Evolution is not Uniform Along Coding Sequences.

Amino acids evolve at different speeds within protein sequences, because their functional and structural roles are different. Notably, amino acids located at the surface of proteins are known to evolve more rapidly than those in the core. In particular, amino acids at the N- and C-termini of protein sequences are likely to be more exposed than those at the core of the folded protein due to their location in the peptidic chain, and they are known to be less structured. Because of these reasons, we would expect that amino acids located at protein termini would evolve faster than residues located inside the chain. Here we test this hypothesis and found that amino acids evolve almost twice as fast at protein termini compared with those in the center, hinting at a strong topological bias along the sequence length. We further show that the distribution of solvent-accessible residues and functional domains in proteins readily explain how structural and functional constraints are weaker at their termini, leading to the observed excess of amino acid substitutions. Finally, we show that the specific evolutionary rates at protein termini may have direct consequences, notably misleading in silico methods used to infer sites under positive selection within genes. These results suggest that accounting for positional information should improve evolutionary models.

Comprehensive mapping of exon junction complex binding sites reveals universal EJC deposition in Drosophila.

BACKGROUND: The exon junction complex (EJC) is involved in most steps of the mRNA life cycle, ranging from splicing to nonsense-mediated mRNA decay (NMD). It is assembled by the splicing machinery onto mRNA in a sequence-independent manner. A fundamental open question is whether the EJC is deposited onto all exon‒exon junctions or only on a subset of them. Several previous studies have made observations supportive of the latter, yet these have been limited by methodological constraints. RESULTS: In this study, we sought to overcome these limitations via the integration of two different approaches for transcriptome-wide mapping of EJCs. Our results revealed that nearly all, if not all, internal exons consistently harbor an EJC in Drosophila, demonstrating that EJC presence is an inherent consequence of the splicing reaction. Furthermore, our study underscores the limitations of eCLIP methods in fully elucidating the landscape of RBP binding sites. Our findings highlight how highly specific (low false positive) methodologies can lead to erroneous interpretations due to partial sensitivity (high false negatives). CONCLUSIONS: This study contributes to our understanding of EJC deposition and its association with pre-mRNA splicing. The universal presence of EJC on internal exons underscores its significance in ensuring proper mRNA processing. Additionally, our observations highlight the need to consider both specificity and sensitivity in RBP mapping methodologies.

Genome wide natural variation of H3K27me3 selectively marks genes predicted to be important for cell differentiation in Phaeodactylum tricornutum.

In multicellular organisms, Polycomb Repressive Complex2 (PRC2) is known to deposit tri-methylation of lysine 27 of histone H3 (H3K27me3) to establish and maintain gene silencing, critical for developmentally regulated processes. The PRC2 complex is absent in both widely studied model yeasts, which initially suggested that PRC2 arose with the emergence of multicellularity. However, its discovery in several unicellular species including microalgae questions its role in unicellular eukaryotes. Here, we use Phaeodactylum tricornutum enhancer of zeste E(z) knockouts and show that P. tricornutum E(z) is responsible for di- and tri-methylation of lysine 27 of histone H3. H3K27me3 depletion abolishes cell morphology in P. tricornutum providing evidence for its role in cell differentiation. Genome-wide profiling of H3K27me3 in fusiform and triradiate cells further revealed genes that may specify cell identity. These results suggest a role for PRC2 and its associated mark in cell differentiation in unicellular species, and highlight their ancestral function in a broader evolutionary context than currently is appreciated.

mTORC1 signaling and primary cilia are required for brain ventricle morphogenesis.

Radial glial cells (RCGs) are self-renewing progenitor cells that give rise to neurons and glia during embryonic development. Throughout neurogenesis, these cells contact the cerebral ventricles and bear a primary cilium. Although the role of the primary cilium in embryonic patterning has been studied, its role in brain ventricular morphogenesis is poorly characterized. Using conditional mutants, we show that the primary cilia of radial glia determine the size of the surface of their ventricular apical domain through regulation of the mTORC1 pathway. In cilium-less mutants, the orientation of the mitotic spindle in radial glia is also significantly perturbed and associated with an increased number of basal progenitors. The enlarged apical domain of RGCs leads to dilatation of the brain ventricles during late embryonic stages (ventriculomegaly), which initiates hydrocephalus during postnatal stages. These phenotypes can all be significantly rescued by treatment with the mTORC1 inhibitor rapamycin. These results suggest that primary cilia regulate ventricle morphogenesis by acting as a brake on the mTORC1 pathway. This opens new avenues for the diagnosis and treatment of hydrocephalus.

PySpacell: A Python Package for Spatial Analysis of Cell Images.

Technologies such as microscopy, sequential hybridization, and mass spectrometry enable quantitative single-cell phenotypic and molecular measurements in situ. Deciphering spatial phenotypic and molecular effects on the single-cell level is one of the grand challenges and a key to understanding the effects of cell-cell interactions and microenvironment. However, spatial information is usually overlooked by downstream data analyses, which usually consider single-cell read-out values as independent measurements for further averaging or clustering, thus disregarding spatial locations. With this work, we attempt to fill this gap. We developed a toolbox that allows one to test for the presence of a spatial effect in microscopy images of adherent cells and estimate the spatial scale of this effect. The proposed Python module can be used for any light microscopy images of cells as well as other types of single-cell data such as in situ transcriptomics or metabolomics. The input format of our package matches standard output formats from image analysis tools such as CellProfiler, Fiji, or Icy and thus makes our toolbox easy and straightforward to use, yet offering a powerful statistical approach for a wide range of applications. © 2019 International Society for Advancement of Cytometry.

Coordination of transcriptional and translational regulations in human epithelial cells infected by Listeria monocytogenes.

The invasion of mammalian cells by intracellular bacterial pathogens reshuffles their gene expression and functions; however, we lack dynamic insight into the distinct control levels that shape the host response. Here, we have addressed the respective contribution of transcriptional and translational regulations during a time-course of infection of human intestinal epithelial cells by an epidemic strain of Listeria monocytogenes, using transcriptome analysis paralleled with ribosome profiling. Upregulations were dominated by early transcriptional activation of pro-inflammatory genes, whereas translation inhibition appeared as the major driver of downregulations. Instead of a widespread but transient shutoff, translation inhibition affected specifically and durably transcripts encoding components of the translation machinery harbouring a 5'-terminal oligopyrimidine motif. Pre-silencing the most repressed target gene (PABPC1) slowed down the intracellular multiplication of Listeria monocytogenes, suggesting that the infected host cell can benefit from the repression of genes involved in protein synthesis and thereby better control infection.

Monitored eCLIP: high accuracy mapping of RNA-protein interactions.

CLIP-seq methods provide transcriptome-wide snapshots of RNA-protein interactions in live cells. Reverse transcriptases stopping at cross-linked nucleotides sign for RNA-protein binding sites. Reading through cross-linked positions results in false binding site assignments. In the 'monitored enhanced CLIP' (meCLIP) method, a barcoded biotinylated linker is ligated at the 5' end of cross-linked RNA fragments to purify RNA prior to the reverse transcription. cDNAs keeping the barcode sequence correspond to reverse transcription read-throughs. Read through occurs in unpredictable proportions, representing up to one fourth of total reads. Filtering out those reads strongly improves reliability and precision in protein binding site assignment.

ALFA: annotation landscape for aligned reads.

BACKGROUND: The last 10 years have seen the rise of countless functional genomics studies based on Next-Generation Sequencing (NGS). In the vast majority of cases, whatever the species, whatever the experiment, the two first steps of data analysis consist of a quality control of the raw reads followed by a mapping of those reads to a reference genome/transcriptome. Subsequent steps then depend on the type of study that is being made. While some tools have been proposed for investigating data quality after the mapping step, there is no commonly adopted framework that would be easy to use and broadly applicable to any NGS data type. RESULTS: We present ALFA, a simple but universal tool that can be used after the mapping step on any kind of NGS experiment data for any organism with available genomic annotations. In a single command line, ALFA can compute and display distribution of reads by categories (exon, intron, UTR, etc.) and biotypes (protein coding, miRNA, etc.) for a given aligned dataset with nucleotide precision. We present applications of ALFA to Ribo-Seq and RNA-Seq on Homo sapiens, CLIP-Seq on Mus musculus, RNA-Seq on Saccharomyces cerevisiae, Bisulfite sequencing on Arabidopsis thaliana and ChIP-Seq on Caenorhabditis elegans. CONCLUSIONS: We show that ALFA provides a powerful and broadly applicable approach for post mapping quality control and to produce a global overview using common or dedicated annotations. It is made available to the community as an easy to install command line tool and from the Galaxy Tool Shed.

Detection and tracking of overlapping cell nuclei for large scale mitosis analyses.

BACKGROUND: Cell culture on printed micropatterns slides combined with automated fluorescent microscopy allows for extraction of tens of thousands of videos of small isolated growing cell clusters. The analysis of such large dataset in space and time is of great interest to the community in order to identify factors involved in cell growth, cell division or tissue formation by testing multiples conditions. However, cells growing on a micropattern tend to be tightly packed and to overlap with each other. Consequently, image analysis of those large dynamic datasets with no possible human intervention has proven impossible using state of the art automated cell detection methods. RESULTS: Here, we propose a fully automated image analysis approach to estimate the number, the location and the shape of each cell nucleus, in clusters at high throughput. The method is based on a robust fit of Gaussian mixture models with two and three components on each frame followed by an analysis over time of the fitting residual and two other relevant features. We use it to identify with high precision the very first frame containing three cells. This allows in our case to measure a cell division angle on each video and to construct division angle distributions for each tested condition. We demonstrate the accuracy of our method by validating it against manual annotation on about 4000 videos of cell clusters. CONCLUSIONS: The proposed approach enables the high throughput analysis of video sequences of isolated cell clusters obtained using micropatterns. It relies only on two parameters that can be set robustly as they reduce to the average cell size and intensity.

Dimerization, oligomerization, and aggregation of human amyotrophic lateral sclerosis copper/zinc superoxide dismutase 1 protein mutant forms in live cells.

More than 100 copper/zinc superoxide dismutase 1 (SOD1) genetic mutations have been characterized. These mutations lead to the death of motor neurons in ALS. In its native form, the SOD1 protein is expressed as a homodimer in the cytosol. In vitro studies have shown that SOD1 mutations impair the dimerization kinetics of the protein, and in vivo studies have shown that SOD1 forms aggregates in patients with familial forms of ALS. In this study, we analyzed WT SOD1 and 9 mutant (mt) forms of the protein by non-invasive fluorescence techniques. Using microscopic techniques such as fluorescence resonance energy transfer, fluorescence complementation, image-based quantification, and fluorescence correlation spectroscopy, we studied SOD1 dimerization, oligomerization, and aggregation. Our results indicate that SOD1 mutations lead to an impairment in SOD1 dimerization and, subsequently, affect protein aggregation. We also show that SOD1 WT and mt proteins can dimerize. However, aggregates are predominantly composed of SOD1 mt proteins.

A novel specific edge effect correction method for RNA interference screenings.

MOTIVATION: High-throughput screening (HTS) is an important method in drug discovery in which the activities of a large number of candidate chemicals or genetic materials are rapidly evaluated. Data are usually obtained by measurements on samples in microwell plates and are often subjected to artefacts that can bias the result selection. We report here a novel edge effect correction algorithm suitable for RNA interference (RNAi) screening, because its normalization does not rely on the entire dataset and takes into account the specificities of such a screening process. The proposed method is able to estimate the edge effects for each assay plate individually using the data from a single control column based on diffusion model, and thus targeting a specific but recurrent well-known HTS artefact. This method was first developed and validated using control plates and was then applied to the correction of experimental data generated during a genome-wide siRNA screen aimed at studying HIV-host interactions. The proposed algorithm was able to correct the edge effect biasing the control data and thus improve assay quality and, consequently, the hit-selection step.

Transfer learning for versatile and training free high content screening analyses.

High content screening (HCS) is a technology that automates cell biology experiments at large scale. A High Content Screen produces a high amount of microscopy images of cells under many conditions and requires that a dedicated image and data analysis workflow be designed for each assay to select hits. This heavy data analytic step remains challenging and has been recognized as one of the burdens hindering the adoption of HCS. In this work we propose a solution to hit selection by using transfer learning without additional training. A pretrained residual network is employed to encode each image of a screen into a discriminant representation. The deep features obtained are then corrected to account for well plate bias and misalignment. We then propose two training-free pipelines dedicated to the two main categories of HCS for compound selection: with or without positive control. When a positive control is available, it is used alongside the negative control to compute a linear discriminant axis, thus building a classifier without training. Once all samples are projected onto this axis, the conditions that best reproduce the positive control can be selected. When no positive control is available, the Mahalanobis distance is computed from each sample to the negative control distribution. The latter provides a metric to identify the conditions that alter the negative control's cell phenotype. This metric is subsequently used to categorize hits through a clustering step. Given the lack of available ground truth in HCS, we provide a qualitative comparison of the results obtained using this approach with results obtained with handcrafted image analysis features for compounds and siRNA screens with or without control. Our results suggests that the fully automated and generic pipeline we propose offers a good alternative to handcrafted dedicated image analysis approaches. Furthermore, we demonstrate that this solution select conditions of interest that had not been identified using the primary dedicated analysis. Altogether, this approach provides a fully automated, reproducible, versatile and comprehensive alternative analysis solution for HCS encompassing compound-based or downregulation screens, with or without positive controls, without the need for training or cell detection, or the development of a dedicated image analysis workflow.

The model diatom Phaeodactylum tricornutum provides insights into the diversity and function of microeukaryotic DNA methyltransferases.

Cytosine methylation is an important epigenetic mark involved in the transcriptional control of transposable elements in mammals, plants and fungi. The Stramenopiles-Alveolate-Rhizaria (SAR) lineages are a major group of ecologically important marine microeukaryotes, including the phytoplankton groups diatoms and dinoflagellates. However, little is known about their DNA methyltransferase diversity. Here, we performed an in-silico analysis of DNA methyltransferases found in marine microeukaryotes and showed that they encode divergent DNMT3, DNMT4, DNMT5 and DNMT6 enzymes. Furthermore, we found three classes of enzymes within the DNMT5 family. Using a CRISPR/Cas9 strategy we demonstrated that the loss of the DNMT5a gene correlates with a global depletion of DNA methylation and overexpression of young transposable elements in the model diatom Phaeodactylum tricornutum. The study provides a view of the structure and function of a DNMT family in the SAR supergroup using an attractive model species.

Cell painting transfer increases screening hit rate.

Drug discovery uses high throughput screening to identify compounds that interact with a molecular target or that alter a phenotype favorably. The cautious selection of molecules used for such a screening is instrumental and is tightly related to the hit rate. In this work, we wondered if cell painting, a general-purpose image-based assay, could be used as an efficient proxy for compound selection, thus increasing the success rate of a specific assay. To this end, we considered cell painting images with 30,000 molecules treatments, and selected compounds that produced a visual effect close to the positive control of an assay, by using the Frechet Inception Distance. We then compared the hit rates of such a preselection with what was actually obtained in real screening campaigns. As a result, cell painting would have permitted a significant increase in the success rate and, even for one of the assays, would have allowed to reach 80% of the hits with 10 times fewer compounds to test. We conclude that images of a cell painting assay can be directly used for compound selection prior to screening, and we provide a simple quantitative approach in order to do so.

Revealing invisible cell phenotypes with conditional generative modeling.

Biological sciences, drug discovery and medicine rely heavily on cell phenotype perturbation and microscope observation. However, most cellular phenotypic changes are subtle and thus hidden from us by natural cell variability: two cells in the same condition already look different. In this study, we show that conditional generative models can be used to transform an image of cells from any one condition to another, thus canceling cell variability. We visually and quantitatively validate that the principle of synthetic cell perturbation works on discernible cases. We then illustrate its effectiveness in displaying otherwise invisible cell phenotypes triggered by blood cells under parasite infection, or by the presence of a disease-causing pathological mutation in differentiated neurons derived from iPSCs, or by low concentration drug treatments. The proposed approach, easy to use and robust, opens the door to more accessible discovery of biological and disease biomarkers.

Choroid plexuses carry nodal-like cilia that undergo axoneme regression from early adult stage.

Choroid plexuses (ChPs) produce cerebrospinal fluid and sense non-cell-autonomous stimuli to control the homeostasis of the central nervous system. They are mainly composed of epithelial multiciliated cells, whose development and function are still controversial. We have thus characterized the stepwise order of mammalian ChP epithelia cilia formation using a combination of super-resolution-microscopy approaches and mouse genetics. We show that ChP ciliated cells are built embryonically on a treadmill of spatiotemporally regulated events, starting with atypical centriole amplification and ending with the construction of nodal-like 9+0 cilia, characterized by both primary and motile features. ChP cilia undergo axoneme resorption at early postnatal stages through a microtubule destabilization process controlled by the microtubule-severing enzyme spastin and mitigated by polyglutamylation levels. Notably, this phenotype is preserved in humans, suggesting a conserved ciliary resorption mechanism in mammals.

Cerebral microcirculation shear stress levels determine Neisseria meningitidis attachment sites along the blood-brain barrier.

Neisseria meningitidis is a commensal bacterium of the human nasopharynx. Occasionally, this bacterium reaches the bloodstream and causes meningitis after crossing the blood-brain barrier by an unknown mechanism. An immunohistological study of a meningococcal sepsis case revealed that neisserial adhesion was restricted to capillaries located in low blood flow regions in the infected organs. This study led to the hypothesis that drag forces encountered by the meningococcus in the bloodstream determine its attachment site in vessels. We therefore investigated the ability of N. meningitidis to bind to endothelial cells in the presence of liquid flow mimicking the bloodstream with a laminar flow chamber. Strikingly, average blood flows reported for various organs strongly inhibited initial adhesion. As cerebral microcirculation is known to be highly heterogeneous, cerebral blood velocity was investigated at the level of individual vessels using intravital imaging of rat brain. In agreement with the histological study, shear stress levels compatible with meningococcal adhesion were only observed in capillaries, which exhibited transient reductions in flow. The flow chamber assay revealed that, after initial attachment, bacteria resisted high blood velocities and even multiplied, forming microcolonies resembling those observed in the septicemia case. These results argue that the combined mechanical properties of neisserial adhesion and blood microcirculation target meningococci to transiently underperfused cerebral capillaries and thus determine disease development.

High content phenotypic cell-based visual screen identifies Mycobacterium tuberculosis acyltrehalose-containing glycolipids involved in phagosome remodeling.

The ability of the tubercle bacillus to arrest phagosome maturation is considered one major mechanism that allows its survival within host macrophages. To identify mycobacterial genes involved in this process, we developed a high throughput phenotypic cell-based assay enabling individual sub-cellular analysis of over 11,000 Mycobacterium tuberculosis mutants. This very stringent assay makes use of fluorescent staining for intracellular acidic compartments, and automated confocal microscopy to quantitatively determine the intracellular localization of M. tuberculosis. We characterised the ten mutants that traffic most frequently into acidified compartments early after phagocytosis, suggesting that they had lost their ability to arrest phagosomal maturation. Molecular analysis of these mutants revealed mainly disruptions in genes involved in cell envelope biogenesis (fadD28), the ESX-1 secretion system (espL/Rv3880), molybdopterin biosynthesis (moaC1 and moaD1), as well as in genes from a novel locus, Rv1503c-Rv1506c. Most interestingly, the mutants in Rv1503c and Rv1506c were perturbed in the biosynthesis of acyltrehalose-containing glycolipids. Our results suggest that such glycolipids indeed play a critical role in the early intracellular fate of the tubercle bacillus. The unbiased approach developed here can be easily adapted for functional genomics study of intracellular pathogens, together with focused discovery of new anti-microbials.

Quantification of protein interaction in living cells by two-photon spectral imaging with fluorescent protein fluorescence resonance energy transfer pair devoid of acceptor bleed-through.

Fluorescence resonance energy transfer (FRET) between fluorescent proteins (FPs) is a powerful method to visualize and quantify protein-protein interaction in living cells. Unfortunately, the emission bleed-through of FPs limits the usage of this complex technique. To circumvent undesirable excitation of the acceptor fluorophore, using two-photon excitation, we searched for FRET pairs that show selective excitation of the donor but not of the acceptor fluorescent molecule. We found this property in the fluorescent cyan fluorescent protein (CFP)/yellow fluorescent protein (YFP) and YFP/mCherry FRET pairs and performed two-photon excited FRET spectral imaging to quantify protein interactions on the later pair that shows better spectral discrimination. Applying non-negative matrix factorization to unmix two-photon excited spectral imaging data, we were able to eliminate the donor bleed-through as well as the autofluorescence. As a result, we achieved FRET quantification by means of a single spectral acquisition, making the FRET approach not only easy and straightforward but also less prone to calculation artifacts. As an application of our approach, the intermolecular interaction of amyloid precursor protein and the adaptor protein Fe65 associated with Alzheimer's disease was quantified. We believe that the FRET approach using two-photon and fluorescent YFP/mCherry pair is a promising method to monitor protein interaction in living cells.

SAGA interacting factors confine sub-diffusion of transcribed genes to the nuclear envelope.

Changes in the transcriptional state of genes have been correlated with their repositioning within the nuclear space. Tethering reporter genes to the nuclear envelope alone can impose repression and recent reports have shown that, after activation, certain genes can also be found closer to the nuclear periphery. The molecular mechanisms underlying these phenomena have remained elusive. Here, with the use of dynamic three-dimensional tracking of a single locus in live yeast (Saccharomyces cerevisiae) cells, we show that the activation of GAL genes (GAL7, GAL10 and GAL1) leads to a confinement in dynamic motility. We demonstrate that the GAL locus is subject to sub-diffusive movement, which after activation can become constrained to a two-dimensional sliding motion along the nuclear envelope. RNA-fluorescence in situ hybridization analysis after activation reveals a higher transcriptional activity for the peripherally constrained GAL genes than for loci remaining intranuclear. This confinement was mediated by Sus1 and Ada2, members of the SAGA histone acetyltransferase complex, and Sac3, a messenger RNA export factor, physically linking the activated GAL genes to the nuclear-pore-complex component Nup1. Deleting ADA2 or NUP1 abrogates perinuclear GAL confinement without affecting GAL1 transcription. Accordingly, transcriptional activation is necessary but not sufficient for the confinement of GAL genes at the nuclear periphery. The observed real-time dynamic mooring of active GAL genes to the inner side of the nuclear pore complex is in accordance with the 'gene gating' hypothesis.