, and even in cases of acute GVHD (aGVHD), suggesting that GVHD occurrence represents a polyclonal donor T cell response to recipient antiges. We also observe that the complexity of the repertoire is significantly diminished after BMT and is not restored even years out post-transplant. The results here provide a new method of quantifying and characterizing post-transplant T cell repertoire reconstitution by further analyzing the mathematical rules governing TCR usage in the context of BMT. This approach may allow for a new means to correlate clinical outcomes with the evolving T cell repertoire post-transplant.

immunology↗

bioRxiv2018

Hybridization, sex specific genomic architecture and local adaptation

While gene flow can reduce the potential for local adaptation, hybridization may conversely provide genetic variation that increases the potential for local adaptation. Hybridization may also affect adaptation through altering sexual dimorphism and sexual conflict, but this remains largely unstudied. Here, we discuss how hybridization may affect sexual dimorphism and conflict due to differential effects of hybridization on males and females, and then how this in turn may affect local adaptation. First, the lower viability of the heterogametic sex in hybrids could shift the balance in sexual conflict. Second, sex-specific inheritance of the mitochondrial genome in hybrids may lead to cyto-nuclear mismatches, for example in the form of \"mothers curse\", with potential consequences for sex-ratio and sex specific expression. Third, transgressive segregation of sexually antagonistic alleles could lead to greater sexual dimorphism in hybrid populations. These mechanisms can reduce sexual conflict and enhance intersexual niche partitioning, increasing the fitness of hybrids. Adaptive introgression of alleles reducing sexual conflict or enhancing intersexual niche partitioning may facilitate local adaptation, and could favour the colonization of novel habitats. We review these consequences of hybridization on sex differences and local adaptation, and discuss how their prevalence and importance could be tested empirically.

evolutionary biology↗