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Background: Microsporidia are a large taxon of intracellular pathogens characterized by extraordinarily streamlined genomes with unusually high sequence divergence and many species-specific adaptations. These unique factors pose challenges for traditional genome annotation methods based on sequence similarity. As a result, many of the microsporidian genomes sequenced to date contain numerous genes of unknown function. Recent innovations in rapid and accurate structure prediction and comparison, together with the growing amount of data in structural databases, provide new opportunities to assist in the functional annotation of newly sequenced genomes. Results: In this study, we established a workflow that combines sequence and structure-based functional gene annotation approaches employing a ChimeraX plugin named ANNOTEX (Annotation Extension for ChimeraX), allowing for visual inspection and manual curation. We employed this workflow on a high-quality telomere-to-telomere sequenced tetraploid genome of Vairimorpha necatrix. First, the 3080 predicted protein-coding DNA sequences, of which 89% were confirmed with RNA sequencing data, were used as input. Next, ColabFold was used to create protein structure predictions, followed by a Foldseek search for structural matching to the PDB and AlphaFold databases. The subsequent manual curation, using sequence and structure-based hits, increased the accuracy and quality of the functional genome annotation compared to results using only traditional annotation tools. Our workflow resulted in a comprehensive description of the V. necatrix genome, along with a structural summary of the most prevalent protein groups, such as the ricin B lectin family. In addition, and to test our tool, we identified the functions of several previously uncharacterized Encephalitozoon cuniculi genes. Conclusion: We provide a new functional annotation tool for divergent organisms and employ it on a newly sequenced, high-quality microsporidian genome t

Prehistoric chewed pitch has proven to be a useful source of ancient DNA, both from humans and their microbiomes. Here we present the metagenomic analysis of three pieces of chewed pitch from Huseby Klev, Sweden, that were dated to 9,890-9,540 before present. The metagenomic profile exposes a Mesolithic oral microbiome that includes opportunistic oral pathogens. We compared the data with healthy and dysbiotic microbiome datasets and we identified increased abundance of periodontitis-associated microbes. In addition, trained machine learning models predicted dysbiosis with 70-80% probability. Moreover, we identified DNA sequences from eukaryotic species such as red fox, hazelnut, red deer and apple. Our results indicate a case of poor oral health during the Scandinavian Mesolithic, and show that pitch pieces have the potential to provide information on material use, diet and oral health.

Background: Exfoliative glaucoma (XFG) is a subtype of open-angle glaucoma characterized by distinctive extracellular fibrils and a yet unknown pathogenesis potentially involving immune-related factors. The aim of this exploratory study was to identify biomarkers for XFG using data from autoimmunity profiling performed on blood samples from a Scandinavian cohort of patients. Methods: Autoantibody screening was analyzed against 258 different protein fragments in blood samples taken from 30 patients diagnosed with XFG and 30 healthy donors. The 258 protein fragments were selected based on a preliminary study performed on 3072 randomly selected antigens and antigens associated with the eye. The “limma” package was used to perform moderated t-tests on the proteomic data to identify differentially expressed reactivity between the groups. Results: Multiple associated genes were highlighted as possible biomarker candidates including FUT2, CDH5, and the LOX family genes. Using seven variables, our binary logistic regression model was able to classify the cases from the controls with an AUC of 0.85, and our reduced model using only one variable corresponding to the FUT2 gene provided an AUC of 0.75, based on LOOCV. Furthermore, over-representation gene analysis was performed to identify pathways that were associated with antigens differentially bound to self-antibodies. This highlighted the enrichment of pathways related to collagen fibril formation and the regulatory molecules mir-3176 and mir-876-5p. Conclusions: This study suggests several potential biomarkers that may be useful in developing further models of the pathology of XFG. In particular, CDH5, FUT2, and the LOX family seem to have a relationship which merits additional exploration., CC BY 4.0 DEED© 2024, The Author(s).Correspondence Address: R. Potter; Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden; email: ryan.potter@his.seOpen access funding provided by University of Gothenburg. This study was funded by Synskadades Vänner Skaraborg (SVS).

Biological variations are being explored at ever-increasing rates through the rapid advancement of analytical techniques. Techniques like massively parallel sequencing empower scientists to accurately differentiate individuals’ genetic compositions, cellular functionalities, and healthy tissue from diseased. The knowledge gained from these techniques brings us ever closer to grasping the complexities of life, contributing to human development. Still, to fully elucidate biological variations in different samples requires novel sensitive and high- throughput techniques, capable of placing everything in its correct context. One such technique gaining promise is droplet barcoding. Droplet barcoding leverages emulsion droplets to segregate samples into their functional components, coupled with barcodes that can group tagged molecules following sequencing. This technique constitutes a versatile tool for studying biological variations in both the phenotype and genotype. This thesis leverages droplet barcoding to explore variations relating to human biology. Droplet barcoding was used to study phenotype variations, looking at protein compositions in single extracellular vesicles (Paper I) and single cells (Paper II). Paper I studies extracellular vesicles which are naturally released from cells. They carry heterogeneous protein signatures that can inform about their cellular origin. Tens of thousands of extracellular vesicles were profiled, including approximately 25,000 from lung cancer patients. From these protein profiles, extracellular vesicles could be grouped into putative subtypes. Paper II presents a novel method for studying single cells which was used to characterize blood-derived immune cells. The method enabled the identification of most major immune cell lineages. Haplotype-resolved genetic variations were analyzed using a linked read sequencing method based on droplet barcoding. Linked-read sequencing conserves long-range information from short-read sequenc, Biologiska variationer utforskas i allt snabbare grad, pådrivet av den snabba utvecklingen av analytiska tekniker. Tekniker som massiv parallellsekvensering möjliggör för forskare att noggrant särskilja individers genetiska sammansättningar, cellernas olika funktioner och frisk vävnad från sjuk. Vetskapen dessa tekniker medför ger oss allt djupare insikter om livsformers komplexitet som främjar mänsklig utveckling. Torts dessa framsteg kräver klarläggandet av biologiska variationer i olika prover nya känsliga tekniker med hög kapacitet, kapabla att placera information i dess rätta sammanhang. En särskilt lovande teknik är droppkodning. Droppkodning utnyttjar emulsionsdropparnas förmåga att separera prover i dess funktionella komponenter kombinerat med DNA-koder för att gruppera märkta molekyler efter sekvensering. Denna teknik utgör ett mångsidigt verktyg för att studera biologiska variationer i både fenotyp och genotyp. Den här avhandlingen utforskar tekniker baserat på droppkodning för att analysera dessa variationer relaterat till människlig biologi. Droppkodning användes i analys av fenotypvariationer genom att studera proteinsignaturer hos enskilda extracellulära vesiklar (Artikel I) samt enskilda celler (Artikel II). Artikel I studerar extracellulära vesiklar, vilka är partiklar som naturligt släpps ut från celler. Dessa vesiklar bär på heterogena protein-signaturer som kan informera om dess cellulära härkomst. I studien undersöks proteinsignaturer från tiotusentals extracellulära vesiklar, inklusive cirka 25 000 från lungcancerpatienter. Utifrån dessa signaturer kunde extracellulära vesiklar sedan grupperas i potentiella subtyper. Artikel II presenterar en ny metod för att studera enskilda celler, som användes för att karakterisera immunceller från blod. Metoden möjliggjorde identifiering av de flesta stora immuncellspopulationerna. Haplotyp-upplösta genotypvariationer analyserades med en metod för länk, QC 2024-02-15

Organisms throughout the tree of life have evolved distinct ways to regulate gene expression. Some of these processes involve non-coding RNAs (ncRNAs), which are not translated but functional nonetheless. These ncRNAs are of utmost importance, with dysregulation of some causing severe developmental effects or even being lethal. In order to get a better fundamental understanding of gene regulation, and the ncRNAs that evolved to regulate gene expression, we study this in Amoebozoa. Members of this taxon vary greatly in lifestyle and organismal complexity. Some are strictly unicellular, free-living, whereas others, such as the social amoeba Dictyostelium discoideum can transition between unicellular and multicellular lifestyles. D. discoideum features a variety of small ncRNAs. Among these are the microRNAs. microRNAs have mostly been studied in plants and animals, where they are believed to have evolved convergently, and hypothesized to have played a role when these taxa evolved multicellular lifestyles. At what point the D. discoideum microRNAs evolved, how they function, and if they are involved in its multicellular lifestyle are fundamental questions addressed in this thesis. Here, we studied the evolution and function of microRNAs in a broad set of species belonging to Amoebozoa. We could identify microRNAs in all studied amoebae, and concluded that they are probably not involved in the evolution of multicellularity. To in detail investigate the evolution of microRNAs, we performed comparative genomics using D. discoideum and the close relative Dictyostelium firmibasis. For this, we sequenced, assembled and annotated the genome of the latter. At this point, our findings suggest that the microRNAs evolved several times in Amoebozoa, although we cannot rule out if they have a deep evolutionary history. The Class I RNAs are another type of ncRNAs. These, on the other hand, are only present in the social amoebae. They are hypothesized to regulate the transition fro

Predicting protein structures has long been an area of active research in the field ofbioinformatics. Great strides have recently been made in this area by googles DeepMindteam. They developed an AI called AlphaFold which is able to make the most accuratepredictions of protein structures as of date. With the advent of AlphaFold some considerthe problem solved. There is however an area in protein prediction that has lagged be-hind, that of multi conformational prediction. There are proteins that can take on oneout of several active forms in the body. Making predictions for these are harder than forsingle conformational proteins due to an increase in complexity and a lack of data. Apromising solution to this problem is to introduce noise to the input data AlphaFold usesto create a wider range of predictions. In this thesis multi conformational prediction withdifferent methods to introduce noise is evaluated. Dropout, disclosing templates, untar-geted Multiple sequence alignment(MSA) subsampling and targeted MSA subsamplingwere used. It was concluded introducing noise did indeed improve the prediction of mul-tiple conformations. Among them, MSA subsampling seemed to be the most effective,especially untargeted MSA subsampling. Dropout also seemed to slightly improve theresults while excluding template information did little to nothing. AlphaFold was unableto predict both structures for 6 out of 16 structures, even with introduced noise. No clearreason for why this could be determined, but the leading hypothesis is that AlhpaFoldwas unable to extract sufficient information about both conformations from the MSAdata for these proteins.

Background The common house spider Parasteatoda tepidariorum represents an emerging new model organism of arthropod evolutionary and developmental (EvoDevo) studies. Recent technical advances have resulted in the first single-cell sequencing (SCS) data on this species allowing deeper insights to be gained into its early development, but mid-to-late stage embryos were not included in these pioneering studies. Results Therefore, we performed SCS on mid-to-late stage embryos of Parasteatoda and characterized resulting cell clusters by means of in-silico analysis (comparison of key markers of each cluster with previously published information on these genes). In-silico prediction of the nature of each cluster was then tested/verified by means of additional in-situ hybridization experiments with additional markers of each cluster. Conclusions Our data show that SCS data reliably group cells with similar genetic fingerprints into more or less distinct clusters, and thus allows identification of developing cell types on a broader level, such as the distinction of ectodermal, mesodermal and endodermal cell lineages, as well as the identification of distinct developing tissues such as subtypes of nervous tissue cells, the developing heart, or the ventral sulcus (VS). In comparison with recent other SCS studies on the same species, our data represent later developmental stages, and thus provide insights into different stages of developing cell types and tissues such as differentiating neurons and the VS that are only present at these later stages.

Vascular diseases are the largest cause of death globally and impose a major global burden on healthcare. The gold standard for treating vascular diseases is the transplantation of autologous veins, if applicable. Alternative treatments still suffer from shortcomings, including low patency, lack of growth potential, the need for repeated intervention, and a substantial risk of developing infections. The use of a vascular ECM scaffold reconditioned with the patient's own cells has shown successful results in preclinical and clinical studies. In this study, we have compared the proteomes of personalized tissue-engineered veins of humans and pigs. By applying tandem mass tag (TMT) labeling LC/MS-MS, we have investigated the proteome of decellularized (DC) veins from humans and pigs and reconditioned (RC) DC veins produced through perfusion with the patient's whole blood in STEEN solution, applying the same technology as used in the preclinical studies. The results revealed high similarity between the proteomes of human and pig DC and RC veins, including the ECM texture after decellularization and reconditioning. In addition, functional enrichment analysis showed similarities in signaling pathways and biological processes involved in the immune system response. Furthermore, the classification of proteins involved in immune response activity that were detected in human and pig RC veins revealed proteins that evoke immunogenic responses, which may lead to graft rejection, thrombosis, and inflammation. However, the results from this study imply the initiation of wound healing rather than an immunogenic response, as both systems share the same processes, and no immunogenic response was reported in the preclinical and clinical studies. Finally, our study assessed the application of STEEN solution in tissue engineering and identified proteins that may be useful for the prediction of successful transplantations., CC BY-NC-ND 4.0 DEED© 2024 The Authors. Published by American Chemical Society.Correspondence Address: N. Ghosheh; Systems Biology Research Center, School of Bioscience, University of Skövde, Skövde, SE-541 28, Sweden; email: nidal.ghosheh@his.seThe study was supported by VERIGRAFT AB (Gothenburg, Sweden), XVIVO Perfusion AB (Gothenburg, Sweden), and the University of Skövde under a grant from the Knowledge Foundation [2018/0125]. The authors appreciate the Proteogenomics unit at SciLifeLab (Solna, Sweden) and Georgios Mermelekas for the proteomics service. The authors also appreciate the scientific advice from Anne-Li Sigvardsson and the laboratory support from Carina Ström.

Even when split into several chromosomes, DNA molecules that make up our genome are too long to fit into the cell nuclei unless massively folded. Such folding must accommodate the need for timely access to selected parts of the genome by transcription factors, RNA polymerases, and DNA replication machinery. Here, we review our current understanding of the genome folding inside the interphase nuclei. We consider the resulting genome architecture at three scales with a particular focus on the intermediate (meso) scale and summarize the insights gained from recent experimental observations and diverse computational models.

In this thesis, two algorithms that detect cross-contamination in tumor samples sequenced with next-generation sequencing (NGS) were evaluated. In genomic medicine, NGS is commonly used to sequence tumor DNA to detect disease-associated genetic variants and determine the most suitable treatment option. Targeted NGS panels are often employed to screen for genetic variations in a selection of specific tumor-associated genes. NGS handles samples from multiple patients in parallel, which poses a risk of cross-contamination between samples. Contamination is a significant issue in the interpretation of NGS results, as it can lead to the incorrect identification of genetic variants and, consequently, incorrect treatment. Therefore, contamination detection is a crucial quality control steps in the analysis of NGS data. Numerous algorithms for detection of cross-contamination have been developed, but many of these algorithms are not suited for small, targeted NGS panels, and several are not developed for tumor data. In this thesis, GATK's CalculateContamination and a self-created algorithm called ContaCheck were evaluated on simulated tumor NGS data. NGS samples were generated in silico with a Python script called BAMSynth and mixed to simulate cross-contamination with contamination rates between 1% and 50%. ContaCheck accurately detected contaminations ranging from 3% to 50% and identified the correct contaminant with an accuracy of 94%. CalculateContamination, on the other hand, detected contaminations ranging from 1% to 15% relatively accurately, but consistently failed to detect high level contaminations. The study showed that ContaCheck outperformed CalculateContamination on simulated NGS data, but to determine which algorithm is the best on real data and determine ContaCheck's applicability in a clinical setting, the algorithms need to be further evaluated on real tumor NGS samples.

Understanding and exploring macromolecular dynamics is crucial for drug development, studying biological functions, and protein engineering, and thus remain a core focus in structural biology. However, revealing partially-occupied states in X-ray crystallography faces challenges due to inherent averaging over a population of proteins in different states, obscuring low-occupancy states in experimentally determined electron densities. One way forward is offered by analysing multiple datasets together, in particular the variations of occupancies found between equivalent datasets, i.e. data collected from different crystals grown in equivalent experimental conditions. In this thesis, I consider real datasets as realisations of a distribution of possible occupancy sets, and try to fit a model against this data using maximum likelihood methods. The resulting network outputs maps resembling electron density maps, containing small differences but does not yet reveal underlying states for the residues. This is believed to be caused by a local minimum, as the model optimises the maps to replicate an average of the real data.

Sepsis, a life-threatening condition with alarmingly high mortality rates, demands the development of improved diagnostic methods to better understand and manage the disease. Enterococcus spp., significant contributors to sepsis and known for their multidrug resistance, urgently require thorough and detailed investigation to devise effective treatment strategies and healthcare interventions. This study explores the genomic characterization of Enterococcus spp. isolates from patients with suspected community-onset sepsis in Sweden, using whole-genome sequencing. Data was processed through an in-house developed bioinformatics pipeline, including quality control with FastQC, sequence trimming with Trimmomatic, and assembly with Unicycler and QUAST, generating reliable FASTA files for the downstream analysis. Subsequently, different gene annotation tools were applied for the genotypic species identification, prediction of antibiotic resistance genes, plasmid replicons, and determination of sequence type. Moreover, the results from the genotypic characterization were compared to those obtained using routine microbiological methods based on cultures followed by MALDI-TOF MS for species identification and disk diffusion for antimicrobial resistance testing. The results revealed a high prevalence of resistance genes, particularly for macrolide, lincosamide, and streptogramin antibiotics. A notable finding was the high discordance (83.2%) between phenotypic and genotypic methods in detecting resistance, highlighting the complexity of correlating phenotypic antibiotic resistance with genotypic predictions. Additionally, a statistically significant higher prevalence of antibiotic resistance genes was observed in E. faecium compared to E. faecalis (p=0.001). Furthermore, a high diversity of sequence types among Enterococcus spp. isolates was detected by multi-locus sequencing typing, with ST6 (14%) as the most prevalent for E. faecalis and ST192 (37%) for E. faecium. In conclus, Det finns övrigt digitalt material (t.ex. film-, bild- eller ljudfiler) eller modeller/artefakter tillhörande examensarbetet som ska skickas till arkivet.There are other digital material (eg film, image or audio files) or models/artifacts that belongs to the thesis and need to be archived.

The development of single-cell RNA sequencing enabled the high throughput characterization of cell populations with unprecedented detail. Yet, it failed in capturing the spatial localization of individual cells. Overcoming this, different spatial profiling methods have been developed in recent years, with in situ sequencing (ISS) being among the most powerful solutions ISS is a targeted spatially-resolved transcriptomics method designed to detect the expression of hundreds of genes in situ in a single experiment. For this, ISS employs padlock probes, a type of oligonucleotide designed to specifically hybridize on the targeted regions, with rolling circle amplification and a combinatorial detection of the transcripts imaged. Due to its throughput and resolution, ISS is seen as a useful tool to create high content molecular maps of tissues, being of special use for building spatial atlases. However, due to its recent development, it’s still unclear how this should be done. The work presented in this thesis explores ISS as a tool for building large spatially-resolved atlases of cell types. In paper I, we compare the performance of cDNA-based ISS with the High Sensitivity Library Preparation Kit, developed by CARTANA AB. We identify this product to be fivefold more sensitive than cDNA-based ISS due to its improved chemistry. In addition, we show that this increased sensitivity enhances the analytical capabilities of the resulting data. In paper II, we build a topographic atlas of the developmental human lung. We identify 83 different cell types and states, including a novel type of GHRL-positive neuroendocrine cell. We further elucidate the developmental origin multiple populations, defining their location in situ and predicting potential interactions. In paper III, we create a topographic atlas of the adult human lung. We combine multiple spatial transcriptomic technologies to generate spatial maps of the populations found in the adult lung. We decipher regional

microRNA are short non-coding RNAs and important post transcriptional gene regulators. miRNAs are found in all animals that have been studied, in numbers that largely correlate with organismal complexity. For instance, nematodes have around 200 miRNA genes, while humans have more than 600 miRNA genes. Mutant animals that are deficient in miRNAs generally exhibit gross developmental defects or embryonic lethality, underlining the importance of these regulators. Given that an estimated ~60% of human mRNAs are targeted by miRNAs in some cellular context, it is not surprising that these regulators are involved in numerous biological processes, ranging from the formation of cell identity to development and human diseases. Even though miRNAs have been systematically studied for over a decade, fundamental questions regarding their biogenesis and function remain unanswered. microRNAs are unevenly distributed between cell types and within homogeneous cell populations, affecting the transcriptomes of individual cells. The vast majority of miRNA studies have been conducted on large pools of cells, and little is known about the biogenesis and function in individual cells. To understand their effect on gene regulation, single-cell measurements are crucial. This thesis introduces techniques that allow us to extend our understanding about microRNA at the resolution of single cells. In Paper I, we develop and establish agoTRIBE – the first sequencing-based method to measure regulatory interactions between miRNAs and their mRNA targets transcriptome-wide in single cells. We applied Smart-seq3 single-cell RNA sequencing to detect increased editing transcriptome-wide in key miRNA targets and found substantial differential targeting across the cell cycle and in mixed cell populations. This method overcame limitations of current methods and allowed for study of heterogeneity in miRNA targeting across individual cells. In Paper II, we further explored miRNA targeting landscape in single

Background: Despite Sweden's rich legacy in entomology, a significant portion of its insect fauna remains poorly studied. Addressing this and other biodiversity knowledge gaps, the Swedish government unveiled the Swedish Taxonomy Initiative (STI) in 2002, with the ambitious goal of documenting and scientifically describing all multicellular species in the country. One of the largest projects funded by STI is the Swedish Malaise Trap Project (SMTP). The SMTP project, the data resulting from it, and the analyses of that data constitute the core of the current thesis. Methods and Results: The SMTP deployed 73 Malaise traps across 55 diverse habitats from 2003 to 2009, capturing an estimated 20 million insects. The catch has been sorted to over 300 taxonomic fractions suitable for further processing by taxonomic experts. The sorted material has been studied by over 100 taxonomists, identifying 4,000 species in about 1% of the total material. A third of these were previously unrecorded in Sweden, including nearly 700 potentially new to science. The SMTP represents a significant community effort and we describe the history, organization, logistics and methodology of the SMTP project, with a focus on the lessons learned along the way and the optimized workflows that resulted in the end. The SMTP output was used to estimate the species richness and composition of the Swedish insect fauna. This included expert assessments, analysis of new species discovery rates, and statistical extrapolations from abundance and incidence data, including a novel non-parametric estimator. These methods converged on an estimate of 33,000 species, 26% of which were unknown at the inventory’s start, and 15% of which still await discovery. To improve the speed and accuracy of the analysis of Malaise trap samples, we introduced morphotype barcoding, combining manual sorting into morphospecies with individual DNA barcoding of representative specimens. Morphotype barcoding is shown to offer more acc

Multicellular organisms form many different cell types from one genome, which requires differential gene activity. The Polycomb system upholds the correct gene expression programs by epigenetically silencing genes that encode critical transcription regulators. It is defined by the protein complexes Polycomb Repressive Complexes 1 and 2 (PRC1 and PRC2). PRC2 methylates lysine 27 on histone H3 (H3K27) on nucleosomes. This is necessary for the repression, but it is not known why. In Drosophila melanogaster both PRC1 and PRC2 bind to DNA elements called PREs near the target genes. In human cells, PRC2 are tethered to CpG islands, but PRC1 tethering is not well understood. In paper I of the thesis we uncover the first comprehensive catalogue of DNA elements, Polycomb Tethering Elements (PTEs), that target PRC1 to human developmental genes. PTEs and CpG islands may be intermixed—forming a PRE equivalent—or offset from each other. Genes equipped with PTEs have low transcription and are stochastically reactivated upon deletion of their PTE. In paper II, we used a computational model to stochastically simulate both the random and targeted methylation by PRC2, to understand the dynamics of H3K27 methylation. The model was constrained by data, such as the levels of methylation in cells, allosteric stimulation of PRC2 by H3K27 trimethylation, and the differing catalytic efficiency of each successive methyl transfer to H3K27. We used it to investigate PRC2’s allosteric stimulation, the relationship between the rates of methylation and demethylation and cell cycle length, and how the rapid embryonic development of D. melanogaster affects the maternal contribution of H3K27me3 to the embryo. In paper III we used polymer modelling to investigate how chromatin folding by PRC1-H3K27me3 interactions affects contacts inside loci repressed by the Polycomb system. With these three studies, this thesis combines experimental and computational methods to further our understanding of epigenet

Inferring relations from correlational data allows researchers across the sciences to uncover complex connections between variables for insights into the underlying mechanisms. The researchers often represent inferred relations using Gaussian graphical models, requiring regularization to sparsify the models. Acknowledging that the modular structure of these inferred networks is often studied, we suggest module-based regularization to balance under- and overfitting. Compared with the graphical lasso, a standard approach using the Gaussian log-likelihood for estimating the regularization strength, this approach better recovers and infers modular structure in noisy synthetic and real data. The module-based regularization technique improves the usefulness of Gaussian graphical models in the many applications where they are employed.

Summary: Nanometa Live presents a user-friendly interface designed for real-time metagenomic data analysis and pathogen identification utilizing Oxford Nanopore Technologies’ MinION and Flongle flow cells. It offers an efficient workflow and graphical interface for the visualization and interpretation of metagenomic data as it is being generated. Key features include automated BLAST validation, streamlined handling of custom Kraken2 databases, and a simplified graphical user interface for enhanced user experience. Nanometa Live is particularly notable for its capability to run without constant internet or server access once installed, setting it apart from similar tools. It provides a comprehensive view of taxonomic composition and facilitates the detection of user-defined pathogens or other species of interest, catering to both researchers and clinicians. Availability and implementation: Nanometa Live has been implemented as a local web application using the Dash framework with Snakemake handling the data processing. The source code is freely accessible on the GitHub repository at https://github.com/FOIBioinformatics/nanometa_live and it is easily installable using Bioconda. It includes containerization support via Docker and Singularity, ensuring ease of use, reproducibility, and portability.

Metabolic fluxes and their control mechanisms are fundamental in cellular metabolism, offering insights for the study of biological systems and biotechnological applications. However, quantitative and predictive understanding of controlling biochemical reactions in microbial cell factories, especially at the system level, is limited. In this work, we present ARCTICA, a computational framework that integrates constraint-based modelling with machine learning tools to address this challenge. Using the model cyanobacterium Synechocystis sp. PCC 6803 as chassis, we demonstrate that ARCTICA effectively simulates global-scale metabolic flux control. Key findings are that (i) the photosynthetic bioproduction is mainly governed by enzymes within the Calvin-Benson-Bassham (CBB) cycle, rather than by those involve in the biosynthesis of the end-product, (ii) the catalytic capacity of the CBB cycle limits the photosynthetic activity and downstream pathways and (iii) ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) is a major, but not the most, limiting step within the CBB cycle. Predicted metabolic reactions qualitatively align with prior experimental observations, validating our modelling approach. ARCTICA serves as a valuable pipeline for understanding cellular physiology and predicting rate-limiting steps in genome-scale metabolic networks, and thus provides guidance for bioengineering of cyanobacteria.

In genome engineering, the integration of incoming DNA has been dependent on enzymes produced by dividing cells, which has been a bottleneck toward increasing DNA insertion frequencies and accuracy. Recently, RNA-guided transposition with CRISPR-associated transposase (CAST) was reported as highly effective and specific in Escherichia coli. Here, we developed Golden Gate vectors to test CAST in filamentous cyanobacteria and to show that it is effective in Anabaena sp. strain PCC 7120. The comparatively large plasmids containing CAST and the engineered transposon were successfully transferred into Anabaena via conjugation using either suicide or replicative plasmids. Single guide (sg) RNA encoding the leading but not the reverse complement strand of the target were effective with the protospacer-associated motif (PAM) sequence included in the sgRNA. In four out of six cases analyzed over two distinct target loci, the insertion site was exactly 63 bases after the PAM. CAST on a replicating plasmid was toxic, which could be used to cure the plasmid. In all six cases analyzed, only the transposon cargo defined by the sequence ranging from left and right elements was inserted at the target loci; therefore, RNA-guided transposition resulted from cut and paste. No endogenous transposons were remobilized by exposure to CAST enzymes. This work is foundational for genome editing by RNA-guided transposition in filamentous cyanobacteria, whether in culture or in complex communities. [GRAPHICS] .

Comprehensive characterization of structural variation in natural populations has only become feasible in the last decade. To investigate the population genomic nature of structural variation, reproducible and high-confidence structural variation callsets are first required. We created a population-scale reference of the genome-wide landscape of structural variation across 33 Nordic house sparrows (Passer domesticus). To produce a consensus callset across all samples using short-read data, we compare heuristic-based quality filtering and visual curation (Samplot/PlotCritic and Samplot-ML) approaches. We demonstrate that curation of structural variants is important for reducing putative false positives and that the time invested in this step outweighs the potential costs of analyzing short-read-discovered structural variation data sets that include many potential false positives. We find that even a lenient manual curation strategy (e.g. applied by a single curator) can reduce the proportion of putative false positives by up to 80%, thus enriching the proportion of high-confidence variants. Crucially, in applying a lenient manual curation strategy with a single curator, nearly all (>99%) variants rejected as putative false positives were also classified as such by a more stringent curation strategy using three additional curators. Furthermore, variants rejected by manual curation failed to reflect the expected population structure from SNPs, whereas variants passing curation did. Combining heuristic-based quality filtering with rapid manual curation of structural variants in short-read data can therefore become a time- and cost-effective first step for functional and population genomic studies requiring high-confidence structural variation callsets.

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.

One of the scopes of Systems Biology is to propose mathematical models that best capture the dynamic behavior of intra-cellular processes. In this regard, the last two decades have brought up a shift in the field, with technological advances now allowing researchers to access a wide range of high-throughput technologies at an affordable cost. These techniques allow to simultaneously interrogate thousands of variables, such as genome-wide transcriptomics and proteomics. However, parallel to these technological advances, there is a growing need for mathematical models that are suited to integrate measurements obtained from different cellular processes. In this thesis we aim to model combinations of three commonly used high-throughput data: epigenetic (namely ATAC-seq and DNA methylation), transcriptomic (RNA-seq) and proteomic data (MASS-spectrometry). In the first work we analyze paired ATAC-seq and RNA-seq data to integrate measurements of (i) chromatin openness, (ii) transcription factors (TFs) availability and (iii) gene expression. To model these data, we use elementary causal motifs, a class of mathematical models which is suited to represent causal interactions between three nodes. Indeed, our analysis shows that the elementary causal motifs in the data are enriched for biologically relevant TF-gene interactions. Moreover, a significant overlap is observed between the causal motifs identified in datasets representing similar cell stimuli, suggesting that causal motifs represent a robust biological signal. This work is then extended to include another class of high-throughput data: MASS-spectrometry. More precisely, we propose a framework to model the flow of events that goes from chromatin remodeling to splice variants expression, and from splice variants to protein synthesis. As the underlying graph becomes more complex than the previous case, a more general mathematical framework is considered: Bayesian networks. Interestingly, this work shows that most putat

Cellular organization is believed to be modular, meaning cellular functions are carried out by modules composed of clusters of genes, proteins and metabolites that are interconnected, co-regulated or physically interacting. In turn, these modules interact together and thereby form complex networks that taken together is considered to be the interactome. Modern high-throughput biological techniques have made high-scale accurate quantification of these biological molecules possible, the so called omics. The simultaneous measurement of these molecules enables a picture of the state of a cell at a resolution that was never before possible. Mapping these measurements aids greatly to elucidate a network structure of interactions. The ever growing size of public repositories for omics data has ushered in the advent of biology as a (big) data science and opens the door for data hungry machine learning approaches in biology. Complex diseases are multi-factorial and arise from a combination of genetic, environmental and lifestyle factors. Additionally, diagnosis and treatment is complicated by the fact that these genetic, environmental and lifestyle factors can vary between patients and may or may not give rise to different disease phenotypes that still classify as the same disease. Genetically, there is substantial heterogeneity among patients and therefore the emergence of a disease phenotype cannot be attributed to a single genetic mutation but rather to a combination of various mutations that may vary from patient to patient. As complex diseases can have different root causes but give rise to a similar disease phenotype, the implication is that different root causes perturb similar components in the interactome. Most of the work in this thesis is aimed at developing methods and computational pipelines to identify, analyze and evaluate these perturbed disease specific sub-networks in the interactome, so called disease modules. We started by collecting popular disease mo

Besides long-term average temperature increases, climate change is projected to result in a higher frequency of marine heatwaves. Coastal zones are some of the most productive and vulnerable ecosystems, with many stretches already under anthropogenic pressure. Microorganisms in coastal areas are central to marine energy and nutrient cycling and therefore, it is important to understand how climate change will alter these ecosystems. Using a long-term heated bay (warmed for 50 years) in comparison with an unaffected adjacent control bay and an experimental short-term thermal (9 days at 6–35 °C) incubation experiment, this study provides new insights into how coastal benthic water and surface sediment bacterial communities respond to temperature change. Benthic bacterial communities in the two bays reacted differently to temperature increases with productivity in the heated bay having a broader thermal tolerance compared with that in the control bay. Furthermore, the transcriptional analysis showed that the heated bay benthic bacteria had higher transcript numbers related to energy metabolism and stress compared to the control bay, while short-term elevated temperatures in the control bay incubation experiment induced a transcript response resembling that observed in the heated bay field conditions. In contrast, a reciprocal response was not observed for the heated bay community RNA transcripts exposed to lower temperatures indicating a potential tipping point in community response may have been reached. In summary, long-term warming modulates the performance, productivity, and resilience of bacterial communities in response to warming., Is included in the dissertation as a manuscript titled: Climate change related warming reduces thermal sensitivity of performance and metabolic plasticity of benthic zone bacterial communities

The adult brain tumor glioblastoma (GBM) is characterized by short survival and a lack of efficient treatments. Median survival is 15 months from time of diagnosis and the 5-year survival rate is only 7 %. There is an urgent need for more efficient treatment against GBM, but there are many challenges, including the high extent of heterogeneity of GBM. The tumoral heterogeneity of GBM ranges from interpatient to intratumoral. The aim of this thesis has been to address unanswered questions relating to the intratumoral heterogeneity of GBM, with three specific focuses; (1) the organization of GBM cell state transitions (paper I and III), (2) the regulation of cell states and cell state transitions (paper II), and (3) targeted interventions against cell states (paper II and IV). In paper I, we develop an experimental-computational method to measure and quantify cell state transitions. We find that GBM cell states organize hierarchically, with a clear “source state” feeding cells downwards in the hierarchy towards a “sink state” with negative growth rate, but with multi-directional transitions between intermediate states. In paper II, we address the lack of computational methods to identify regulators of intratumoral heterogeneity by developing an algorithm called scRegClust that uses scRNA-seq data to estimate regulatory programs. Through an integrative study of the regulatory landscape of neuro-oncology we find two potential regulators of the macrophage-induced mesenchymal transition in GBM. In paper III, we explore the energy-concept as a way of measuring differentiation potential of single cells, instead of relying on gene markers or gene signatures of stemness. We fit a model called the Ising model from statistical mechanics to scRNA-seq data and show both on synthetic and real data that the estimated Ising energy is a good measure of a cell’s differentiation potential, where high Ising energy indicate a high degree of stemness. Finally, in paper IV, another experi

The 2022 Metrics of the Human Proteome from the HUPO Human Proteome Project (HPP) show that protein expression has now been credibly detected (neXtProt PE1 level) for 18 407 (93.2%) of the 19 750 predicted proteins coded in the human genome, a net gain of 50 since 2021 from data sets generated around the world and reanalyzed by the HPP. Conversely, the number of neXtProt PE2, PE3, and PE4 missing proteins has been reduced by 78 from 1421 to 1343. This represents continuing experimental progress on the human proteome parts list across all the chromosomes, as well as significant reclassifications. Meanwhile, applying proteomics in a vast array of biological and clinical studies continues to yield significant findings and growing integration with other omics platforms. We present highlights from the Chromosome-Centric HPP, Biology and Disease-driven HPP, and HPP Resource Pillars, compare features of mass spectrometry and Olink and Somalogic platforms, note the emergence of translation products from ribosome profiling of small open reading frames, and discuss the launch of the initial HPP Grand Challenge Project, “A Function for Each Protein”.

Background: The Living Atlas is an open source platform used to collect, visualise and analyse biodiversity data from multiple sources, and serves as the national biodiversity data hub in many countries. Although powerful, the Living Atlas has had limited func-tionality for species occurrence data derived from DNA sequences. As a step toward integrating this fast-growing data source into the platform, we developed the Ampli-con Sequence Variant (ASV) portal: a web interface to sequence-based biodiversity observations in the Living Atlas.Results: The ASV portal allows data providers to submit denoised metabarcoding output to the Living Atlas platform via an intermediary ASV database. It also enables users to search for existing ASVs and associated Living Atlas records using the Basic Local Alignment Search Tool, or via filters on taxonomy and sequencing details. The ASV portal is a Python-Flask/jQuery web interface, implemented as a multi-container docker service, and is an integral part of the Swedish Biodiversity Data Infrastructure. Conclusion: The ASV portal is a web interface that effectively integrates biodiversity data derived from DNA sequences into the Living Atlas platform., QC 20230127

Malaria inflicts the highest rate of morbidity and mortality among the vector-borne diseases. The dramatic bottleneck of parasite numbers that occurs in the gut of the obligatory mosquito vector provides a promising target for novel control strategies. Using single-cell transcriptomics, we analyzed Plasmodium falciparum development in the mosquito gut, from unfertilized female gametes through the first 20 h after blood feeding, including the zygote and ookinete stages. This study revealed the temporal gene expression of the ApiAP2 family of transcription factors and of parasite stress genes in response to the harsh environment of the mosquito midgut. Further, employing structural protein prediction analyses, we found several upregulated genes predicted to encode intrinsically disordered proteins (IDPs), a category of proteins known for their importance in regulation of transcription, translation, and protein-protein interactions. IDPs are known for their antigenic properties and may serve as suitable targets for antibody- or peptide-based transmission suppression strategies. In total, this study uncovers the P. falciparum transcriptome from early to late parasite development in the mosquito midgut, inside its natural vector, which provides an important resource for future malaria transmission-blocking initiatives.

Shigella is a Gram-negative bacterium that invades the human gut epithelium. The resulting infection, shigellosis, is the deadliest bacterial diarrheal disease. Much of the information about the genes dictating the pathophysiology of Shigella, both on the chromosome and the virulence plasmid, was obtained by classical reverse genetics. However, technical limitations of the prevalent mutagenesis techniques restrict the generation of mutants in a single reaction to a small number, preventing large-scale targeted mutagenesis of Shigella and the subsequent assessment of phenotype. We adopted a CRISPR-Cas-dependent approach, where a nickase Cas9 and cytidine deaminase fusion is guided by single guide RNA (sgRNA) to introduce targeted C→T transitions, resulting in internal stop codons and premature termination of translation. In proof-of-principle experiments using an mCherry fluorescent reporter, we were able to generate loss-of-function mutants in both Escherichia coli and Shigella flexneri with up to 100% efficacy. Using a modified fluctuation assay, we determined that under optimized conditions, the frequency of untargeted mutations introduced by the Cas9-deaminase fusion was in the same range as spontaneous mutations, making our method a safe choice for bacterial mutagenesis. Furthermore, we programmed the method to mutate well-characterized chromosomal and plasmid-borne Shigella flexneri genes and found the mutant phenotype to be similar to those of the reported gene deletion mutants, with no apparent polar effects at the phenotype level. This method can be used in a 96-well-plate format to increase the throughput and generate an array of targeted loss-of-function mutants in a few days.

Mercury (Hg) methylation genes (hgcAB) mediate the formation of the toxic methylmercury and have been identified from diverse environments, including freshwater and marine ecosystems, Arctic permafrost, forest and paddy soils, coal-ash amended sediments, chlor-alkali plants discharges and geothermal springs. Here we present the first attempt at a standardized protocol for the detection, identification and quantification of hgc genes from metagenomes. Our Hg-cycling microorganisms in aquatic and terrestrial ecosystems (Hg-MATE) database, a catalogue of hgc genes, provides the most accurate information to date on the taxonomic identity and functional/metabolic attributes of microorganisms responsible for Hg methylation in the environment. Furthermore, we introduce "marky-coco", a ready-to-use bioinformatic pipeline based on de novo single-metagenome assembly, for easy and accurate characterization of hgc genes from environmental samples. We compared the recovery of hgc genes from environmental metagenomes using the marky-coco pipeline with an approach based on coassembly of multiple metagenomes. Our data show similar efficiency in both approaches for most environments except those with high diversity (i.e., paddy soils) for which a coassembly approach was preferred. Finally, we discuss the definition of true hgc genes and methods to normalize hgc gene counts from metagenomes.

Postmortem metabolomics can assist death investigations by characterizing metabolic fingerprints differentiating causes of death. Hypoglycemia-related deaths, including insulin intoxications, are difficult to identify and, thus, presumably underdiagnosed. This investigation aims to differentiate insulin intoxication deaths by metabolomics, and identify a metabolic fingerprint to screen for unknown hypoglycemia-related deaths. Ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry data were obtained from 19 insulin intoxications (hypo), 19 diabetic comas (hyper), and 38 hangings (control). Screening for potentially unknown hypoglycemia-related deaths was performed using 776 random postmortem cases. Data were processed using XCMS and SIMCA. Multivariate modeling revealed group separations between hypo, hyper, and control groups. A metabolic fingerprint for the hypo group was identified, and analyses revealed significant decreases in 12 acylcarnitines, including nine hydroxylated-acylcarnitines. Screening of random postmortem cases identified 46 cases (5.9%) as potentially hypoglycemia-related, including six with unknown causes of death. Autopsy report review revealed plausible hypoglycemia-cause for five unknown cases. Additionally, two diabetic cases were found, with a metformin intoxication and a suspicious but unverified insulin intoxication, respectively. Further studies are required to expand on the potential of postmortem metabolomics as a tool in hypoglycemia-related death investigations, and the future application of screening for potential insulin intoxications.

Transposable elements constitute a substantial portion of most eukaryotic genomes and their activity can lead to developmental and neuronal defects. In the germline, transposon activity is antagonized by the PIWI-interacting RNA pathway tasked with repression of transposon transcription and degrading transcripts that have already been produced. However, most of the genes required for transposon control are not expressed outside the germline, prompting the question: what causes deleterious transposons activity in the soma and how is it managed? Here, we show that disruptions of the Histone 3 lysine 36 methylation machinery led to increased transposon transcription in Drosophila melanogaster brains and that there is division of labour for the repression of transposable elements between the different methyltransferases Set2, NSD, and Ash1. Furthermore, we show that disruption of methylation leads to somatic activation of key genes in the PIWI-interacting RNA pathway and the preferential production of RNA from dual-strand piRNA clusters., Originally included in thesis in manuscript form.

In this work we present a novel method to extract potential hub genes, transcription factors and regions with densely interconnected protein-protein-interaction networks from RNAseq data. To achieve this we deploy variational autoencoders, a generative machine learning framework, and extract the gene-wise reconstruction errors. This reconstruction error produced during training is considered as a measurement of impact for a gene on the transcriptome here. The method can handle big datasets (3.5Gb and more) in reasonable time on computers for domestic usage without any gpu-acceleration. This circumstance allows users without access to large amounts of computational resources to also work with expression data of large size. The final ranking based on reconstruction errors underlies less of a bias compared to most hub gene inference methods currently available. Also no prior gene regulatory network inference is required. However, the introduction of a bias can help to focus on certain genes of interest. Here we biased by using genes present in the STRING data base to also ease the following analysis. Analysis of reconstruction error showed a tendency for genes with low reconstruction error to capture genes with central meaning to the data set used for training. In case of healthy cells this was genes associated with house keeping mechanisms and for breast cancer data those genes were associated to breast cancer. In breast cancer specific data we found for example a high frequency of HOX family members linked specifically to breast cancer. For data covering different types of cancer here the picture was broader and covered a wide range of genes associated with different types of cancer. There also was a high enrichment of transcription factors present in the genes with low reconstruction error. Not only the regions with lowest reconstruction error will reveal a high enrichment for transcription factors, also other regions show transcription factor enrichment. Transc

The functional and anatomical characteristics of cancer cells vary among patients. Additionally, therapeutic approaches display varying responses in different individuals and cancer types due to the anatomical and functional complexity of tumor. Prognosis, and responsiveness to therapy depends on the tissue architecture of the tumor microenvironment (TME). TME cells, including immune cells, endothelial cells, stromal cells, and their subtypes, coexist with cancer cells. The cellular and spatial architecture of the TME show significant variation across and within individuals. There is an important correlation between cell function and its spatial organization in the tissue. To unravel this organization, typical clustering is applied on spatial omics data to find discrete clusters based on local cellular abundance. Alternatively, graph-based methods are used to define clusters of cells that are closest to each other, using community-detection methods. In order to better understand the rules governing the design, formation, and interactions of the TME, the Niche-Phenotype Mapping (NIPMAP) analysis pipeline, developed based on ideas from community ecology and machine learning, was reimplemented on a new and different type of data called Hyperplexed Immunofluorescence Imaging (HIFI) from mouse glioblastoma multiforme cancer (GBM) tissue sections. NIPMAP identified cellular niches and their interactions in this dataset. Niche abundance and their cell type composition were dynamic in response to ionizing radiation (IR) treatment and relapseHausser. Testing different numbers of archetypes, resulted in different optimal niche numbers for different condition groups. So, the optimal niche is not only specific to different tissue and cancer types but also to the treatment and other experimental conditions.

Antimicrobial resistance (AMR) is rapidly becoming a public health issue, as more and more infectious bacteria become resistant to our known antibiotics. Suggested reasons for the proliferation of these strains are misuse and overuse of antimicrobials, especially on an industrial level, in agriculture, livestock and aquaculture. These resistances are not unique for pathogenic bacteria but originates in nature where complicated systems of microbes interact with each other. Freshwater environments hold special interest as they provide drinking water, have unique biodiversity, and provide other ecosystem services. Soil environments have the most diverse microbial communities and are often the source of new discoveries in microbial functions and AMR interactions. Since the vast majority of microbes can’t be maintained in pure culture or replicated in the laboratory setting, metagenomic methods have proven to be vital for understanding the diversity and occurrence of AMR in the environment that would otherwise have remained unexplored and unaccounted for. By producing metagenomic pipelines that utilize parallel computing to handle vast amounts of data, a catalogue of AMR in 390 samples mainly from Scandinavia was created from two datasets. One of the datasets had been previously processed and published, while the other was managed from raw reads to metagenomic assembled bins. This revealed a difference in the distribution of resistance mechanisms that microbes utilize to achieve AMR based on lifestyle and that AMR can be found in most taxa as well as in any freshwater and soil environments.

Följande studie behandlar utvecklandet av automatiserade cellsegmenteringsmodeller med avsikt att identifiera gränser mellan celler i hjärtvävnad. Syftet är att möjliggöra analys av data genererad från multiplexed error-robust in situ hybridization (MERFISH). MERFISH är en spatial transcriptomics-teknik som till skillnad från exempelvis single-cell RNA sequencing (ScRNA-seq) och single molecule fluorescence in situ hybridization (smFISH), möjliggör profilering av hundratals RNA-sekvenser hos enskilda celler utan att förlora dess rumsliga kontext. I Kosuri laboratoriet på Salk Institute of Biological Studies i San Diego tillämpas MERFISH på mushjärtan. Syftet är att få en djupare insikt i hur celler är organiserade i friska hjärtan, och hur denna struktur ändras i och med åldring och sjukdom. Att extrahera meningsfull information från MERFISH medför dock en betydande utmaning - en exakt cellsegmentering. Studien bidrar följaktligen till utvecklandet av segmenteringsmodeller för att kringgå de utmaningar som står i vägen för all efterföljande analys. Då klassiska segmenteringsalgoritmer är otillräckliga för att segmentera den komplexa vävnad som hjärtat utgörs av, tillämpades några av dagens mest avancerade och framstående maskininlärningsalgoritmer inom fältet, kallade Cellpose och Omnipose. Givet den täta och heterogena hjärtvävnaden, som härstammar från en bred distribution av celltyper och geometrier, utvecklades två separata modeller; en för att täcka både mindre celler och kardiomyocyter skurna på tvärsnittet; och en för att enbart segmentera kardiomyocyter skurna i longitudinell riktning. Den förstnämnda modellen utvecklades och tränades i Cellpose, och uppnådde en träffsäkerhet på 91.2%. Modellen för longitudinella kardiomyocyter utvecklades istället både i Cellpose och Omnipose för att utvärdera vilket nätverk som är bäst lämpat för ändamålet. Ingen av nätverken lyckades uppnå en tillräckligt hög träffsäkerhet för att vara applicerbar, och är därmed i behov a, The following study delves into the development of automated cell segmentation models, with the intention of identifying boundaries between cells in the cardiac tissue for analysing spatial transcriptomics data. Addressing the limitations of alternative techniques like single-cell RNA sequencing (ScRNA-seq) and single molecule fluorescence in situ hybridization (smFISH), the study underscores the innovative use of multiplexed error-robust fluorescence in situ hybridization (MERFISH) deployed by the Kosuri Lab at Salk Institute for Biological Studies. This advanced imaging-based technique allows for a single-cell transcriptome profiling of hundreds of different transcripts while retaining the spatial context of the tissue. The technique can accordingly reveal how the organization of cells within a healthy heart is altered during disease. However, the extraction of meaningful data from MERFISH poses a significant challenge - accurate cell segmentation. This thesis therefore presents the development of a robust model for cell boundary identification within cardiac tissue, leveraging some of the advanced supervised machine learning algorithms in the field, named Cellpose and Omnipose. Due to the dense and highly heterogeneous tissue- stemming from a wide distribution of cell types and shapes- two separate models had to be developed; one that covers the smaller cells and the cross-sectioned cardiomyocytes, and correspondingly one to cover the longitudinal cardiomyocytes. The cross-section model was successfully developed to achieve an accuracy of 91.2%, whereas the longitudinal model still needs further improvements before being implemented. The thesis acknowledges potential areas for improvement, emphasizing the need to further improve the segmentation of longitudinal cardiomyocytes, tackle the challenges with segmenting cells within fibrotic regions of the diseased heart, as well as achieving a precise 3D cell segmentation. Nonetheless, the generated models have paved

In this study, a novel method has been assessed to bridge the gap between bioinformatics and ecological conservation efforts to gain evidence to further base conservational plans on. Herein, the validity of using a provisional host metagenome-assembled metagenome to decontaminate the data from host contamination was concluded. To achieve this, 11 samples of increasing host contamination were devised by simulating reads from 100 genomes representing Platanthera bifolia and Platanthera chlorantha endophytic root microbiomes. By following the Critical Interpretation of Metagenome Interpretation benchmarking framework, the method was evaluated on assembly and binning performance. The study concluded strong negative correlations with host contamination that is derived by the lowered proportion of endophytic sequence depth at the higher host contamination levels. Furthermore, statistically significant difference between the control and the perfect GHOST-MAGNET was determined when accounting for the proportion of bins being endophytic.

Only 40 years ago, just three individuals made the journey from Finland/Russia to found the current wolf population in the southwest of Sweden. This population, that to this date descends from less than 10 founders, has a substantial increased extinction risk due to inbreeding. Several previous studies have used SNPs to monitor the level of inbreeding and homozygosity in the population, as well as measure immigration and the inflow of new genetic material. This study uses both short- and long-read data to discover structural variants (SVs) and small indels in the population, so that they may be used to extend the analyses and provide more insight into the current state of the Scandinavian wolf population. After the calling of the SVs, strict filtering and manual curation were applied to the data, thereby removing many false positive calls and increasing confidence in the remaining SVs. Short-read and long-read SV-callers found 31,800 and 57,821 SVs respectively, with relatively little overlap between the two sets. By far, the most common SV-types were deletions and insertions, at about 30,000 each with varying length ranging from a 50 base pairs to several tens of Mbp. Analyses on the data, such as PCAs and parent-offspring trio analyses, reveal high-confidence calls and consistent results between SV-types and SV-callers, as well as a low estimated genotyping error rate. PCAs performed on the SVs resembled those performed on SNPs, which strengthens the credibility of the identified variants. Finally, this study suggests several alternative steps for possible improvement to the dataset, along with some proposals for subsequent research topics that may use the variants discovered in this study.

The Central Arctic Ocean is a repository of rich and diverse biota, whose major portion is one of the most important drivers of global biogeochemical cycles, including carbon cycling. In this study, the functional potential of the microbiota to fix carbon with alternative carbon fixation pathways were investigated along with their chemolithotrophic characteristics. Samples from two expeditions MOSAiC & SAS-Oden (2019-2021) resulted in metagenomic data consisting of about 1200 mOTUs (metagenomic Operational Taxonomic Units). Kofamscan based annotation explained by KEGG pathways database was analysed to explore prevalence of the alternative Carbon Fixation pathways across different taxa. From the six carbon fixation pathways, three were consolidated for their presence (rTCA, DC-HP, HP-HP). In order to explain the other metabolic processes that these organisms employ to survive, a functional annotation tool for metabolic pathways was used. that Reductive Tricarboxylic acid cycle pathway was found to be most present and observed in 5 out of 6 mOTUs selected from filtering the dataset. The taxa include bacterial phyla Proteobacteria, Actinobacteria, Chloroflexota and Marinisomatota and archaeal phyla Thermoplasmota. However, for the other pathways and less studied organisms less resolution were observed across the dataset for the presence of other pathways. These CFPs found were also supported by oxidation of inorganic compounds with high redox potentials. This study provides a glimpse of the metabolic potential of the Central Arctic Ocean microbiota, shines light on the importance of understanding and unravelling the intricacies of this rich and diverse environment.

Glioblastoma is the fastest-growing, and the most common malignant brain tumour in adults. It is normally treated with surgery and radio- or chemotherapy, but the approximate life expectancy is of 15 months with a high probability of cancer recurring. Therefore, there is a need for decreasing its severity. Bulk and single-cell RNA sequencing allow the identification of cellular states in tumours affected by cell-intrinsic and extrinsic factors. Four different cellular states have been identified in glioblastoma: neural progenitor-like, oligodendrocyte progenitor-like, astrocyte-like, and mesenchymal-like. As glioblastoma is an immunosuppressive tumour, it can alter the immune system and increase the tumour's immune escaping by segregating immunosuppressive factors or interacting with the brain microenvironment.Two datasets were used in this study to explore if the localization of the tumour in the brain microenvironment and the tendency of glioblastomas to activate microglial cells are due to particular ligand-receptor interactions. Data quality control was applied to both datasets and SingleCellSignalR and CellphoneDB packages were used to predict the possible interactions. A total of seven experiments were designed for this study. The first dataset, GBmap, allowed us to do a comparison between tumour cells and microglia, tumour cells and other cell types in the brain, and the four cellular states of glioblastoma with microglia and macrophages. Next, healthy microglia from GBmap was used to compare with the tumour bulk data from the second dataset, HGCC. The bootstrap technique was performed to compare bulk data vs single-cell data, and a comparison between tumour cells and microglia or other cell types was analysed.Results showed specific and shared interactions between cell types or cellular states, revealing the different localization of the tumour cells depends on the expressed ligand-receptor pairs. Also, a total of four patterns of interactions were found in

Coccidiosis caused by Eimeria parasites is a worldwide problem, affecting chickens and leading to great losses in the poultry industry. Current vaccines are costly and non-optimal, and the parasite has developed resistance to the anticoccidials in use. To be able to develop more efficient and cost-effective vaccines, further research into the immune response in poultry is needed. Here, we have analyzed immune chickens undergoing a secondary E. tenella infection using dual RNA-seq, as well as compared the immune response of the immune chickens to that of naïve chickens. Samples were taken from caecal tissue where the parasites replicate at six timepoints between 0 and 10 days post infection. The reads were put through a bioinformatic pipeline for preprocessing, mapping, counting and differential expression analysis. Using this we found 69 differentially expressed chicken genes (DEGs) in the secondary infections.The results show that DEGs are mainly found 1 and 2 days post infection (dpi), and a large proportion are interferon (IFN) stimulated genes. Compared to samples from naïve chickens, the immune chickens also expressed fewer cytokines and chemokines and the responses are lower at late time points (4 and 10 dpi). There are also lower counts of parasites in the immune chickens. These results show that immune chickens have a much faster response to E. tenellacompared to that of naïve chickens, and that there is a clear IFN-signature. We hypothesize that IFN-mediated inhibition of parasite replication is an important effector mechanism in protective immunity to Eimeria infection.

Metagenomics studies the totality of genomes of all species in a microbial community. It is a young, growing field with medical, industrial, and ecological applications. Abundant metagenomic data is being produced today, but there is a lack of interpretation and visualization tools. The aim of this project was to create Nanometa Live: a user-friendly, real-time data processing pipeline and graphical user interface that enables visualization of the general species content in a sample, as well as detection of a set of predetermined pathogens. The pipeline was created using Snakemake, with classification by Kraken 2, and sequence validation by BLAST, with the input of the pipeline being fastq batch files from an Oxford Nanopore. The interface was coded in Python using the framework Dash, and utilizes the data produced by the pipeline to visualize results. A Sankey plot and a list of most abundant taxa displays the general species content, while a separate table and a gauge, colored to show the pathogenicity of the sample, displays the user-determined pathogens that the program looks for. Further exploration of the species composition is enabled by a sunburst plot and an icicle chart. Nanometa Live is a fully functioning prototype and can be considered on par with existing tools when it comes to analysis speed, computer requirements, and general user-friendliness. Its strengths are ease of interpretation and flexibility in visualizations, with weaknesses being lack of functionality, such as antibiotic resistance detection, and imperfections in code, structure and packaging.

Lung cancer is the leading cause of cancer-related deaths worldwide and is divided into two broad histological types, small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC). Network module-based approach is applied to lung cancer subtypes in order to analyze and compare the results with previous literature and thus discover new genetic biomarkers and/or confirm previously discovered ones. Data were extracted and analyzed in GEO2R, later protein-protein interaction (PPI)networks were generated through STRING. Functional modules and genesoverlapping between modules were identified using Cytoscape plugins MCODE and ModuLand, which were compared subsequently. The tools complement each other as MCODE can help visualize the neighbors of nodes identified by ModuLand while ModuLand can help identify significant genes as MCODE identifies all genes equally. Venny was used to analyze the overlapping genes between the subtypes and FunRichfor functional enrichment. The results were consistent with findings of previous literature. ModuLand highlighted nodes previously reported to have a role in various types of cancer including lung cancer, which involved two common proteins: CDK1and HIGD1B. The two functional networks showed clusters belonging to the mitoticsister chromatid segregation. Perhaps the main defective part in the cell cycle of lungcancer is chromatin-related. In conclusion by establishing functional modules and highlighting common genes between the modules for each subtype can shed light on potential mechanisms and further support previous discoveries. Several important genes have been identified at the centre of highly interconnected biological complexes that could serve as candidate biomarkers and hallmarks for future studies.

Interactions between proteins are directly involved in most biological processes and are essential for the correct functioning of every form of life. The nature of protein-protein interactions allows functional assemblies of hundreds of protein chains. Given the enormous complexity and the pivotal role of protein interactions in life’s mechanics, the necessity to obtain a complete comprehension of such mechanisms is just as big as the challenge to achieve such knowledge. In the last few decades, experimental procedures constantly improved, dramatically increasing the available structural data for protein interactions. Unfortunately, experimental methods require a lot of time and resources and cannot always be applied with the same degree of success. Several computational methods have been developed in parallel with experimental procedures to overcome such limitations. Therefore, this thesis focused on screening existing computational methods and adopting them to improve the overall accuracy in solving structures of protein-complexes. In the first paper, I propose a simple rigid-body docking framework to test several interface predictors and their ability to drive a protein-protein docking procedure. Next, in the second paper, I display a method to adapt the trRosetta deep neural network to predict inter-residues distances and dihedral angle constraints for full protein complexes. The same concept is then improved in the third paper with FoldDock, an adaptation of Alphafold2 to work on multiple protein sequences and produce the corresponding complex. Finally, in the fourth paper, the FoldDock pipeline is applied to a large dataset of protein pairwise interactions derived from the hu.MAP and HuRI datasets, resulting in the characterization of more than 3000 high-confidence structural models.

Motivation: With advances in long-read transcriptome sequencing, we can now fully sequence transcripts, which greatly improves our ability to study transcription processes. A popular long-read transcriptome sequencing technique is Oxford Nanopore Technologies (ONT), which through its cost-effective sequencing and high throughput, has the potential to characterize the transcriptome in a cell. However, due to transcript variability and sequencing errors, long cDNA reads need substantial bioinformatic processing to produce a set of isoform predictions from the reads. Several genome and annotation-based methods exist to produce transcript predictions. However, such methods require high-quality genomes and annotations and are limited by the accuracy of long-read splice aligners. In addition, gene families with high heterogeneity may not be well represented by a reference genome and would benefit from reference-free analysis. Reference-free methods to predict transcripts from ONT, such as RATTLE, exist, but their sensitivity is not comparable to reference-based approaches.Results: We present isONform, a high-sensitivity algorithm to construct isoforms from ONT cDNA sequencing data. The algorithm is based on iterative bubble popping on gene graphs built from fuzzy seeds from the reads. Using simulated, synthetic, and biological ONT cDNA data, we show that isONform has substantially higher sensitivity than RATTLE albeit with some loss in precision. On biological data, we show that isONform's predictions have substantially higher consistency with the annotation-based method StringTie2 compared with RATTLE. We believe isONform can be used both for isoform construction for organisms without well-annotated genomes and as an orthogonal method to verify predictions of reference-based methods.Availability and implementation

Prediction of orthologs is an important bioinformatics pursuit that is frequently used for inferring protein function and evolutionary analyses. The InParanoid database is a well known resource of ortholog predictions between a wide variety of organisms. Although orthologs have historically been inferred at the level of full-length protein sequences, many proteins consist of several independent protein domains that may be orthologous to domains in other proteins in a way that differs from the full-length protein case. To be able to capture all types of orthologous relations, conventional full-length protein orthologs can be complemented with orthologs inferred at the domain level. We here present InParanoiDB 9, covering 640 species and providing orthologs for both protein domains and full-length proteins. InParanoiDB 9 was built using the faster InParanoid-DIAMOND algorithm for orthology analysis, as well as Domainoid and Pfam to infer orthologous domains. InParanoiDB 9 is based on proteomes from 447 eukaryotes, 158 bacteria and 35 archaea, and includes over one billion predicted ortholog groups. A new website has been built for the database, providing multiple search options as well as visualization of groups of orthologs and orthologous domains. This release constitutes a major upgrade of the InParanoid database in terms of the number of species as well as the new capability to operate on the domain level. InParanoiDB 9 is available at https://inparanoidb.sbc.su.se/.

Real-time surveillance is a crucial element in the response to infectious disease outbreaks. However, the interpretation of incidence data is often hampered by delays occurring at various stages of data gathering and reporting. As a result, recent values are biased downward, which obscures current trends. Statistical nowcasting techniques can be employed to correct these biases, allowing for accurate characterization of recent developments and thus enhancing situational awareness. In this paper, we present a preregistered real-time assessment of eight nowcasting approaches, applied by independent research teams to German 7-day hospitalization incidences during the COVID-19 pandemic. This indicator played an important role in the management of the outbreak in Germany and was linked to levels of non-pharmaceutical interventions via certain thresholds. Due to its definition, in which hospitalization counts are aggregated by the date of case report rather than admission, German hospitalization incidences are particularly affected by delays and can take several weeks or months to fully stabilize. For this study, all methods were applied from 22 November 2021 to 29 April 2022, with probabilistic nowcasts produced each day for the current and 28 preceding days. Nowcasts at the national, state, and age-group levels were collected in the form of quantiles in a public repository and displayed in a dashboard. Moreover, a mean and a median ensemble nowcast were generated. We find that overall, the compared methods were able to remove a large part of the biases introduced by delays. Most participating teams underestimated the importance of very long delays, though, resulting in nowcasts with a slight downward bias. The accompanying prediction intervals were also too narrow for almost all methods. Averaged over all nowcast horizons, the best performance was achieved by a model using case incidences as a covariate and taking into account longer delays than the other approaches. Fo