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Thesis advisor: Tim van Opijnen / Thesis advisor: Welkin Johnson / Pathogenic bacteria can experience various stress factors during an infection including antibiotics and the host immune system. Whether a pathogen will establish an infection largely depends on its survival-success while enduring these stress factors. We reasoned that the ability to predict whether a pathogen will survive under and/or adapt to a stressful condition will provide great diagnostic and prognostic value. However, it is unknown what information is needed to enable such predictions. We hypothesized that under a stressful condition, a bacterium triggers responses that indicate how the stress is experienced in the genome, thereby correctly identifying a stress response holds the key to enabling such predictions. Bacterial stress responses have long been studied by determining how small groups of individual genes or pathways respond to certain environmental triggers. However, the conservation of these genes and the manner in which they respond to a stress can vary widely across species. Thus, this thesis sought to achieve a genome-wide and systems-level understanding of a bacterial stress response with the goal to identify signatures that enable predictions of survival and adaptation outcomes in a pathogen- and stress-independent manner. Here, we first set up a multi-omics framework that maps out a stress response on a genome-wide level using the human respiratory pathogen Streptococcus pneumoniae as a model organism. Under an environmental stress, gene fitness changes are determined by transposon insertion sequencing (Tn-Seq) which represents the phenotypic response. Differential expression is profiled by RNA-Seq which represents as the transcriptional response. Much to our surprise, the phenotypic response and transcriptional response are separated on different genes, meaning that differentially expressed genes are poor indicators of genes that contribute to the fitness of the bacterium. By devising and performing topological network analysis, we show that phenotypic and transcriptional responses are coordinated under evolutionary familiar stress, such as nutrient depletion and host infection, in both Gram-positive and -negative pathogens. However, such coordination is lost under the relatively unfamiliar stress of antibiotic treatment. We reasoned that this could mean that a generalizable stress response signature might exist that indicates the level to which a bacterium is adapted to a stress. By extending stress response profiling to 9 antibiotics and 3 nutrient depletion conditions, we found that such a signature indeed exists and can be captured by the level of transcriptomic disruption, defined by us as transcriptomic entropy. Centered on entropy, we constructed predictive models that perform with high accuracy for both survival outcomes and antibiotic sensitivity across 7 species. To further develop these models with the goal to eventually enable predictions on disease progression, we developed a dual RNA-Seq technique that maps out the transcriptomic responses of both S. pneumoniae and its murine host during lung infection. Preliminary data show that a high entropy is observed in the pathogen’s transcriptome during clearance (a failed infection) compared to a successful/severe infection, while the host transcriptome exhibits a pro-inflammatory and active immune response under the severe infection. Lastly, we characterized evolutionary trajectories that lead to long-term survival success of S. pneumoniae, for instance this means that the bacterium successfully adapts to the presence of an antibiotic and becomes resistant or can grow successfully in the absence of a formerly critical nutrient. These trajectories show that adaptive mutations tend to occur in genes closely related to the adapted stress. Additionally, independent of the stress, adaptation triggers rewiring of transcriptional responses resulting in a change in entropy from high to low. Most importantly, we demonstrate that by combining multi-omics profiles with additional genomic data including gene conservation and expression plasticity, and feeding this into machine learning models, that adaptive evolution can become (at least partially) predictable. Additionally, the genetic diversity in bacterial genomes across different strains and species can indeed influence a bacterium’s adaptation trajectory. In conclusion, this thesis presents a substantial collection of multi-omics stress response profiles of S. pneumoniae and other pathogenic bacteria under various environmental and clinically-relevant stresses. By demonstrating the feasibility of predictions on bacterial survival and adaptive outcomes, this thesis paves the way towards future improvements on infectious disease prognostics and forecasting the emergence of antibiotic resistance. / Thesis (PhD) — Boston College, 2020. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Biology.

La tomate (Solanum lycopersicum) [...] présente de nombreux avantages : facilité de culture, temps de génération court, connaissances et ressources importantes, génome séquencé, facilité de transformation... Le développement du fruit est un procédé complexe hautement régulé et divisible en quatre étapes principales : la division cellulaire, l'expansion cellulaire, l’étape appelé « turning » et la maturation. Chaque étape est associée à un phénotype, qui lui-même découle de changements à différents niveaux cellulaires. [...]. Grâce aux récents progrès technologiques et en particulier au développement des «techniques omiques», comme la génomique, la transcriptomique, la protéomique, la métabolomique, les principaux composants cellulaires peuvent désormais être étudiés à haut densité. Dans ce contexte, l'objectif de mon doctorat était d'effectuer une analyse protéomique quantitative du développement du fruit de tomate puis d’intégrer les données «omiques» à la fois par des analyses statistiques et par la modélisation mathématique. Le premier chapitre rapporte les résultats de quantification du protéome de fruit de tomate réalisé en collaboration avec la plateforme PAPPSO (INRA, Gif-sur-Yvette). Des échantillons collectés à neuf stades de développement du fruit de tomate ont été extraits et le protéome quantifié, en absence de marquage, par chromatographie liquide couplée à la spectrométrie de masse (LC-MS/MS). Ensuite, j'ai cherché la méthode la plus adaptée, testant un ensemble de filtres sur les données, pour obtenir une quantification précise des protéines à partir des intensités ioniques (XIC). Au total, j’ai pu obtenir la quantification absolue de 2494 protéines en utilisant une méthode basée sur la modélisation de l'intensité des peptides. [...] Le deuxième chapitre est consacré aux résultats obtenus par analyses combinées d’«omiques» au cours du développement du fruit de tomate. La transcriptomique a été réalisée en collaboration avec Genotoul GeT (Toulouse) et le groupe Usadel (RWTH Aachen University, Allemagne). Grâce à l’ajout d’étalons internes, plus de 20000 transcrits ont été quantifiés de manière absolue à chacune des neuf étapes de développement. Cette quantification a ensuite été validée par comparaison avec des données de concentration de 71 transcrits précédemment obtenues par PCR quantitative. Enfin, nous avons cherché à intégrer les quatre niveaux de données - transcriptome, protéome, métabolome et activome- afin d‘identifier les principales variables associées au développement. Pour ces quatre niveaux, les analyses ont confirmé que l’entrée en maturation s’accompagne de changements majeurs et révélé une grande similarité entre la fin et le début du développement, notamment au niveau du métabolisme énergétique. Le troisième chapitre porte sur les résultats de modélisation de la traduction protéique obtenus grâce à la quantification absolue du transcriptome et du protéome. Afin d’expliquer la corrélation décroissante observée au cours du développement entre les concentrations en protéines et celles des transcrits correspondants, nous avons résolu un modèle mathématique de la traduction protéique basé sur une équation différentielle ordinaire et impliquant deux constantes de vitesse: pour la synthèse et la dégradation de la protéine. La résolution de cette équation, validée par un critère de qualité basé un intervalle de confiance fermé, a conduit à l'estimation de ces constantes pour plus de 1000 protéines. [...] Enfin le dernier chapitre décrit l’ensemble du matériel et des méthodes utilisées pour obtenir les différents résultats présentés dans le manuscrit. Dans le domaine de la biologie des systèmes, ce travail illustre comment l'intégration de multiples données «omiques» et la modélisation mécanistique basée sur la quantification absolue des «omiques» peut révéler de nouvelles propriétés des composants cellulaires. / The interest of the tomato (Solanum lycopersicum) fruit has spread in plant science where it is used as the model for fleshy fruit. The valuable advantages of the tomato fruit are numerous: an ease of culture, a short generation time, a high knowledge with important resources, a sequenced genome, an ease for transforming…. The development of tomato fruit is a complex regulated process, divided in four main steps: cell division, cell expansion, turning and ripening. Each step is characterized by a phenotype resulting from changes at different cellular levels. Thus, gene expression, protein abundance, enzyme activities, metabolic fluxes and metabolite concentrations show significant changes during these steps. Thanks to recent technologies advances and in particular the development of ‘omics techniques’, such as genomic, transcriptomic, proteomic, metabolomic, the main cell components can now be analysed by high-throughput. In this context, the objective of my PhD was to perform a quantitative proteomic analysis of the tomato fruit development and then integrate omics data both by statistical analyses and by mathematical modelling. The first chapter focused on results obtained for the quantitative proteomic developed in collaboration with the PAPPSO platform (INRA, Gif-sur-Yvette). Samples were harvested at nine stages of tomato fruit development, total proteome was extracted and quantified by label-free LC-MS/MS. Then I searched for the most appropriate method, testing a set of filters on the data, to obtain an absolute label-free protein quantification from ion intensities (XIC). Finally, I obtained the absolute quantification of 2494 proteins using a method based on peptides intensity modelling. The quantification of proteins by LC-MS/MS was then validated by comparison with 32 enzymatic capacities used as proxy for protein abundance. The second chapter was dedicated to the results of integrative omics analyses throughout tomato fruit development. First, transcriptomic has been performed in collaboration with Genotoul GeT (Toulouse) and Usadel‘lab (RWTH Aachen University, Germany). Using spikes in the experimental design, more than 20000 transcripts have been quantitatively determined at the nine stages of development. Then, this absolute quantification of the tomato transcriptome has been cross-validated with 71 transcripts previously measured by qRT-PCR. Finally, we integrated the four omics datasets- transcriptome, proteome, metabolome and activome – in order to identify key variables of the tomato fruit development. For the four levels, analyses confirmed that the entrance in maturation phase was accompanied by major changes, and revealed a great similarity between the end and the beginning of development, especially in the energy metabolism. The third chapter focuses on modelling results of the protein translation based on the absolute quantification of transcriptomic and proteomic. To explain the decreasing correlation observed between proteins and transcripts concentration throughout development, we proposed a mathematical model of protein translation based on an ordinary differential equation and involving two rate constants (for synthesis and degradation of the protein). The resolution of this equation, validated by a quality criteria based on a closed confidence interval, led to the estimation of the rate constants for more than 1000 proteins. These results were then compared with previous published data reported for plants and more widely in eukaryotic cells. Finally, the last chapter describes all the materials and methods used to obtain the results presented in the manuscript.In the systems biology context, this work illustrates how integration of multiple omics datasets and mechanistic modelling based on absolute omics quantification can reveal new properties of cellular component.

Le carcinome hépatocellulaire (CHC) est la forme la plus fréquente de cancer du foie et l’une des principales causes de mortalité par cancer dans le monde. Il s’agit d’une maladie de mauvais pronostic, aux ressources thérapeutiques limitées, hétérogène sur le plan immunophénotypique et génomique, qui se développe très souvent sur un foie remanié cirrhotique. Les études moléculaires ont révélé plusieurs sous-classes de CHC caractérisés par des signatures génomiques et protéomiques distinctes. Au cours de mon travail de thèse, nous avons contribué à améliorer notre compréhension de la biologie des CHC et des classifications moléculaires en cartographiant le génome non-codant de tumeurs de CHC induites par des virus hépatotropes (VHB, VHC) et en étudiant la sous-classe moléculaire de CHC la plus agressive KRT19-positif. Nous avons établi la première carte de transcriptome à ARN non codants du CHC et révélé une forte activation intra-tumorale des rétrotransposons à LTR, qui sont principalement inhibés dans les cellules hépatiques normales, dans des CHC induits par les VHB et VHC. Certains des transcrits dérivés de LTR se sont révélés être des régulateurs clés de l’expression génique et donc activer la croissance cellulaire. Dans la deuxième étude, nous identifions une nouvelle voie de régulation des CHC KRT19-positif affectant le métabolisme énergétique de ces tumeurs. Les CHC KRT19-positif sont des tumeurs fortement glycolytiques liée à une activation de la réponse à l’hypoxie. L’excès de production par les CHC KRT19-positif de l’oncométabolite 2-hydroxyglutarate en absence de mutation des gènes IDH1 et IDH2 était associé à un profil aberrant hyperméthylé sur la lysine 9 de l’histone H3 (H3K9me3) suggérant une répression de la transcription notamment des gènes impliqués dans la différenciation cellulaire. / Hepatocellular carcinoma (HCC) is the main primary liver cancer and one of the most leading cause of cancer-related death worldwide. This heterogeneous disease with a worse prognosis has been subjected of numerous studies aimed to establish global phenotypic profiles. During my thesis, I dedicated my work to improve these classifications by identifying signatures on the non-coding genome and working on a very aggressive form of HCC expressing progenitors markers. With help of a Japanese team, we demonstrated that LTR-derived ncRNAs were active in HCC and that correlation correlates with expression of common cancer markers (GPC3) ans TP53 mutations. This signature can also be used to discriminate HCCs at high risk of recurrence. Finally, we have showed that these LTRs are detectable on prenoplastic stages in the Mdr2 KO mouse model. In parallel, I worked on HCC that expresses progenitor markers such as cytokeratin 19. Using proteomic and transcriptionnal approaches and in silico analyses, we propose that the occurrence of this type of cancer id due to an hypoxic event likely related to trans-arterial chemoembolization. These tumors have a highly glycolytic phenotype with production of an oncometabolite (2-hydroxyglutarate) that has been generally foubd in IDH1/2 mutated cholangiocarcinomas. Finally we suggest the use of metformin, type 2 diabetes drug, to reverse metabolic reprogramming and restore sensitivity to chemotherapy

La qualité germinative (vigueur) des semences est un caractère agronomique majeur. Elle correspond à la capacité d'un lot de semences à germer de façon rapide et homogène dans une large gamme de conditions environnementales. Cette qualité germinative est notamment contrôlée par une interaction antagoniste entre deux phytohormones, l'acide abscissique (ABA) qui induit et maintient la dormance et les gibbérellines (GAs) qui stimulent la germination et la croissance de la plantule. La dormance, qui correspond à un blocage physiologique de la germination, est un paramètre non souhaitable d'un point de vue agronomique. Par conséquent la compréhension de la régulation hormonale sur la qualité des semences représente un intérêt fort pour la communauté scientifique mais aussi pour les acteurs de la filière "semences". De nombreuses études ont démontré l'existence d’une importante régulation de la synthèse des protéines au cours de l’imbibition des graines. Cette régulation traductionnelle contribuerait à la mise en place des programmes métaboliques différents en fonction de l’état physiologique des semences pour maintenir un état de dormance ou initier le processus de germination.Le travail réalisé dans le cadre de cette thèse s’est concentré à apporter des éléments nouveaux sur le rôle de l’ABA dans la détermination de la qualité physiologique des semences au cours du développement de la graine et au cours de la germination. L’impact de l’ABA a été particulièrement décortiqué à l’aide de mutant d’Arabidopsis thaliana présentant des teneurs en ABA endogènes contrastées. Par une approche multi-omique combinant des analyses transcriptomiques, protéomiques et métabolomiques, nous avons étudié les mécanismes moléculaires et biochimiques associées avec la mise en place de la qualité physiologique des semences en relation avec l’ABA. Nos résultats ont montré qu’au-delà du contenu en ABA, l’origine tissulaire de cette hormone dans les graines gouverne de nombreux réarrangements métaboliques qui participent au déterminisme de la profondeur de dormance et de la vigueur germinative. Il apparaît un lien entre l’ABA et l’activité traductionnelle, étroitement associé au métabolisme énergétique et à l’homéostasie RedOx.L’effet de l’ABA sur l’activité traductionnelle a été suivi par une adaptation des méthodes SILAC (stable-isotope labelled amino acids in cell culture) aux grains d’Arabidopsis. Cette technique a été utilisée pour décrire la dynamique du protéome dépendante du contenu en ABA des graines au cours de leur imbibition. Nos résultats montrent que cette approche originale permet d’enrichir les connaissances sur la biologie fondamentale des semences. En effet, nous avons montré que l’ABA est un régulateur clé de la synthèse protéique dans les graines et est un contributeur majeur dans la mise en place des différents programmes traductionnels. Cette approche a montré que l’ABA exerce un contrôle sur la traduction de plus de 400 ARNm au cours de l’imbibition des graines et ouvre de nouvelles pistes pour la compréhension de la régulation de la synthèse protéique chez les semences et chez les plantes. Ces données générées offrent un nouveau regard sur le processus germinatif et de sa régulation par l’ABA.Sur la base des données existantes au laboratoire et celles générées au cours de cette thèse, nous avons également développé une utilisation de bio-marqueurs pour l’évaluation de la qualité des semences et nous avons mis au point des traitements de semences innovants. Ces technologies ont été développé en accord avec les attentes des industriels de la filière « semences ». La récente obtention d’un financement pour ce projet de recherche appliquée démontre la complémentarité des recherches effectuées au sein du laboratoire avec les besoin des industriels de la filière « semences ». / Germination vigor is a main concern in agriculture. High seed vigor is defined as the capacity of a seed lot to germinate rapidly, uniformly and in a wide range of environmental conditions. Seed quality is controlled by a dynamic balance between two antagonistic hormones, abscisic acid (ABA), which induces and maintains dormancy and gibberellins (GAs), which stimulate seed germination and seedling establishment. Seed dormancy corresponds to a block to the completion of germination and is an undesirable characteristic from an agronomic point of view. Thus, investigation of seed quality toward a better understanding of hormonal regulation is of fundamental concern for scientific community and seed industry.Recent studies have highlighted the intensive regulation of protein synthesis during seed germination. Translational regulation would govern the implementation of different metabolic programs during seed imbibition in order to maintain seed dormancy or to initiate the germination process. In this thesis, we explore the role of ABA in the control of germination quality during seed development and seed germination, using Arabidopsis thaliana mutant displaying contrasted ABA content.By combined “omic” approaches, we have highlighted the impact of ABA level on metabolic rearrangements during seed maturation. Our results showed that ABA origin in the seeds governs many metabolic rearrangements controlling dormancy depth and germination vigor. In addition, the present work suggests an intimate linkage between translational activity and ABA content, in association with energetic pathways and redox homeostasis.The impact of ABA on proteome turnover during seed germination was studied by adapting a metabolic labeling of neosynthesized proteins based on SILAC methods (stable isotope labelled amino acids in cell culture) to Arabidopsis seeds. Our results suggest that ABA is a key regulator of protein synthesis and modulates metabolic changes during seed imbibition. Indeed, this novel approach has highlighted that ABA controls the translation of more than 400 mRNAs during seed imbibition. This work provides an original perspective on the contribution of ABA and mRNA translation in seed germination and provides a valuable basis for further investigation of translational regulation in seeds and in plants.Based on existing data and those generated during this thesis, we also developed innovative seed treatments and new biomarkers for seed quality assessment. Recent funding for a maturation program dedicated to improve these biotechnologies demonstrates that our research meets the needs of seed industry.

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Advanced molecular profiling technologies, utilizing the entire human genome, have opened new avenues to study biological systems. In recent decades, the generation of vast volumes of multi-omics data, spanning a broad range of phenotypes. Development of advanced bioinformatics tools to identify informative biomarkers from these data becomes increasingly important. These tools are crucial to extract meaningful biomarkers from this data, especially for understanding the biological pathways responsible for disease development. The identification of signature genes and the analysis of differentially networked genes are two fundamental and critically important tasks. However, many current methodologies employ test statistics that don't align perfectly with the signature definition, potentially leading to the identification of imprecise signatures. It may be challenging because the test statistics employed by many prevailing methods fall short of fulfilling the exact definition of a marker genes, inherently leaving them susceptible to deriving inaccurate features. The problem is further compounded when attempting to identify marker genes across biologically diverse samples, especially when comparing more than two biological conditions. Additionally, traditional differential group analysis or co-expression analysis under singular conditions often falls short in certain scenarios. For instance, the subtle expression levels of transcription factors (TFs) make their detection daunting, despite their pivotal role in guiding gene expression. Pinpointing the intricate network landscape of complex ailments and isolating core genes for subsequent analysis are challenging tasks. Yet, these marker genes are instrumental in identifing potential pivotal pathways. Multi-omics data, with its inherent complexity and diversity, presents unique challenges that traditional methods might struggle to address effectively. Recognizing this, our team sought to introduce new and innovative techniques specifically designed to handle this intricate dataset. To overcome these challenges, it is vital to develop and adopt innovative methods tailored to handle the complexity and diversity inherent in multi-omics data. In response to these challenges, we have pioneered the Cosine-based One-sample Test (COT), a method meticulously crafted for the analysis of biologically diverse samples. Tailored to discern marker genes across a spectrum of subtypes using their expression profiles, COT employs a one-sample test framework. The test statistic within COT utilizes cosine similarity, comparing a molecule's expression profile across various subtypes with the precise mathematical representation of ideal marker genes. To ensure ease of application and accessibility, we've encapsulated the COT workflow within a Python package. To assess its effectiveness, we undertook an exhaustive evaluation, juxtaposing the marker genes detection capabilities of COT against its contemporaries. This evaluation employed realistic simulation data. Our findings indicated that COT was not only adept at handling gene expression data but was also proficient with proteomics data. This data, sourced from enriched tissue or cell subtype samples, further accentuated COT's superior performance. We demonstrated the heightened effectiveness of COT when applied to gene expression and proteomics data originating from distinct tissue or cell subtypes. This led to innovative findings and hypotheses in several biomedical case studies. Additionally, we have enhanced the Differential Dependency Network (DDN) framework to detect network rewiring between different conditions where significantly rewired network modes serve as informative biomarkers. Using cross-condition data and a block-wise Lasso network model, DDN detects significant network rewiring together with a subnetwork of hub molecular entities. In DDN 3.0, we took the imbalanced sample size into the consideration, integrated several acceleration strategies to enable it to handle large datasets, and enhanced the network presentation for more informative network displays including color-coded differential dependency network and gradient heatmap. We applied it to the simulated data and real data to detect critical changes in molecular network topology. The current tool stands as a valuable blueprint for the development and validation of mechanistic disease models. This foundation aids in offering a coherent interpretation of data, deepening our understanding of disease biology, and sparking new hypotheses ripe for subsequent validation and exploration. As we chart our future course, our vision is to expand the scope of tools like COT and DDN 3.0, explore the vast realm of multi-omics data, including those from longitudinal studies or clinical trials. We're looking at incorporating datasets from longitudinal studies and clinical trials – domains where data complexity scales to new heights. We believe that these tools can facilitate more nuanced and comprehensive understanding of disease development and progression. Furthermore, by integrating these methods with other advanced bioinformatics and machine learning tools, we aim to create a holistic pipeline that will allow for seamless extraction of significant biomarkers and actionable insights from multi-omics data. This is a promising step towards precision medicine, where individual genomic information can guide personalized treatment strategies. / Doctor of Philosophy / Recent advances in technology have allowed us to study human biology on a much larger scale than ever before. These technologies have produced a lot of data on many different types of traits. As a result, it's becoming increasingly important to develop tools that can sift through this data and find meaningful biomarkers – essentially, indicators that can help us understand what causes diseases. Two key parts of this process are identifying 'signature genes' and analyzing groups of genes that work together differently depending on the circumstances. But, current methods have their drawbacks – they don't always pick out the right genes and can struggle when comparing more than two groups at once. There are also other challenges when it comes to identifying groups of genes that express differently or work together under one set of conditions. For instance, some important genes – known as transcription factors (TFs) – control the activity of other genes. But because TFs are often expressed at low levels, they're hard to detect, even though they play a key role in controlling gene activity. And, it can be tough to identify 'hub' genes, which are central to gene networks and can help us understand the potential key pathways in diseases. To address these challenges, we introduced the Cosine based One-sample Test (COT), a novel approach to identify pivotal genes across diverse samples. COT gauges the alignment of a gene's expression profile with the quintessential marker genes' definition. Our evaluations underscore COT's robust performance, paving the way for deeper disease understanding. Further enhancing our toolkit, we've refined the Differential Dependency Network (DDN), a method to unravel the dynamic interplay of genes under diverse conditions. DDN 3.0 is a more robust iteration, adept at accommodating varied sample sizes, efficiently processing vast datasets, and offering richer visualizations of gene networks. Its prowess in pinpointing crucial alterations in gene networks is noteworthy. The Cosine based One-sample Test (COT) and the Differential Dependency Network (DDN) are revolutionary tools, poised to significantly elevate genomics research. COT, with its precision in gauging the alignment of a gene's expression pattern with predefined ideal gene markers, emerges as an invaluable asset in the hunt for marker genes. It acts as a fine-tuned sieve, meticulously screening vast datasets to unveil these crucial genetic signposts. On the other hand, DDN offers a comprehensive framework to decipher the intricate web of gene interactions under diverse conditions. It meticulously analyzes the interplay between genes, spotlighting potential 'hub' genes and highlighting shifts in their dynamic relationships. Together, COT and DDN not only pave the way for the identification of pivotal marker genes but also furnish a richer, more nuanced understanding of the genomic landscape. By leveraging these tools, researchers are empowered to unravel the intricate tapestry of genes, laying the foundation for groundbreaking discoveries in genomics. Looking to the future, we plan to apply COT and DDN 3.0 to more complex datasets. We believe these tools will give us a better understanding of how diseases develop and progress. By integrating these methods with other advanced tools, we're aiming to create a complete system for extracting important biomarkers and insights from this complex data. This is a big step towards precision medicine, where a person's unique genetic information could guide their treatment strategy.

Insights into the modes of action (MoAs) of xenobiotics are of utmost importance for the definition of adverse outcome pathways (AOPs), which are essential for a mechanism-based risk assessment. A well-established strategy to reveal MoAs of xenobiotics is the use of omics. However, often an even more comprehensive approach is needed, which can be achieved using multi-omics. Since the immune system plays a central role in the defense against foreign substances and pathogens, with the innate immune system building a first barrier, we systematically reviewed multi-omics studies investigating the effects of xenobiotics on macrophages. Surprisingly, only nine publications were identified, combining proteomics with transcriptomics or metabolomics. We summarized pathways and single proteins, transcripts, or metabolites, which were described to be affected upon treatment with xenobiotics in the reviewed studies, thus revealing a broad range of effects. In summary, we show that macrophages are a relevant model system to investigate the toxicological effects induced by xenobiotics. Furthermore, the multi-omics approaches led to a more comprehensive overview compared to only one omics layer with slight advantages for combinations that complement each other directly, e.g., proteome and metabolome.

<p dir="ltr">The vaginal microbiome and female sex hormones are both involved in the development and progression of gynecological pathologies. The individual mechanisms by which the vaginal microbiome leads to disease progression and how female sex hormones are known. However, the mechanisms by which the vaginal microbiome regulates female sex hormones, such as progesterone, are not well understood. This study seeks to understand how the vaginal microbiome regulates progesterone receptor (PGR) expression via secondary analysis of host and vaginal microbiome multi-omics data from the Partners PrEP cohort. This dataset consists of cervicovaginal samples of women enrolled in the Partners PrEP study. Partial Least Squares Regression (PLSR) models were created for each biological data type (microbial composition, metabolomics, metaproteomics) to assess how these factors regulate PGR expression. Significant factors were identified through variable importance of projection (VIP) and correlation analysis. Partial correlation analysis and follow-up PLSR models incorporating clinical and demographic variables were performed to assess the robustness of the vaginal microbiome-PGR associations. The PLSR models indicated lower PGR expression was associated with <i>G. vaginalis,</i> and higher PGR expression was associated with <i>Lactobacillus </i>species. Cytosine, guanine, and tyrosine were among metabolites significantly associated with higher PGR expression and experimentally determined to be produced by <i>Lactobacillus</i> species. Conversely, citrulline and succinate were associated with lower PGR expression and experimentally determined to be produced by <i>G. vaginalis</i>. The models indicated that bacterial metabolic pathways involved in glucose metabolism, such as glucagon signaling and starch and sugar metabolism, may regulate PGR expression. Demographic phenotypes were also considered from the dataset and did not significantly alter the association between the biological explanatory variables and PGR expression. The results indicate that guanine, cytosine, succinate, starch and sucrose metabolism, and glycolysis gluconeogenesis may be regulators of PGR abundance and function. The models suggest vaginal microbiome factors could play a role in gynecological conditions where progesterone signaling is suppressed. Future experimental work is needed to validate the results of these models and support their use as predictive tools to understand the role of the vaginal microbiome.</p>

Mining interspecies interactions remain a challenge due to the complex nature of microbial communities and the need for computational power to handle big data. Our meta-analysis indicates that genetic potential alone does not resolve all issues involving mining of microbial interactions. Nevertheless, it can be used as the starting point to infer synergistic interspecies interactions and to limit the search space (i.e., number of species and metabolic reactions) to a manageable size. A reduced search space decreases the number of additional experiments necessary to validate the inferred putative interactions. As validation experiments, we examine how multi-omics and state of the art imaging techniques may further improve our understanding of species interactions’ role in ecosystem processes. Finally, we analyze pros and cons from the current methods to infer microbial interactions from genetic potential and propose a new theoretical framework based on: (i) genomic information of key members of a community; (ii) information of ecosystem processes involved with a specific hypothesis or research question; (iii) the ability to identify putative species’ contributions to ecosystem processes of interest; and, (iv) validation of putative microbial interactions through integration of other data sources.