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Cuticular hydrocarbons (CHC) abound on the surface of arthropods. In spite of their simple structure (molecules of carbon and hydrogen atoms), they provide pivotal functions in insects: their hydrophobic properties confer the insects a means to regulate water balance and avoid desiccation, whereas their diversity has enhanced their use as signals and cues in a wide range of communication and recognition processes. Although the study of CHC in insects over the past two decades has provided great insight into the wide range of functions they play, there is still a gap in understanding how they diversify and evolve. In this thesis, I have used members of the family Chrysididae to explore patterns of diversification of CHC. Most of the species of cuckoo wasps in this study are specialized parasitoids or kleptoparasites of mainly solitary hymenopteran hosts. Other hosts of the family include butterflies or stick insects. Cuckoo wasps are a particular interesting model to study the evolution of cuticular hydrocarbons because of their chemical adaptations that allow them to remain unrecognized by their hosts. Chemical insignificance (the reduction of the total amount of CHC on the cuticle) and chemical mimicry (the de novo production of CHC profiles resembling those of their female host) have been described in some representatives of the family and unpublished evidence suggests chemical deception is widespread in Chrysididae (Chapter 2). Nonetheless, to trace the evolution of any trait of interest, a reliable phylogenetic reconstruction of the family is required. Therefore, the first study of this thesis constitutes the largest and to-date most reliable phylogenetic reconstruction of the family Chrysididae, which includes representatives of 186 species of cuckoo wasps. While the results of this phylogenetic reconstruction are consistent with previous ideas on the relationships of subfamilies and tribes, it shows the existence of several non-monophyletic genera (Chapter 3). CHC are involved in intraspecific recognition, often acting as contact sex pheromones. Nevertheless, it is not yet understood to what extent CHC profiles differ between the two sexes and whether some compound classes are more prevalent in one or the other sex. So far, no comparison of CHC profiles of males and females has been done for more than a dozen of related species. In Chapter 4, I describe and compare CHC profiles of females and males of 58 species of cuckoo wasps in order to evaluate whether and to what extent CHC profiles of these species differ between the sexes. I demonstrated that CHC profiles of cuckoo wasps are frequently (more than 90% of the species analyzed) and strongly dimorphic (both sexes of a given species tend to produce very different CHC compounds). Methyl-branched compounds tend to be more prevalent in males (especially dimethyl-branched compounds) and unsaturated compounds prevail in females. Moreover, a sex-specific pattern in the distribution of the double bond position of alkenes was evident: internal double bond positions (> 11) occur predominantly in males, whereas alkenes with the doublé bond at position 9 were more abundant and frequent in females (Chapter4). In Chapter5, I investigated how CHC profiles of cuckoo wasps differ across species. Are CHC profiles of cuckoo wasps species-specific, enabling their use as cues for species recognition? How do CHC profiles resemble phylogenetic relatedness? In Chapter 5, I try to answer these questions by comparing CHC profiles of 59 species of cuckoo wasps. CHC profiles of cuckoo wasps are shown to be species (and sex-) specific. I show that CHC profiles are useful as a complementary tool to help delimiting taxonomically difficult sibling species. Moreover, the evaluation of CHC profiles of five commonly occurring species within a genus, showed little or no geographical variation. However, CHC profiles of closely related species may differ strongly among each other, not being useful to track the evolutionary history of species (Chapter 5). Sexual selection is generally credited for generating striking sexual dimorphism by causing changes in male traits. Most often, sexual selection has a stronger effect on males, who compete for access to and may be selected by females, thus male traits may rapidly evolve. Nevertheless, in cuckoo wasps, it appears that it is the female sex the one evolving faster changes, with females of very closely related species showing extremely divergent profiles. One plausible reason for this disparity is that natural selection acting on female’s CHC profiles may be stronger than sexual selection on males (Chapter 6). Since females of cuckoo wasps are most probably engaged in an evolutionary arms race with their female hosts, CHC profiles of female cuckoo wasps are likely rapidly evolving, thus explaining part of the strong observed sexual dimorphism of CHC (Chapter 6). In fact, Chapter 7 shows evidence of a possible ongoing evolutionary arms race between five cuckoo wasps of the genus Hedychrum and their hosts. Hedychrum species parasitize either Coleoptera-hunting or Hymenoptera-hunting digger wasps. Since the coleopteran prey of the former digger wasps is naturally better protected against fungus infestation, these wasps do not embalm their prey with alkene-enriched secretions as do the Hymenoptera-hunting digger wasps. Thus, Coleoptera-hunting digger wasps can apparently diversify their profiles to escape chemical mimicry. Interestingly, only female cuckoo wasps of these hosts have started producing the same compound classes and even the same CHC compounds as those of their hosts. Male cuckoo wasps, however retain an alkene-enriched CHC profile that reflects the molecular phylogeny of the genus (Chapter 7). Whereas, a larger number of parasite-host comparisons may be needed to further conclude that an arms race between cuckoo wasps and their hosts is capable of generating sexual dimorphism of cuckoo wasps, this thesis constitutes the first effort towards this, providing a starting point for further studies. Finally, I provide some methodological tools that may help in speeding up the sometimes cumbersome process of analyzing and identifying CHC profiles. One of the most time-demanding steps in the processing of CHC data is the alignment of CHC chromatograms. This process is often done manually, because alignment programs are mostly designed for metabolomics or are just recently being developed. I analyzed CHC profiles using a combined approach with two freely available programs. I used AMDIS (Automated Mass Spectral Deconvolution and Identification System, http://chemdata.nist.gov/mass-spc/amdis/) to deconvolute and automatically identify all CHC of interest present in a chromatogram. I then developed a series of R scripts to correct for potential, unavoidable errors while processing CHC chromatograms with AMDIS. Chapter 8 explains this procedure. In the next chapter, I developed a program that helps in the identification of one commonly occurring class of hydrocarbons. The limited number of linear alkanes (only one per carbon atom) and their characteristic diagnostic ion allows a rapid and unambigous identification of these substances. In opposition, unsaturated and methyl-branched compounds are more difficult to identify, as a result of the much larger diversity of existing compounds. To identify unsaturated compounds a derivatization is necessary to determine the position of the double bond. Methyl-branched alkanes, however can be identified from the original chromatogram if their diagnostic ions are known. Nonetheless, polymethyl-branched alkanes (e.g., compounds with two or more methyl groups along the chain) are often difficult to identify, because they may appear in mixes (e.g., 3,7 diMeC27 and 3,9 diMeC27), and tables containing the diagnostic ions are not easily available. Therefore, I developed a program that creates a table with all possiblemethyl-branched compounds containing up to 4 methyl groups, and that provides their diagnostic ions and a calculated retention index. This may allow a much faster identification of the methyl-branched compound a researcher is dealing with, without having to lose time in the tedious calculations by hand. The program is able to correctly identify, or at least, greatly reduce the number of possible options for the identification of an unknown methyl-branched compound. Thus, using this tool, most methyl-branched compounds can be readily identified (Chapter 9). This thesis ends with a general discussion (Chapter 10). Overall, this work provides a comprehensive overview of the diversity of cuticular hydrocarbons of cuckoo wasps. The analyses presented here shed light on the emergence and evolution of interspecific diversity and intraspecific sexual dimorphism of CHC profiles. In addition, two technical methods have been developed that could greatly facilitate the CHC analysis of insects.
Background: ApaH like phosphatases (ALPHs) originate from the bacterial ApaH protein and are present in eukaryotes of all eukaryotic super-groups; still, only two proteins have been functionally characterised. One is ALPH1 from the Kinetoplastid Trypanosoma brucei that we recently found to be the mRNA decapping enzyme of the parasite. mRNA decapping by ALPHs is unprecedented in eukaryotes, which usually use nudix hydrolases, but the bacterial ancestor protein ApaH was recently found to decap non-conventional caps of bacterial mRNAs. These findings prompted us to explore whether mRNA decapping by ALPHs is restricted to Kinetoplastida or more widespread among eukaryotes.
Results: We screened 824 eukaryotic proteomes with a newly developed Python-based algorithm for the presence of ALPHs and used the data to refine phylogenetic distribution, conserved features, additional domains and predicted intracellular localisation of ALPHs. We found that most eukaryotes have either no ALPH (500/824) or very short ALPHs, consisting almost exclusively of the catalytic domain. These ALPHs had mostly predicted non-cytoplasmic localisations, often supported by the presence of transmembrane helices and signal peptides and in two cases (one in this study) by experimental data. The only exceptions were ALPH1 homologues from Kinetoplastida, that all have unique C-terminal and mostly unique N-terminal extension, and at least the T. brucei enzyme localises to the cytoplasm. Surprisingly, despite of these non-cytoplasmic localisations, ALPHs from all eukaryotic super-groups had in vitro mRNA decapping activity.
Conclusions: ALPH was present in the last common ancestor of eukaryotes, but most eukaryotes have either lost the enzyme since, or use it exclusively outside the cytoplasm in organelles in a version consisting of the catalytic domain only. While our data provide no evidence for the presence of further mRNA decapping enzymes among eukaryotic ALPHs, the broad substrate range of ALPHs that includes mRNA caps provides an explanation for the selection against the presence of a cytoplasmic ALPH protein as a mean to protect mRNAs from unregulated degradation. Kinetoplastida succeeded to exploit ALPH as their mRNA decapping enzyme, likely using the Kinetoplastida-unique N- and C-terminal extensions for regulation.
N-MYC is a member of the human MYC proto-oncogene family, which comprises three transcription factors (C-, N- and L-MYC) that function in multiple biological processes. Deregulated expression of MYC proteins is linked to tumour initiation, maintenance and progression. For example, a large fraction of neuroblastoma displays high N-MYC levels due to an amplification of the N-MYC encoding gene. MYCN-amplified neuroblastoma depend on high N-MYC protein levels, which are maintained by Aurora-A kinase. Aurora-A interaction with N-MYC interferes with degradation of N-MYC via the E3 ubiquitin ligase SCFFBXW7. However, the underlying mechanism of Aurora-A-mediated stabilisation of N-MYC remains to be elucidated.
To identify novel N-MYC interacting proteins, which could be involved in N-MYC stabilisation by Aurora-A, a proteomic analysis of purified N-MYC protein complexes was conducted. Since two alanine mutations in MBI of N-MYC, T58A and S62A (N-MYC mut), disable Aurora-A-mediated stabilisation of N-MYC, N-MYC protein complexes from cells expressing either N-MYC wt or mut were analysed. Proteomic analysis revealed that N-MYC interacts with two deubiquitinating enzymes, USP7 and USP11, which catalyse the removal of ubiquitin chains from target proteins, preventing recognition by the proteasome and subsequent degradation. Although N-MYC interaction with USP7 and USP11 was confirmed in subsequent immunoprecipitation experiments, neither USP7, nor USP11 was shown to be involved in the regulation of N-MYC stability. Besides USP7/11, proteomic analyses identified numerous additional N-MYC interacting proteins that were not described to interact with MYC transcription factors previously. Interestingly, many of the identified N-MYC interaction partners displayed a preference for the interaction with N-MYC wt, suggesting a MBI-dependent interaction. Among these were several proteins, which are involved in three-dimensional organisation of chromatin domains and transcriptional elongation by POL II. Not only the interaction of N-MYC with proteins functioning in elongation, such as the DSIF component SPT5 and the PAF1C components CDC73 and CTR9, was validated in immunoprecipitation experiments, but also with the POL III transcription factor TFIIIC and topoisomerases TOP2A/B. ChIP-sequencing analysis of N-MYC and TFIIIC subunit 5 (TFIIIC5) revealed a large number of joint binding sites in POL II promoters and intergenic regions, which are characterised by the presence of a specific motif that is highly similar to the CTCF motif. Additionally, N-MYC was shown to interact with the ring-shaped cohesin complex that is known to bind to CTCF motifs and to assist the insulator protein CTCF. Importantly, individual ChIP experiments demonstrated that N-MYC, TFIIIC5 and cohesin subunit RAD21 occupy joint binding sites comprising a CTCF motif.
Collectively, the results indicate that N-MYC functions in two biological processes that have not been linked to MYC biology previously. Furthermore, the identification of joint binding sites of N-MYC, TFIIIC and cohesin and the confirmation of their interaction with each other suggests a novel function of MYC transcription factors in three-dimensional organisation of chromatin.
While our knowledge about the roles of microbes and viruses in the ocean has increased tremendously due to recent advances in genomics and metagenomics, research on marine microbial eukaryotes and zooplankton has benefited much less from these new technologies because of their larger genomes, their enormous diversity, and largely unexplored physiologies. Here, we use a metatranscriptomics approach to capture expressed genes in open ocean Tara Oceans stations across four organismal size fractions. The individual sequence reads cluster into 116 million unigenes representing the largest reference collection of eukaryotic transcripts from any single biome. The catalog is used to unveil functions expressed by eukaryotic marine plankton, and to assess their functional biogeography. Almost half of the sequences have no similarity with known proteins, and a great number belong to new gene families with a restricted distribution in the ocean. Overall, the resource provides the foundations for exploring the roles of marine eukaryotes in ocean ecology and biogeochemistry.
Non–Small-Cell Lung Cancer (NSCLC) is the most frequent human lung cancer and a major cause of death due to its high rate of metastasis1. These facts emphasize the urgent need for the investigation of new targets for anti-metastatic therapy. Up to now a number of genes and gene products have been identified that positively or negatively affect the probability of established human tumor cell lines to metastasize2. Previously, together with the group of Professor Ulf Rapp, we have described the first conditional mouse model for metastasis of NSCLC and identified a gene, c-MYC, that is able to orchestrate all steps of this process. We could identify potential markers for detection of metastasis and highlighted GATA4, which is exclusively expressed during lung development, as a target for future therapeutic intervention2. However, the mechanism underlying this metastatic conversion remained to be identified, and was therefore the focus of the present work. Here, GATA4 is identified as a MYC target in the development of metastasis and epigenetic alterations at the GATA4 promoter level are shown after MYC expression in NSCLC in vivo and in vitro. Such alterations include site-specific demethylation that accompanies the displacement of the MYC-associated zinc finger protein (MAZ) from the GATA4 promoter, which leads to GATA4 expression. Histone modification analysis of the GATA4 promoter revealed a switch from repressive histone marks to active histone marks after MYC binding, which corresponds to active GATA4 expression. This work identifies a novel epigenetic mechanism by which MYC activates GATA4 leading to metastasis in NSCLC, suggesting novel potential targets for the development of anti-metastatic therapy.
In the fast-evolving landscape of biomedical research, the emergence of big data has presented researchers with extraordinary opportunities to explore biological complexities. In biomedical research, big data imply also a big responsibility. This is not only due to genomics data being sensitive information but also due to genomics data being shared and re-analysed among the scientific community. This saves valuable resources and can even help to find new insights in silico. To fully use these opportunities, detailed and correct metadata are imperative. This includes not only the availability of metadata but also their correctness. Metadata integrity serves as a fundamental determinant of research credibility, supporting the reliability and reproducibility of data-driven findings. Ensuring metadata availability, curation, and accuracy are therefore essential for bioinformatic research. Not only must metadata be readily available, but they must also be meticulously curated and ideally error-free. Motivated by an accidental discovery of a critical metadata error in patient data published in two high-impact journals, we aim to raise awareness for the need of correct, complete, and curated metadata. We describe how the metadata error was found, addressed, and present examples for metadata-related challenges in omics research, along with supporting measures, including tools for checking metadata and software to facilitate various steps from data analysis to published research.
Highlights
• Data awareness and data integrity underpins the trustworthiness of results and subsequent further analysis.
• Big data and bioinformatics enable efficient resource use by repurposing publicly available RNA-Sequencing data.
• Manual checks of data quality and integrity are insufficient due to the overwhelming volume and rapidly growing data.
• Automation and artificial intelligence provide cost-effective and efficient solutions for data integrity and quality checks.
• FAIR data management, various software solutions and analysis tools assist metadata maintenance.
Since ancient times aging has also been regarded as a disease, and humankind has always strived to extend the natural lifespan. Analyzing the genes involved in aging and disease allows for finding important indicators and biological markers for pathologies and possible therapeutic targets. An example of the use of omics technologies is the research regarding aging and the rare and fatal premature aging syndrome progeria (Hutchinson-Gilford progeria syndrome, HGPS). In our study, we focused on the in silico analysis of differentially expressed genes (DEGs) in progeria and aging, using a publicly available RNA-Seq dataset (GEO dataset GSE113957) and a variety of bioinformatics tools. Despite the GSE113957 RNA-Seq dataset being well-known and frequently analyzed, the RNA-Seq data shared by Fleischer et al. is far from exhausted and reusing and repurposing the data still reveals new insights. By analyzing the literature citing the use of the dataset and subsequently conducting a comparative analysis comparing the RNA-Seq data analyses of different subsets of the dataset (healthy children, nonagenarians and progeria patients), we identified several genes involved in both natural aging and progeria (KRT8, KRT18, ACKR4, CCL2, UCP2, ADAMTS15, ACTN4P1, WNT16, IGFBP2). Further analyzing these genes and the pathways involved indicated their possible roles in aging, suggesting the need for further in vitro and in vivo research. In this paper, we (1) compare “normal aging” (nonagenarians vs. healthy children) and progeria (HGPS patients vs. healthy children), (2) enlist genes possibly involved in both the natural aging process and progeria, including the first mention of IGFBP2 in progeria, (3) predict miRNAs and interactomes for WNT16 (hsa-mir-181a-5p), UCP2 (hsa-mir-26a-5p and hsa-mir-124-3p), and IGFBP2 (hsa-mir-124-3p, hsa-mir-126-3p, and hsa-mir-27b-3p), (4) demonstrate the compatibility of well-established R packages for RNA-Seq analysis for researchers interested but not yet familiar with this kind of analysis, and (5) present comparative proteomics analyses to show an association between our RNA-Seq data analyses and corresponding changes in protein expression.
Machine learning techniques are excellent to analyze expression data from single cells. These techniques impact all fields ranging from cell annotation and clustering to signature identification. The presented framework evaluates gene selection sets how far they optimally separate defined phenotypes or cell groups. This innovation overcomes the present limitation to objectively and correctly identify a small gene set of high information content regarding separating phenotypes for which corresponding code scripts are provided. The small but meaningful subset of the original genes (or feature space) facilitates human interpretability of the differences of the phenotypes including those found by machine learning results and may even turn correlations between genes and phenotypes into a causal explanation. For the feature selection task, the principal feature analysis is utilized which reduces redundant information while selecting genes that carry the information for separating the phenotypes. In this context, the presented framework shows explainability of unsupervised learning as it reveals cell-type specific signatures. Apart from a Seurat preprocessing tool and the PFA script, the pipeline uses mutual information to balance accuracy and size of the gene set if desired. A validation part to evaluate the gene selection for their information content regarding the separation of the phenotypes is provided as well, binary and multiclass classification of 3 or 4 groups are studied. Results from different single-cell data are presented. In each, only about ten out of more than 30000 genes are identified as carrying the relevant information. The code is provided in a GitHub repository at https://github.com/AC-PHD/Seurat_PFA_pipeline.
Summary
Chapters I & II: General Introduction & General Methods
Agriculture is confronted with a rampant loss of biodiversity potentially eroding ecosystem service potentials and adding up to other stressors like climate change or the consequences of land-use change and intensive management. To counter this ‘biodiversity crisis’, agri-environment schemes (AES) have been introduced as part of ecological intensification efforts. These AES combine special management regimes with the establishment of tailored habitats to create refuges for biodiversity in agricultural landscapes and thus ensure biodiversity mediated ecosystem services such as pest control. However, little is known about how well different AES habitats fulfil this purpose and whether they benefit ecosystem services in adjacent crop fields. Here I investigated how effective different AES habitats are for restoring biodiversity in different agricultural landscapes (Chapter V) and whether they benefit natural pest control in adjacent oilseed rape (Chapter VI) and winter cereal fields (Chapter VII). I recorded biodiversity and pest control potentials using a variety of different methods (Chapters II, V, VI & VII). Moreover, I validated the methodology I used to assess predator assemblages and predation rates (Chapters III & IV).
Chapter III: How to record ground dwelling predators?
Testing methodology is critical as it ensures scientific standards and trustworthy results. Pitfall traps are widely used to record ground dwelling predators, but little is known about how different trap types affect catches. I compared different types of pitfall traps that had been used in previous studies in respect to resulting carabid beetle assemblages. While barrier traps collected more species and deliver more complete species inventories, conventional simple pitfall traps provide reliable results with comparatively little handling effort. Placing several simple pitfall traps in the field can compensate the difference while still saving handling effort.
Chapter IV: How to record predation rates?
A plethora of methods has been proposed and used for recording predation rates, but these have rarely been validated before use. I assessed whether a novel approach to record predation, the use of sentinel prey cards with glued on aphids, delivers realistic results. I compared different sampling efforts and showed that obtained predation rates were similar and could be linked to predator (carabid beetle) densities and body-sizes (a proxy often used for food intake rates). Thus, the method delivers reliable and meaningful predation rates.
Chapter V: Do AES habitats benefit multi-taxa biodiversity?
The main goal of AES is the conservation of biodiversity in agricultural landscapes. I investigated how effectively AES habitats with different temporal continuity fulfil this goal in differently structured landscapes. The different AES habitats investigated had variable effects on local biodiversity. Temporal continuity of AES habitats was the most important predictor with older, more temporally continuous habitats harbouring higher overall biodiversity and different species assemblages in most taxonomic groups than younger AES habitats. Results however varied among taxonomic groups and natural enemies were equally supported by younger habitats. Semi-natural habitats in the surrounding landscape and AES habitat size were of minor importance for local biodiversity and had limited effects. This stresses that newly established AES habitats alone cannot restore farmland biodiversity. Both AES habitats as well as more continuous semi-natural habitats synergistically increase overall biodiversity in agricultural landscapes.
Chapter VI: The effects of AES habitats on predators in adjacent oilseed rape fields
Apart from biodiversity conservation, ensuring ecosystem service delivery in agricultural landscapes is a crucial goal of AES. I therefore investigated the effects of adjacent AES habitats on ground dwelling predator assemblages in oilseed rape fields. I found clear distance decay effects from the field edges into the field centres on both richness and densities of ground dwelling predators. Direct effects of adjacent AES habitats on assemblages in oilseed rape fields however were limited and only visible in functional traits of carabid beetle assemblages. Adjacent AES habitats doubled the proportion of predatory carabid beetles indicating a beneficial role for pest control. My results show that pest control potentials are largest close to the field edges and beneficial effects are comparably short ranged.
Chapter VII: The effects of AES habitats on pest control in adjacent cereal fields
Whether distance functions and potential effects of AES habitats are universal across crops is unknown. Therefore, I assessed distance functions of predators, pests, predation rates and yields after crop rotation in winter cereals using the same study design as in the previous year. Resulting distance functions were not uniform and differed from those found in oilseed rape in the previous year, indicating that the interactions between certain adjacent habitats vary with habitat and crop types. Distance functions of cereal-leaf beetles (important cereal pests) and parasitoid wasps were moreover modulated by semi-natural habitat proportion in the surrounding landscapes. Field edges buffered assemblage changes in carabid beetle assemblages over crop rotation confirming their important function as refuges for natural enemies. My results emphasize the beneficial role of field edges for pest control potentials. These findings back the calls for smaller field sizes and more diverse, more heterogeneously structured agricultural landscapes.
Chapter VIII: General Discussion
Countering biodiversity loss and ensuring ecosystem service provision in agricultural landscapes is intricate and requires strategic planning and restructuring of these landscapes. I showed that agricultural landscapes could benefit maximally from (i) a mixture of AES habitats and semi-natural habitats to support high levels of overall biodiversity and from (ii) smaller continuously managed agricultural areas (i.e. smaller field sizes or the insertion of AES elements within large fields) to maximize natural pest control potentials in crop fields. I propose a mosaic of younger AES habitats and semi-natural habitats to support ecosystem service providers and increase edge density for ecosystem service spillover into adjacent crops. The optimal extent and density of this network as well as the location in which AES and semi-natural habitats interact most beneficially with adjacent crops need further investigation. My results provide a further step towards more sustainable agricultural landscapes that simultaneously allow biodiversity to persist and maintain agricultural production under the framework of ecological intensification.
By promoting ceramide release at the cytosolic membrane leaflet, the neutral sphingomyelinase 2 (NSM) is capable of organizing receptor and signalosome segregation. Its role in T cell receptor (TCR) signaling remained so far unknown. We now show that TCR-driven NSM activation is dispensable for TCR clustering and initial phosphorylation, but of crucial importance for further signal amplification. In particular, at low doses of TCR stimulatory antibodies, NSM is required for Ca\(^{2+}\) mobilization and T cell proliferation. NSM-deficient T cells lack sustained CD3ζ and ZAP-70 phosphorylation and are unable to polarize and stabilize their microtubular system. We identified PKCζ as the key NSM downstream effector in this second wave of TCR signaling supporting dynamics of microtubule-organizing center (MTOC). Ceramide supplementation rescued PKCζ membrane recruitment and MTOC translocation in NSM-deficient cells. These findings identify the NSM as essential in TCR signaling when dynamic cytoskeletal reorganization promotes continued lateral and vertical supply of TCR signaling components: CD3ζ, Zap70, and PKCζ, and functional immune synapses are organized and stabilized via MTOC polarization.