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The inclusion of exotic germplasm serves as a crucial means to enhance allelic and
consequently phenotypic diversity in inbred crop species. Such species have experienced a reduction in diversity due to artificial selection focused on a limited set of traits. The natural biodiversity within ecosystems presents an opportunity to explore various traits influencing plant survival, reproductive fitness and yield potential. In agricultural research, the study of wild species closely related to cultivated plants serves as a means to comprehend the genetic foundations of past domestication events and the polymorphisms essential for future breeding efforts to develop superior varieties. In order to examine the metabolic composition, pinpoint quantitative trait loci (QTL) and facilitate their resolution an extensive large-scale analysis of metabolic QTL (mQTL) was conducted on tomato backcross inbred lines (BILs) derived from a cross between the wild species S. pennellii (5240) incorporated into the background of S. lycopersicum cv. LEA determinate inbred which can be grown in open fields and cv. TOP indeterminate which can be grown in greenhouse conditions. A large number of mQTL associated with primary secondary and lipid metabolism in fruit were identified across the two BIL populations. Epistasis, the interactions between genes at different loci, has been an interest in molecular and quantitative genetics for many decades. The study of epistasis requires the analysis of very large populations with multiple independent genotypes that carry specific genomic regions. In order to understand the genetic basis of tomato fruit metabolism, I extended the work to investigate epistatic interactions of the genomic regions. In addition, two candidate genes were identified through quantitative trait loci underlying fruit-specific sucrose and jasmonic acid derivatives. Finally, in this study, I assessed the genetic framework of fruit metabolic traits with a high level of detail, utilizing the newly created Solanum pennellii (5240) backcrossed introgression lines (n=3000). This investigation resulted in the discovery of promising candidate loci associated with significant fruit quality traits, including those to the abundance of glutamic acid and aspartic acid crucial elements contributing to the development of acidity and flavors.
Omics and male infertility
(2022)
Male infertility is a multifaceted disorder affecting approximately 50% of male partners in infertile couples.
Over the years, male infertility has been diagnosed mainly through semen analysis, hormone evaluations, medical records and physical examinations, which of course are fundamental, but yet inefficient, because 30% of male infertility cases remain idiopathic. This dilemmatic status of the unknown needs to be addressed with more sophisticated and result-driven technologies and/or techniques.
Genetic alterations have been linked with male infertility, thereby unveiling the practicality of investigating this disorder from the "omics" perspective.
Omics aims at analyzing the structure and functions of a whole constituent of a given biological function at different levels, including the molecular gene level (genomics), transcript level (transcriptomics), protein level (proteomics) and metabolites level (metabolomics). In the current study, an overview of the four branches of omics and their roles in male infertility are briefly discussed; the potential usefulness of assessing transcriptomic data to understand this pathology is also elucidated.
After assessing the publicly obtainable transcriptomic data for datasets on male infertility, a total of 1385 datasets were retrieved, of which 10 datasets met the inclusion criteria and were used for further analysis.
These datasets were classified into groups according to the disease or cause of male infertility.
The groups include non-obstructive azoospermia (NOA), obstructive azoospermia (OA), non-obstructive and obstructive azoospermia (NOA and OA), spermatogenic dysfunction, sperm dysfunction, and Y chromosome microdeletion.
Findings revealed that 8 genes (LDHC, PDHA2, TNP1, TNP2, ODF1, ODF2, SPINK2, PCDHB3) were commonly differentially expressed between all disease groups.
Likewise, 56 genes were common between NOA versus NOA and OA (ADAD1, BANF2, BCL2L14, C12orf50, C20orf173, C22orf23, C6orf99, C9orf131, C9orf24, CABS1, CAPZA3, CCDC187, CCDC54, CDKN3, CEP170, CFAP206, CRISP2, CT83, CXorf65, FAM209A, FAM71F1, FAM81B, GALNTL5, GTSF1, H1FNT, HEMGN, HMGB4, KIF2B, LDHC, LOC441601, LYZL2, ODF1, ODF2, PCDHB3, PDHA2, PGK2, PIH1D2, PLCZ1, PROCA1, RIMBP3, ROPN1L, SHCBP1L, SMCP, SPATA16, SPATA19, SPINK2, TEX33, TKTL2, TMCO2, TMCO5A, TNP1, TNP2, TSPAN16, TSSK1B, TTLL2, UBQLN3).
These genes, particularly the above-mentioned 8 genes, are involved in diverse biological processes such as germ cell development, spermatid development, spermatid differentiation, regulation of proteolysis, spermatogenesis and metabolic processes.
Owing to the stage-specific expression of these genes, any mal-expression can ultimately lead to male infertility.
Therefore, currently available data on all branches of omics relating to male fertility can be used to identify biomarkers for diagnosing male infertility, which can potentially help in unravelling some idiopathic cases.
We recently demonstrated that the sympathetic nervous system can be voluntarily activated following a training program consisting of cold exposure, breathing exercises, and meditation. This resulted in profound attenuation of the systemic inflammatory response elicited by lipopolysaccharide (LPS) administration. Herein, we assessed whether this training program affects the plasma metabolome and if these changes are linked to the immunomodulatory effects observed. A total of 224 metabolites were identified in plasma obtained from 24 healthy male volunteers at six timepoints, of which 98 were significantly altered following LPS administration. Effects of the training program were most prominent shortly after initiation of the acquired breathing exercises but prior to LPS administration, and point towards increased activation of the Cori cycle. Elevated concentrations of lactate and pyruvate in trained individuals correlated with enhanced levels of anti-inflammatory interleukin (IL)-10. In vitro validation experiments revealed that co-incubation with lactate and pyruvate enhances IL-10 production and attenuates the release of pro-inflammatory IL-1 beta and IL-6 by LPS-stimulated leukocytes. Our results demonstrate that practicing the breathing exercises acquired during the training program results in increased activity of the Cori cycle. Furthermore, this work uncovers an important role of lactate and pyruvate in the anti-inflammatory phenotype observed in trained subjects.
We recently demonstrated that the sympathetic nervous system can be voluntarily activated following a training program consisting of cold exposure, breathing exercises, and meditation. This resulted in profound attenuation of the systemic inflammatory response elicited by lipopolysaccharide (LPS) administration. Herein, we assessed whether this training program affects the plasma metabolome and if these changes are linked to the immunomodulatory effects observed. A total of 224 metabolites were identified in plasma obtained from 24 healthy male volunteers at six timepoints, of which 98 were significantly altered following LPS administration. Effects of the training program were most prominent shortly after initiation of the acquired breathing exercises but prior to LPS administration, and point towards increased activation of the Cori cycle. Elevated concentrations of lactate and pyruvate in trained individuals correlated with enhanced levels of anti-inflammatory interleukin (IL)-10. In vitro validation experiments revealed that co-incubation with lactate and pyruvate enhances IL-10 production and attenuates the release of pro-inflammatory IL-1 beta and IL-6 by LPS-stimulated leukocytes. Our results demonstrate that practicing the breathing exercises acquired during the training program results in increased activity of the Cori cycle. Furthermore, this work uncovers an important role of lactate and pyruvate in the anti-inflammatory phenotype observed in trained subjects.
Plant metabolism serves as the primary mechanism for converting assimilated carbon into essential compounds crucial for plant growth and ultimately, crop yield. This renders it a focal point of research with significant implications. Despite notable strides in comprehending the genetic principles underpinning metabolism and yield, there remains a dearth of knowledge regarding the genetic factors responsible for trait variation under varying environmental conditions. Given the burgeoning global population and the advancing challenges posed by climate change, unraveling the intricacies of metabolic and yield responses to water scarcity became increasingly important in safeguarding food security.
Our research group has recently started to work on the genetic resources of legume species. To this end, the study presented here investigates the metabolic diversity across five different legume species at a tissue level, identifying species-specific biosynthesis of alkaloids as well as iso-/flavonoids with diverse functional groups, namely prenylation, phenylacylation as well as methoxylation, to create a resource for follow up studies investigation the metabolic diversity in natural diverse populations of legume species.
Following this, the second study investigates the genetic architecture of drought-induced changes in a global common bean population. Here, a plethora of quantitative trait loci (QTL) associated with various traits are identified by performing genome-wide association studies (GWAS), including for lipid signaling. On this site, overexpression of candidates highlighted the induction of several oxylipins reported to be pivotal in coping with harsh environmental conditions such as water scarcity.
Diverging from the common bean and GWAS, the following study focuses on identifying drought-related QTL in tomato using a bi-parental breeding population. This descriptive study highlights novel multi-omic QTL, including metabolism, photosynthesis as well as fruit setting, some of which are uniquely assigned under drought. Compared to conventional approaches using the bi-parental IL population, the study presented improves the resolution by assessing further backcrossed ILs, named sub-ILs.
In the final study, a photosynthetic gene, namely a PetM subunit of the cytochrome b6f complex encoding gene, involved in electron flow is characterized in an horticultural important crop. While several advances have been made in model organisms, this study highlights the transition of this fundamental knowledge to horticultural important crops, such as tomato, and investigates its function under differing light conditions. Overall, the presented thesis combines different strategies in unveiling the genetic components in multi-omic traits under drought using conventional breeding populations as well as a diverse global population. To this end, it allows a comparison of either approach and highlights their strengths and weaknesses.
Aging is a complex process characterized by several factors, including loss of genetic and epigenetic information, accumulation of chronic oxidative stress, protein damage and aggregates and it is becoming an emergent drug target. Therefore, it is the utmost importance to study aging and agerelated diseases, to provide treatments to develop a healthy aging process. Skeletal muscle is one of the earliest tissues affected by age-related changes with progressive loss of muscle mass and function from 30 years old, effect known as sarcopenia. Several studies have shown the accumulation of protein aggregates in different animal models, as well as in humans, suggesting impaired proteostasis, a hallmark of aging, especially regarding degradation systems. Thus, different publications have explored the role of the main proteolytic systems in skeletal muscle from rodents and humans, like ubiquitin proteasomal system (UPS) and autophagy lysosomal system (ALS), however with contradictory results. Yet, most of the published studies are performed in muscles that comprise more than one fiber type, that means, muscles composed by slow and fast fibers. These fiber types, exhibit different metabolism and contraction speed; the slow fibers or type I display an oxidative metabolism, while fast fibers function towards a glycolytic metabolism ranging from fast oxidative to fast glycolytic fibers. To this extent, the aim of this thesis sought to understand on how aging impacts both fiber types not only regarding proteostasis but also at a metabolome and transcriptome network levels. Therefore, the first part of this thesis, presents the differences between slow oxidative (from Soleus muscle) and fast glycolytic fibers (Extensor digitorum longus, EDL) in terms of degradation systems and how they cope with oxidative stress during aging, while the second part explores the differences between young and old EDL muscle transcriptome and metabolome, unraveling molecular features. More specifically, the results from the present work show that slow oxidative muscle performs better at maintaining the function of UPS and ALS during aging than EDL muscle, which is clearly affected, accounting for the decline in the catalytic activity rates and accumulation of autophagy-related proteins. Strinkingly, transcriptome and metabolome analyses reveal that fast glycolytic muscle evidences significant downregulation of mitochondrial related processes and damaged mitochondria morphology during aging, despite of having a lower oxidative metabolism compared to oxidative fibers. Moreover, predictive analyses reveal a negative association between aged EDL gene signature and lifespan extending interventions such as caloric restriction (CR). Although, CR intervention does not alter the levels of mitochondrial markers in aged EDL muscle, it can reverse the higher mRNA levels of muscle damage markers. Together, the results from this thesis give new insights about how different metabolic muscle fibers cope with age-related changes and why fast glycolytic fibers are more susceptible to aging than slow oxidative fibers.
Cells are built from a variety of macromolecules and metabolites. Both, the proteome and the metabolome are highly dynamic and responsive to environmental cues and developmental processes. But it is not their bare numbers, but their interactions that enable life. The protein-protein (PPI) and protein-metabolite interactions (PMI) facilitate and regulate all aspects of cell biology, from metabolism to mitosis. Therefore, the study of PPIs and PMIs and their dynamics in a cell-wide context is of great scientific interest. In this dissertation, I aim to chart a map of the dynamic PPIs and PMIs across metabolic and cellular transitions. As a model system, I study the shift from the fermentative to the respiratory growth, known as the diauxic shift, in the budding yeast Saccharomyces cerevisiae. To do so, I am applying a co-fractionation mass spectrometry (CF-MS) based method, dubbed protein metabolite interactions using size separation (PROMIS). PROMIS, as well as comparable methods, will be discussed in detail in chapter 1.
Since PROMIS was developed originally for Arabidopsis thaliana, in chapter 2, I will describe the adaptation of PROMIS to S. cerevisiae. Here, the obtained results demonstrated a wealth of protein-metabolite interactions, and experimentally validated 225 previously predicted PMIs. Applying orthogonal, targeted approaches to validate the interactions of a proteogenic dipeptide, Ser-Leu, five novel protein-interactors were found. One of those proteins, phosphoglycerate kinase, is inhibited by Ser-Leu, placing the dipeptide at the regulation of glycolysis.
In chapter 3, I am presenting PROMISed, a novel web-tool designed for the analysis of PROMIS- and other CF-MS-datasets. Starting with raw fractionation profiles, PROMISed enables data pre-processing, profile deconvolution, scores differences in fractionation profiles between experimental conditions, and ultimately charts interaction networks. PROMISed comes with a user-friendly graphic interface, and thus enables the routine analysis of CF-MS data by non-computational biologists.
Finally, in chapter 4, I applied PROMIS in combination with the isothermal shift assay to the diauxic shift in S. cerevisiae to study changes in the PPI and PMI landscape across this metabolic transition. I found a major rewiring of protein-protein-metabolite complexes, exemplified by the disassembly of the proteasome in the respiratory phase, the loss of interaction of an enzyme involved in amino acid biosynthesis and its cofactor, as well as phase and structure specific interactions between dipeptides and enzymes of central carbon metabolism.
In chapter 5, I am summarizing the presented results, and discuss a strategy to unravel the potential patterns of dipeptide accumulation and binding specificities. Lastly, I recapitulate recently postulated guidelines for CF-MS experiments, and give an outlook of protein interaction studies in the near future.
The availability of high-throughput data from transcriptomics and metabolomics technologies provides the opportunity to characterize the transcriptional effects on metabolism. Here we propose and evaluate two computational approaches rooted in data reduction techniques to identify and categorize transcriptional effects on metabolism by combining data on gene expression and metabolite levels. The approaches determine the partial correlation between two metabolite data profiles upon control of given principal components extracted from transcriptomics data profiles. Therefore, they allow us to investigate both data types with all features simultaneously without doing preselection of genes. The proposed approaches allow us to categorize the relation between pairs of metabolites as being under transcriptional or post-transcriptional regulation. The resulting classification is compared to existing literature and accumulated evidence about regulatory mechanism of reactions and pathways in the cases of Escherichia coil, Saccharomycies cerevisiae, and Arabidopsis thaliana.
Due to global climate change providing food security for an increasing world population is a big challenge. Especially abiotic stressors have a strong negative effect on crop yield. To develop climate-adapted crops a comprehensive understanding of molecular alterations in the response of varying levels of environmental stresses is required. High throughput or ‘omics’ technologies can help to identify key-regulators and pathways of abiotic stress responses. In addition to obtain omics data also tools and statistical analyses need to be designed and evaluated to get reliable biological results.
To address these issues, I have conducted three different studies covering two omics technologies. In the first study, I used transcriptomic data from the two polymorphic Arabidopsis thaliana accessions, namely Col-0 and N14, to evaluate seven computational tools for their ability to map and quantify Illumina single-end reads. Between 92% and 99% of the reads were mapped against the reference sequence. The raw count distributions obtained from the different tools were highly correlated. Performing a differential gene expression analysis between plants exposed to 20 °C or 4°C (cold acclimation), a large pairwise overlap between the mappers was obtained. In the second study, I obtained transcript data from ten different Oryza sativa (rice) cultivars by PacBio Isoform sequencing that can capture full-length transcripts. De novo reference transcriptomes were reconstructed resulting in 38,900 to 54,500 high-quality isoforms per cultivar. Isoforms were collapsed to reduce sequence redundancy and evaluated, e.g. for protein completeness level (BUSCO), transcript length, and number of unique transcripts per gene loci. For the heat and drought tolerant aus cultivar N22, I identified around 650 unique and novel transcripts of which 56 were significantly differentially expressed in developing seeds during combined drought and heat stress. In the last study, I measured and analyzed the changes in metabolite profiles of eight rice cultivars exposed to high night temperature (HNT) stress and grown during the dry and wet season on the field in the Philippines. Season-specific changes in metabolite levels, as well as for agronomic parameters, were identified and metabolic pathways causing a yield decline at HNT conditions suggested.
In conclusion, the comparison of mapper performances can help plant scientists to decide on the right tool for their data. The de novo reconstruction of rice cultivars without a genome sequence provides a targeted, cost-efficient approach to identify novel genes responding to stress conditions for any organism. With the metabolomics approach for HNT stress in rice, I identified stress and season-specific metabolites which might be used as molecular markers for crop improvement in the future.
To develop and investigate detailed mathematical models of metabolic processes is one of the primary challenges in systems biology. However, despite considerable advance in the topological analysis of metabolic networks, kinetic modeling is still often severely hampered by inadequate knowledge of the enzyme-kinetic rate laws and their associated parameter values. Here we propose a method that aims to give a quantitative account of the dynamical capabilities of a metabolic system, without requiring any explicit information about the functional form of the rate equations. Our approach is based on constructing a local linear model at each point in parameter space, such that each element of the model is either directly experimentally accessible or amenable to a straightforward biochemical interpretation. This ensemble of local linear models, encompassing all possible explicit kinetic models, then allows for a statistical exploration of the comprehensive parameter space. The method is exemplified on two paradigmatic metabolic systems: the glycolytic pathway of yeast and a realistic-scale representation of the photosynthetic Calvin cycle.