Unveiling the dynamics of acetylation and phosphorylation in SGBS and 3T3-L1 adipogenesis.

Obesity, characterized by enlarged and dysfunctional adipose tissue, is among today's most pressing global public health challenges with continuously increasing prevalence. Despite the importance of post-translational protein modifications (PTMs) in cellular signaling, knowledge of their impact on adipogenesis remains limited. Here, we studied the temporal dynamics of transcriptome, proteome, central carbon metabolites, and the acetyl- and phosphoproteome during adipogenesis using LC-MS/MS combined with PTM enrichment strategies on human (SGBS) and mouse (3T3-L1) adipocyte models. Both cell lines exhibited unique PTM profiles during adipogenesis, with acetylated proteins being enriched for central energy metabolism, while phosphorylated proteins related to insulin signaling and organization of cellular structures. As candidates with strong correlation to the adipogenesis timeline we identified CD44 and the acetylation sites FASN_K673 and IDH_K272. While results generally aligned between SGBS and 3T3-L1 cells, details appeared cell line specific. Our datasets on SGBS and 3T3-L1 adipogenesis dynamics are accessible for further mining.

deepFPlearn +: enhancing toxicity prediction across the chemical universe using graph neural networks.

SUMMARY: Sophisticated approaches for the in silico prediction of toxicity are required to support the risk assessment of chemicals. The number of chemicals on the global chemical market and the speed of chemical innovation stand in massive contrast to the capacity for regularizing chemical use. We recently proved our ready-to-use application deepFPlearn as a suitable approach for this task. Here, we present its extension deepFPlearn+ incorporating (i) a graph neural network to feed our AI with a more sophisticated molecular structure representation and (ii) alternative train-test splitting strategies that involve scaffold structures and the molecular weights of chemicals. We show that the GNNs outperform the previous model substantially and that our models can generalize on unseen data even with a more robust and challenging test set. Therefore, we highly recommend the application of deepFPlearn+ on the chemical inventory to prioritize chemicals for experimental testing or any chemical subset of interest in monitoring studies. AVAILABILITY AND IMPLEMENTATION: The software is compatible with python 3.6 or higher, and the source code can be found on our GitHub repository: https://github.com/yigbt/deepFPlearn. The data underlying this article are available in Zenodo, and can be accessed with the link below: https://zenodo.org/record/8146252. Detailed installation guides via Docker, Singularity, and Conda are provided within the repository for operability across all operating systems.

HBEGF-TNF induce a complex outer retinal pathology with photoreceptor cell extrusion in human organoids.

Human organoids could facilitate research of complex and currently incurable neuropathologies, such as age-related macular degeneration (AMD) which causes blindness. Here, we establish a human retinal organoid system reproducing several parameters of the human retina, including some within the macula, to model a complex combination of photoreceptor and glial pathologies. We show that combined application of TNF and HBEGF, factors associated with neuropathologies, is sufficient to induce photoreceptor degeneration, glial pathologies, dyslamination, and scar formation: These develop simultaneously and progressively as one complex phenotype. Histologic, transcriptome, live-imaging, and mechanistic studies reveal a previously unknown pathomechanism: Photoreceptor neurodegeneration via cell extrusion. This could be relevant for aging, AMD, and some inherited diseases. Pharmacological inhibitors of the mechanosensor PIEZO1, MAPK, and actomyosin each avert pathogenesis; a PIEZO1 activator induces photoreceptor extrusion. Our model offers mechanistic insights, hypotheses for neuropathologies, and it could be used to develop therapies to prevent vision loss or to regenerate the retina in patients suffering from AMD and other diseases.

AI for predicting chemical-effect associations at the chemical universe level-deepFPlearn.

Many chemicals are present in our environment, and all living species are exposed to them. However, numerous chemicals pose risks, such as developing severe diseases, if they occur at the wrong time in the wrong place. For the majority of the chemicals, these risks are not known. Chemical risk assessment and subsequent regulation of use require efficient and systematic strategies. Lab-based methods-even if high throughput-are too slow to keep up with the pace of chemical innovation. Existing computational approaches are designed for specific chemical classes or sub-problems but not usable on a large scale. Further, the application range of these approaches is limited by the low amount of available labeled training data. We present the ready-to-use and stand-alone program deepFPlearn that predicts the association between chemical structures and effects on the gene/pathway level using a combined deep learning approach. deepFPlearn uses a deep autoencoder for feature reduction before training a deep feed-forward neural network to predict the target association. We received good prediction qualities and showed that our feature compression preserves relevant chemical structural information. Using a vast chemical inventory (unlabeled data) as input for the autoencoder did not reduce our prediction quality but allowed capturing a much more comprehensive range of chemical structures. We predict meaningful-experimentally verified-associations of chemicals and effects on unseen data. deepFPlearn classifies hundreds of thousands of chemicals in seconds. We provide deepFPlearn as an open-source and flexible tool that can be easily retrained and customized to different application settings at https://github.com/yigbt/deepFPlearn.

Evolution and Phylogeny of MicroRNAs - Protocols, Pitfalls, and Problems.

MicroRNAs are important regulators in many eukaryotic lineages. Typical miRNAs have a length of about 22nt and are processed from precursors that form a characteristic hairpin structure. Once they appear in a genome, miRNAs are among the best-conserved elements in both animal and plant genomes. Functionally, they play an important role in particular in development. In contrast to protein-coding genes, miRNAs frequently emerge de novo. The genomes of animals and plants harbor hundreds of mutually unrelated families of homologous miRNAs that tend to be persistent throughout evolution. The evolution of their genomic miRNA complement closely correlates with important morphological innovation. In addition, miRNAs have been used as valuable characters in phylogenetic studies. An accurate and comprehensive annotation of miRNAs is required as a basis to understand their impact on phenotypic evolution. Since experimental data on miRNA expression are limited to relatively few species and are subject to unavoidable ascertainment biases, it is inevitable to complement miRNA sequencing by homology based annotation methods. This chapter reviews the state of the art workflows for homology based miRNA annotation, with an emphasis on their limitations and open problems.

A Multi-Omics Analysis of Mucosal-Associated-Invariant T Cells Reveals Key Drivers of Distinct Modes of Activation.

The function of mucosal-associated invariant T (MAIT) cells highly depends on the mode of activation, either by recognition of bacterial metabolites via their T cell receptor (TCR) or in a TCR-independent manner via cytokines. The underlying molecular mechanisms are not entirely understood. To define the activation of MAIT cells on the molecular level, we applied a multi-omics approach with untargeted transcriptomics, proteomics and metabolomics. Transcriptomic analysis of E. coli- and TCR-activated MAIT cells showed a distinct transcriptional reprogramming, including altered pathways, transcription factors and effector molecules. We validated the consequences of this reprogramming on the phenotype by proteomics and metabolomics. Thus, and to distinguish between TCR-dependent and -independent activation, MAIT cells were stimulated with IL12/IL18, anti-CD3/CD28 or both. Only a combination of both led to full activation of MAIT cells, comparable to activation by E. coli. Using an integrated network-based approach, we identified key drivers of the distinct modes of activation, including cytokines and transcription factors, as well as negative feedback regulators like TWIST1 or LAG3. Taken together, we present novel insights into the biological function of MAIT cells, which may represent a basis for therapeutic approaches to target MAIT cells in pathological conditions.

Mouse Retinal Organoid Growth and Maintenance in Longer-Term Culture.

Using retinal organoid systems, organ-like 3D tissues, relies implicitly on their robustness. However, essential key parameters, particularly retinal growth and longer-term culture, are still insufficiently defined. Here, we hypothesize that a previously optimized protocol for high yield of evenly-sized mouse retinal organoids with low variability facilitates assessment of such parameters. We demonstrate that these organoids reliably complete retinogenesis, and can be maintained at least up to 60 days in culture. During this time, the organoids continue to mature on a molecular and (ultra)structural level: They develop photoreceptor outer segments and synapses, transiently maintain its cell composition for about 5-10 days after completing retinogenesis, and subsequently develop pathologic changes - mainly of the inner but also outer retina and reactive gliosis. To test whether this organoid system provides experimental access to the retina during and upon completion of development, we defined and stimulated organoid growth by activating sonic hedgehog signaling, which in patients and mice in vivo with a congenital defect leads to enlarged eyes. Here, a sonic hedgehog signaling activator increased retinal epithelia length in the organoid system when applied during but not after completion of development. This experimentally supports organoid maturation, stability, and experimental reproducibility in this organoid system, and provides a potential enlarged retina pathology model, as well as a protocol for producing larger organoids. Together, our study advances the understanding of retinal growth, maturation, and maintenance, and further optimizes the organoid system for future utilization.

Potential Co-Factors of an Intraoral Contact Allergy-A Cross-Sectional Study.

The aim of this cross-sectional study was to evaluate the frequency of dental allergens and potential co-factors, especially hypothyroidism, for patients with an intraoral contact allergy. From 2015 to 2016, patients with confirmed symptoms of an intraoral contact allergy (study group SG n = 50) were recruited in the dental clinic of the University of Leipzig. The participants of the control group (CG n = 103) were patients without oral diseases or intraoral symptoms of a contact allergy. For the data collection, a new "Allergy questionnaire" was developed. Information on allergies and general diseases were collected. The statistical analysis was carried out with SPSS 23.0. Sensitizations/allergies to metals and composites were higher in SG compared to CG. Of all study participants (n = 148), 14.2% (n = 21) had a nickel allergy. In 18% (n = 8) of the SG a cobalt allergy based on all metal allergens could be seen. In addition, an association between a nickel and cobalt allergy was found. Hypothyroidism occurred significantly more frequently (p = 0.049) in SG than in CG. Sensitizations and allergies can occur to metals in dental alloys. Hypothyroidism increased the risk of having an allergy threefold.

A probabilistic version of Sankoff's maximum parsimony algorithm.

The number of genes belonging to a multi-gene family usually varies substantially over their evolutionary history as a consequence of gene duplications and losses. A first step toward analyzing these histories in detail is the inference of the changes in copy number that take place along the individual edges of the underlying phylogenetic tree. The corresponding maximum parsimony minimizes the total number of changes along the edges of the species tree. Incorrectly determined numbers of family members however may influence the estimates drastically. We therefore augment the analysis by introducing a probabilistic model that also considers suboptimal assignments of changes. Technically, this amounts to a partition function variant of Sankoff's parsimony algorithm. As a showcase application, we reanalyze the gain and loss patterns of metazoan microRNA families. As expected, the differences between the probabilistic and the parsimony method is moderate, in this limit of T→0, i.e. very little tolerance for deviations from parsimony, the total number of reconstructed changes is the same. However, we find that the partition function approach systematically predicts fewer gains and more loss events, showing that the data admit co-optimal solutions among which the parsimony approach selects biased representatives.

Prospects and challenges of multi-omics data integration in toxicology.

Exposure of cells or organisms to chemicals can trigger a series of effects at the regulatory pathway level, which involve changes of levels, interactions, and feedback loops of biomolecules of different types. A single-omics technique, e.g., transcriptomics, will detect biomolecules of one type and thus can only capture changes in a small subset of the biological cascade. Therefore, although applying single-omics analyses can lead to the identification of biomarkers for certain exposures, they cannot provide a systemic understanding of toxicity pathways or adverse outcome pathways. Integration of multiple omics data sets promises a substantial improvement in detecting this pathway response to a toxicant, by an increase of information as such and especially by a systemic understanding. Here, we report the findings of a thorough evaluation of the prospects and challenges of multi-omics data integration in toxicological research. We review the availability of such data, discuss options for experimental design, evaluate methods for integration and analysis of multi-omics data, discuss best practices, and identify knowledge gaps. Re-analyzing published data, we demonstrate that multi-omics data integration can considerably improve the confidence in detecting a pathway response. Finally, we argue that more data need to be generated from studies with a multi-omics-focused design, to define which omics layers contribute most to the identification of a pathway response to a toxicant.

uap: reproducible and robust HTS data analysis.

BACKGROUND: A lack of reproducibility has been repeatedly criticized in computational research. High throughput sequencing (HTS) data analysis is a complex multi-step process. For most of the steps a range of bioinformatic tools is available and for most tools manifold parameters need to be set. Due to this complexity, HTS data analysis is particularly prone to reproducibility and consistency issues. We have defined four criteria that in our opinion ensure a minimal degree of reproducible research for HTS data analysis. A series of workflow management systems is available for assisting complex multi-step data analyses. However, to the best of our knowledge, none of the currently available work flow management systems satisfies all four criteria for reproducible HTS analysis. RESULTS: Here we present uap, a workflow management system dedicated to robust, consistent, and reproducible HTS data analysis. uap is optimized for the application to omics data, but can be easily extended to other complex analyses. It is available under the GNU GPL v3 license at https://github.com/yigbt/uap. CONCLUSIONS: uap is a freely available tool that enables researchers to easily adhere to reproducible research principles for HTS data analyses.

Specific induction of the unique GPR15 expression in heterogeneous blood lymphocytes by tobacco smoking.

Purpose: In the peripheral blood, it has been shown that smoking is, to date, the only specific condition leading to an increase in GPR15+ T cells. We, therefore, aimed to characterize GPR15-expressing blood T cells in more detail. Materials and Methods: The whole transcriptome by RNAseq as a proxy for protein expression was analyzed in GPR15+ and GPR15- T cells. A deep immuno-phenotyping was conducted for the identification of T cell subtypes. Results: The expression of GPR15 seemed to be unique, not concomitantly accompanied with the expression of another protein. According to different T cell subtypes, there is no single cell type prominently represented in GPR15+ T cells. The individually different proportions of GPR15+ cells among each GPR15-expressing T cell subtypes in blood were strongly associated with chronic smoking. Indeed, the frequency of GPR15+ T cell subtypes can be effectively used as a highly convincing biomarker for tobacco smoking. Conclusions: While the chronic smoking-induced enrichment of GPR15+ T cells in blood might indicate a systemic inflammation, by the widespread presence in different T cell subtypes, GPR15 could feature a general impact on maintaining the systemic homeostasis to putatively prevent harm from smoking.

The fungal snoRNAome.

Small nucleolar RNAs (snoRNAs) are essential players in the rRNA biogenesis due to their involvement in the nucleolytic processing of the precursor and the subsequent guidance of nucleoside modifications. Within the kingdom Fungi, merely a few species-specific surveys have explored their snoRNA repertoire. However, the wide range of the snoRNA landscape spanning all major fungal lineages has not been mapped so far, mainly because of missing tools for automatized snoRNA detection and functional analysis. For the first time, we report here a comprehensive inventory of fungal snoRNAs together with a functional analysis and an in-depth investigation of their evolutionary history including innovations, deletions, and target switches. This large-scale analysis, incorporating more than 120 snoRNA families with more than 7700 individual snoRNA sequences, catalogs and clarifies the landscape of fungal snoRNA families, assigns functions to previously orphan snoRNAs, and increases the number of sequences by 450%. We also show that the snoRNAome is subject to ongoing rearrangements and adaptations, e.g., through lineage-specific targets and redundant guiding functions.