Biology subjects

Ernst, M.

Publications and source records attributed to Ernst, M..

At least 37 records · Page 2Linked to original sources

bioRxiv2022

Recalibrating vision-for-action requires years after sight restoration from congenital blindness

Being able to perform goal-directed actions requires predictive, feed-forward control, including a mapping between the visually estimated target locations and the motor commands reaching for them. When the mapping is perturbed, e.g., due to muscle fatigue or optical distortions, we are quickly able to recalibrate the sensorimotor system to update this mapping. Here we investigated whether early visual and visuomotor experience is essential for developing the ability to recalibrate. To this end, we assessed young individuals deprived from vision due to dense congenital bilateral cataracts, who were surgically treated for sight restoration only years after birth. We compared their recalibration performance to such distortion to that of age-matched sighted controls. Their recalibration performance was impaired right after surgery. This finding cannot be explained by their still lower visual acuity, since blurring vision in controls to a matching degree did not lead to comparable behavior. Nevertheless, the recalibration ability of cataract-treated participants improved with experience, matching controls performance after around 2 years from surgery. Thus, the lack of early visual experience affects visuomotor recalibration. However, this ability is not lost but slowly develops after sight restoration, highlighting the importance of sensorimotor experience for brain plasticity late in life.

neuroscience↗

bioRxiv2021

The minor spliceosome offers a therapeutically viable target for the treatment of a broad spectrum of cancers

Minor splicing is a second splicing system required for the correct expression of [~]700 human minor intron-containing genes (MIGs). Many MIGs are expressed in vigorously proliferating cells and are frequently dysregulated in cancer including BRAF, ERK, JNK and p38. Minor splicing is carried out by the minor spliceosome which comprises several unique components, including a 65kDa protein encoded by RNPC3. We show that Rnpc3 heterozygosity reduces tumour burden in a broad spectrum of in vivo cancer settings, without harming normal tissues. Using the collective power of zebrafish, mouse and human cancer models, we reveal a sequence of events connecting Rnpc3 deficiency and impaired splicing of MIGs to DNA damage and activation of a Tp53-dependent transcriptional program that restricts tumour burden by inducing cell cycle arrest and apoptosis. Interrogation of human liver and lung cancer transcriptomes curated in TCGA revealed that the expression of many of the genes encoding protein components of the minor spliceosome is upregulated in these cancers. This is accompanied by upregulation of the expression of MIGs that are enriched in cell cycle and DNA damage pathways. These findings suggest that cancer cells can invoke mechanisms to increase the efficiency of minor splicing to support their high proliferation rates. Finally, Kaplan Meier survival analysis shows that highly expressed MIGs are frequently associated with poor patient survival. Taken together, these results indicate that the minor spliceosome offers a therapeutically viable target for the treatment of a broad spectrum of cancers.

cancer biology↗

bioRxiv2021

Biomolecular solid-state NMR spectroscopy at highest field: the gain in resolution at 1200 MHz

Progress in NMR in general and in biomolecular applications in particular is driven by increasing magnetic-field strengths leading to improved resolution and sensitivity of the NMR spectra. Recently, persistent superconducting magnets at a magnetic field strength (magnetic induction) of 28.2 T corresponding to 1200 MHz proton resonance frequency became commercially available. We present here a collection of high-field NMR spectra of a variety of proteins, including molecular machines, membrane proteins and viral capsids and others. We show this large panel in order to provide an overview over a range of representative systems under study, rather than a single best performing model system. We discuss both carbon-13 and proton-detected experiments, and show that in 13C spectra substantially higher numbers of peaks can be resolved compared to 850 MHz while for 1H spectra the most impressive increase in resolution is observed for aliphatic side-chain resonances.

biophysics↗

bioRxiv2021

EHF is essential for epidermal and colonic epithelial homeostasis and suppresses Apc-initiated colonic tumorigenesis

BackgroundEts homologous factor (EHF) is a member of the epithelial-specific Ets (ESE) transcription factors. EHF is specifically expressed in epithelial tissues, however its role in development and epithelial homeostasis is largely uncharacterized. MethodsWe generated a novel mouse strain in which the Ets DNA binding domain (exon 8) of Ehf was flanked by loxP sites (EhfLox/Lox). To inactivate Ehf in the whole body, EhfLox/Lox mice were crossed to CMVCre mice, which were then bred out to generate germline Ehf null (Ehf-/-) mice. To inactivate Ehf specifically in the intestinal epithelium, EhfLox/Lox mice were bred to tamoxifen-inducible VillinCre-ERT2 mice. EhfLox/Lox mice were also crossed to tamoxifen-inducible Cdx2CreERT2; ApcLox/+ mice to determine the impact of Ehf deletion on Apc-initiated colon cancer development. ResultsTranscripts encoding the Ets binding domain of EHF were effectively deleted in all tissues in Ehf-/- mice. Ehf-/- mice were born at the expected Mendelian ratio, but showed reduced body weight gain and developed a series of pathologies during their lifespan that led the majority of Ehf-/- mice to reach an ethical endpoint within one year of age. Most prominent of these were the development of papillomas in the chin, and abscesses in the preputial glands (males) or vulvae (females) which showed evidence of Staphylococcus and Proteus infection. Consistent with the development of papillomas, the epidermis of Ehf-/- mice showed evidence of mild hyperplasia. A subset of Ehf-/- mice also developed cataracts and corneal ulcers. EHF is highly expressed in the colonic epithelium and Ehf-/- mice displayed increased susceptibility to dextran sodium sulphate-induced colitis. This phenotype was confirmed in intestinal-specific Ehf knockout mice, and histopathological analyses revealed reduced numbers of goblet cells and extensive transcriptional reprogramming in the colonic epithelium. Finally, colon-specific deletion of Ehf enhanced Apc-initiated adenoma development, unveiling a novel, tumour suppressive role for EHF in colorectal cancer. ConclusionThe Ets DNA-binding domain of EHF is essential for post-natal homeostasis of the epidermis and colonic epithelium, and functions as a tumour suppressor in the colon.

cell biology↗

bioRxiv2021

Dual MAPK and HDAC inhibition rewires the apoptotic rheostat to trigger colorectal cancer cell death

The EGFR/RAS/MEK/ERK signalling pathway (ERK/MAPK) is hyper-activated in most colorectal cancers (CRCs). A current limitation of inhibitors of this pathway is that they primarily induce cytostatic effects in CRC cells. Nevertheless, these drugs do induce expression of pro-apoptotic factors, suggesting they may prime CRC cells to undergo apoptosis. As histone deacetylase inhibitors (HDACi) induce expression of multiple pro-apoptotic proteins, we examined whether they could synergize with ERK/MAPK inhibitors to trigger CRC cell apoptosis. Combined MEK/ERK and HDAC inhibition synergistically induced apoptosis in CRC cell lines and patient-derived tumour organoids in vitro, and attenuated Apc-initiated adenoma formation in vivo. Mechanistically this effect was mediated through induction of the BH3-only pro-apoptotic proteins BIM and BMF. Importantly, we demonstrate that this treatment paradigm can be tailored to specific MAPK genotypes in CRCs, by combining HDACi with EGFR, KRASG12C or BRAFV600 inhibitors in KRAS/BRAFWT; KRASG12C, BRAFV600E CRC cell lines respectively. These findings identify a novel ERK/MAPK genotype-targeted treatment paradigm for colorectal cancer.

cancer biology↗

bioRxiv2020

The ant fungus garden acts as an external digestive system

Most animals digest their food within their own bodies, but some do not. Many species of ants grow fungus gardens that pre-digest food as an essential step of the ants nutrient uptake. To better understand this digestion process, we generated a 3D molecular map of an Atta texana fungus garden, revealing chemical modifications mediated by the fungus garden as plant material passes through.

systems biology↗

bioRxiv2020

Chemically-informed Analyses of Metabolomics Mass Spectrometry Data with Qemistree

Untargeted mass spectrometry is employed to detect small molecules in complex biospecimens, generating data that are difficult to interpret. We developed Qemistree, a data exploration strategy based on hierarchical organization of molecular fingerprints predicted from fragmentation spectra, represented in the context of sample metadata and chemical ontologies. By expressing molecular relationships as a tree, we can apply ecological tools, designed around the relatedness of DNA sequences, to study chemical composition.

bioinformatics↗

bioRxiv2020

Prefrontal responses during proactive and reactive inhibition are differentially impacted by stress in anorexia and bulimia nervosa

BackgroundBinge-eating is a distressing, transdiagnostic eating disorder symptom associated with impulsivity, particularly in negative mood states. Neuroimaging studies of bulimia nervosa (BN) report reduced activity in fronto-striatal regions implicated in self-regulatory control. However, it remains unknown if negative affective states, including stress, impair self-regulation, and, if so, whether such self-regulatory deficits generalize to binge-eating in underweight individuals (i.e., the bingeing/purging subtype of anorexia nervosa; AN-BP). MethodsWe determined the effect of acute stress on inhibitory control in 85 women (33 BN, 22 AN-BP, 30 matched controls). Participants underwent repeated functional MRI scanning, during performance of the stop-signal anticipation task, a validated measure of proactive (i.e., anticipation of stopping) and reactive (outright stopping) inhibition. Neural and behavioral responses to induced, psychological stress and a control task were evaluated on two separate days. ResultsWomen with BN had reduced proactive inhibition while prefrontal responses were increased in both AN-BP and BN. Reactive inhibition was neurally and behaviorally intact in both diagnostic groups. Both AN-BP and BN groups showed distinct, stress-induced changes in prefrontal activity during both proactive and reactive inhibition. However, task performance was not significantly affected by stress. ConclusionsThese findings offer novel evidence of reduced proactive inhibition in BN, yet inhibitory control deficits did not generalize to AN-BP. While both groups showed altered neural responses during inhibition following stress, neither group demonstrated stress-induced performance deficits. As such, our findings counsel against a simplistic, stress-induced failure of regulation as a holistic explanation for binge-eating in these conditions.

neuroscience↗

bioRxiv2020

Algorithmic Learning for Auto-deconvolution of GC-MS Data to Enable Molecular Networking within GNPS.

Gas chromatography-mass spectrometry (GC-MS) represents an analytical technique with significant practical societal impact. Spectral deconvolution is an essential step for interpreting GC-MS data. No public GC-MS repositories that also enable repository-scale analysis exist, in part because deconvolution requires significant user input. We therefore engineered a scalable machine learning workflow for the Global Natural Product Social Molecular Networking (GNPS) analysis platform to enable the mass spectrometry community to store, process, share, annotate, compare, and perform molecular networking of GC-MS data. The workflow performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization, using a Fast Fourier Transform-based strategy to overcome scalability limitations. We introduce a "balance score" that quantifies the reproducibility of fragmentation patterns across all samples. We demonstrate the utility of the platform with breathomics analysis applied to the early detection of oesophago-gastric cancer, and by creating the first molecular spatial map of the human volatilome.

bioinformatics↗

bioRxiv2019

Feature-based Molecular Networking in the GNPS Analysis Environment

Molecular networking has become a key method used to visualize and annotate the chemical space in non-targeted mass spectrometry-based experiments. However, distinguishing isomeric compounds and quantitative interpretation are currently limited. Therefore, we created Feature-based Molecular Networking (FBMN) as a new analysis method in the Global Natural Products Social Molecular Networking (GNPS) infrastructure. FBMN leverages feature detection and alignment tools to enhance quantitative analyses and isomer distinction, including from ion-mobility spectrometry experiments, in molecular networks.

bioinformatics↗

bioRxiv2019

Repository-scale Co- and Re-analysis of Tandem Mass Spectrometry Data

Metabolomics data are difficult to find and reuse, even in public repositories. We, therefore, developed the Reanalysis of Data User (ReDU) interface (https://redu.ucsd.edu/), a community- and data-driven approach that solves this problem at the repository scale. ReDU enables public data discovery and co- or re-analysis via uniformly formatted, publicly available MS/MS data and metadata in the Global Natural Product Social Molecular Networking Platform (GNPS), consistent with findable, accessible, interoperable, and reusable (FAIR) principles.1

bioinformatics↗

bioRxiv2019

MolNetEnhancer: enhanced molecular networks by integrating metabolome mining and annotation tools

Metabolomics has started to embrace computational approaches for chemical interpretation of large data sets. Yet, metabolite annotation remains a key challenge. Recently, molecular networking and MS2LDA emerged as molecular mining tools that find molecular families and substructures in mass spectrometry fragmentation data. Moreover, in silico annotation tools obtain and rank candidate molecules for fragmentation spectra. Ideally, all structural information obtained and inferred from these computational tools could be combined to increase the resulting chemical insight one can obtain from a data set. However, integration is currently hampered as each tool has its own output format and efficient matching of data across these tools is lacking. Here, we introduce MolNetEnhancer, a workflow that combines the outputs from molecular networking, MS2LDA, in silico annotation tools (such as Network Annotation Propagation or DEREPLICATOR) and the automated chemical classification through ClassyFire to provide a more comprehensive chemical overview of metabolomics data whilst at the same time illuminating structural details for each fragmentation spectrum. We present examples from four plant and bacterial case studies and show how MolNetEnhancer enables the chemical annotation, visualization, and discovery of the subtle substructural diversity within molecular families. We conclude that MolNetEnhancer is a useful tool that greatly assists the metabolomics researcher in deciphering the metabolome through combination of multiple independent in silico pipelines.

biochemistry↗

bioRxiv2019

MASST: A Web-based Basic Mass Spectrometry Search Tool for Molecules to Search Public Data.

CorrespondenceWe introduce a web-enabled small-molecule mass spectrometry (MS) search engine. To date, no tool can query all the public small-molecule tandem MS data in metabolomics repositories, greatly limiting the utility of these resources in clinical, environmental and natural product applications. Therefore, we introduce a Mass Spectrometry Search Tool (MASST) (https://proteosafe-extensions.ucsd.edu/masst/), that enables the discovery of molecular relationships among accessible public metabolomics and natural product tandem mass spectrometry data (MS/MS).

bioinformatics↗

bioRxiv2019

Disrupted mechanobiology links the molecular and cellular phenotypes in familial dilated cardiomyopathy

Familial dilated cardiomyopathy (DCM) is a leading cause of sudden cardiac death and a major indicator for heart transplant. The disease is frequently caused by mutations of sarcomeric proteins; however, it is not well understood how these molecular mutations lead to alterations in cellular organization and contractility. To address this critical gap in our knowledge, we studied the molecular and cellular consequences of a DCM mutation in troponin-T, {Delta}K210. We determined the molecular mechanism of {Delta}K210 and used computational modeling to predict that the mutation should reduce the force per sarcomere. In mutant cardiomyocytes, we found that {Delta}K210 not only reduces contractility, but also causes cellular hypertrophy and impairs cardiomyocytes ability to adapt to changes in substrate stiffness (e.g., heart tissue fibrosis that occurs with aging and disease). These results link the molecular and cellular phenotypes and implicate alterations in mechanosensing as an important factor in the development of DCM.

biophysics↗

bioRxiv2019

Implementations of the chemical structural and compositional similarity metric in R and Python

MotivationTandem mass spectrometry (MS/MS) has the potential to substantially improve metabolomics by acquiring spectra of fragmented ions. These fragmentation spectra can be represented as a molecular network, by measuring cosine distances between them, thus identifying signals from the same or similar molecules. Metrics that enable comparison between pairs of samples based on their metabolite profiles are in great need. Taking inspiration from the successful phylogeny-aware beta-diversity measures used in microbiome research, integrating chemical similarity information about the features in addition to their abundances could lead to better insights when comparing metabolite profiles. Chemical Structural and Compositional Similarity (CSCS) is a recently published similarity metric comparing the full set of signals and their chemical similarity between two samples. Efficient, scalable and easily accessible implementations of this algorithm is currently lacking. Here, we present an easily accessible and scalable implementation of CSCS in both python and R, including a version not weighted by intensity information. ResultsWe provide a new implementation of the CSCS algorithm that is over 300 times faster than the published implementation in R, making the algorithm suitable for large-scale metabolomics applications. We also show that adding chemical information enriches existing methods. Furthermore, the R implementation includes functions for exporting molecular networks directly from the mass spectral molecular networking platform GNPS for ease of use for downstream applications. Contactbrejnrod@sund.ku.dk Availabilitygithub.com/askerdb/rCSCS, github.com/askerdb/pyCSCS

bioinformatics↗

bioRxiv2018

An integrated genome-wide multi-omics analysis of gene expression dynamics in the preimplantation mouse embryo

Early mouse embryos have an atypical translational machinery comprised of cytoplasmic lattices, poorly competent for translation. Thus, the impact of transcriptomic changes on the operational levels of proteins has likely been overestimated in the past. To find out, we used liquid chromatography-tandem mass spectrometry to detect and quantify 6,550 proteins in the oocyte and in six developmental stages (from zygote to blastocyst) collected in triplicates, and we also performed mRNA sequencing.\n\nIn contrast to the known split between the 2-cell and 4-cell stages at the transcript level, on the protein level the oocyte-to-embryo transition appeared to last until the morula stage. In general, protein abundance profiles were weakly correlated with those of their cognate mRNAs and we found little or no concordance between changes in protein and transcript expression relative to the oocyte at early stages. However, concordance increased towards morula and blastocyst, hinting at a more direct coupling of proteins with transcripts at these stages, in agreement with the increase in free ribosome abundance. Independent validation by immunofluorescence and qPCR confirmed the existence of genes featuring strongly positively and negatively correlated protein and transcript. Moreover, consistent coverage of most known protein complexes indicates that our dataset represents a large fraction of the expressed proteome. Finally, we identified 20 markers, including members of the endoplasmic reticulum pathway, for discriminating between early and late stages.\n\nThis resource contributes towards closing the gap between the predicted phenotype, based on mRNA, and the actual phenotype, based on protein, of the mouse embryo.

developmental biology↗

bioRxiv2018

Deciphering complex metabolite mixtures by unsupervised and supervised substructure discovery and semi-automated annotation from MS/MS spectra

O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=94 SRC=\"FIGDIR/small/491506_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (19K):\norg.highwire.dtl.DTLVardef@1382d8borg.highwire.dtl.DTLVardef@13636a4org.highwire.dtl.DTLVardef@3d7decorg.highwire.dtl.DTLVardef@1161d62_HPS_FORMAT_FIGEXP M_FIG Integration of MS2LDA substructure discovery with MAGMa spectral annotations and ClassyFire term predictions complemented with MotifDB significantly advance metabolite annotation.\n\nC_FIG Complex metabolite mixtures are challenging to unravel. Mass spectrometry (MS) is a widely used and sensitive technique to obtain structural information on complex mixtures. However, just knowing the molecular masses of the mixtures constituents is almost always insufficient for confident assignment of the associated chemical structures. Structural information can be augmented through MS fragmentation experiments whereby detected metabolites are fragmented giving rise to MS/MS spectra. However, how can we maximize the structural information we gain from fragmentation spectra?\n\nWe recently proposed a substructure-based strategy to enhance metabolite annotation for complex mixtures by considering metabolites as the sum of (bio)chemically relevant moieties that we can detect through mass spectrometry fragmentation approaches. Our MS2LDA tool allows us to discover - unsupervised - groups of mass fragments and/or neutral losses termed Mass2Motifs that often correspond to substructures. After manual annotation, these Mass2Motifs can be used in subsequent MS2LDA analyses of new datasets, thereby providing structural annotations for many molecules that are not present in spectral databases.\n\nHere, we describe how additional strategies, taking advantage of i) combinatorial in-silico matching of experimental mass features to substructures of candidate molecules, and ii) automated machine learning classification of molecules, can facilitate semi-automated annotation of substructures. We show how our approach accelerates the Mass2Motif annotation process and therefore broadens the chemical space spanned by characterized motifs. Our machine learning model used to classify fragmentation spectra learns the relationships between fragment spectra and chemical features. Classification prediction on these features can be aggregated for all molecules that contribute to a particular Mass2Motif and guide Mass2Motif annotations.\n\nTo make annotated Mass2Motifs available to the community, we also present motifDB: an open database of Mass2Motifs that can be browsed and accessed programmatically through an API. MotifDB is integrated within ms2lda.org, allowing users to efficiently search for characterized motifs in their own experiments. We expect that with an increasing number of Mass2Motif annotations available through a growing database we can more quickly gain insight in the constituents of complex mixtures. That will allow prioritization towards novel or unexpected chemistries and faster recognition of known biochemical building blocks.

bioinformatics↗

bioRxiv2018

Comprehensive mass spectrometry-guided plant specialized metabolite phenotyping reveals metabolic diversity in the cosmopolitan plant family Rhamnaceae

Plants produce a myriad of specialized metabolites to overcome their sessile habit and combat biotic as well as abiotic stresses. Evolution has shaped specialized metabolite diversity, which drives many other aspects of plant biodiversity. However, until recently, large-scale studies investigating specialized metabolite diversity in an evolutionary context have been limited by the impossibility to identify chemical structures of hundreds to thousands of compounds in a time-feasible manner. Here, we introduce a workflow for large-scale, semi-automated annotation of specialized metabolites, and apply it for over 1000 metabolites of the cosmopolitan plant family Rhamnaceae. We enhance the putative annotation coverage dramatically, from 2.5 % based on spectral library matches alone to 42.6 % of total MS/MS molecular features extending annotations from well-known plant compound classes into the dark plant metabolomics matter. To gain insights in substructural diversity within the plant family, we also extract patterns of co-occurring fragments and neutral losses, so-called Mass2Motifs, from the dataset; for example, only the Ziziphoid clade developed the triterpenoid biosynthetic pathway, whereas the Rhamnoid clade predominantly developed diversity in flavonoid glycosides, including 7-O-methyltransferase activity. Our workflow provides the foundations towards the automated, high-throughput chemical identification of massive metabolite spaces, and we expect it to revolutionize our understanding of plant chemoevolutionary mechanisms.

plant biology↗