EML2-S constitutes a new class of proteins that recognizes and regulates the dynamics of tyrosinated microtubules

EML2 and EML4 splice variants regulate microtubule remodelling during neuronal cell differentiation †

Neurons depend on microtubule organization for axon and dendrite formation during differentiation. Yet, our understanding of how microtubules are remodelled during this process is far from complete. Echinoderm microtubule-associated protein-like (EML) is a family of microtubule-associated proteins (MAPs) highly expressed in neuronal cells. Database analysis revealed that EMLs are subject to alternative splicing in their N-terminal regions, leading to the production of one long (L) variant containing a complete N-terminus and TAPE domain, and possibly several short (S) variants with a truncated N-terminal region. We investigated EML4 and EML2 splice variants during SH-SY5Y neuronal cell differentiation, examining their expression, localisation, and effects on neurite outgrowth and branching. We found that differentiation led to decreased expression of the EML4-L, but not the -S variant. Moreover, expression of the EML2-L variant increased, while EML2-S expression decreased. Overexpression of EML2-L and EML2-S led to increased and decreased neurite lengths, respectively. Interestingly, neurite branching increased in EML2-S transfected cells. Using immunofluorescence microscopy, we found that EML2-L and EML4-L but not EML2-S and EML4-S localised strongly to interphase microtubules regardless of whether cells had differentiated or not. Depletion of both EML4 and EML2 led to neurite outgrowth. We propose that the presence of the N-terminal microtubule-binding region in the long variants of EML2 and EML4 promotes stabilisation of microtubules and neurite extension while short variants favour branching. Together, these data suggest that regulated expression and localisation of different splice variants of EML proteins are crucial to microtubule remodelling during neuronal differentiation.

Regulation of microtubule growth rates and their impact on chromosomal instability

Microtubules are polymers of α/β tubulin dimers that build the mitotic spindle, which segregates duplicated chromosomes during cell division. Microtubule function is governed by dynamic instability, whereby cycles of growth and shrinkage contribute to the forces necessary for chromosome movement. Regulation of microtubule growth velocity requires cell cycle-dependent changes in expression, localization and activity of microtubule-associated proteins (MAPs) as well as tubulin post-translational modifications that modulate microtubule dynamics. It has become clear that optimal microtubule growth velocities are required for proper chromosome segregation and ploidy maintenance. Suboptimal microtubule growth rates can result from altered activity of MAPs and could lead to aneuploidy, possibly by disrupting the establishment of microtubule bundles at kinetochores and altering the mechanical forces required for sister chromatid segregation. Future work using high-resolution, low-phototoxicity microscopy and novel fluorescent markers will be invaluable in obtaining deeper mechanistic insights into how microtubule processes contribute to chromosome segregation.

Identification of Recurrence-associated Gene Signatures and Machine Learning-based Prediction in IDH-Wildtype Histological Glioblastoma

Glioblastoma (GBM) is a highly aggressive brain tumor with frequent recurrence, yet the molecular mechanisms driving recurrence remain poorly understood. Identifying recurrence-associated genes may improve prognosis and treatment strategies. We applied weighted gene co-expression network analysis (WGCNA) to transcriptomic data from IDH-wildtype histological GBM in the CGGA-693 (n = 190) and CGGA-325 (n = 111) cohorts to identify recurrence-associated genes. These genes were validated using RT-qPCR and single-cell RNA sequencing (scRNA-seq) datasets (GSE174554, GSE131928). Their associations with immune cell composition were analyzed. Finally, we evaluated 113 machine learning algorithms to develop a multi-gene predictive model for GBM recurrence, with model performance assessed using receiver operating characteristic (ROC) curves and confusion matrix analysis. We identified eight recurrence-associated genes (CERS2, EML2, FNBP1, ICOSLG, MFAP3L, NPC1, ROGDI, SLAIN1) that were significantly differentially expressed between primary and recurrent GBM. The scRNA-seq analysis revealed cell-type-specific expression patterns, with eight genes predominantly enriched in oligodendrocytes, malignant GBM subtypes, and immune cells. Immune cell deconvolution showed significant alterations in macrophage polarization and NK cell activation in recurrent GBM. Machine learning analysis demonstrated that random forest (RF) was the most effective model, achieving AUC values of 0.998, 0.968, and 0.998 in the training, CGGA-693 validation, and CGGA-325 validation cohorts, respectively, suggesting high predictive accuracy. This study identifies novel recurrence-associated molecular signatures and establishes a machine learning-based predictive model in IDH-wildtype histological GBM.

How does the tubulin code facilitate directed cell migration?

Besides being a component of the cytoskeleton that provides structural integrity to the cell, microtubules also serve as tracks for intracellular transport. As the building units of the mitotic spindle, microtubules distribute chromosomes during cell division. By distributing organelles, vesicles, and proteins, they play a pivotal role in diverse cellular processes, including cell migration, during which they reorganize to facilitate cell polarization. Structurally, microtubules are built up of α/β-tubulin dimers, which consist of various tubulin isotypes that undergo multiple post-translational modifications (PTMs). These PTMs allow microtubules to differentiate into functional subsets, influencing the associated processes. This text explores the current understanding of the roles of tubulin PTMs in cell migration, particularly detyrosination and acetylation, and their implications in human diseases.

Virtual staining from bright-field microscopy for label-free quantitative analysis of plant cell structures

The applicability of a deep learning model for the virtual staining of plant cell structures using bright-field microscopy was investigated. The training dataset consisted of microscopy images of tobacco BY-2 cells with the plasma membrane stained with the fluorescent dye PlasMem Bright Green and the cell nucleus labeled with Histone-red fluorescent protein. The trained models successfully detected the expansion of cell nuclei upon aphidicolin treatment and a decrease in the cell aspect ratio upon propyzamide treatment, demonstrating its utility in cell morphometry. The model also accurately documented the shape of Arabidopsis pavement cells in both wild type and the bpp125 triple mutant, which has an altered pavement cell phenotype. Metrics such as cell area, circularity, and solidity obtained from virtual staining analyses were highly correlated with those obtained by manual measurements of cell features from microscopy images. Furthermore, the versatility of virtual staining was highlighted by its application to track chloroplast movement in Egeria densa. The method was also effective for classifying live and dead BY-2 cells using texture-based machine learning, suggesting that virtual staining can be applied beyond typical segmentation tasks. Although this method still has some limitations, its non-invasive nature and efficiency make it highly suitable for label-free, dynamic, and high-throughput analyses in quantitative plant cell biology.

α-tubulin detyrosination fine-tunes kinetochore-microtubule attachments

Post-translational cycles of α-tubulin detyrosination and tyrosination generate microtubule diversity, the cellular functions of which remain largely unknown. Here we show that α-tubulin detyrosination regulates kinetochore-microtubule attachments to ensure normal chromosome oscillations and timely anaphase onset during mitosis. Remarkably, detyrosinated α-tubulin levels near kinetochore microtubule plus-ends depend on the direction of chromosome motion during metaphase. Proteomic analyses unveil that the KNL-1/MIS12/NDC80 (KMN) network that forms the core microtubule-binding site at kinetochores and the microtubule-rescue protein CLASP2 are enriched on tyrosinated and detyrosinated microtubules during mitosis, respectively. α-tubulin detyrosination enhances CLASP2 binding and NDC80 complex diffusion along the microtubule lattice in vitro. Rescue experiments overexpressing NDC80, including variants with slower microtubule diffusion, suggest a functional interplay with α-tubulin detyrosination for the establishment of a labile kinetochore-microtubule interface. These results offer a mechanistic explanation for how different detyrosinated α-tubulin levels near kinetochore microtubule plus-ends fine-tune load-bearing attachments to both growing and shrinking microtubules.

GraphPI: Efficient Protein Inference with Graph Neural Networks

The integration of deep learning approaches in biomedical research has been transformative, enabling breakthroughs in various applications. Despite these strides, its application in protein inference is impeded by the scarcity of extensively labeled data sets, a challenge compounded by the high costs and complexities of accurate protein annotation. In this study, we introduce GraphPI, a novel framework that treats protein inference as a node classification problem. We treat proteins as interconnected nodes within a protein-peptide-PSM graph, utilizing a graph neural network-based architecture to elucidate their interrelations. To address label scarcity, we train the model on a set of unlabeled public protein data sets with pseudolabels derived from an existing protein inference algorithm, enhanced by self-training to iteratively refine labels based on confidence scores. Contrary to prevalent methodologies necessitating data set-specific training, our research illustrates that GraphPI, due to the well-normalized nature of Percolator features, exhibits universal applicability without data set-specific fine-tuning, a feature that not only mitigates the risk of overfitting but also enhances computational efficiency. Our empirical experiments reveal notable performance on various test data sets and deliver significantly reduced computation times compared to common protein inference algorithms.

Structural basis for α-tubulin-specific and modification state-dependent glutamylation

Microtubules have spatiotemporally complex posttranslational modification patterns. Tubulin tyrosine ligase-like (TTLL) enzymes introduce the most prevalent modifications on α-tubulin and β-tubulin. How TTLLs specialize for specific substrate recognition and ultimately modification-pattern generation is largely unknown. TTLL6, a glutamylase implicated in ciliopathies, preferentially modifies tubulin α-tails in microtubules. Cryo-electron microscopy, kinetic analysis and single-molecule biochemistry reveal an unprecedented quadrivalent recognition that ensures simultaneous readout of microtubule geometry and posttranslational modification status. By binding to a β-tubulin subunit, TTLL6 modifies the α-tail of the longitudinally adjacent tubulin dimer. Spanning two tubulin dimers along and across protofilaments (PFs) ensures fidelity of recognition of both the α-tail and the microtubule. Moreover, TTLL6 reads out and is stimulated by glutamylation of the β-tail of the laterally adjacent tubulin dimer, mediating crosstalk between α-tail and β-tail. This positive feedback loop can generate localized microtubule glutamylation patterns. Our work uncovers general principles that generate tubulin chemical and topographic complexity.

Comprehensive analysis of EML2 as a prognostic biomarker in colon cancer

BACKGROUND

Echinoderm microtubule-associated protein-like 2 (EML2), a gene located on 19q13.32, is overexpressed in various cancers and has been identified as a prognostic factor. However, the function and carcinogenic mechanism of EML2 in colon cancer is yet to be explored.

METHODS

This study aimed to demonstrate the relationship between EML2 expression and colon cancer using The Cancer Genome Atlas (TCGA) database. The EML2 expression, including GSE33113 and GSE39923, was validated in colon cancer in the Gene Expression Omnibus (GEO) database. The Receiver Operating Characteristic (ROC) curves were used to assess the feasibility of EML2 as a distinguishing factor from the area under the curve (AUC) scores. In addition, Cox regression and logistic regression analyses were conducted to evaluate the factors linked to the prognosis of colon cancer. Moreover, the STRING tool was used to establish the EML2 binding protein network. The enrichment analysis cluster Profiler of the R package was utilized to investigate the function of EML2. The relationship between the immune infiltration and EML2 expression level in colon cancer was investigated by the R package Gene Set Variation Analysis (GSVA) and the single sample Gene Set Enrichment Analysis (ssGSEA) method in the Tumor Immune Estimation Resource (TIMER) database.

RESULTS

Pan-cancer data analysis revealed that EML2 expression was higher in most cancers, including colon cancer. This outcome was in line with the findings of the GEO database. The ROC curve demonstrated that EML2 can serve as a diagnostic biomarker for colon cancer (AUC = 0.738). High EML2 expression was associated with poorer overall survival (OS; P = 0.004). Moreover, the results of the enrichment and immune infiltration analysis revealed that high EML2 expression correlated with regulation of the infiltration level of GTPase binding and some immune cell types like NK cells and NK CD56 bright cells.

CONCLUSION

The findings revealed that colon cancer tissues had a higher EML2 expression than normal colon epithelial tissues. This phenomenon was significantly associated with poor prognosis and altered immune cell infiltration. Consequently, EML2 has shown the capacity to serve as a prognostic biomarker for patients diagnosed with colon cancer.

The Tubulin Code, from Molecules to Health and Disease

Microtubules are essential dynamic polymers composed of α/β-tubulin heterodimers. They support intracellular trafficking, cell division, cellular motility, and other essential cellular processes. In many species, both α-tubulin and β-tubulin are encoded by multiple genes with distinct expression profiles and functionality. Microtubules are further diversified through abundant posttranslational modifications, which are added and removed by a suite of enzymes to form complex, stereotyped cellular arrays. The genetic and chemical diversity of tubulin constitute a tubulin code that regulates intrinsic microtubule properties and is read by cellular effectors, such as molecular motors and microtubule-associated proteins, to provide spatial and temporal specificity to microtubules in cells. In this review, we synthesize the rapidly expanding tubulin code literature and highlight limitations and opportunities for the field. As complex microtubule arrays underlie essential physiological processes, a better understanding of how cells employ the tubulin code has important implications for human disease ranging from cancer to neurological disorders.

Microtubule detyrosination by VASH1/SVBP is regulated by the conformational state of tubulin in the lattice

Tubulin, a heterodimer of α- and β-tubulin, is a GTPase that assembles into microtubule (MT) polymers whose dynamic properties are intimately coupled to nucleotide hydrolysis. In cells, the organization and dynamics of MTs are further tuned by post-translational modifications (PTMs), which control the ability of MT-associated proteins (MAPs) and molecular motors to engage MTs. Detyrosination is a PTM of α-tubulin, wherein its C-terminal tyrosine residue is enzymatically removed by either the vasohibin (VASH) or MT-associated tyrosine carboxypeptidase (MATCAP) peptidases. How these enzymes generate specific patterns of MT detyrosination in cells is not known. Here, we use a novel antibody-based probe to visualize the formation of detyrosinated MTs in real time and employ single-molecule imaging of VASH1 bound to its regulatory partner small-vasohibin binding protein (SVBP) to understand the process of MT detyrosination in vitro and in cells. We demonstrate that the activity, but not binding, of VASH1/SVBP is much greater on mimics of guanosine triphosphate (GTP)-MTs than on guanosine diphosphate (GDP)-MTs. Given emerging data showing that tubulin subunits in GTP-MTs are in expanded conformation relative to tubulin subunits in GDP-MTs, we reasoned that the lattice conformation of MTs is a key factor that gates the activity of VASH1/SVBP. We show that Taxol, a drug known to expand the MT lattice, promotes MT detyrosination and that CAMSAP2 and CAMSAP3 are two MAPs that spatially regulate detyrosination in cells. Collectively, our work shows that VASH1/SVBP detyrosination is regulated by the conformational state of tubulin in the MT lattice and that this is spatially determined in cells by the activity of MAPs.

DCX-EMAP is a core organizer for the ultrastructure of Drosophila mechanosensory organelles

Mechanoreceptor cells develop specialized mechanosensory organelles (MOs), where force-sensitive channels and supporting structures are organized in an orderly manner to detect forces. It is intriguing how MOs are formed. Here, we address this issue by studying the MOs of fly ciliated mechanoreceptors. We show that the main structure of the MOs is a compound cytoskeleton formed of short microtubules and electron-dense materials (EDMs). In a knock-out mutant of DCX-EMAP, this cytoskeleton is nearly absent, suggesting that DCX-EMAP is required for the formation of the MOs and in turn fly mechanotransduction. Further analysis reveals that DCX-EMAP expresses in fly ciliated mechanoreceptors and localizes to the MOs. Moreover, it plays dual roles by promoting the assembly/stabilization of the microtubules and the accumulation of the EDMs in the MOs. Therefore, DCX-EMAP serves as a core ultrastructural organizer of the MOs, and this finding provides novel molecular insights as to how fly MOs are formed.

Mechanistic Analysis of CCP1 in Generating ΔC2 α-Tubulin in Mammalian Cells and Photoreceptor Neurons

An important post-translational modification (PTM) of α-tubulin is the removal of amino acids from its C-terminus. Removal of the C-terminal tyrosine residue yields detyrosinated α-tubulin, and subsequent removal of the penultimate glutamate residue produces ΔC2-α-tubulin. These PTMs alter the ability of the α-tubulin C-terminal tail to interact with effector proteins and are thereby thought to change microtubule dynamics, stability, and organization. The peptidase(s) that produces ΔC2-α-tubulin in a physiological context remains unclear. Here, we take advantage of the observation that ΔC2-α-tubulin accumulates to high levels in cells lacking tubulin tyrosine ligase (TTL) to screen for cytosolic carboxypeptidases (CCPs) that generate ΔC2-α-tubulin. We identify CCP1 as the sole peptidase that produces ΔC2-α-tubulin in TTLΔ HeLa cells. Interestingly, we find that the levels of ΔC2-α-tubulin are only modestly reduced in photoreceptors of ccp1-/- mice, indicating that other peptidases act synergistically with CCP1 to produce ΔC2-α-tubulin in post-mitotic cells. Moreover, the production of ΔC2-α-tubulin appears to be under tight spatial control in the photoreceptor cilium: ΔC2-α-tubulin persists in the connecting cilium of ccp1-/- but is depleted in the distal portion of the photoreceptor. This work establishes the groundwork to pinpoint the function of ΔC2-α-tubulin in proliferating and post-mitotic mammalian cells.

Contributions of microtubule dynamics and transport to presynaptic and postsynaptic functions

Microtubules (MT) are elongated, tubular, cytoskeletal structures formed from polymerization of tubulin dimers. They undergo continuous cycles of polymerization and depolymerization, primarily at their plus ends, termed dynamic instability. Although this is an intrinsic property of MTs, there are a myriad of MT-associated proteins that function in regulating MT dynamic instability and other dynamic processes that shape the MT array. Additionally, MTs assemble into long, semi-rigid structures which act as substrates for long-range, motor-driven transport of many different types of cargoes throughout the cell. Both MT dynamics and motor-based transport play important roles in the function of every known type of cell. Within the last fifteen years many groups have shown that MT dynamics and transport play ever-increasing roles in the neuronal function of mature neurons. Not only are neurons highly polarized cells, but they also connect with one another through synapses to form complex networks. Here we will focus on exciting studies that have illuminated how MTs function both pre-synaptically in axonal boutons and post-synaptically in dendritic spines. It is becoming clear that MT dynamics and transport both serve important functions in synaptic plasticity. Thus, it is not surprising that disruption of MTs, either through hyperstabilization or destabilization, has profound consequences for learning and memory. Together, the studies described here suggest that MT dynamics and transport play key roles in synaptic function and when disrupted result in compromised learning and memory.

Microtubule cytoskeleton: Revealing new readers of the tubulin code

Microtubule networks are thought to be controlled by an elaborate program of tubulin posttranslational modifications and proteins that selectively bind to modified states. A new study identifies proteins that bind tyrosinated tubulin, revealing a novel recognition mechanism.