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Alternative splicing (AS) enables differential inclusion of exons from a given transcript, thereby contributing to the transcriptome and proteome diversity. Aberrant AS patterns play major roles in the development of different pathologies, including breast cancer. N⁶-methyladenosine (m⁶A), the most abundant internal modification of eukaryotic mRNA, influences tumor progression and metastasis of breast cancer, and it has been recently linked to AS regulation. Here, we identify a specific AS signature associated with breast tumorigenesis in vitro. We characterize for the first time the role of METTL3 in modulating breast cancer-associated AS programs, expanding the role of the m⁶A-methyltransferase in tumorigenesis. Specifically, we find that both m⁶A deposition in splice site boundaries and in splicing and transcription factor transcripts, such as MYC, direct AS switches of specific breast cancer-associated transcripts. Finally, we show that five of the AS events validated in vitro are associated with a poor overall survival rate for patients with breast cancer, suggesting the use of these AS events as a novel potential prognostic biomarker.

The genome encodes the metabolic and functional capabilities of an organism and should be a major determinant of its ecological niche. Yet, it is unknown if the niche can be predicted directly from the genome. Here, we conduct metagenomic binning on 123 water samples spanning major environmental gradients of the Baltic Sea. The resulting 1961 metagenome-assembled genomes represent 352 species-level clusters that correspond to 1/3 of the metagenome sequences of the prokaryotic size-fraction. By using machine-learning, the placement of a genome cluster along various niche gradients (salinity level, depth, size-fraction) could be predicted based solely on its functional genes. The same approach predicted the genomes’ placement in a virtual niche-space that captures the highest variation in distribution patterns. The predictions generally outperformed those inferred from phylogenetic information. Our study demonstrates a strong link between genome and ecological niche and provides a conceptual framework for predictive ecology based on genomic data.

The rate of meiotic recombination is one of the central factors determining genome-wide levels of linkage disequilibrium which has important consequences for the efficiency of natural selection and for the dissection of quantitative traits. Here we present a new, high-resolution linkage map for Populus tremula that we use to anchor approximately two thirds of the P. tremula draft genome assembly on to the expected 19 chromosomes, providing us with the first chromosome-scale assembly for P. tremula (Table 2). We then use this resource to estimate variation in recombination rates across the P. tremula genome and compare these results to recombination rates based on linkage disequilibrium in a large number of unrelated individuals. We also assess how variation in recombination rates is associated with a number of genomic features, such as gene density, repeat density and methylation levels. We find that recombination rates obtained from the two methods largely agree, although the LD-based method identifies a number of genomic regions with very high recombination rates that the map-based method fails to detect. Linkage map and LD-based estimates of recombination rates are positively correlated and show similar correlations with other genomic features, showing that both methods can accurately infer recombination rate variation across the genome. Recombination rates are positively correlated with gene density and negatively correlated with repeat density and methylation levels, suggesting that recombination is largely directed toward gene regions in P. tremula.

Enteropathogenic Yersinia species can infect many mammalian organs such as the small intestine, cecum, Peyer’s patches, liver, spleen, and lung and cause diseases that resemble a typhoid-like syndrome, as seen for other enteropathogens. We found that sublethal infection doses of Y. pseudotuberculosis gave rise to asymptomatic persistent infection in mice and identified the cecal lymphoid follicles as the primary site for colonization during persistence. Persistent Y. pseudotuberculosis is localized in the dome area, often in inflammatory lesions, as foci or as single cells, and also in neutrophil exudates in the cecal lumen. This new mouse model for bacterial persistence in cecum has potential as an investigative tool for deeper understanding of bacterial adaptation and host immune defense mechanisms during persistent infection. Here, we investigated the nature of the persistent infection established by Y. pseudotuberculosis in mouse cecal tissue using in vivo RNA-seq of bacteria during early and persistent stages of infection. Comparative analysis of the bacterial transcriptomes revealed that Y. pseudotuberculosis undergoes transcriptional reprogramming with drastic down-regulation of T3SS virulence genes during persistence in the cecum. At the persistent stage, the expression pattern in many respects resembles the pattern seen in vitro at 26°C. Genes that are up-regulated during persistence are genes involved in anaerobiosis, chemotaxis, and protection against oxidative and acidic stress, which indicates the influence of different environmental cues. We found that the Crp/CsrA/RovA regulatory cascades influence the pattern of bacterial gene expression during persistence. Furthermore, we show that ArcA, Fnr, FrdA, WrbA, RovA, and RfaH play critical roles in persistence. An extended investigation of the transcriptional regulator rfaH employing mouse infection studies, phenotypic characterizations, and RNA-seq transcriptomics analyses indicated that this gene product contributes to establishment of infection and confirmed that it regulates O-antigen biosynthesis genes in Y. pseudotuberculosis. The RNA-seq results also suggest that rfaH has a relatively global effect. Furthermore, we also found that the dynamics of the cecal tissue organization and microbial composition shows changes during different stages of the infection. Taken together, based on our findings, we speculate that this enteropathogen initiates infection by using its virulence factors in meeting the innate immune response in the cecal tissue. Later on, these factors lead to dysbiosis in the local microbiota and altered tissue organization. At later stages of the infection, the pathogen adapts to the environment in the cecum by reprogramming its transcriptome from a highly virulent mode to a more environmentally adaptable mode for survival and shedding. The in vivo transcriptomic analyses for essential genes during infections present strong candidates for novel targets for antimicrobials.

We recently found that Yersinia pseudotuberculosis can be used as a model of persistent bacterial infections. We performed in vivo RNA-seq of bacteria in small cecal tissue biopsies at early and persistent stages of infection to determine strategies associated with persistence. Comprehensive analysis of mixed RNA populations from infected tissues revealed that Y. pseudotuberculosis undergoes transcriptional reprogramming with drastic down-regulation of T3SS virulence genes during persistence when the pathogen resides within the cecum. At the persistent stage, the expression pattern in many respects resembles the pattern seen in vitro at 26oC, with for example, up-regulation of flagellar genes and invA. These findings are expected to have impact on future rationales to identify suitable bacterial targets for new antibiotics. Other genes that are up-regulated during persistence are genes involved in anaerobiosis, chemotaxis, and protection against oxidative and acidic stress, which indicates the influence of different environmental cues. We found that the Crp/CsrA/RovA regulatory cascades influence the pattern of bacterial gene expression during persistence. Furthermore, arcA, fnr, frdA, and wrbA play critical roles in persistence. Our findings suggest a model for the life cycle of this enteropathogen with reprogramming from a virulent to an adapted phenotype capable of persisting and spreading by fecal shedding.

Pathogenic Yersinia cause a manifold of diseases in humans ranging from mild gastroenteritis (Y. pseudotuberculosis and Y. enterocolitica) to pneumonic and bubonic plague (Y. pestis), while all three have a common virulence strategy that relies on a well-studied type III secretion system and its effector proteins to colonize the host and evade immune responses. However, the role of other protein secretion and/or translocation systems in virulence of Yersinia species is not well known. In this thesis, we sought to investigate the contribution of twin-arginine translocation (Tat) pathway and its secreted substrates to the physiology and virulence of Y. pseudotuberculosis. Tat pathway uniquely exports folded proteins including virulence factors across the cytoplasmic membranes of bacteria. The proteins exported by Tat pathway contain a highly conserved twin-arginine motif in the N-terminal signal peptide. We found that the loss of Tat pathway causes a drastic change of the transcriptome of Y. pseudotuberculosis in stationary phase at environmental temperature with differential regulation of genes involved in virulence, carbon metabolism and stress responses. Phenotypic analysis revealed novel phenotypes of the Tat-deficient strain with defects in iron acquisition, acid resistance, copper oxidation and envelope integrity, which we were partly able to associate with the related Tat substrates. Moreover, increased glucose consumption and accumulation of intracellular fumarate were observed in response to inactivation of Tat pathway implicating a generic effect in cellular physiology. We evaluated the direct role of 22 in silico predicted Tat substrate mutants in the mouse infection model and found only one strain, ΔsufI, exhibited a similar degree of attenuation as Tat-deficient strain. Comparative in vivo characterization studies demonstrated a minor defect for ΔsufI in colonization of intestinal tissues compared to the Tat-deficient strain during early infection, whereas both SufI and TatC were required for dissemination from mesenteric lymph nodes and further systemic spread during late infection. This verifies that SufI has a major role in attenuation seen for the Tat deficient strain both during late infection and initial colonization. It is possible that other Tat substrates such as those involved in iron acquisition and copper resistance also has a role in establishing infection. Further phenotypic analysis indicated that SufI function is required for cell division and stress-survival. Transcriptomic analysis revealed that the highest number of differentially regulated genes in response to loss of Tat and SufI were involved in metabolism and transport. Taken together, this thesis presents a thorough analysis of the involvement of Tat pathway in the overall physiology and virulence strategies of Y. pseudotuberculosis. Finally, we propose that strong effects in virulence render TatC and SufI as potential targets for development of novel antimicrobial compounds

The Twin-arginine translocation (Tat) system mediates secretion of folded proteins that in bacteria, plants and archaea are identified via an N-terminal signal peptide. Tat systems are associated with virulence in many bacterial pathogens and our previous studies revealed that Tat deficient Yersinia pseudotuberculosis was severely attenuated for virulence. Aiming to identify Tat-dependent pathways and phenotypes of relevance for in vivo infection, we analysed the global transcriptome of parental and ∆tatC mutant strains of Y. pseudotuberculosis during exponential and stationary growth at 26^oC and 37^oC. The most significant changes in the transcriptome of the ∆tatC mutant were seen at 26^oC during stationary phase growth and these included the altered expression of genes related to virulence, stress responses and metabolism. Subsequent phenotypic analysis based on these transcriptome changes revealed several novel Tat-dependent phenotypes including decreased YadA expression, impaired growth under iron-limiting and high copper conditions as well as acidic pH and SDS. Several functionally related Tat substrates were also verified to contribute to these phenotypes. Interestingly, the phenotypic defects observed in the Tat-deficient strain were generally more pronounced than in mutants lacking the Tat substrate predicted to contribute to that specific function. Altogether, this provides new insight into the impact of Tat deficiency on in vivo fitness and survival/replication of Y. pseudotuberculosis during infection.

Plants and other green organisms harvest sunlight by green chlorophyll pigments and covertit to chemical energy (sugars) and oxygen in a process called photosynthesis providing the foundation for life on Earth. Although it is unanimously believed that oceanic phytoplanktons are the main contributors to the global photosynthesis, the contribution of coniferous boreal forests distributed across vast regions of the northern hemisphere cannot be undermined. Hence boreal forests account signifificantly for social, economical and environmental sustainability. Not only do conifers thrive in the tundra regions with extreme climate, but they also maintain their needles green over the boreal winter. A question remains; what makes them so resilient? In this respect, we aimed to understand the remarkable winter adaptation strategies in two dominant boreal coniferous species,i.e., Pinus sylvestris and Picea abies. First, we mapped the transcriptional landscape in Norway spruce (Picea abies) needles over the annual cycle. Transcriptional changes in the nascent needles reflflected a sequence of developmental processes and active vegetative growth during early summer and summer. Later after maturation, transcriptome reflflected activated defense against biotic factors and acclimationin response to abiotic environmental cues such as freezing temperatures during winter. Secondly, by monitoring the photosynthetic performance of Scot pine needles, we found that the trees face extreme stress during the early spring (Feb-Mar) when sub-zero temperatures are accompanied by high solar radiation. At this time, drastic changes occur in the thylakoid membranes of the chloroplast that allows the mixing of photosystem I and photosystem II that typically remain laterally segregated. This triggers direct energy transfer from PSII to PSI and thus protects PSII from damage. Furthermore, we found that this loss of lateral segregation may be a consequence of triple phosphorylationof Lhcb1 (Light harvesting complex1 of photosystem II). The structural changes in thylakoid membranes also lead to changes inthe thylakoid macro domain organisationand pigment protein composition. Furthermore, we discovered that while PSII is protected by direct energy transfer, the protection of PSI is provided through photoreduction of oxygen by flavodiiron proteins, which in turn allows P700 to stay in an oxidised state necessary for direct energy transfer. These coordinated cascades of changes concomitantly protect both PSI and PSII to maintain the needles green over the winter.

Norway spruce is a boreal forest tree species of significant ecological and economic importance. Hence there is a strong imperative to dissect the genetics underlying important wood quality traits in the species. We performed a functional genome-wide association study (GWAS) of 17 wood traits in Norway spruce using 178 101 single nucleotide polymorphisms (SNPs) generated from exome genotyping of 517 mother trees. The wood traits were defined using functional modelling of wood properties across annual growth rings. We applied a Least Absolute Shrinkage and Selection Operator (LASSO-based) association mapping method using a functional multilocus mapping approach that utilizes latent traits, with a stability selection probability method as the hypothesis testing approach to determine a significant quantitative trait locus. The analysis provided 52 significant SNPs from 39 candidate genes, including genes previously implicated in wood formation and tree growth in spruce and other species. Our study represents a multilocus GWAS for complex wood traits in Norway spruce. The results advance our understanding of the genetics influencing wood traits and identifies candidate genes for future functional studies.

Here we study a spatio-temporal prey-predator model with ratio-dependent functional response and nonlocal interaction term in the prey growth. For a clear understanding of the effect of nonlocal interaction on the resulting stationary and non-stationary patterns, we consider the nonlocal interaction term in prey growth only to describe the nonlocal intra-specific competition due to limited resources for the prey. First we obtain the patterns exhibited by the basic model in the absence of nonlocal interaction and then explore the effect of nonlocal interaction on the resulting patterns. We demonstrate the stabilizing role of nonlocal interaction as it induces stationary pattern from periodic and chaotic regimes with an increase in the range of nonlocal interaction. The existence of multiple branches of stationary solutions, bifurcating from homogeneous steady-state as well as non-stationary patterns, is illustrated with the help of numerical continuation technique.

Soybean (Glycine max) is a major legume crop worldwide, providing a critical source of protein and oil. The release of the soybean genome fuelled several transcriptome projects comprising multiple developmental stages and environmental conditions. Nevertheless, the global transcriptional patterns of embryonic axes during germination remain unknown. Here we report the analysis of ~1.58 billion RNA-Seq reads from soybean embryonic axes at five germination stages. Our results support the early activation of processes that are critical for germination, such as glycolysis, Krebs cycle and cell wall remodelling. Strikingly, only 3 hours after imbibition there is a preferential up-regulation of protein kinases and transcription factors, particularly from the LOB domain family, implying that transcriptional and post-transcriptional regulation play major roles early after imbibition. Lipid mobilization and glyoxylate pathways are also transcriptionally active in the embryonic axes, indicating that the local catabolism of oil reserves in the embryonic axes contributes to energy production during germination. We also present evidence supporting abscisic acid inactivation and the up-regulation of gibberellin, ethylene and brassinosteroid pathways. Further, there is a remarkable differential activation of paralogous genes in these hormone signalling pathways. Taken together, our results provide insights on the regulation and biochemistry of soybean germination.

In this study, we examine the impact of transcriptional network rearrangements driven by horizontal gene acquisition in PhoP and SlyA regulons using as a case study a phytopathosystem comprised of potato tubers and the soft-rot pathogen Pectobacterium brasiliense 1692 (Pb1692). Genome simulations and statistical analyses uncovered the tendency of PhoP and SlyA networks to mobilize lineage-specific traits predicted as horizontal gene transfer at late infection, highlighting the prominence of regulatory network rearrangements in this stage of infection. The evidence further supports the circumscription of two horizontally acquired quorum-sensing regulators (carR and expR1) by the PhoP network. By recruiting carR and expR1, the PhoP network also impacts certain host adaptation- and bacterial competition-related systems, seemingly in a quorum sensing-dependent manner, such as the type VI secretion system, carbapenem biosynthesis, and plant cell wall-degrading enzymes (PCWDE) like cellulases and pectate lyases. Conversely, polygalacturonases and the type III secretion system (T3SS) exhibit a transcriptional pattern that suggests quorum-sensing-independent regulation by the PhoP network. This includes an uncharacterized novel phage-related gene family within the T3SS gene cluster that has been recently acquired by two Pectobacterium species. The evidence further suggests a PhoP-dependent regulation of carbapenem- and PCWDE-encoding genes based on the synthesized products' optimum pH. The PhoP network also controls slyA expression in planta, which seems to impact carbohydrate metabolism regulation, especially at early infection, when 76.2% of the SlyA-regulated genes from that category also require PhoP to achieve normal expression levels.

IMPORTANCE: Exchanging genetic material through horizontal transfer is a critical mechanism that drives bacteria to efficiently adapt to host defenses. In this report, we demonstrate that a specific plant-pathogenic species (from the Pectobacterium genus) successfully integrated a population density-based behavior system (quorum sensing) acquired through horizontal transfer into a resident stress-response gene regulatory network controlled by the PhoP protein. Evidence found here underscores that subsets of bacterial weaponry critical for colonization, typically known to respond to quorum sensing, are also controlled by PhoP. Some of these traits include different types of enzymes that can efficiently break down plant cell walls depending on the environmental acidity level. Thus, we hypothesize that PhoP's ability to elicit regulatory responses based on acidity and nutrient availability fluctuations has strongly impacted the fixation of its regulatory connection with quorum sensing. In addition, another global gene regulator, known as SlyA, was found under the PhoP regulatory network. The SlyA regulator controls a series of carbohydrate metabolism-related traits, which also seem to be regulated by PhoP. By centralizing quorum sensing and slyA under PhoP scrutiny, Pectobacterium cells added an advantageous layer of control over those two networks that potentially enhances colonization efficiency.

F-box proteins constitute a large gene family that regulates processes from hormone signaling to stress response. F-box proteins are the substrate recognition modules of SCF E3 ubiquitin ligases. Here we report very distinct trends in family size, duplication, synteny and transcription of F-box genes in two nitrogen-fixing legumes, Glycine max (soybean) and Medicago truncatula (alfafa). While the soybean FBX genes emerged mainly through segmental duplications (including whole-genome duplications), M. truncatula genome is dominated by locally-duplicated (tandem) F-box genes. Many of these young FBX genes evolved complex transcriptional patterns, including preferential transcription in different tissues, suggesting that they have probably been recruited to important biochemical pathways (e.g. nodulation and seed development).

Soft-rot Enterobacteriaceae (SRE), typified by Pectobacterium and Dickeya genera, are phytopathogenic bacteria inflicting soft-rot disease in crops worldwide. By combining genomic information from 100 SRE with whole-transcriptome data sets, we identified novel genomic and transcriptional associations among key pathogenicity themes in this group. Comparative genomics revealed solid linkage between the type I secretion system (T1SS) and the carotovoricin bacteriophage (Ctv) conserved in 96.7% of Pectobacterium genomes. Moreover, their coactivation during infection indicates a novel functional association involving T1SS and Ctv. Another bacteriophage-borne genomic region, mostly confined to less than 10% of Pectobacterium strains, was found, presumably comprising a novel lineage-specific prophage in the genus. We also detected the transcriptional coregulation of a previously predicted toxin/immunity pair (WHH and SMI1_KNR4 families), along with the type VI secretion system (T6SS), which includes hcp and/or vgrG genes, suggesting a role in disease development as T6SS-dependent effectors. Further, we showed that another predicted T6SS-dependent endonuclease (AHH family) exhibited toxicity in ectopic expression assays, indicating antibacterial activity. Additionally, we report the striking conservation of the group 4 capsule (GFC) cluster in 100 SRE strains which consistently features adjacently conserved serotype-specific gene arrays comprising a previously unknown organization in GFC clusters. Also, extensive sequence variations found in gfcA orthologs suggest a serotype-specific role in the GfcABCD machinery.

Importance: Despite the considerable loss inflicted on important crops yearly by Pectobacterium and Dickeya diseases, investigations on key virulence and interbacterial competition assets relying on extensive comparative genomics are still surprisingly lacking for these genera. Such approaches become more powerful over time, underpinned by the growing amount of genomic information in public databases. In particular, our findings point to new functional associations among well-known genomic themes enabling alternative means of neutralizing SRE diseases through disruption of pivotal virulence programs. By elucidating novel transcriptional and genomic associations, this study adds valuable information on virulence candidates that could be decisive in molecular applications in the near future. The utilization of 100 genomes of Pectobacterium and Dickeya strains in this study is unprecedented for comparative analyses in these taxa, and it provides novel insights on the biology of economically important plant pathogens.

The MODOMICS database has been, since 2006, a manually curated and centralized resource, storing and distributing comprehensive information about modified ribonucleosides. Originally, it only contained data on the chemical structures of modified ribonucleosides, their biosynthetic pathways, the location of modified residues in RNA sequences, and RNA-modifying enzymes. Over the years, prompted by the accumulation of new knowledge and new types of data, it has been updated with new information and functionalities. In this new release, we have created a catalog of RNA modifications linked to human diseases, e.g., due to mutations in genes encoding modification enzymes. MODOMICS has been linked extensively to RCSB Protein Data Bank, and sequences of experimentally determined RNA structures with modified residues have been added. This expansion was accompanied by including nucleotide 5'-monophosphate residues. We redesigned the web interface and upgraded the database backend. In addition, a search engine for chemically similar modified residues has been included that can be queried by SMILES codes or by drawing chemical molecules. Finally, previously available datasets of modified residues, biosynthetic pathways, and RNA-modifying enzymes have been updated. Overall, we provide users with a new, enhanced, and restyled tool for research on RNA modification. MODOMICS is available at https://iimcb.genesilico.pl/modomics/.

Background: Pancreatic ductal adenocarcinoma (PDAC) is a very aggressive malignancy with a 5-year survival of 10 %. Surgery is the only curative treatment. Unfortunately, few patients are eligible for surgery due to late detection. Thus, we need ways to detect the disease at an earlier stage and for that good screening biomarkers could be used. Previous studies have analyzed circulating analytes in prospective studies to identify early PDAC signals. One such class is microRNAs (miRNAs). MicroRNAs are non-coding RNAs of around 22 nucleotides that act as post- transcriptional regulators by interaction with messenger RNAs (mRNAs). The function of a miRNA can be elucidated by target prediction, to identify its potential targets, followed by enrichment analysis of the predicted targets. Challenges with this approach includes a lot of false positives being generated and that miRNAs can perform their role in a tissue- or disease-specific manner. Other classes of analytes that have previously been studied in prospective PDAC cohorts are metabolites and proteins. 

Aims: This thesis has three aims. First, to build a miRNA functional analysis pipeline with correlation support between miRNA and its predicted target genes. Second, to identify potential circulating biomarkers for early detection of PDAC using multi-omics. Third, to identify potential prognostic metabolites in a prospective PDAC cohort.

Methods: We used publicly available data from the cancer genome atlas-pancreatic adenocarcinoma (TCGA-PAAD) and pre-diagnostic plasma samples from the Northern Sweden Health and Disease Study. We built a pipeline in R including miRNA, mRNA, and protein expression data from TCGA-PAAD for in silico miRNA functional analysis. Pre- diagnostic plasma samples from future PDAC patients as well as matched healthy controls were analyzed using multi- omics. Tissue polypeptide specific antigen (TPS) was analyzed by enzyme linked immunosorbent assay in 267 future PDAC samples and 320 healthy controls. Metabolomics and clinical biomarkers (carbohydrate antigen (CA) 19-9, carcinoembryonic antigen (CEA), and CA 15-3) were profiled in 100 future PDAC samples and 100 healthy controls using liquid chromatography-mass spectrometry (MS), gas chromatography-MS, and multi-plex technology. Of these, a subset of 39 future PDAC patients and 39 healthy controls were profiled for 2083 microRNAs using targeted sequencing and 644 proteins using proximity extension assays. Circulating levels of multi-omics analytes were analyzed using conditional or unconditional logistic regression. Least absolute shrinkage and selection operator (LASSO) in combination with 500 bootstrap iterations identified the most informative variables. The prognostic value of metabolites was assessed using cox regression. Multi-omics factor analysis (MOFA) and data integration analysis for biomarker discovery using latent components (DIABLO) were used for multi-omics integration analyses.

Results: An automated pipeline was built consisting of 1) miRNA target prediction, 2) correlation analyses between miRNA and its targets on mRNA and protein expression levels, and 3) functional enrichment of correlated targets to identify enriched Kyoto encyclopedia of genes and genomes (KEGG) pathways and gene ontology (GO) terms for a specific miRNA. The pipeline was run for all microRNAs (~700) detected in the TCGA-PAAD cohort. These results can be downloaded from a shiny app (https://emmbor.shinyapps.io/mirfa/). TPS was not altered in pre-diagnostic PDAC patients up to 24 years prior to diagnosis, but increased at diagnosis (OR = 1.03, 95 % CI: 1.01-1.05). Internal area under curves of 0.74, 0.80, and 0.88 were achieved for five metabolites, two proteins, and two miRNAs that were selected by LASSO and bootstrap iterations, in combination with CA 19-9. Neither MOFA nor DIABLO separated well between future PDAC cases and healthy controls. 

Conclusions: Our bioinformatics pipeline for in silico functional analysis of microRNAs successfully identifies enriched KEGG pathways and GO terms for miRNA isoforms. The investigated plasma samples are heterogeneous, but among the analyzed variables, we identified five metabolites, two proteins, and two microRNAs with highest potential for early PDAC detection. CA 19-9 levels increased closer to diagnosis. We identified five fatty acids that could be studied in a diagnostic PDAC cohort as prognostic biomarkers. 

Background: MicroRNAs (miRNAs) are small RNAs that regulate gene expression at a post-transcriptional level and are emerging as potentially important biomarkers for various disease states, including pancreatic cancer. In silico-based functional analysis of miRNAs usually consists of miRNA target prediction and functional enrichment analysis of miRNA targets. Since miRNA target prediction methods generate a large number of false positive target genes, further validation to narrow down interesting candidate miRNA targets is needed. One commonly used method correlates miRNA and mRNA expression to assess the regulatory effect of a particular miRNA.

The aim of this study was to build a bioinformatics pipeline in R for miRNA functional analysis including correlation analyses between miRNA expression levels and its targets on mRNA and protein expression levels available from the cancer genome atlas (TCGA) and the cancer proteome atlas (TCPA). TCGA-derived expression data of specific mature miRNA isoforms from pancreatic cancer tissue was used.

Results: Fifteen circulating miRNAs with significantly altered expression levels detected in pancreatic cancer patients were queried separately in the pipeline. The pipeline generated predicted miRNA target genes, enriched gene ontology (GO) terms and Kyoto encyclopedia of genes and genomes (KEGG) pathways. Predicted miRNA targets were evaluated by correlation analyses between each miRNA and its predicted targets. MiRNA functional analysis in combination with Kaplan-Meier survival analysis suggest that hsa-miR-885-5p could act as a tumor suppressor and should be validated as a potential prognostic biomarker in pancreatic cancer.

Conclusions: Our miRNA functional analysis (miRFA) pipeline can serve as a valuable tool in biomarker discovery involving mature miRNAs associated with pancreatic cancer and could be developed to cover additional cancer types. Results for all mature miRNAs in TCGA pancreatic adenocarcinoma dataset can be studied and downloaded through a shiny web application at https://emmbor.shinyapps.io/mirfa/.

The volume of data on past environmental and climate changes, as well as human interactions with these, has long since passed the level where it is manageable outside of large scale database systems. The Strategic Environmental Archaeology Database project aims to not only store and disseminate such data, but also provide tools for querying and analysing them, whilst maintaining a close connection with the archaeological and ecological data that are essential for their comprehensive interpretation. Large scale, geographically and chronologically unrestricted databases provide us with essentially unlimited scope for putting individual sites into a broader context and applying locally collated data to the investigation of earth system level changes. By providing integrated access to data from a variety of proxies, including plant macrofossils, pollen, insects and geochemistry, along with dating evidence, more complex questions can be answered where any single proxy would not be able to provide comprehensive answers.

BugsCEP is a research and teaching aid for palaeoentomology, entomology and ecology. As well as habitat and distribution data, it includes tools for climate and environmental reconstruction, and facilities for storing site based abundance/collection data. A variety of searching and reporting functions greatly augment the efficiency of beetle based research.

Bugs is built around a comprehensive database of beetle ecology and European fossil records which has been accumulated over the past 20 years.

This report provides an overview of the progress and results of the VR:KFI infrastructure projects 2007-7494 and (825-)2010-5976. It should be considered as a status report in an on-going long-term research infrastructure development project.

Tree biotechnology will soon reach a mature state where it will influence the overall supply of fiber, energy and wood products. We are now ready to make the transition from identifying candidate genes, controlling important biological processes, to discovering the detailed molecular function of these genes on a broader, more holistic, systems biology level. In this paper, a strategy is outlined for informative data generation and integrated modeling of systematic changes in transcript, protein and metabolite profiles measured from hybrid aspen samples. The aim is to study characteristics of common changes in relation to genotype-specific perturbations affecting the lignin biosynthesis and growth. We show that a considerable part of the systematic effects in the system can be tracked across all platforms and that the approach has a high potential value in functional characterization of candidate genes.

The axolotl (Ambystoma mexicanum) is the vertebrate model system with the highest regeneration capacity. Experimental tools established over the past 100 years have been fundamental to start unraveling the cellular and molecular basis of tissue and limb regeneration. In the absence of a reference genome for the Axolotl, transcriptomic analysis become fundamental to understand the genetic basis of regeneration.

Here we present one of the most diverse transcriptomic data sets for Axolotl by profiling coding and non coding RNAs from diverse tissues. We reconstructed a population of 115,906 putative protein coding mRNAs as full ORFs (including isoforms). We also identified 352 conserved miRNAs and 297 novel putative mature miRNAs.

Systematic enrichment analysis of gene expression allowed us to identify tissue-specific protein-coding transcripts. We also found putative novel and conserved microRNAs which potentially target mRNAs which are reported as important disease candidates in heart and liver.

Protein coding genes have been extensively studied in both plant and animal genomes, while non-coding portions of the genomes were considered not relevant for a long time. This was due to the fact that non-coding led immediately to not functional, until the discovery of let-7, the first conserved miRNA, in Caenorhabditis elegans. From here on, several studies on small RNAs (sRNAs) were performed, while long non-coding RNAs (lncRNAs) have risen to attention in the last two decades, also because of their usage as diagnostic biomarkers in cancer. Studies to assign function to RNAs have progressed more slowly in plants compared to the animal kingdom and there is still a lot to explore even in the protein coding space, above all if we consider huge genomes like Norway spruce and Scots pine, so the non-coding part of the genome still represents an abyss to discover. In my PhD I mostly focused on a subclass of non-coding RNAs in Norway spruce and aspen. Long non-coding RNAs are considered arbitrarily longer than 200 nucleotides (nt) and can have one small open reading frame (sORF, length < 300 nt) coding for a short peptide (not a complete protein). lncRNAs tend to be expressed at lower levels than genes, but with precise spatio-temporal patterns. They are mostly expressed in particular tissues, stages of a biological process and/or particular conditions, that are often related to biotic or abiotic stresses. They have low levels of sequence homology conservation, even in close related species. In particular, I studied the class of lncRNAs located in the intergenic space, the long intergenic non-coding RNAs (lincRNAs). 

In the first part of this thesis, I developed a pipeline to identify lincRNAs. This pipeline allows to identify in silico bona fide lincRNAs starting from an RNA-Sequencing dataset. It is an ensemble method, considering different tools and the characteristics of lincRNAs. 

In the second part of this thesis, I focused on functionally annotating lincRNAs. To achieve this challenge, I decided to use the guilt-by-association strategy. This method relies on a co-expression network containing both lincRNAs and protein coding genes. Through a functional enrichment of the protein coding genes, it is possible to transfer the same annotation to a lincRNA co-expressed in the same module. I have also tried to relate lincRNAs to a possible function in the de novo methylation of DNA via the RdDM pathway in Norway spruce.

In the last part of this thesis, I identified lincRNAs expressed during leaf development in aspen and produced CRISPR-Cas9 mutants lacking the sequence of two lincRNAs in order to provide a functional validation. 

In general, RNA-Sequencing has enabled and advanced the identification of lincRNAs, and this thesis demonstrates an implemented strategy to identify and assign putative functional information to lincRNAs, deepening the knowledge in the non-coding abyss.

Long non-coding RNAs (lncRNAs) have emerged as important regulators of many bio- logical processes, although their regulatory roles remain poorly characterized in woody plants, especially in gymnosperms. A major challenge of working with lncRNAs is to assign functional annotations, since they have a low coding potential and low cross-species conservation.

We utilised an existing RNA-Sequencing resource and performed short RNA sequencing of somatic embryogenesis developmental stages in Norway spruce (Picea abies L. Karst). We implemented a pipeline to identify lncRNAs located within the intergenic space (lincRNAs) and generated a co-expression network including protein coding, lincRNA and miRNA genes.

To assign putative functional annotation, we employed a guilt-by-association approach using the co-expression network and integrated these results with annota- tion assigned using semantic similarity and co-expression. Moreover, we evaluated the relationship between lincRNAs and miRNAs, and identified which lincRNAs are conserved in other species. We identified lincRNAs with clear evidence of differential expression during somatic embryogenesis and used network connectivity to identify those with the greatest regulatory potential.

This work provides the most comprehensive view of lincRNAs in Norway spruce and is the first study to perform global identification of lincRNAs during somatic embryogen- esis in conifers. The data have been integrated into the expression visualisation tools at the PlantGenIE.org web resource to enable easy access to the community. This will facilitate the use of the data to address novel questions about the role of lincRNAs in the regulation of embryogenesis and facilitate future comparative genomics studies.

Gene transcription in bacteria often starts some nucleotides upstream of the start codon. Identifying the specific Transcriptional Start Site (TSS) is essential for genetic manipulation, as in many cases upstream of the start codon there are sequence elements that are involved in gene expression regulation. Taken into account the classical gene structure, we are able to identify two kinds of transcriptional start site: primary and secondary. A primary transcriptional start site is located some nucleotides upstream of the translational start site, while a secondary transcriptional start site is located within the gene encoding sequence.Here, we present a step by step protocol for genome-wide transcriptional start sites determination by differential RNA-sequencing (dRNA-seq) using the enteric pathogen Shigella flexneri serotype 5a strain M90T as model. However, this method can be employed in any other bacterial species of choice. In the first steps, total RNA is purified from bacterial cultures using the hot phenol method. Ribosomal RNA (rRNA) is specifically depleted via hybridization probes using a commercial kit. A 5′-monophosphate-dependent exonuclease (TEX)-treated RNA library enriched in primary transcripts is then prepared for comparison with a library that has not undergone TEX-treatment, followed by ligation of an RNA linker adaptor of known sequence allowing the determination of TSS with single nucleotide precision. Finally, the RNA is processed for Illumina sequencing library preparation and sequenced as purchased service. TSS are identified by in-house bioinformatic analysis.Our protocol is cost-effective as it minimizes the use of commercial kits and employs freely available software.

Background: Affordable high-throughput DNA and RNA sequencing technologies are allowing genomic analysis of plant and animal populations and as a result empowering new systems genetics approaches to study complex traits. The availability of intuitive tools to browse and analyze the resulting large-scale genetic and genomic datasets remain a significant challenge. Furthermore, these integrative genomics approaches require innovative methods to dissect the flow and interconnectedness of biological information underlying complex trait variation. The Plant Genome Integrative Explorer (PlantGenIE.org) is a multi-species database and domain that houses online tools for model and woody plant species including Eucalyptus. Since the Eucalyptus Genome Integrative Explorer (EucGenIE) is integrated within PlantGenIE, it shares genome and expression analysis tools previously implemented within the various subdomains (ConGenIE, PopGenIE and AtGenIE). Despite the success in setting up integrative genomics databases, online tools for systems genetics modelling and high-resolution dissection of complex trait variation in plant populations have been lacking.

Results: We have developed qtlXplorer (https://eucgenie.org/QTLXplorer) for visualizing and exploring systems genetics data from genome-wide association studies including quantitative trait loci (QTLs) and expression-based QTL (eQTL) associations. This module allows users to, for example, find co-located QTLs and eQTLs using an interactive version of Circos, or explore underlying genes using JBrowse. It provides users with a means to build systems genetics models and generate hypotheses from large-scale population genomics data. We also substantially upgraded the EucGenIE resource and show how it enables users to combine genomics and systems genetics approaches to discover candidate genes involved in biotic stress responses and wood formation by focusing on two multigene families, laccases and peroxidases.

Conclusions: qtlXplorer adds a new dimension, population genomics, to the EucGenIE and PlantGenIE environment. The resource will be of interest to researchers and molecular breeders working in Eucalyptus and other woody plant species. It provides an example of how systems genetics data can be integrated with functional genetics data to provide biological insight and formulate hypotheses. Importantly, integration within PlantGenIE enables novel comparative genomics analyses to be performed from population-scale data.

Many ecological interactions of aspens and their relatives (Populus spp.) are affected by products of the phenylpropanoid pathway synthesizing condensed tannins (CTs), whose production involves trade-offs with other ecologically important compounds and with growth. Genes of this pathway are candidates for investigating the role of selection on ecologically important, polygenic traits. We analysed sequences from 25 genes representing 10 steps of the CT synthesis pathway, which produces CTs used in defence and lignins used for growth, in 12 individuals of European aspen (Populus tremula). We compared these to homologs from P.trichocarpa, to a control set of 77 P. tremula genes, to genome-wide resequencing data and to RNA-seq expression levels, in order to identify signatures of selection distinct from those of demography. In Populus, pathway position exerts a strong influence on the evolution of these genes. Nonsynonymous diversity, divergence and allele frequency shifts (Tajima's D) were much lower than for synonymous measures. Expression levels were higher, and the direction of selection more negative, for upstream genes than for those downstream. Selective constraints act with increasing intensity on upstream genes, despite the presence of multiple paralogs in most gene families. Pleiotropy, expression level, flux control and codon bias appear to interact in determining levels and patterns of variation in genes of this pathway, whose products mediate a wide array of ecological interactions for this widely distributed species.

Global analysis of protein phosphorylation by mass spectrometry proteomic techniques has emerged in the last decades as a powerful tool in biological and biomedical research. However, there are several factors that make the global study of the phosphoproteome more challenging than measuring non-modified proteins. The low stoichiometry of the phosphorylated species and the need to retrieve residue specific information require particular attention on sample preparation, data acquisition and processing to ensure reproducibility, qualitative and quantitative robustness and ample phosphoproteome coverage in phosphoproteomic workflows. Aiming to investigate the effect of different variables in the performance of proteome wide phosphoprotein analysis protocols, ProteoRed-ISCIII and EuPA launched the Proteomics Multicentric Experiment 11 (PME11). A reference sample consisting of a yeast protein extract spiked in with different amounts of a phosphomix standard (Sigma/Merck) was distributed to 31 laboratories around the globe. Thirty-six datasets from 23 laboratories were analyzed. Our results indicate the suitability of the PME11 reference sample to benchmark and optimize phosphoproteomics strategies, weighing the influence of different factors, as well as to rank intra and inter laboratory performance.

Adverse environmental conditions are detrimental to plant growth and development. Acclimation to abiotic stress conditions involves activation of signaling pathways which often results in changes in gene expression via networks of transcription factors (TFs). Mediator is a highly conserved co-regulator complex and an essential component of the transcriptional machinery in eukaryotes. Some Mediator subunits have been implicated in stress-responsive signaling pathways; however, much remains unknown regarding the role of plant Mediator in abiotic stress responses. Here, we use RNA-seq to analyze the transcriptional response of Arabidopsis thaliana to heat, cold and salt stress conditions. We identify a set of common abiotic stress regulons and describe the sequential and combinatorial nature of TFs involved in their transcriptional regulation. Furthermore, we identify stress-specific roles for the Mediator subunits MED9, MED16, MED18 and CDK8, and putative TFs connecting them to different stress signaling pathways. Our data also indicate different modes of action for subunits or modules of Mediator at the same gene loci, including a co-repressor function for MED16 prior to stress. These results illuminate a poorly understood but important player in the transcriptional response of plants to abiotic stress and identify target genes and mechanisms as a prelude to further biochemical characterization.

MOTIVATION: RNA sequencing is becoming a standard for expression profiling experiments and many tools have been developed in the past few years to analyze RNA-Seq data. Numerous 'Bioconductor' packages are available for next-generation sequencing data loading in R, e.g. ShortRead and Rsamtools as well as to perform differential gene expression analyses, e.g. DESeq and edgeR. However, the processing tasks lying in between these require the precise interplay of many Bioconductor packages, e.g. Biostrings, IRanges or external solutions are to be sought.

RESULTS: We developed 'easyRNASeq', an R package that simplifies the processing of RNA sequencing data, hiding the complex interplay of the required packages behind a single functionality.

AVAILABILITY: The package is implemented in R (as of version 2.15) and is available from Bioconductor (as of version 2.10) at the URL: http://bioconductor.org/packages/release/bioc/html/easyRNASeq.html, where installation and usage instructions can be found.

CONTACT: delhomme@embl.de.

BACKGROUND: Plants can perceive and respond to mechanical signals. For instance, cortical microtubule (CMT) arrays usually reorganize following the predicted maximal tensile stress orientation at the cell and tissue level. While research in the last few years has started to uncover some of the mechanisms mediating these responses, much remains to be discovered, including in most cases the actual nature of the mechanosensors. Such discovery is hampered by the absence of adequate quantification tools that allow the accurate and sensitive detection of phenotypes, along with high throughput and automated handling of large datasets that can be generated with recent imaging devices.

RESULTS: Here we describe an image processing workflow specifically designed to quantify CMT arrays response to tensile stress in time-lapse datasets following an ablation in the epidermis - a simple and robust method to change mechanical stress pattern. Our Fiji-based workflow puts together several plugins and algorithms under the form of user-friendly macros that automate the analysis process and remove user bias in the quantification. One of the key aspects is also the implementation of a simple geometry-based proxy to estimate stress patterns around the ablation site and compare it with the actual CMT arrays orientation. Testing our workflow on well-established reporter lines and mutants revealed subtle differences in the response over time, as well as the possibility to uncouple the anisotropic and orientational response.

CONCLUSION: This new workflow opens the way to dissect with unprecedented detail the mechanisms controlling microtubule arrays re-organization, and potentially uncover the still largely elusive plant mechanosensors.

The Swedish Childhood Tumor Biobank (BTB) is a nonprofit national infrastructure for collecting tissue samples and genomic data from pediatric patients diagnosed with central nervous system (CNS) and other solid tumors. The BTB is built on a multidisciplinary network established to provide the scientific community with standardized biospecimens and genomic data, thereby improving knowledge of the biology, treatment and outcome of childhood tumors. As of 2022, over 1100 fresh-frozen tumor samples are available for researchers. We present the workflow of the BTB from sample collection and processing to the generation of genomic data and services offered. To determine the research and clinical utility of the data, we performed bioinformatics analyses on next-generation sequencing (NGS) data obtained from a subset of 82 brain tumors and patient blood-derived DNA combined with methylation profiling to enhance the diagnostic accuracy and identified germline and somatic alterations with potential biological or clinical significance. The BTB procedures for collection, processing, sequencing, and bioinformatics deliver high-quality data. We observed that the findings could impact patient management by confirming or clarifying the diagnosis in 79 of the 82 tumors and detecting known or likely driver mutations in 68 of 79 patients. In addition to revealing known mutations in a broad spectrum of genes implicated in pediatric cancer, we discovered numerous alterations that may represent novel driver events and specific tumor entities. In summary, these examples reveal the power of NGS to identify a wide number of actionable gene alterations. Making the power of NGS available in healthcare is a challenging task requiring the integration of the work of clinical specialists and cancer biologists; this approach requires a dedicated infrastructure, as exemplified here by the BTB.

Computational models that can explain and predict complex sub-cellular, cellular, and tissue-level drug response mechanisms could speed drug discovery and prioritize patient-specific treatments (i.e., precision medicine). Some models are mechanistic with detailed equations describing known (or supposed) physicochemical processes, while some are statistical or machine learning-based approaches, that explain datasets but have no mechanistic or causal guarantees. These two types of modeling are rarely combined, missing the opportunity to explore possibly causal but data-driven new knowledge while explaining what is already known. Here, we explore combining machine learned associations with mechanistic models to develop computational models that could more fully represent cellular behavior. In this proposed MEMMAL (MEchanistic Modeling with MAchine Learning) framework, machine learning/statistical models built using omics datasets provide predictions for new interactions between genes and proteins where there is physicochemical uncertainty. These interactions are used as a basis for new reactions in mechanistic models. As a test case, we focused on incorporating novel IFNγ/PD-L1 related associations into a large-scale mechanistic model for cell proliferation and death to better recapitulate the recently released NIH LINCS Consortium MCF10A dataset and enable description of the cellular response to checkpoint inhibitor immunotherapies. This work is a template for combining big-data-inferred interactions with mechanistic models, which could be more broadly applicable for building multi-scale precision medicine and whole cell models.

Behçet's Disease (BD) is a multi-systemic, auto-inflammatory disorder that is characterized by recurrent episodes of inflammatory manifestations affecting skin, mucosa, eyes, blood vessels, joints and several other organs. BD is classified as a multifactorial disease with an important contribution of genetics. Genetic studies suggest that there is a strong association of BD with a Class I major histocompatibility complex antigen, named HLA-B*51, along with several other weaker associations with genes encoding proteins involved in inflammation. However, pathogenic mechanisms associated with these genetic variations and their interactions with the environment have not been elucidated yet. In this paper, we present a mathematical model for BD based on a dynamical systems perspective that captures especially the relapsing nature of the disease. We propose a disease progression mechanism and construct a model, in the form of coupled ordinary differential equations (ODEs), which reveals the occurrence pattern of the disease in the population. According to our model, the disease has three distinct modes describing different phenotypes of people carrying HLA-B*51 tissue antigen, namely, the Healthy Carrier, the Potential Patient and the Active Patient. We herein present an exemplary mathematical model for BD, for the first time in the literature, that concisely captures the actions of many cell types together with genetic and environmental effects. The proposed model provides insight into this complex inflammatory disease which may lead to identification of new tools for its treatment and prevention.

Robust identification of context-specific network features that control cellular phenotypes remains a challenge. We here introduce MOBILE (Multi-Omics Binary Integration via Lasso Ensembles) to nominate molecular features associated with cellular phenotypes and pathways. First, we use MOBILE to nominate mechanisms of interferon-γ (IFNγ) regulated PD-L1 expression. Our analyses suggest that IFNγ-controlled PD-L1 expression involves BST2 , CLIC2 , FAM83D , ACSL5 , and HIST2H2AA3 genes, which were supported by prior literature. We also compare networks activated by related family members transforming growth factor-beta 1 (TGFβ1) and bone morphogenetic protein 2 (BMP2) and find that differences in ligand-induced changes in cell size and clustering properties are related to differences in laminin/collagen pathway activity. Finally, we demonstrate the broad applicability and adaptability of MOBILE by analyzing publicly available molecular datasets to investigate breast cancer subtype specific networks. Given the ever-growing availability of multi-omics datasets, we envision that MOBILE will be broadly useful for identification of context-specific molecular features and pathways.

Aberrant signaling through insulin (Ins) and insulin-like growth factor I (IGF1) receptors contribute to the risk and advancement of many cancer types by activating cell survival cascades. Similarities between these pathways have thus far prevented the development of pharmacological interventions that specifically target either Ins or IGF1 signaling. To identify differences in early Ins and IGF1 signaling mechanisms, we developed a dual receptor (IGF1R & InsR) computational response model. The model suggested that ribosomal protein S6 kinase (RPS6K) plays a critical role in regulating MAPK and Akt activation levels in response to Ins and IGF1 stimulation. As predicted, perturbing RPS6K kinase activity led to an increased Akt activation with Ins stimulation compared to IGF1 stimulation. Being able to discern differential downstream signaling, we can explore improved anti-IGF1R cancer therapies by eliminating the emergence of compensation mechanisms without disrupting InsR signaling.

Abstract Mechanistic models of how single cells respond to different perturbations can help integrate disparate big data sets or predict response to varied drug combinations. However, the construction and simulation of such models have proved challenging. Here, we developed a python-based model creation and simulation pipeline that converts a few structured text files into an SBML standard and is high-performance- and cloud-computing ready. We applied this pipeline to our large-scale, mechanistic pan-cancer signaling model (named SPARCED) and demonstrate it by adding an IFNγ pathway submodel. We then investigated whether a putative crosstalk mechanism could be consistent with experimental observations from the LINCS MCF10A Data Cube that IFNγ acts as an anti-proliferative factor. The analyses suggested this observation can be explained by IFNγ-induced SOCS1 sequestering activated EGF receptors. This work forms a foundational recipe for increased mechanistic model-based data integration on a single-cell level, an important building block for clinically-predictive mechanistic models.

Insulin and insulin-like growth factor I (IGF1) influence cancer risk and progression through poorly understood mechanisms. To better understand the roles of insulin and IGF1 signaling in breast cancer, we combined proteomic screening with computational network inference to uncover differences in IGF1 and insulin induced signaling. Using reverse phase protein array, we measured the levels of 134 proteins in 21 breast cancer cell lines stimulated with IGF1 or insulin for up to 48 h. We then constructed directed protein expression networks using three separate methods: (i) lasso regression, (ii) conventional matrix inversion, and (iii) entropy maximization. These networks, named here as the time translation models, were analyzed and the inferred interactions were ranked by differential magnitude to identify pathway differences. The two top candidates, chosen for experimental validation, were shown to regulate IGF1/insulin induced phosphorylation events. First, acetyl-CoA carboxylase (ACC) knock-down was shown to increase the level of mitogen-activated protein kinase (MAPK) phosphorylation. Second, stable knock-down of E-Cadherin increased the phospho-Akt protein levels. Both of the knock-down perturbations incurred phosphorylation responses stronger in IGF1 stimulated cells compared with insulin. Overall, the time-translation modeling coupled to wet-lab experiments has proven to be powerful in inferring differential interactions downstream of IGF1 and insulin signaling, in vitro.

Actinorhizal plants are in symbiosis with the nitrogen-fixating soil bacterium Frankia, which forms nodules in the plant root. However, several studies also report other endophytic bacteria appearing in the nodules, but their function and interaction with the host plant or Frankia is not yet understood. This thesis used a bioinformatic approach to investigate the metagenomes of eighteen actinorhizal nodule samples to find out which bacteria are present, how the microbiomes differed from each other, and if the genomes of non-Frankia inhabitants could give indications of any functions. The results showed that the bacterial composition, richness, and diversity differed among the samples, especially between the samples sequenced from the field versus those primarily cultivated in a greenhouse. All samples had a substantial number of sequencing reads belonging to potential endophytes, such as strains of Enterobacteria, Pseudomonas, Streptomyces, Micromonospora, Mycobacteria and Pseudonocardia. There seemed to be a common microbial community shared among the plants on a family level, since no significant difference was found in the core microbiomes between the field and greenhouse groups. Some sequences found in the metagenomes were annotated as potential functions of the fellow travellers, such as antibiotic synthesis, proteins involved in regulating abiotic stresses, but also probable plant damaging compounds rather associated with pathogens than symbionts.

Long non-coding RNAs contribute to dosage compensation in both mammals and Drosophila by inducing changes in the chromatin structure of the X-chromosome. In Drosophila melanogaster, roX1 and roX2 are long non-coding RNAs that together with proteins form the male-specific lethal (MSL) complex, which coats the entire male X-chromosome and mediates dosage compensation by increasing its transcriptional output. Studies on polytene chromosomes have demonstrated that when both roX1 and roX2 are absent, the MSL-complex becomes less abundant on the male X-chromosome and is relocated to the chromocenter and the 4^thchromosome. Here we address the role of roX RNAs in MSL-complex targeting and the evolution of dosage compensation in Drosophila. We performed ChIP-seq experiments which showed that MSL-complex recruitment to high affinity sites (HAS) on the X-chromosome is independent of roX and that the HAS sequence motif is conserved in D. simulans. Additionally, a complete and enzymatically active MSL-complex is recruited to six specific genes on the 4^thchromosome. Interestingly, our sequence analysis showed that in the absence of roX RNAs, the MSL-complex has an affinity for regions enriched in Hoppel transposable elements and repeats in general. We hypothesize that roX mutants reveal the ancient targeting of the MSL-complex and propose that the role of roX RNAs is to prevent the binding of the MSL-complex to heterochromatin.

1. With accelerated land conversion and global heating at northern latitudes, it becomes crucial to understand, how life histories of animals in extreme environments adapt to these changes. Animals may either adapt by adjusting foraging behavior or through physiological responses, including adjusting their energy metabolism or both. Until now, it has been difficult to study such adaptations in free‐ranging animals due to methodological constraints that prevent extensive spatiotemporal coverage of ecological and physiological data.

2. Through a novel approach of combining DNA‐metabarcoding and nuclear magnetic resonance (NMR)‐based metabolomics, we aim to elucidate the links between diets and metabolism in Scandinavian moose Alces alces over three biogeographic zones using a unique dataset of 265 marked individuals.

3. Based on 17 diet items, we identified four different classes of diet types that match browse species availability in respective ecoregions in northern Sweden. Individuals in the boreal zone consumed predominantly pine and had the least diverse diets, while individuals with highest diet diversity occurred in the coastal areas. Males exhibited lower average diet diversity than females.

4. We identified several molecular markers indicating metabolic constraints linked to diet constraints in terms of food availability during winter. While animals consuming pine had higher lipid, phospocholine, and glycerophosphocholine concentrations in their serum than other diet types, birch‐ and willow/aspen‐rich diets exhibit elevated concentrations of several amino acids. The individuals with highest diet diversity had increased levels of ketone bodies, indicating extensive periods of starvation for these individuals.

5. Our results show how the adaptive capacity of moose at the eco‐physiological level varies over a large eco‐geographic scale and how it responds to land use pressures. In light of extensive ongoing climate and land use changes, these findings pave the way for future scenario building for animal adaptive capacity.

Ostreococcus spp. are unicellular organisms with one of the simplest cellular organizations. The sequencing of the genomes of different Ostreococcus species has reinforced this status since Ostreococcus tauri has one most compact nuclear genomes among eukaryotic organisms. Despite this, it has retained a number of genes, setting it apart from other organisms with similar small genomes. Ostreococcus spp. feature a substantial number of selenocysteine-containing proteins, which, due to their higher catalytic activity compared to their selenium-lacking counterparts, may require a reduced quantity of proteins. Notably, O. tauri encodes several ammonium transporter genes, that may provide it with a competitive edge for acquiring nitrogen (N). This characteristic makes it an intriguing model for studying the efficient use of N in eukaryotes. Under conditions of low N availability, O. tauri utilizes N from abundant proteins or amino acids, such as L-arginine, similar to higher plants. However, the presence of a nitric oxide synthase (L-arg substrate) sheds light on a new metabolic pathway for L-arg in algae. The metabolic adaptations of O. tauri to day and night cycles offer valuable insights into carbon and iron metabolic configuration. O. tauri has evolved novel strategies to optimize iron uptake, lacking the classic components of the iron absorption mechanism. Overall, the cellular and genetic characteristics of Ostreococcus contribute to its evolutionary success, making it an excellent model for studying the physiological and genetic aspects of how green algae have adapted to the marine environment. Furthermore, given its potential for lipid accumulation and its marine habitat, it may represent a promising avenue for third-generation biofuels.

Background: Cluster analysis, and in particular hierarchical clustering, is widely used to extract information from gene expression data. The aim is to discover new classes, or sub-classes, of either individuals or genes. Performing a cluster analysis commonly involve decisions on how to; handle missing values, standardize the data and select genes. In addition, pre processing, involving various types of filtration and normalization procedures, can have an effect on the ability to discover biologically relevant classes. Here we consider cluster analysis in a broad sense and perform a comprehensive evaluation that covers several aspects of cluster analyses, including normalization.

Result: We evaluated 2780 cluster analysis methods on seven publicly available 2-channel microarray data sets with common reference designs. Each cluster analysis method differed in data normalization (5 normalizations were considered), missing value imputation (2), standardization of data (2), gene selection (19) or clustering method (11). The cluster analyses are evaluated using known classes, such as cancer types, and the adjusted Rand index. The performances of the different analyses vary between the data sets and it is difficult to give general recommendations. However, normalization, gene selection and clustering method are all variables that have a significant impact on the performance. In particular, gene selection is important and it is generally necessary to include a relatively large number of genes in order to get good performance. Selecting genes with high standard deviation or using principal component analysis are shown to be the preferred gene selection methods. Hierarchical clustering using Ward's method, k-means clustering and Mclust are the clustering methods considered in this paper that achieves the highest adjusted Rand. Normalization can have a significant positive impact on the ability to cluster individuals, and there are indications that background correction is preferable, in particular if the gene selection is successful. However, this is an area that needs to be studied further in order to draw any general conclusions.

Conclusions: The choice of cluster analysis, and in particular gene selection, has a large impact on the ability to cluster individuals correctly based on expression profiles. Normalization has a positive effect, but the relative performance of different normalizations is an area that needs more research. In summary, although clustering, gene selection and normalization are considered standard methods in bioinformatics, our comprehensive analysis shows that selecting the right methods, and the right combinations of methods, is far from trivial and that much is still unexplored in what is considered to be the most basic analysis of genomic data.

Despite the great morphological diversity of insects, there is a regularity in their digestive functions, which is apparently related to their physiology. In the present work we report the de novo midgut transcriptomes of four non-model insects from four distinct orders: Spodoptera frugiperda (Lepidoptera), Musca domestica (Diptera), Tenebrio molitor (Coleoptera) and Dysdercus peruvianus (Hemiptera). We employed a computational strategy to merge assemblies obtained with two different algorithms, which substantially increased the quality of the final transcriptomes. Unigenes were annotated and analyzed using the eggNOG database, which allowed us to assign some level of functional and evolutionary information to 79.7% to 93.1% of the transcriptomes. We found interesting transcriptional patterns, such as: i) the intense use of lysozymes in digestive functions of M. domestica larvae, which are streamlined and adapted to feed on bacteria; ii) the up-regulation of orthologous UDP-glycosyl transferase and cytochrome P450 genes in the whole midguts different species, supporting the existence of an ancient defense frontline to counter xenobiotics; iii) evidence supporting roles for juvenile hormone binding proteins in the midgut physiology, probably as a way to activate genes that help fight anti-nutritional substances (e.g. protease inhibitors). The results presented here shed light on the digestive and structural properties of the digestive systems of these distantly related species. Furthermore, the produced datasets will also be useful for scientists studying these insects.

Key message: Here we uncover the major evolutionary events shaping the evolution of the GID1 family of gibberellin receptors in land plants at the sequence, structure and gene expression levels.

Abstract: Gibberellic acid (gibberellin, GA) controls key developmental processes in the life cycle of land plants. By interacting with the GIBBERELLIN INSENSITIVE DWARF1 (GID1) receptor, GA regulates the expression of a wide range of genes through different pathways. Here we report the systematic identification and classification of GID1s in 54 plants genomes, encompassing from bryophytes and lycophytes, to several monocots and eudicots. We investigated the evolutionary relationship of GID1s using a comparative genomics framework and found strong support for a previously proposed phylogenetic classification of this family in land plants. We identified lineage-specific expansions of particular subfamilies (i.e. GID1ac and GID1b) in different eudicot lineages (e.g. GID1b in legumes). Further, we found both, shared and divergent structural features between GID1ac and GID1b subgroups in eudicots that provide mechanistic insights on their functions. Gene expression data from several species show that at least one GID1 gene is expressed in every sampled tissue, with a strong bias of GID1b expression towards underground tissues and dry legume seeds (which typically have low GA levels). Taken together, our results indicate that GID1ac retained canonical GA signaling roles, whereas GID1b specialized in conditions of low GA concentrations. We propose that this functional specialization occurred initially at the gene expression level and was later fine-tuned by mutations that conferred greater GA affinity to GID1b, including a Phe residue in the GA-binding pocket. Finally, we discuss the importance of our findings to understand the diversification of GA perception mechanisms in land plants.

Until recently, the majority of genomics assays have been performed on bulk tissue samples containing multiple cell types. Tissues such as the wood formation zone in trees contain a complex mix of cell types organised in three-dimensional space. Moreover, cells within the wood formation zone represent a continual developmental progression from meristematic cambial initials through to cell death. This spatiotemporal developmental gradient and cell type information are not assayed by bulk samples. New and improved sampling methods coupled to next-generation sequencing assays are enabling the generation of high spatial resolution and single-cell transcriptomics data, offering unprecedented insight into the biology of unique cell types and cell developmental programs. We overview the application of these approaches to the study of wood development, in particular, and highlight challenges associated with the analysis of such data.

Understanding complex biological systems requires functional characterization of specialized tissue domains. However, existing strategies for generating and analysing high-throughput spatial expression profiles were developed for a limited range of organisms, primarily mammals. Here we present the first available approach to generate and study highresolution, spatially resolved functional profiles in a broad range of model plant systems. Our process includes highthroughput spatial transcriptome profiling followed by spatial gene and pathway analyses. We first demonstrate the feasibility of the technique by generating spatial transcriptome profiles from model angiosperms and gymnosperms microsections. In Arabidopsis thaliana we use the spatial data to identify differences in expression levels of 141 genes and 189 pathways in eight inflorescence tissue domains. Our combined approach of spatial transcriptomics and functional profiling offers a powerful new strategy that can be applied to a broad range of plant species, and is an approach that will be pivotal to answering fundamental questions in developmental and evolutionary biology.