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Results for “cell biology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2020

Understanding the compositional changes in LB Lennox medium during growth of Escherichia coli DH5α in a 1L bioreactor

Compositional changes in growth medium represents dynamic interplay between cell growth, biomass formation, and energy maintenance, with concomitant decrease in nutrients and increase in secreted metabolites and metabolic byproducts. Such information is important for quantifying microbial physiological response both at the population and cellular level, with respect to understanding subtle differences in microbial growth response, as well as supporting model building efforts in metabolic engineering. With the desire to understand molecular weight changes in components of LB Lennox medium as well as broth fractions present during cultivation of Escherichia coli DH5 (ATCC 53868) at 37 {degrees}C, 400 rpm stirring and 1 VVM aeration in a 1 L bioreactor, this study used a combination of gel filtration chromatography (GFC) and reversed phase high performance liquid chromatography (RP-HPLC) for determining changes to molecular weight of different fractions of the growth medium. Experiment results revealed the difficulty of fractionating the culture broth with RP-HPLC, where no distinct peaks of narrow retention time width were obtained. More importantly, the column used for GFC was unable to differentiate small molecular weight changes on the order of a few tens to few hundred Da through a refractive index detector. Together, GFC and RP-HPLC highlighted the difficulty of fractionating LB Lennox culture broth into different distinct fractions. Finally, the study validated the use of 194 nm as detection wavelength for visualizing the chromatogram of LB Lennox medium eluted from a C-18 reversed phase column during liquid chromatography. Collectively, GFC and RP-HPLC could not fractionate LB Lennox broth of E. coli DH5 into distinct fractions for further analysis by identification techniques such as mass spectrometry. Given the inherent complexity of complex medium such as LB Lennox, clean separation of the medium into every component with high purity may be impossible. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=47 SRC="FIGDIR/small/311373v1_ufig1.gif" ALT="Figure 1"> View larger version (7K): org.highwire.dtl.DTLVardef@2d0861org.highwire.dtl.DTLVardef@814ebcorg.highwire.dtl.DTLVardef@158328borg.highwire.dtl.DTLVardef@15e6844_HPS_FORMAT_FIGEXP M_FIG C_FIG Short descriptionBroad elution profile and lack of distinct peaks in chromatogram of LB Lennox broth at 6 hours post inoculation with Escherichia coli DH5 (ATCC 53868), after attempted fractionation by C-18 reversed phase high performance liquid chromatography (RP-HPLC), revealed that separation could not be achieved with a complex mixture such as a microbiology broth. Specifically, while the objective of understanding compositional changes to culture medium during growth presents tremendous opportunities for determining the dynamic conversion of nutrients into metabolites and byproducts, difficulty of profiling all chemical constituents in a complex broth mixture meant that current model building efforts for metabolic engineering remains primitive with respect to the consortia of metabolic reactions occurring in situ at the cellular level. Subject areasbiotechnology, biochemistry, cell biology, microbiology, analytical chemistry

biochemistry↗

bioRxiv2020

ICP-MS analysis of metal and metalloid concentrations of common microbiological growth media reveals presence of heavy metals

Trace elements such as cobalt and manganese play important roles as cofactors of enzymes, and thus, they collectively impact on biochemistry and cellular metabolism. Hence, it is of importance to gain an understanding of the trace metal and metalloid concentrations of growth medium in order to fully account for the growth performance of the bacterium. But, this aspect of microbial cell cultivation is usually neglected. Advent of instrumented techniques for metals and metalloid analysis such as inductively coupled plasma mass spectrometry (ICP-MS) has significantly facilitated trace element analysis at the parts per billion (ppb) to parts per million (ppm) level in aqueous matrixes free of organic interference. In this work, ICP-MS was utilized as the principal tool for profiling the range of metals and metalloids in different microbiological growth medium ranging from minimal salts medium to complex chemically undefined medium. Growth media examined include: LB Lennox, LB Lennox + 2 g/L glucose, LB Lennox (buffered, 89 mM phosphate), LB Lennox (buffered, 89 mM phosphate) + 6 g/L glucose, formulated medium + 6 g/L glucose, Tryptic Soy Broth, M9 medium, and M9 + 1 g/L yeast extract medium. Results reveal detection of elevated concentrations of chromium, copper and cadmium in different versions of LB Lennox and Tryptic Soy Broth. Concentrations of the above heavy metals in the tens of ppm range meant that the growth media could only support the growth of environmental bacteria with some resistance to heavy metal toxicity. On the other hand, no heavy metals were detected in M9 minimal salts medium or a modified version with supplementation of 1 g/L yeast extract. This indicated that minimal salts media may have less of a problem with heavy metal contamination compared to chemically undefined microbiological growth media. Collectively, the results highlight the essentiality of conducting a comprehensive profiling experiment for detecting different metals and metalloids at trace levels in microbiological growth medium. Such data would in addition to offering a deeper understanding of some peculiar growth behaviour from some microorganisms, may also help identify contamination issue during manufacture that preclude use of the media in cultivating many laboratory domesticated microorganisms. Subject areasbiochemistry, biotechnology, cell biology, microbiology, biochemical engineering,

microbiology↗

bioRxiv2020

MiSiC, a general deep learning-based method for the high-throughput cell segmentation of complex bacterial communities

Studies of microbial communities by live imaging require new tools for the robust identification of bacterial cells in dense and often inter-species populations, sometimes over very large scales. Here, we developed MiSiC, a general deep-learning-based segmentation method that automatically segments a wide range of spatially structured bacterial communities with very little parameter adjustment, independent of the imaging modality. Using a bacterial predator-prey interaction model, we demonstrate that MiSiC enables the analysis of interspecies interactions, resolving processes at subcellular scales and discriminating between species in millimeter size datasets. The simple implementation of MiSiC and the relatively low need in computing power make its use broadly accessible to fields interested in bacterial interactions and cell biology.

microbiology↗

bioRxiv2020

Multiscale and multimodal reconstruction of cortical structure and function

We present a semi-automated reconstruction of L2/3 mouse primary visual cortex from 3 million cubic microns of electron microscopic images, including pyramidal and inhibitory neurons, astrocytes, microglia, oligodendrocytes and precursors, pericytes, vasculature, mitochondria, and synapses. Visual responses of a subset of pyramidal cells are included. The data are being made publicly available, along with tools for programmatic and 3D interactive access. The density of synaptic inputs onto inhibitory neurons varies across cell classes and compartments. We uncover a compartment-specific correlation between mitochondrial coverage and synapse density. Frequencies of connectivity motifs in the graph of pyramidal cells are predicted quite accurately from node degrees using the configuration model of random graphs. Cells receiving more connections from nearby cells exhibit stronger and more reliable visual responses. These example findings illustrate the resources utility for relating structure and function of cortical circuits as well as for neuronal cell biology.

neuroscience↗

bioRxiv2020

A novel family of secreted proteins linked to plant gall development

In an elaborate form of inter-species exploitation, many insects hijack plant development to induce novel plant organs called galls that provide the insect with a source of nutrition and a temporary home. Galls result from dramatic reprogramming of plant cell biology driven by insect molecules, but the roles of specific insect molecules in gall development have not yet been determined. Here we study the aphid Hormaphis cornu, which makes distinctive "cone" galls on leaves of witch hazel Hamamelis virginiana. We found that derived genetic variants in the aphid gene determinant of gall color (dgc) are associated with strong downregulation of dgc transcription in aphid salivary glands, upregulation in galls of seven genes involved in anthocyanin synthesis, and deposition of two red anthocyanins in galls. We hypothesize that aphids inject DGC protein into galls, and that this results in differential expression of a small number of plant genes. Dgc is a member of a large, diverse family of novel predicted secreted proteins characterized by a pair of widely spaced cysteine-tyrosine-cysteine (CYC) residues, which we named BICYCLE proteins. Bicycle genes are most strongly expressed in the salivary glands specifically of galling aphid generations, suggesting that they may regulate many aspects of gall development. Bicycle genes have experienced unusually frequent diversifying selection, consistent with their potential role controlling gall development in a molecular arms race between aphids and their host plants. One Sentence SummaryAphid bicycle genes, which encode diverse secreted proteins, contribute to plant gall development.

genetics↗

bioRxiv2020

The meadow jumping mouse genome and transcriptome suggest mechanisms of hibernation

Hibernating mammals exhibit medically relevant phenotypes, but the genetic basis of hibernation remains poorly understood. Using the meadow jumping mouse (Zapus hudsonius), we investigated the genetic underpinnings of hibernation by uniting experimental and comparative genomic approaches. We assembled a Z. hudsonius genome and identified widespread expression changes during hibernation in genes important for circadian rhythm, membrane fluidity, and cell cycle arrest. Tissue-specific gene expression changes during torpor encompassed Wnt signaling in the brain and structural and transport functions in the kidney brush border. Using genomes from the closely related Zapus oregonus (previously classified as Z. princeps) and leveraging a panel of hibernating and non-hibernating rodents, we found selective pressure on genes involved in feeding behavior, metabolism, and cell biological processes potentially important for function at low body temperature. Leptin stands out with elevated conservation in hibernating rodents, implying a role for this metabolic hormone in triggering fattening and hibernation. These findings illustrate that mammalian hibernation requires adaptation at all levels of organismal form and function and lay the groundwork for future study of hibernation phenotypes.

genomics↗

bioRxiv2020

Signalling Network of Breast Cancer Cells in Response to Progesterone

Breast cancer cells enter into the cell cycle following progestin exposure by the activation of signalling cascades involving a plethora of enzymes, transcription factors and co-factors that transmit the external signal from the cell membrane to chromatin, ultimately leading to a change of the gene expression program. Although many of the events within the signalling network have been described in isolation, how they globally team up to generate the final cell response is unclear. In this study we use antibody microarrays and phosphoproteomics to provide a dynamic global signalling map that reveals new key regulated proteins and links between previously known and novel pathways. Detailed analysis of the data revealed intriguing changes in protein complexes involved in nuclear structure, EMT, cell adhesion, as well as transcription factors previously not associated with breast cancer proliferation. As different post-translational modifications can mediate complex crosstalk mechanisms and massive PARylation is also rapidly induced by progestins, we provide details of important chromatin regulatory complexes containing both phosphorylated and PARylated proteins. This study contributes an important resource for the scientific community, as it identifies novel players and connections meaningful for breast cancer cell biology and potentially relevant for cancer management. (197 w)

molecular biology↗

bioRxiv2020

Performance of deep learning restoration methods for the extraction of particle dynamics in noisy microscopy image sequences

Image-based particle tracking is an essential tool to answer research questions in cell biology and beyond. A major challenge of particle tracking in living systems is that low light exposure is required to avoid phototoxicity and photobleaching. In addition, high-speed imaging used to fully capture particle motion dictates fast image acquisition rates. Short exposure times come at the expense of tracking accuracy. This is generally true for quantitative microscopy approaches and particularly relevant to single molecule tracking where the number of photons emitted from a single chromophore is limited. Image restoration methods based on deep learning dramatically improve the signal-to-noise ratio in low-exposure datasets. However, it is not clear whether images generated by these methods yield accurate quantitative measurements such as diffusion parameters in (single) particle tracking experiments. Here, we evaluate the performance of two popular deep learning denoising software packages for particle tracking, using synthetic datasets and movies of diffusing chromatin as biological examples. With synthetic data, both supervised and unsupervised deep learning restored particle motions with high accuracy in two-dimensional datasets, whereas artifacts were introduced by the denoisers in 3D datasets. Experimentally, we found that, while both supervised and unsupervised approaches improved the number of trackable particles and tracking accuracy, supervised learning generally outperformed the unsupervised approach, as expected. We also highlight that with extremely noisy image sequences, deep learning algorithms produce deceiving artifacts, which underscores the need to carefully evaluate the results. Finally, we address the challenge of selecting hyper-parameters to train convolutional neural networks by implementing a frugal Bayesian optimizer that rapidly explores multidimensional parameter spaces, identifying networks yielding optional particle tracking accuracy. Our study provides quantitative outcome measures of image restoration using deep learning. We anticipate broad application of the approaches presented here to critically evaluate artificial intelligence solutions for quantitative microscopy.

biophysics↗

bioRxiv2020

Antagonistic fungal enterotoxins intersect at multiple levels with host innate immune defences.

Animals and plants need to defend themselves from pathogen attack. Their defences drive innovation in virulence mechanisms, leading to never-ending cycles of co-evolution in both hosts and pathogens. A full understanding of host immunity therefore requires examination of pathogen virulence strategies. Here, we take advantage of the well-studied innate immune system of Caenorhabditis elegans to dissect the action of two virulence factors from its natural fungal pathogen Drechmeria coniospora. We show that these two enterotoxins have strikingly different effects when expressed individually in the nematode epidermis. One is able to interfere with diverse aspects of host cell biology, altering vesicle trafficking and preventing the key STAT-like transcription factor STA-2 from activating defensive antimicrobial peptide gene expression. The second, potentially as a consequence of a host surveillance mechanism, increases STA-2 levels in the nucleus, modifies the nucleolus, and causes increased defence gene expression. Our results highlight the remarkably complex and potentially antagonistic mechanisms that come into play in the interaction between co-evolved hosts and pathogens.

microbiology↗

bioRxiv2020

Establishment of a highly efficient gene disruption strategy to analyze and manipulate lipid co-regulatory networks

Gene disruption has been dramatically facilitated by genome editing tools. Despite improvements in gene disruption rates in cultured cells, clone isolation remains routinely performed to obtain mutants, potentially leading to artifacts due to clonal variation in cellular phenotypes. Here we report GENF, a highly efficient strategy to disrupt genes without isolating clones, which can be multiplexed. Using it, we obtained reliable lipidomics datasets from mutant cells without being affected by variances related to clone isolation. Through this, we found that an enzyme involved in congenital generalized lipodystrophy regulates glycerophospholipids with specific acyl-chains. We also demonstrate the possibility to dissect complex lipid co-regulatory mechanisms, explaining cell adaptations to altered lipid metabolism. With its simplicity and the avoidance of cloning-related artifacts, GENF is likely to contribute to many cell biology studies, especially those involving -omics approaches.

genetics↗

bioRxiv2020

The tumor suppressor protein PTEN undergoes amyloid-like aggregation in tumor cells.

Protein aggregation is an underappreciated mechanism that may contribute to the loss- and oncogenic-gain-of-function of mutant tumor suppressors such as p53 and axin. In the present study, we describe amyloid-like aggregation behaviour of the second most frequently mutated tumor suppressor in human cancer, PTEN. In silico analysis revealed a particularly high aggregation vulnerability for this protein, which was corroborated by in vitro aggregation assays. In cultured tumor cells, we found that under stress conditions, PTEN readily undergoes amyloid-like aggregation as a result of mutation. However, we also show that severe dysregulation of protein homeostasis may lead to aggregation of wild-type PTEN. These observations were supported by a small survey of patient-derived uterine tumor tissues, which found that more than 25% of tumors analyzed displayed wild-type PTEN aggregation. Finally, in an exploratory clinical study we found that PTEN aggregation status was correlated with a decline in clinical outcome. Our findings establish that the tumor suppressor PTEN is highly aggregation-prone and our work suggests that protein aggregation might be an underestimated but prevalent component of cancer cell biology.

cancer biology↗

bioRxiv2020

Design of smart antibody mimetics with photosensitive switches

As two prominent examples of intracellular single-domain antibodies or antibody mimetics derived from synthetic protein scaffolds, monobodies and nanobodies are gaining wide applications in cell biology, structural biology, synthetic immunology, and theranostics. We introduce herein a generally-applicable method to engineer light-controllable monobodies and nanobodies, designated as moonbody and sunbody, respectively. These engineered antibody-like modular domains enable rapid and reversible antibody-antigen recognition by utilizing light. By paralleled insertion of two LOV2 modules into a single sunbody and the use of bivalent sunbodies, we substantially enhance the range of dynamic changes of photo-switchable sunbodies. Furthermore, we demonstrate the use of moonbodies or sunbodies to precisely control protein degradation, gene transcription, and base editing by harnessing the power of light.

synthetic biology↗

bioRxiv2020

Population genomics of the maize pathogen Ustilago maydis: demographic history and role of virulence clusters in adaptation

The tight interaction between pathogens and their hosts results in reciprocal selective forces that impact the genetic diversity of the interacting species. The footprints of this selection differ between pathosystems because of distinct life-history traits, demographic histories, or genome architectures. Here, we studied the genome-wide patterns of genetic diversity of 22 isolates of the causative agent of the corn smut disease, Ustilago maydis, originating from five locations in Mexico, the presumed center of origin of this species. In this species, many genes encoding secreted effector proteins reside in so-called virulence clusters in the genome, an arrangement that is so far not found in other filamentous plant pathogens. Using a combination of population genomic statistical analyses, we assessed the geographical, historical and genome-wide variation of genetic diversity in this fungal pathogen. We report evidence of two partially admixed subpopulations that are only loosely associated with geographic origin. Using the multiple sequentially Markov coalescent model, we inferred the demographic history of the two pathogen subpopulations over the last 0.5 million years. We show that both populations experienced a recent strong bottleneck starting around 10,000 years ago, coinciding with the assumed time of maize domestication. While the genome average genetic diversity is low compared to other fungal pathogens, we estimated that the rate of non-synonymous adaptive substitutions is three times higher in genes located within virulence clusters compared to non-clustered genes, including non-clustered effector genes. These results highlight the role that these singular genomic regions play in the evolution of this pathogen. Significance statementThe maize pathogen Ustilago maydis is a model species to study fungal cell biology and biotrophic host-pathogen interactions. Population genetic studies of this species, however, were so far restricted to using a few molecular markers, and genome-wide comparisons involved species that diverged more than 20 million years ago. Here, we sequenced the genomes of 22 Mexican U. maydis isolates to study the recent evolutionary history of this species. We identified two co-existing populations that went through a recent bottleneck and whose divergence date overlaps with the time of maize domestication. Contrasting the patterns of genetic diversity in different categories of genes, we further showed that effector genes in virulence clusters display a high rate of adaptive mutations, highlighting the importance of these effector arrangements for the adaptation of U. maydis to its host.

genomics↗

bioRxiv2020

Label2label: Using deep learning and dual-labelling to retrieve cellular structures in fluorescence images

Fluorescence microscopy is an essential tool in cell biology to visualise the spatial distribution of proteins that dictates their role in cellular homeostasis, dynamic cellular processes, and dysfunction during disease. However, unspecific binding of the antibodies that are used to label a cellular target often leads to high background signals in the images, decreasing the contrast of a cellular structure of interest. Recently, convolutional neural networks (CNNs) have been successfully employed for denoising and upsampling in fluorescence microscopy, but current image restoration methods cannot correct for background signals originating from the label. Here, we report a new method to train a CNN as content filter for non-specific signals in fluorescence images that does not require a clean benchmark, using dual-labelling to generate the training data. We name this method label2label (L2L). In L2L, a CNN is trained with image pairs of two non-identical labels that target the same cellular structure of interest. We show that after L2L training a network restores images not only with reduced image noise but also label-induced unspecific fluorescence signal in images of a variety of cellular structures, resulting in images with enhanced structural contrast. By implementing a multi-scale structural similarity loss function, the performance of the CNN as a content filter is further enhanced, for example, in STED images of caveolae. We show evidence that, for this loss function, sample differences in the training data significantly decrease so-called hallucination effects in the restorations that we otherwise observe when training the CNN with images of the same label. We also assess the performance of a cycle generative adversarial network as a content filter after L2L training with unpaired image data. Lastly, we show that a CNN can be trained to separate structures in superposed fluorescence images of two different cellular targets, allowing multiplex imaging with microscopy setups where the number of excitation sources or detectors is limited.

bioinformatics↗

bioRxiv2021

Convergent extension requires adhesion-dependent biomechanical integration of cell crawling and junction contraction

Convergent extension is an evolutionarily conserved collective cell movement that elongates the body axis of almost all animals and is required for the morphogenesis of several organ systems. Decades of study have revealed two distinct mechanisms of cell movement during CE, one based on cell crawling and the other on junction contraction. How these two behaviors collaborate during CE is not understood. Here, using quantitative live cell imaging we show that these two modes act both independently and in concert during CE, but that cell movement is more effective when the two modes are integrated via mechano-reciprocity. Based on these findings, we developed a novel computational model that for the first time treats crawling and contraction independently. This model not only confirmed the biomechanical efficacy of integrating the two modes, but also revealed for the first time how the two modes -and their integration- are influenced by cell adhesion. Finally, we use these new insights to further understand the complex CE phenotype resulting from loss of the C-cadherin interacting catenin Arvcf. These data are significant for providing new biomechanical and cell biological insights into a fundamental morphogenetic process that is implicated in human neural tube defects and skeletal dysplasias.

developmental biology↗

bioRxiv2021

Female gametophyte expressed Arabidopsis thaliana lipid transfer proteins AtLtpI.4 and AtLtpI.8 provide a link between callose homeostasis, pollen tube guidance, and fertilization success

Non-specific lipid transfer proteins (LTPs) represent a sub-class among the large family of Cysteine-rich proteins (CRPs) specific to land plants. LTPs possess a hydrophobic cavity, enabling them to bind and stabilize a variety of lipid molecules outside membranes. In line with the existence of an N-terminal signal peptide, secreted LTPs represent a well-suited mobile signal carrier in the plants extracellular matrix. Thus, LTPs are currently considered as key players to mediate the bulk flow of lipids between membranes/compartments as well as the buildup of lipid barrier polymers including cutin and suberin. Here, we show that floral expressed Arabidopsis thaliana AtLtpI.4 (AtLTP2) and AtLtpI.8 (AtLTP5), mutually control cell-cell communication between growing pollen tubes and ovules during fertilization. Arabidopsis mutants lacking functional AtLtpI.4 and AtLtpI.8 exhibit significantly reduced fertilization success. Cross-pollination and cell biological analyses revealed that AtLtpI.4/I.8 double mutants are impaired in pollen tube guidance towards ovules. Our finding that the AtLtpI.4/I.8 phenotype correlates with aberrant callose depositions in the micropylar region during ovule development suggests that both LTPs represent novel players of a joint signaling pathway that controls callose homeostasis in the female gametophyte.

plant biology↗

bioRxiv2021

Glycoproteomics of Haloferax volcanii reveals an extensive glycoproteome and concurrence of different N-glycosylation pathways

Glycosylation is one of the most complex post-translational protein modifications. Its importance has been established not only for eukaryotes but also for a variety of prokaryotic cellular processes, such as biofilm formation, motility and mating. However, comprehensive glycoproteomic analyses are largely missing in prokaryotes. Here we extend the phenotypic characterisation of N-glycosylation pathway mutants in Haloferax volcanii and provide a detailed glycoproteome for this model archaeon through the mass spectrometric analysis of intact glycopeptides. Using in-depth glycoproteomic datasets generated for the wild-type and mutant strains as well as a reanalysis of datasets within the Archaeal Proteome Project, we identify the largest archaeal glycoproteome described so far. We further show that different N-glycosylation pathways can modify the same glycosites under the same culture conditions. The extent and complexity of the Hfx. volcanii N-glycoproteome revealed here provides new insights into the roles of N-glycosylation in archaeal cell biology.

biochemistry↗

bioRxiv2021

Protein domain patterns reveal a functional diversity of the archaeal Cdv system and give insights into the origin of the eukaryotic ESCRT system

The Endosomal Sorting Complex Required for Transport (ESCRT) system is a multi-protein machinery that is involved in cell division of both Eukaryotes and Archaea. This spread across super-phyla suggests that a precursor ESCRT machinery existed already at an evolutionary early stage of life, making it a promising candidate for the (re)construction of a minimal cell division machinery. There are, however, only few experimental data about ESCRT machineries in Archaea, due to high technical challenges in cultivation and microscopy. Here, we analyse the proteins of archaeal ESCRT machineries bioinformatically on a domain level, to enable mechanistical comparison without such challenging experiments. First, we infer that there are at least three different cell division mechanisms utilizing ESCRT proteins in archaea, probably similar in their constriction mechanisms but different in membrane tethering. Second, we show that ESCRT proteins in Asgard archaea are highly similar to eukaryotic ESCRT proteins, strengthening the recently developed idea that all Eukaryotes descended from archaea. Third, we reconstruct a plausible evolutionary development of ESCRT machineries and suggest that a simple ESCRT-based constriction machinery existed in the last archaeal common ancestor. These findings not only give very interesting insights into the evolution of cell division in Archaea and Eukaryotes, but also reveal new research avenues by suggesting hypothesis-driven experiments for both, cell biology and bottom-up synthetic biology.

molecular biology↗