Högskolan i Skövde

Extracellular vesicles are emitted by almost all cell types. They play an important role in cell-to-cell communication by passing biomolecules such as mRNAs to other cells by endocytosis. It is crucial to isolate and purify them from complex body fluids for studying exosomes. Various techniques, including ultracentrifugation, ultrafiltration, precipitation kits, and immunoprecipitation, are used to isolate exosomes (Bu et al., 2019). Each of these techniques has a significant impact on the properties and purity of the EVs obtained. This project aims to understand the effects of different isolation methods on exosome content by comparing the methods of UC and SEC, with three objectives. The first objective was to compare UC and SEC samples and identify DEGs from native EVs. The second objective was to analyze DEG functional annotation between isolation methods to understand their impact on EV content in biological processes and cellular components. The last objective was to investigate the protein-protien interactions (PPI) between the differentially expressed genes. To investigate the effects of EVs isolation methods at the transcriptional level, RNA-seq data were analyzed from a dataset of three different cell lines, including human lung epithelial cells (HTB-177), umbilical vein endothelial cells (HUVEC), and cardiac progenitor cells (CPC). RNA-seq analysis used an available transcriptomic dataset of EV samples isolated by UC and SEC methods. It identified 10, 15509, and 8995 DEGs from HTB, HUVEC, and CPC, respectively, and mapped them to pathways using EnrichR software. The study found that isolation methods and cell line sources affect analysis results. EnrichR analysis revealed the isolation method's impact on exosomal RNA content and regulation of biological processes.

Low rates of allergy are observed in children growing up on a farm, but the factors that contribute to this protective effect are unclear. This study aimed to investigate if living on a farm alters the infant gut microbiota and thereby reduces the risk of developing allergic diseases in later childhood. This study was based on the prospective Farmflora cohort, which included 28 children living on a farm and 35 control children living in a rural area, who were followed from birth to 8 years of age. The gut microbiota was analyzed from 122 fecal samples collected from 48 children during the first 6 months after birth, using quantitative microbiome profiling. This novel method integrates 16S rRNA gene sequencing data with total bacterial load to obtain absolute counts of bacterial abundance. A significant increase in microbial diversity was seen in the gut microbiota of all the infants in the cohort over the first 6 months after birth. Similar alpha and beta diversity levels were observed in the farm and the control children. However, Sutterella, Megasphaera, and Dorea were more abundant in the gut microbiota of farm children. It has previously been observed that the farm environment was associated with low rates of allergy in children at 3 years. Taxa Akkermansia was more abundant in the gut microbiota of infants who were evaluated with allergy at 3 years. In addition, children who were healthy at 8 years had a higher abundance of Bifidobacterium in their gut microbiota at 6 months of age. However, the abundance of Bifidobacterium could not be linked to farm residence in this study. The findings were consistent with previous studies which link the higher abundance of Sutterella, Dorea, and Megasphaera with protection against allergic diseases. In conclusion, the study observed differences in the gut microbiota of children growing up on a farm, who have low rates of allergy, and showed Bifidobacterium may be protective against allergy development.

Sepsis is a dangerous and potentially fatal condition that has a mysterious origin, underscoring the significance of prompt and accurate diagnosis and treatment. Bacterial whole-genome sequencing, which is widely used in clinical microbiology, stands at the forefront of sequencing technologies, particularly to combat sepsis. The aim of this thesis is to improve sepsis treatment by examining the genetic characteristics and drug resistance patterns of the common sepsis-causing bacteria Pseudomonas and Proteus spp., by analyzing the whole-genome sequencing data of bacterial isolates using an in-house-developed pipeline. The result was compared with a commercial cloud-based platform from 1928 Diagnostic (Gothenburg, Sweden), as well as the results from a clinical laboratory. Using Illumina HiSeq X next-generation sequencing technology, whole-genome data from 88 isolates of Pseudomonas and Proteus spp. was obtained. The isolates were obtained during a prospective observational study of community-onset severe sepsis and septic shock in adults at Skaraborg Hospital in Sweden's western region. The collected isolates were characterized using approved laboratory techniques, such as phenotypic antibiotic susceptibility testing (AST) in accordance with EUCAST guidelines and species identification by MALDI-TOF MS analysis. The species identification result matched the phenotypic method, with the exception of two isolates from Pseudomonas samples and four isolates from Proteus samples. When benchmarking the in-house pipeline and 1928 platform for Pseudomonas spp., predicted 97% of the isolates were resistant to at least one class of the tested antibiotics, of which 94% shows multi-drug resistance. In phenotypes, 88% of the isolates had at least one antibiotic resistance future, of which 68% shows multi-drug resistance. The most prevalent sequence types (STs) identified were ST 3285 and ST111 (9.3%) and ST564 and ST17 (6.98%) each, and both pipelines accurately predicted the number of multilocus types. The in-house pipeline reported 9820 Pseudomonas virulence genes, with PhzB1, a metabolic factor, being the most common gene. It was discovered that there was a significant correlation between the virulence factor gene count and the multilocus sequence typing (MLST) (p = 0.00001). With a Simpson's Diversity Index of 0.98, the urine culture specimens showed the greatest ST diversity. Plasmids were detected in twelve samples (20.93%) in total. In general, this study provided a detailed description of the bacterial future for Pseudomonas and Proteus organisms using WGS data. This research shows the applicability of the in-house and 1928 pipelines in the identification of sepsis-causing organisms with accuracy. It also showed the need for an organized and easy-to-use international pipeline to implement and analyze WGS bacterial data and to compare it with laboratory results as needed.

Pancreatic Ductal Adenocarcinomas initiates in the exocrine part of the pancreatic tissue and represents over 90% of all the pancreatic cancers. Pancreatic Ductal Adenocarcinomas are extremely aggressive and are one of the most lethal malignant neoplasms. The five-year relative survival is currently less than 8% of the patients. The main reason behind such a low survival rate is that most of the cases are diagnosed at a very late stage. Although substantial advancement in pancreatic cancer research has been done, there has not been any remarkable significance in the mortality to incidence ratio. This is mainly a result of the scarce of early diagnostic characteristic symptoms and reliable biomarkers besides the unresponsiveness to the treatments. In this study, transcriptomics and proteomics data were used for the construction of a genome-scale metabolic model that was used in the detection of altered metabolic pathways, genes and metabolites using gene set analysis and reporter metabolites analysis. As a result, altered metabolic pathways in PDAC tumours were detected, including the lipid metabolism-related pathways as well as carbohydrate metabolism, in addition to nucleotide metabolism, which are considered as potential candidates for diagnostic biomarkers. Moreover, classification of the filtered DIRAC tightly regulated network genes, based on their prognostic values from the pathology atlas, detected two groups of PDAC patients that have significantly different survival outcome. The differential expression analysis of the two groups showed that six of the eight genes used in clustering were showing significantly altered expression, which suggests their importance in PDAC patient stratification. As a conclusion, this study shows the valuable outcome of the GEM reconstructions and other systems-level analyses for elucidating the underlying altered metabolic mechanisms of PDAC. Such analyses results should provide more insights into the biomarker discovery and developing of potential treatments.

Sepsis is a life-threatening organ dysfunction that is caused by a dysregulated host response to infection. Sepsis is a substantial health care and economic burden worldwide and is one of the most common reasons for admission to the hospital and intensive care unit. Early diagnosis and targeted treatment of sepsis are the bases to reduce the mortality and morbidity. Conventional blood culturing is the gold standard method for sepsis diagnostics. However, blood culturing is a time consuming method, requiring at least 48 to 72 hours to get the first results with very low sensitivity and specificity. The aim of this study was to determine and assess the direct sepsis-related costs for PCR-based diagnostic strategies (SeptiFast and POC/LAB). A mathematical model was constructed to compare PCR-based diagnostic strategies with the conventional blood culturing. Three case scenarios were investigated based on data from the United Kingdom, Spain and the Czech Republic. It was found that, POC/LAB was the most cost effective strategy in all countries if it could reduce the hospitalization length of stay with at least 3 days in the normal hospital ward and 1 day in the intensive care unit. Reducing the hospitalization length of stay had the greatest impact on the economic outcomes. While, reducing the costs of the diagnostic strategies did not show a remarkable effect on the economic results. In conclusion, the findings suggest that PCR-rapid diagnostic methods could be cost-effective for the diagnosis of patients with sepsis if they could reduce the hospitalization length of stay.

Common variable immunodeficiency (CVID) is a primary immune deficiency present in about 1 in 25,000 people, characterized by recurrent infections, low serum immunoglobulin (Ig) levels (IgG, IgA, and sometimes IgM), and reduced vaccine responses. It is genetically a heterogeneous illness that often affects adults or teenagers and requires lifetime clinical care. CVID patients experience recurrent or chronic sinopulmonary tract infections, gastrointestinal disorders, and malignancies. Ig reconstitution administered intravenously or subcutaneously is the main treatment. Although the fundamental causes of CVID are still undefined, studies suggest that a variety of variables, including impaired somatic hypermutation (SHM), B cell maturation, primary B cell dysfunctions, abnormalities in T cells, and antigen-presenting cells are implicated. Understanding the molecular mechanisms driving this disease's genome regulation requires a deep understanding of the gene expression. It is today possible to study both coding and non-coding sections of RNA using next-generation RNA-seq, which allows detecting differentially expressed genes in massive amounts of data, particularly in multifaceted illnesses like CVID. The aim of this study was to identify the differentially expressed genes between unstimulated (ex vivo) and stimulated (in vitro) naïve B cells of CVID patients and healthy donors (HD), and also to identify the underlying biological processes by gene enrichment analysis. The results of this study showed that both in CVID and HD, the stimulated and unstimulated cells were well separated. In the gene set analysis, it was discovered that significantly enriched CVID pathways were mostly involved in immune system-related processes such as adaptive immune response, cytoplasmic translation, granulocyte activation, lymphocyte activation, and lymphocyte differentiation. Therefore, the transcriptomic analysis of this study concluded that the majority of the genes that regulate the immune cell activation process function may have a greater impact on CVID patients than on HD which helps to understand the immunological defects in CVID patients. 

The multifunctional purpose of hepatocytes, the functional liver cells within the metabolic, endocrine and secretory functions highlights key importance in emphasizing the research and treatment methods that utilize these cells. Forming 80% of the liver's cells, hepatocytes are involved in many of the primary functions of the liver including the delivery of immune response against pathogens and aiding in the detoxification of drugs. As a result, it provides a valuable basis for medical research. Through the findings of Ghosheh et al. (2017), a method of generating mature hepatocytes was achieved through the human pluripotent stem cells (HPSC), but the generation of hepatocytes in which all the genes are expressed at the right amount through this method proves to be a difficult endeavor. The primary goal of this project is to utilize the established findings to enhance and improve the efficacy of the process that goes behind the generation of mature hepatocytes. The approach towards the current project was initiated with culturing and differentiating three human embryonic stem cell lines and three human-induced pluripotent stem cell lines into mature hepatocytes. In the study mentioned, k-means clustering along with Pearson correlation as the distant measure was run in R to subdivide the top 2000 genes with the highest differential expression into 10 clusters. The cluster data from this paper was used to do the current study, in which the up-regulated and down-regulated gene were first identified for clusters 2, 4 & 6 and 9. The interactions of up-regulated genes in these clusters were further analyzed using Enrichr to identify the different miRNAs for various genes from the clusters. Within cluster 2, a total of 8 genes showed the possibility of being regulated using 4 miRNAs. Transcription factors were also identified for cluster 2 and a combination of HNF1A, EP300, AHR, NFKB1 and HIF1A could repress 8 genes that were not repressed by miRNAs. In cluster 4 & 6, most of the up-regulated genes showcased tumorigenicity and all 20 genes identified could be regulated with the combination of 7 miRNAs. In cluster 9, a combination of 11 miRNAscould be used to regulate 26 out of 27 genes that were analyzed. Ensuring that stem cell products do not turn cancerous is a priority in the medical field. Conducting the analyses of the other clusters aside from 2, 4 & 6 and 9 will prove highly beneficial in reducing the risks pertaining to stem cell mutation due to overexpression of genes.

Sepsis is defined as a syndrome of life-threatening organ dysfunction caused by a dysregulated host response to an infection. Early detection of sepsis and immediate treatment with antibiotics is critical for patient outcomes. Haemophilus influenzae (H. influenzae) is a gram-negative bacteria known to be a human-adapted pathogen that may cause a variety of communityacquired infections such as sepsis. A rapid increase in beta-lactam resistance in H. influenzae has been noticed and has become a major problem in clinical care. By implementing bacterial whole genome sequencing (WGS) in the clinical laboratory, it can provide a great amount of information such as species identification, serotype identification, antimicrobial resistance prediction, typing for epidemiologic purposes and tracking infectious disease outbreaks. The aim of this study was to analyze WGS data for clinical H. influenzae isolates using an in-house developed bioinformatic pipeline and an automated 1928 Diagnostics platform to evaluate and compare the predicted results in terms of species identification, prediction of resistance and virulence genes. Furthermore, the predicted genotypic antibiotic resistance genes were compared to the phenotypic antimicrobial susceptibility testing obtained from the clinical laboratory. For the in-house developed pipeline, the analysis of H. influenzae WGS data started with quality control and preprocessing (trimming) of FASTQ files. Following, de novo assembly and quality assessment of assembled contigs and lastly gene annotation tools were performed. For 1928 Diagnostics, the untrimmed FASTQ files were uploaded to the 1928 platform. Species identification resulted in a high agreement of predicted H. influenzae for both phenotypic and genotypic methods except for one sample that may have been contaminated. The analysis of antibiotic resistance genes resulted in both in-house developed pipeline and 1928 Diagnostics having a high agreement regarding the prediction of broad-spectrum beta-lactamase in six clinical isolates, all of which predicted bla TEM-1B. The four most common sequence types found in the MLST analysis from the in-house pipeline were ST159, ST388, ST14 and ST12. The analysis of virulence genes yielded a large number of different virulence genes and each of the identified virulence genes codes a specific function that is crucial to the pathogenesis of H. influenzae. In conclusion, the obtained results provide valuable insights into using WGS-based analysis as a reliable tool for determining the pathogen characteristics in clinical bacterial isolates.

Neoehrlichia mikurensis is a bacterium transmitted to humans by tick bites, usually from Ixodes ricinus species, causing an infection known as neoehrlichiosis. The importance of B cells in host defence against this infection has previously been demonstrated in patients undergoing anti-B cell therapy for lymphoma. As a causal relationship between N. mikurensis and malignant B-cell lymphomas has not yet been established, the identification of a genetic signature caused by the infection would be a step forward in the development of therapeutics. Findings have been published on the heavy chain repertoire of the major B-cell clonotypes of N. mikurensis-positive lymphoma patients, and it is essential to gain a deeper understanding of the broader heavy chain repertoire. That can be achieved by analysing other aspects such as VDJ gene frequency, somatic hypermutations in complementarity determining regions (CDRs) and framework regions (FWRs) of the variable domain, and the physicochemical properties of the H-CDR3 repertoire, the latter being the most hypervariable part of the heavy chain and a major determinant of its specificity. Specifically, eight infected lymphoma patients were analysed for these variables. In short, the analysis of the results separates the data into at least two distinct groups of patients studied. It was also possible to group the patients by integrating the data into heatmap representations that clustered the patients in an unsupervised manner based on the use of IGHV, IGHD and IGHJ gene frequencies and the physicochemical properties of H-CDR3. Interestingly, this correlated closely with the lymphoma classifications of the patients infected with N. mikurensis. Although the sample size is not very large, and work is currently being to recruit more patients and controls, these results are promising as not only may there be an underlying signature related to the infection, but they may also help to separate the heavy chain repertoire data into groups, which in turn may help to diagnose lymphoma patients more accurately and perhaps more quickly and find the right treatment from the outset. 

One of the main components of atherosclerotic plaque is the production and accumulation of cholesterol crystals (CCs), which could serve as a biomarker of atherosclerosis. Atherosclerosis is a chronic inflammatory artery disease that is the root cause of myocardial infarction and stroke. Endothelial dysfunction is one of the main contributors to the development of atherosclerosis. The aim of the study was to investigate whether Human Umbilical Vein Endothelial Cells (HUVECs) can uptake CCs and to examine CCs-induced inflammatory response in HUVECs. Using molecular and functional techniques, the distinctive characteristic of CC-mediated immune response was discovered in HUVECs. CCs were mostly taken up by HUVECs by macropinocytosis and phagocytosis. CCs were found to induce Signal Transducer of Activators of Transcription (STAT) 3 phosphorylation and Interleukin (IL)-6 release in HUVEC. In addition, Caspase activation and recruitment domain 8 (CARD8) knockdown drastically reduced CCs uptake and CCs induced IL-6 expression in HUVECs. Moreover, the stem cell growth factor (SCGF)-b protein release was downregulated in response to CCs. IL-1A and Colony Stimulating Factor (CSF) 2 were identified as the topmost hub nodes interacting with all other differentially expressed proteins. A significant increase in neutrophil adhesion on HUVECS was found in response to CCs and conditioned medium from CCs-treated HUVECs. In conclusion, the study findings suggest that the CCs induceSTAT3-mediated IL-6 release and neutrophil adhesion, thereby promoting inflammation in HUVECs.

Type 2 diabetes (T2D) is a widespread metabolic disorder characterized by chronic hyperglycemia and insulin resistance (IR), impacting over 90% of diabetes cases globally. Despite extensive research, the precise molecular mechanisms of T2D remain unclear, highlighting the need for further investigation into its pathophysiology. This study aims to elucidate the relationship between pancreatic islet cell electrophysiological activity and gene expression profiles to enhance our understanding of T2D’s cellular mechanisms.

Pancreatic islets from non-diabetic mice and humans were isolated, dissociated into single cells, and analyzed using high-density microelectrode arrays (HD-MEA) for electrophysiological across various glucose concentrations. The electrophysiological was monitored over 3-5 days, with validation through calcium fluorescence imaging. Single-cell RNA sequencing (scRNA-seq) was employed to analyze gene expression changes for alpha and beta cell types.

Results revealed distinct electrophysiological patterns in response to glucose, with variations inspike frequency. Differential gene expression analysis identified gene expression changes in alpha and beta cells. The TRIB3 and FFAR4 genes were found to be downregulated in T2D compared tonon-diabetic non-obese (NN) individuals, and both are involved in glucose transmembrane transport. Additionally, FFAR4 showed reduced expression in T2D relative to non-diabetic obese(NO) individuals, and is associated with peptide hormone secretion. Conversely, PDE4B was upregulated in T2D compared to NO individuals, impacting calcium channel voltage gating and cAMP signaling pathways. FXYD2, related to sodium channel regulation, was also downregulated in T2D compared to NO individuals. In alpha cells, the genes TMEM236, TMEM45B, and SYTL5 were upregulated in T2D compared to both NN and NO individuals, suggesting alterations in cellular membrane dynamics and signaling pathways.

The integration of HD-MEA with scRNA-seq provided insights into the functional and transcriptomic profiles of human pancreatic islets under different metabolic states. Findings highlight significant changes in gene expression and electrophysiology, emphasizing the impactof T2D and obesity on islet function. While this study enhances our understanding of islet dysfunction in T2D, it also points to limitations such as small sample sizes and the focus on only alpha and beta cells, suggesting the need for broader studies to further elucidate T2D mechanisms.

The increasing number of biological databases today requires that users are able to search more efficiently among as well as in individual databases. One of the most widespread problems is redundancy, i.e. the problem of duplicated information in sets of data. This thesis aims at implementing an algorithm that distinguishes from other related attempts by using the genomic positions of sequences, instead of similarity based sequence comparisons, when making a sequence data set non-redundant. In an automatic updating procedure the algorithm drastically increases the possibility to update and to maintain the topicality of a non-redundant database. The procedure creates a biologically sound non-redundant data set with accuracy comparable to other algorithms focusing on making data sets non-redundant

Diffuse large B-cell lymphoma is a common non-Hodgkin lymphoma and covers about one third of global non-Hodgkin lymphoma cases each year. The lymphoma is characterised by B-cells with large nuclei which makes the cells unable to function normally as a part of the immune response. MicroRNAs, which are small noncoding RNA, can be used as biomarkers for the diagnosis, prognosis and treatment in many different diseases. However, the pathways regulated by microRNAs in connection to diffuse large B-cell lymphoma are not well understood. The aim of this study was to find hub microRNAs and their pathways in diffuse large B-cell lymphoma. This was done by identifying differentially expressed microRNAs, finding their target genes and building networks of the microRNAs and their targets. Gene ontology terms and KEGG pathways were identified for each network. This study found three microRNA hubs hsa-let-7f-1-3p, hsa-miR-200c-3p, and hsa-miR-507. Each of them has been shown to regulate tumours in other cancer types though they have not been connected to diffuse large B-cell lymphoma before. The target genes have also been found to play central roles in general tumour formation. This study found that known tumour suppressor genes were down-regulated while genes that aided in tumour formation were up-regulated. The gene ontology analysis showed that many of the target genes take part in the normal development of the human body, but the KEGG pathways were concentrated around cancer formation. The microRNAs show promise as prognostic markers and targets for developing treatments.

Inflammatory Bowel Disease (IBD) is a complicated and multifaceted illness marked by chronic inflammation of the gastrointestinal system. Understanding the underlying molecular causes of IBD is critical for creating tailored therapeutics and better patient outcomes. This thesis compares Over-Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA) methods on two datasets, GSE66407 and GSE186582. Three ORA tools—ClusterProfiler, WebGestalt, and Enrichr—were tested to determine their sensitivity, specificity, and robustness in discovering enriched pathways. The results showed that the techniques were quite consistent, especially in the GSE186582 dataset, where there was perfect overlap in pathway discovery. However, small differences were seen in the GSE66407 dataset, with ClusterProfiler and Enrichr detecting distinct pathways. Furthermore, GSEA was conducted using clusterProfiler, WebGestalt, and fgsea, which provided a dynamic analysis of the whole ranked list of genes. GSEA results from both datasets consistently revealed immune-related pathways involved in IBD pathogenesis, including IL-17 signaling, NF-kappaB signaling, and TNF signaling. Particularly, ClusterProfiler, WebGestalt, and fgsea both identified many distinct pathways, demonstrating differences in pathway recognition depending on the approach utilized. While ORA approaches are strong and accurate for quickly identifying pathways, GSEA may give a more detailed knowledge of the biological processes by capturing minor gene expression variations.

Background: There exist few, if any, practical guidelines for predictive and falsifiable multi-omic data integration that systematically integrate existing knowledge. Disease modules are popular concepts for interpreting genome-wide studies in medicine but have so far not been systematically evaluated and may lead to corroborating multi-omic modules. Result: We assessed eight module identification methods in 57 previously published expression and methylation studies of 19 diseases using GWAS enrichment analysis. Next, we applied the same strategy for multi-omic integration of 20 datasets of multiple sclerosis (MS), and further validated the resulting module using both GWAS and risk-factor-associated genes from several independent cohorts. Our benchmark of modules showed that in immune-associated diseases modules inferred from clique-based methods were the most enriched for GWAS genes. The multi-omic case study using MS data revealed the robust identification of a module of 220 genes. Strikingly, most genes of the module were differentially methylated upon the action of one or several environmental risk factors in MS (n = 217, P = 10− 47) and were also independently validated for association with five different risk factors of MS, which further stressed the high genetic and epigenetic relevance of the module for MS. Conclusions: We believe our analysis provides a workflow for selecting modules and our benchmark study may help further improvement of disease module methods. Moreover, we also stress that our methodology is generally applicable for combining and assessing the performance of multi-omic approaches for complex diseases. 

Alzheimer’s disease (AD) is an aging-related neurodegenerative disorder with large implications for society and individuals. AD is a multi-factor disorder, with these factors having a direct or indirect correlation with each other. Despite many studies with different aspects on molecular and cellular pathways, there is still no specific treatment for AD. Identification of potential pathogenic factors can be done by transcriptomic studies of differentially expressed genes (DEGs), but the outcomes have been contradictory. Using both bioinformatics and meta-analysis methods can be useful for removing such inconsistencies. A useful and common approach for a better understanding of neurodegenerative disease is to assess its molecular causes, by comparing the gene expression levels in healthy and disease tissues. Next-generation RNA-sequencing is a valuable method for analyzing both coding and non-coding regions of RNA, and it has made it possible to identify differentially expressed genes in large-scale data. The aim of the current study was to get a better understanding of the transcriptional changes in AD models, and identify differentially expressed genes between healthy and AD individuals from the adult mouse brain model as well as detecting AD pathways. In this study, the transcriptomes of purified neuron, astrocyte and microglia cells from mouse brains were analyzed using publicly available RNA-seq datasets. The DEGs were identified for all three mentioned cell types using DESeq2 and EdgeR packages. All statistical analyses were performed by R software and the DEGs detected by DESeq2 and edgeR, respectively, were compared using Venn diagrams. Additionally, analyzing the AD pathway was performed using GOrilla tool for visualizing the enriched gene ontology (GO) terms in the list of ranked genes. From this project, it was found that there were very few significantly DEGs between AD and healthy samples in neuron cells, while there were more DEGs in astrocyte and microglia cells. In conclusion, comparing DESeq2 and egeR packages using Venn diagrams showed a slight advantage of DESeq2 in detection accuracy, since it was able to identify more DEGs than edgeR. Moreover, analyzing AD pathway using GOrilla tool indicated that identified enriched GO terms by each cell type differed from each other. For astrocytes, more enriched GO terms were identified than for microglia cells, while no significant enriched GO terms were detected for neuron cells.

The rapid and reliable detection of pathogens is of utmost importance in healthcare settings to ensure the appropriate treatment thereby reducing morbidity and mortality for the patient. Current culturing, PCR based and NGS species detection methods are time consuming (Opota et al., 2015), limited in their detection (Buckley et al., 2015), or require specialist skills and are expensive (Basho and Eterovic., 2015). Oxford Technologies Nanopore devices could provide detailed genomic sequencing at a fraction of the cost and without the need for technical bioinformatic skills. This study evaluates the MinION device and analysis tools to suggest best practice. Classification and genotyping of 12 Klebsiella isolates were performed using EPI2ME automated workflows and manual de novo assembly.  Automated workflows using raw MinION reads provided clinically relevant information identified in ~6hrs. Manual de novo assembly and analysis used hybrid, and single source data took >24hrs. The inclusion of MinION long reads overcome problems assembling short reads. Hybrid genomes provided the most contiguous and highly detailed contigs. MinION only read assemblies contained more errors but still identified similar genotypic findings. Automated workflows are rapid and require minimal bioinformatic know-how. There should be a dialogue between clinicians and bioinformaticians to develop bespoke analysis tools.  Although challenges remain around compatible kits and vulnerable flowcells long read sequencing can be an effective tool for species detection and pathogen typing. Furthermore, hybrid assemblies have the potential to advance our genome detailing and discovery.

Megasatellites are polymorphic tandem repetitive sequences with repeat-units longer than or equal to 1000 base pairs. The novel algorithm Megasatfinder predicts megasatellites in the human genome. A structured method of analysing the algorithm is developed and conducted. The analysis method consists of six test scenarios. Scripts are created, which execute the algorithm using various parameter settings. Three nucleotide sequences are applied; a real sequence extracted from the human genome and two random sequences, generated using different base probabilities. Usability and accuracy are investigated, providing the user with confidence in the algorithm and its output. The results indicate that Megasatfinder is an excellent tool for the detection of megasatellites and that the generated results are highly reliable. The results of the complete analysis suggest alterations in the default parameter settings, presented as user guidelines, and state that artificially generated sequences are not applicable as models for real DNA in computational simulations.

Multiple sclerosis is a common neurological disorder, characterized by increasing disability over time for the affected patient. The disease is considered an autoimmune disorder in which the immune system causes damage to nerves in the central nervous system through demyelination and inflammation. It is currently not understood what causes the disease, but both genetic susceptibility as well as environmental- and lifestyle factors are thought to contribute to disease development.In this project, data of known disease-associated risk factors were used to characterize the processes through which they may alter the risk of disease development. Modules for each risk factor was derived from experimental data, using the MODifieR disease-module inference algorithms. Of the five different risk factors included, each module was analysed using the PASCAL tool and disease specific GWAS data to evaluate the relevance towards the disease.Based on the modules derived using the Clique Sum Permutation module inference method a consensus module comprising 126 genes was identified, which proved to be significantly enriched for disease associated SNPs (single nucleotide polymorphisms). Additionally, the risk-factor consensus module was compared to disease specific consensus genes previously obtained within the research group. The comparison showed a significant overlap, which indicates that the methodology may provide means of examine the impact of risk-factors in the context of complex disease.

Prostate cancer (PCa) is the second most common cancer in men worldwide. The non-cancerous PCa tissue is often used as the benign region irrespective of the zone it is collected for biomarker analysis. Signal Transducer and Activator of Transcription 3 (STAT3) is a transcription factor expressed in epithelial and stromal cells controlling the expression of oncogenes with influence on PCa progression and it has been suggested as a prognostic marker for event-free survival. However, zone-specific expression of activated pSTAT3^Ser727 has so far not been investigated. Therefore, this study aimed to: Firstly, validate the non-cancer cores of PCa patients as a representative of normal control for PCa cores. Secondly, investigate the prostate zone-specific expression of pSTAT3^Ser727 in localized hormone naïve PCa. Thirdly, evaluate the prognostic value of zone-specific pSTAT3^Ser727 expression associated with biochemical recurrence (BCR)-free survival using the zone difference approach. Nuclear and cytoplasmic expression of pSTAT3^Ser727 in epithelium and stroma were scored from prostates of bladder cancer patients and localized hormone naïve PCa patients to calculate the H-score. The H-score subtracted value determined its association with BCR-free survival analysed by Kaplan-Meier curves and Cox-regression analysis. Expression of pSTAT3^Ser727 in normal prostates of the bladder cancer patients was not significantly different from the non-cancer core of the PCa patients. Zone-dependent pSTAT3^Ser727 expression was observed in the zones of the prostatic non-cancer core, however, the non-cancer core had a significantly higher expression of pSTAT3^Ser727 than their zone-corresponding cancer core. Kaplan-Meier curves with log-rank test for the cytoplasm of the epithelium showed lower H-score subtracted value associated with shorter time to BCR. H-score subtracted value in the zone was revealed as an independent prognostic marker for BCR-free survival while adjusting for Gleason grade and Pathological T-stage. Conclusively, expression in non-cancer cores from PCa is validated to be represented as a control to the cancer cores, although differences in the zone-specific expression were observed. We showed pSTAT3^Ser727 zone difference expression in the epithelium as an alternative approach to study biomarkers by improving the prognostic value for BCR.  

The liver, the largest gland in the body, performs over 500 essential functions and displays a remarkable capacity for regeneration. When compromised, this regenerative ability can lead to severe conditions such as liver cirrhosis and hepatocellular carcinoma. A potential solution to these liver conditions lies in vitro hepatocyte differentiation from pluripotent stem cells, including human embryonic stem cells and induced pluripotent stem cells. Our study explored the role of epigenetic modifications in this process, specifically DNA methylation and hydroxymethylation. These modifications are crucial in modulating gene expression and cellular phenotypes, which influence the differentiation and function of cells. The results demonstrated significant differential methylation in key regions associated with hepatocyte differentiation, implicating their potential importance in successful in vitro hepatocyte differentiation. Additionally, the utility of the Infinium Methylation EPIC BeadChip for high-throughput analysis was confirmed, revealing a broad spectrum of methylation and hydroxymethylation changes during hepatocyte differentiation. The findings suggest a previously underappreciated role of DNA methylation and hydroxymethylation in influencing hepatocyte differentiation, with potential implications for enhancing the efficiency and stability of in vitro hepatocyte generation. This understanding can inform the development of improved protocols, providing valuable platforms for studying liver diseases and developing novel therapeutics. Future studies will continue to dissect the intricate network of epigenetic regulations, opening new avenues for understanding and treating liver diseases. 

Gemcitabine/carboplatin chemotherapy commonly induces myelosuppression, including neutropenia, leukopenia, and thrombocytopenia. Predicting patients at risk of these adverse drug reactions (ADRs) and adjusting treatments accordingly is a long-term goal of personalized medicine. This study used whole-genome sequencing (WGS) of blood samples from 96 gemcitabine/carboplatin-treated non-small cell lung cancer (NSCLC) patients and gene network modules for predicting myelosuppression. Association of genetic variants in PLINK found 4594, 5019, and 5066 autosomal SNVs/INDELs with p ≤ 1 × 10−3 for neutropenia, leukopenia, and thrombocytopenia, respectively. Based on the SNVs/INDELs we identified the toxicity module, consisting of 215 unique overlapping genes inferred from MCODE-generated gene network modules of 350, 345, and 313 genes, respectively. These module genes showed enrichment for differentially expressed genes in rat bone marrow, human bone marrow, and human cell lines exposed to carboplatin and gemcitabine (p < 0.05). Then using 80% of the patients as training data, random LASSO reduced the number of SNVs/INDELs in the toxicity module into a feasible prediction model consisting of 62 SNVs/INDELs that accurately predict both the training and the test (remaining 20%) data with high (CTCAE 3–4) and low (CTCAE 0–1) maximal myelosuppressive toxicity completely, with the receiver-operating characteristic (ROC) area under the curve (AUC) of 100%. The present study shows how WGS, gene network modules, and random LASSO can be used to develop a feasible and tested model for predicting myelosuppressive toxicity. Although the proposed model predicts myelosuppression in this study, further evaluation in other studies is required to determine its reproducibility, usability, and clinical effect.

Rheumatoid arthritis is an autoimmune disease that affects smaller joints, resulting in bone erosion, joint inflammation, and significant disability. Current rheumatoid arthritis treatments are associated with the risk of relapse and side effects, the latter including immunosuppression.Therefore, developing more efficient therapies is of interest. Memory B cells have been implicated in the disease, given the presence of autoantibodies and discrepancies in memory B cell numbers when comparing patients with healthy individuals. Hence, this study aimed to characterize the memory B cell subsets present in patients with untreated, early rheumatoid arthritis, where no treatment would act as a confounding factor. To elucidate the immune cell heterogeneity, singlecell RNA sequencing was used. Data from three patients was integrated and compared to a public dataset from a healthy donor. Two memory B cell subsets were linked to the production of interleukin-6, with one of them also expressing tumor necrosis factor-α. These cytokines are known to be highly expressed in patients with rheumatoid arthritis. Furthermore, a third memory B cell subset expressed high levels of B cell activating factor receptor, associated with pro-survival signals and likely autoimmunity. In the equivalent subset of the healthy donor this receptor was not upregulated. Targeting these subsets specifically with treatments could improve patient prognosis and reduce side effects. However, further research is needed to evaluate the contribution of these memory B cells in the context of rheumatoid arthritis.

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/ .

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease with poor survival. Novel biomarkers are urgently needed to improve the outcome through early detection. Here, we aimed to discover novel biomarkers for early PDAC detection using multi-omics profiling in pre-diagnostic plasma samples biobanked after routine health examinations.

A nested case-control study within the Northern Sweden Health and Disease Study was designed. Pre-diagnostic plasma samples from 37 future PDAC patients collected within 2.3 years before diagnosis and 37 matched healthy controls were included. We analyzed metabolites using liquid chromatography mass spectrometry and gas chromatography mass spectrometry, microRNAs by HTG edgeseq, proteins by multiplex proximity extension assays, as well as three clinical biomarkers using milliplex technology. Supervised and unsupervised multi-omics integration were performed as well as univariate analyses for the different omics types and clinical biomarkers. Multiple hypothesis testing was corrected using Benjamini-Hochberg's method and a false discovery rate (FDR) below 0.1 was considered statistically significant.

Carbohydrate antigen (CA) 19-9 was associated with PDAC risk (OR [95 % CI] = 3.09 [1.31–7.29], FDR = 0.03) and increased closer to PDAC diagnosis. Supervised multi-omics models resulted in poor discrimination between future PDAC cases and healthy controls with obtained accuracies between 0.429–0.500. No single metabolite, microRNA, or protein was differentially altered (FDR < 0.1) between future PDAC cases and healthy controls.

CA 19-9 levels increase up to two years prior to PDAC diagnosis but extensive multi-omics analysis including metabolomics, microRNAomics and proteomics in this cohort did not identify novel early biomarkers for PDAC.

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.

The problem of functional annotation of novel sequences has been a sigfinicant issue for many laboratories that decided to apply next generation sequencing techniques to less studied species. In particular experiments such as transcriptome analysis heavily suer from this problem due to the impossibility of ascribing their results in a relevant biological context. Several tools have been proposed to solve this problem through homology annotation transfer. The principle behind this strategy is that homologous genes share common functions in dierent organisms, and therefore annotations are transferable between these genes. Commonly, BLAST reports are used to identify a suitable homologousgene in a well annotated species and the annotation is then transferred fromthe homologue to the novel sequence. Not all homologues, however, possess valid functional annotations. The aim of this project was to devise an algorithm to process BLAST reports and provide a criterion to discriminate between homologues with a biologically informative and uninformative annotation, respectively. In addition, all data obtained from the BLAST report isto be stored in a relational database for ease of consultation and visualization. In order to test the solidity of the system, we utilized 750 novel sequences obtained through application of next generation sequencing techniques to Avena sativa samples. This species particularly suits our needs as it represents the typical target for homology annotation transfer: lack of a reference genome and diculty in attributing functional annotation. The system was able to perform all the required tasks. Comparisons between best hits asdetermined by BLAST and best hits as determined by the algorithm showed a significant increase in the biological significance of the results when thealgorithm sorting system was applied.

Background

With the advent of microarray technology, it has become feasible to identify virtually all genes in an organism that are induced by developmental or environmental changes. However, relying solely on gene expression data may be of limited value if the aim is to infer the underlying genetic networks. Development of computational methods to combine microarray data with other information sources is therefore necessary. Here we describe one such method.

Results

By means of our method, previously published Arabidopsis microarray data from cold acclimated plants at six different time points, promoter motif sequence data extracted from ~24,000 Arabidopsis promoters and known transcription factor binding sites were combined to construct a putative genetic regulatory interaction network. The inferred network includes both previously characterised and hitherto un-described regulatory interactions between transcription factor (TF) genes and genes that encode other TFs or other proteins. Part of the obtained transcription factor regulatory network is presented here. More detailed information is available in the additional files.

Conclusion

The rule-based method described here can be used to infer genetic networks by combining data from microarrays, promoter sequences and known promoter binding sites. This method should in principle be applicable to any biological system. We tested the method on the cold acclimation process in Arabidopsis and could identify a more complex putative genetic regulatory network than previously described. However, it should be noted that information on specific binding sites for individual TFs were in most cases not available. Thus, gene targets for the entire TF gene families were predicted. In addition, the networks were built solely by a bioinformatics approach and experimental verifications will be necessary for their final validation. On the other hand, since our method highlights putative novel interactions, more directed experiments could now be performed.

Glaucoma is a major cause of blindness worldwide. The number of cases where vision loss occurs is also increased by the rise in cases that go undiagnosed. The primary causes of this vision loss are the degeneration of the optic nerve and the reduction of retinal ganglion cells. Primary open-angle glaucoma (POAG) represents the most prevalent type of glaucoma, accounting for around two-thirds of all cases. Tears, aqueous humor (AH), and vitreous humor are three fluids in the eyes that contain extra cellular miRNAs. MicroRNAs (miRNAs) are naturally occurring non-coding RNAs and play vital roles in various physiological and pathological processes. The dysregulation of miRNAs in disease states can serve as a basis for biomarker identification. This study aimed to identify and re-evaluatekey miRNAs involved in POAG through a comprehensive analysis that included normalization, differential expression analysis, target gene identification, and Random Forest (RF) classification. Two public datasets were analyzed, which revealed both upregulated and downregulated miRNAs, and with subsequent target gene prediction, hsamiR-1298-5p was identified as a key miRNA with limited connectivity within the miRNet network. Enrichment analysis suggested potential processes involved in POAG, particularly the role of miR-9 in targeting VE-cadherin, leading to the reduction of β-catenin. Further exploration through Random Forest analysis identified important miRNAs, including hsamiR-1298, hsa-miR-574-5p, and hsa-miR-188-5p. These miRNAs have been studied for their involvement in the Wnt/β-caten in signaling pathway but not extensively in glaucoma, indicating a novel area for future research.

Glaucoma is a major cause of blindness worldwide. The number of cases where vision loss occurs is also increased by the rise in cases that go undiagnosed. The primary causes of this vision loss are the degeneration of the optic nerve and the reduction of retinal ganglion cells. Primary open-angle glaucoma (POAG) represents the most prevalent type of glaucoma, accounting for around two-thirds of all cases. Tears, aqueous humor (AH), and vitreous humor are three fluids in the eyes that contain extracellular miRNAs. MicroRNAs(miRNAs) are naturally occurring non-coding RNAs and play vital roles in various physiological and pathological processes. The dysregulation of miRNAs in disease states can serve as a basis for biomarker identification. This study aimed to identify and re-evaluatekey miRNAs involved in POAG through a comprehensive analysis that included normalization, differential expression analysis, target gene identification, and RandomForest (RF) classification. Two public datasets were analyzed, which revealed both upregulated and downregulated miRNAs, and with subsequent target gene prediction, hsamiR-1298-5p was identified as a key miRNA with limited connectivity within the miRNet network. Enrichment analysis suggested potential processes involved in POAG, particularly the role of miR-9 in targeting VE-cadherin, leading to the reduction of β-catenin. Further exploration through Random Forest analysis identified important miRNAs, including hsamiR-1298, hsa-miR-574-5p, and hsa-miR-188-5p. These miRNAs have been studied for their involvement in the Wnt/β-catenin signaling pathway but not extensively in glaucoma, indicating a novel area for future research.

Most ecosystem functions and related services involve species interactions across trophic levels, for example, pollination and biological pest control. Despite this, our understanding of ecosystem function in multitrophic communities is poor, and research has been limited to either manipulation in small communities or statistical descriptions in larger ones. Recent advances in food web ecology may allow us to overcome the trade-off between mechanistic insight and ecological realism. Molecular tools now simplify the detection of feeding interactions, and trait-based approaches allow the application of dynamic food web models to real ecosystems. We performed the first test of an allometric food web model's ability to replicate temporally nonaggregated abundance data from the field and to provide mechanistic insight into the function of predation. We aimed to reproduce and explore the drivers of the population dynamics of the aphid herbivore Rhopalosiphum padi observed in ten Swedish barley fields. We used a dynamic food web model, taking observed interactions and abundances of predators and alternative prey as input data, allowing us to examine the role of predation in aphid population control. The inverse problem methods were used for simultaneous model fit optimization and model parameterization. The model captured >70% of the variation in aphid abundance in five of ten fields, supporting the model-embodied hypothesis that body size can be an important determinant of predation in the arthropod community. We further demonstrate how in-depth model analysis can disentangle the likely drivers of function, such as the community's abundance and trait composition. Analysing the variability in model performance revealed knowledge gaps, such as the source of episodic aphid mortality, and general method development needs that, if addressed, would further increase model success and enable stronger inference about ecosystem function. The results demonstrate that confronting dynamic food web models with abundance data from the field is a viable approach to evaluate ecological theory and to aid our understanding of function in real ecosystems. However, to realize the full potential of food web models, in ecosystem function research and beyond, trait-based parameterization must be refined and extended to include more traits than body size. © 2018 The Authors. Journal of Animal Ecology © 2018 British Ecological Society

Wastewater-based epidemiology (WBE) analyzes wastewater for the presence of biological and chemical substances to make public health conclusions. COVID-19 disease is caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) that infected individuals shed also in their feces, making WBE an alternative way to track SARS-CoV-2 in populations. There are many limitations to the detection and quantification of SARS-CoV-2 from wastewater, such as sample quality, storage conditions or viral concentration. This thesis aims to determine the extent of these limitations and the factors that contribute to them. Other viruses can help the measurements for example Bovine coronavirus (BCoV) can be spiked as a process surrogate, while Pepper mild mottle virus (PMMoV), a fecal biomarker is used to estimate the prevalence of SARS-CoV-2 infection. This study involved two distinct wastewater samples. For method comparison both samples were processed with two methods: virus concentration by electronegative (EN) filtration or direct RNA extraction method. From the RNA extracts RT-qPCR assays were performed to identify and quantify SARS-CoV-2, BCoV, and PMMoV. Based on the obtained cycle threshold (Ct) values, viral gene copy numbers and virus concentration of the original wastewater samples were calculated. Statistical tests were conducted to assess suggested hypothesizes and variations within the data. Results revealed differences in viral contents due to different sample qualities and as a result of freezing and thawing. Furthermore, different sample processing methods led to differences in quantification. In conclusion, improving analysis of SARS-CoV-2 in wastewater using methodologies with better detection efficiency leads to more reliable results.

Lung cancer is one the most dangerous types of all cancer. Several studies have explored the use of machine learning methods to predict and diagnose this cancer. This study explored the potential of decision tree (DT) and random forest (RF) classification models, in the context of a small transcriptome dataset for outcome prediction of different subtypes on lung cancer. In the study we compared the three subtypes; adenocarcinomas (AC), small cell lung cancer (SCLC) and squamous cell carcinomas (SCC) with normal lung tissue by applying the two machine learning methods from caret R package. The DT and RF model and their validation showed different results for each subtype of the lung cancer data. The DT found more features and validated them with better metrics. Analysis of the biological relevance was focused on the identified features for each of the subtypes AC, SCLC and SCC. The DT presented a detailed insight into the biological data which was essential by classifying it as a biomarker. The identified features from this research may serve as potential candidate genes which could be explored further to confirm their role in corresponding lung cancer types and contribute to targeted diagnostics of different subtypes. 

MOTIVATION: Complex diseases are due to the dense interactions of many disease-associated factors that dysregulate genes that in turn form so-called disease modules, which have shown to be a powerful concept for understanding pathological mechanisms. There exist many disease module inference methods that rely on somewhat different assumptions, but there is still no gold standard or best performing method. Hence, there is a need for combining these methods to generate robust disease modules.

RESULTS: We developed MODule IdentiFIER (MODifieR), an ensemble R package of nine disease module inference methods from transcriptomics networks. MODifieR uses standardized input and output allowing the possibility to combine individual modules generated from these methods into more robust disease-specific modules, contributing to a better understanding of complex diseases.

AVAILABILITY: MODifieR is available under the GNU GPL license and can be freely downloaded from https://gitlab.com/Gustafsson-lab/MODifieR and as a Docker image from https://hub.docker.com/r/ddeweerd/modifier.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The human transcriptome is a highly complex system and is often the focus of research, especially when it fails to function properly, causing disease. Indeed, the amount of publicly available transcriptomic data has grown considerably with the advent of high-throughput techniques. Such special cases are often hard to fully dissect, since studies will be confined to limited data samples and multiple biases. An ideal approach would utilize all available data to learn the fundamentals of the human gene expression system and use these insights in the examination of the more limited sample sets relating to specific diseases. This study shows how a neural network model can be created and used to extract relevant disease genes when applied to limited disease datasets and to suggest relevant pharmaceutical compounds. Thus, it presents a step toward a future where artificial intelligence can advance the analysis of human high-throughput data.

MotivationNetwork-based disease modules have proven to be a powerful concept for extracting knowledge about disease mechanisms, predicting for example disease risk factors and side effects of treatments. Plenty of tools exist for the purpose of module inference, but less effort has been put on simultaneously utilizing knowledge about regulatory mechanisms for predicting disease module hub regulators.

ResultsWe developed MODalyseR, a novel software for identifying disease module regulators and reducing modules to the most disease-associated genes. This pipeline integrates and extends previously published software packages MODifieR and ComHub and hereby provides a user-friendly network medicine framework combining the concepts of disease modules and hub regulators for precise disease gene identification from transcriptomics data. To demonstrate the usability of the tool, we designed a case study for multiple sclerosis that revealed IKZF1 as a promising hub regulator, which was supported by independent ChIP-seq data.

Availability and implementationMODalyseR is available as a Docker image at https://hub.docker.com/r/ddeweerd/modalyser with user guide and installation instructions found at https://gustafsson-lab.gitlab.io/MODalyseR/.

Supplementary informationSupplementary data are available at Bioinformatics Advances online.

Background: Real-time quantitative Reverse Transcriptase Polymerase Chain Reaction (qRT-PCR) is recently used for characterization and expression analysis of miRNAs. The data from such experiments need effective analysis methods to produce reliable and high-quality data. For the miRNA prostate cancer qRT-PCR data used in this study, standard housekeeping normalization method fails due to non-stability of endogenous controls used. Therefore, identifying appropriate normalization method(s) for data analysis based on other data driven principles is an important aspect of this study.

Results: In this study, different normalization methods were tested, which are available in the R packages Affy and qpcrNorm for normalization of the raw data. These methods reduce the technical variation and represent robust alternatives to the standard housekeeping normalization method. The performance of different normalization methods was evaluated statistically and compared against each other as well as with the standard housekeeping normalization method. The results suggest that qpcrNorm Quantile normalization method performs best for all methods tested.

Conclusions: The qpcrNorm Quantile normalization method outperforms the other normalization methods and standard housekeeping normalization method, thus proving the hypothesis of the study. The data driven methods used in this study can be applied as standard procedures in cases where endogenous controls are not stable.

Objective: Biallelic titin truncating variants (TTNtv) have been associated with a wide phenotypic spectrum, ranging from complex prenatal muscle diseases with dysmorphic features to adult-onset limb-girdle muscular dystrophy, with or without cardiac involvement. Given the size and complexity of TTN, reaching an unequivocal molecular diagnosis and precise disease prognosis remains challenging. Methods: In this case series, 12 unpublished cases and one already published case with biallelic TTNtv were collected from multiple international medical centers between November 2022 and September 2023. TTN mutations were detected through exome or genome sequencing. Information about familial and personal clinical history was collected in a standardized form. RNA-sequencing and analysis of TTN exon usage were performed on an internal sample cohort including postnatal skeletal muscles, fetal skeletal muscles, postnatal heart muscles, and fetal heart muscles. In addition, publicly available RNA-sequencing data was retrieved from ENCODE. Results: We generated new RNA-seq data on TTN exons and identified genotype–phenotype correlations with prognostic implications for each titinopathy patient (whether worsening or improving in prenatal and postnatal life) using percentage spliced in (PSI) data for the involved exons. Interestingly, thanks to exon usage, we were also able to rule out a titinopathy diagnosis in one prenatal case. Interpretation: This study demonstrates that exon usage provides valuable insights for a more exhaustive clinical interpretation of TTNtv; additionally, it may serve as a model for implementing personalized medicine in many other genetic diseases, since most genes undergo alternative splicing. 

Epigenomics is the study of modifications to the genetic material without changes to the DNA sequence, one such modification is methylation of nucleotides. DNA methylation is associated with gene regulation and is studied in a variety of fields such as cancer and ageing. Quality control is essential when designing research studies to ensure that the end result is not affected by poor quality data. In this study, the aim was to define robust quality parameters for whole methylome sequencing for Illumina next generation sequencing data. Three different library preparation protocols, all designed for methylation analysis, has been compared: Accel-NGS Methyl-Seq DNA library, NEB Next Enzymatic Methyl-seq and SPlinted Ligation Adapter Tagging. All samples were sequenced on the Illumina NovaSeq 6000 with paired-end 150 bp. An evaluation of alignment software was also included in the study. The nf-core methylseq pipeline version 1.6.1 was used to process all samples in the study. The pipeline was run multiple times with different settings depending on library type and software choice. Throughout the study, the parameters puc19, lambda and alignment rate showed consistency whereas overall methylation rate and coverage were affected by origin of sample material and study design. In conclusion, not all proposed quality parameters were suitable for general quality control since study design and origin of sample material have impact, but alignment rate and the controls puc19 and lambda shows great promise for general quality control. Future work to establish sample material specific thresholds for methylation rate is encouraged.

The adaptive arm of the immune system protects from reinfection and is recognized by an immense diversity of cells that interact and coevolve as novel pathogens emerge. At gestation, the mother undergoes profound and complex changes of her immune system to maintain tolerance towards the fetus while providing proper protection against any intruders. Understanding the mechanisms behind this balancing act can help identify the hallmarks of pregnancy well-being and adverse outcomes. Here, the aim was to explore the dynamic changes at gestation and after delivery in sorted B cell populations including naïve and two memory B cell (MBC) subsets named herein CD27^dull and CD27^bright from three pregnant females. The subsets were analyzed using Illumina MiSeq HTS data derived from the full-length immunoglobulin heavy chain variable region transcript incorporated with unique molecular identifiers (UMIs). The data was processed using UMI-guided error corrections and reads with shared sequence identities were assembled into clonotypes. The samples were further analyzed by comparing baseline B cell repertoire properties including variable gene segment usage (IGHV), complementarity determining region 3 (CDR3) sizes and levels of somatic hypermutations (SHMs). The results revealed a dynamic change in IGHV usage profiles and an increase of CDR3 sizes between gestation and after delivery. CD27^bright exhibited higher levels of SHMs than CD27^dull, and SHMs tend to accumulate unequally across the region. CD27^bright increased their replacement-to-silent SHM ratio after delivery. The results shed more light on maternal immune adaptation and provide a framework that can be refined and optimized in future works.

Metabarcoding have emerged as a vital tool for assessing biodiversity and early detecting invasive species. It is a technique where a certain marker gene, or barcode, is amplified from a DNA sample, sequenced and subsequently classified by comparisons to known reference genomes. This allows for identification of species by sampling water, soil, faeces etc. Compared to the traditional visual surveying, it is more cost and time efficient, in addition to scalable. One other benefit is the potential of discovering species that are elusive, scarce or too small or difficult to identify. In the recent years, the Illumina sequencing platform has been used with high accuracy, and has produced very reliable results. The Oxford Nanopore Technologies third generation sequencing platform allows for the sequencing of very long reads, although at a higher error rate. The aim of this project was to compare COI metabarcoding from Illumina sequencing to that of COI metabarcoding using the longread sequencer MinION from Oxford Nanopore Technologies. The comparisons were made on the classification performance, regarding biodiversity metrics and number of non-indigenous species detected, and total number of species detected. A secondary aim was to evaluate the performance of a metagenomic approach, a reverse metabarcoding, where sequenced longreads are classified by multiple barcodes. The DNA in this project was received from the research group SWEDNA, and had been used in their metabarcoding study using the Illumina platform. The DNA originates from three different sample types with varying integrity. Both the gDNA and amplicons of the Folmer region were sequenced with the MinION. Three datasets were generated in total by post sequencing basecalling, one of amplicon and two of gDNA but with different levels of accuracy. These were classified using Kraken2 and a custom made database of reference sequences. The results show that amplicon sequencing is well adapted to community DNA samples of high concentration and integrity. When compared, eDNA samples are ill fitted to the same method and will most likely benefit from a metagenomic approach, preferably using multiple barcodes. The new approach of reverse metabarcoding shows promise but could be further improved and evaluated.

Allogenic stem cell transplantation has shown considerable success in a number of hematological malignancies, in particular in leukemia. The beneficial effect is mediated by donor T cells recognizing patient-specific HLA-binding peptides. These peptides are called minor histocompatibility antigens (miHAs) and are typically caused by single nucleotide polymorphisms. Tissue-specific miHAs have successfully been used in anti-tumor therapy without causing unspecific graft-versus-host reactions. However, only a small number of miHAs have been identified to date, limiting the clinical use.

Here we present an immunoinformatics pipeline for the identification of miHAs. The pipeline can be applied to large-scale miHA screening, for example, in the development of diagnostic tests. Another interesting application is the design of personalized miHA-based cancer therapies based on patient-donor pair-specific miHAs detected by this pipeline. The suggested method covers various aspects of genetic variant detection, effects of alternative transcripts, and HLA-peptide binding. A comparison of our computational pipeline and experimentally derived datasets shows excellent agreement and coverage of the computationally predicted miHAs.

A large number of biological pathways have been elucidated recently, and there is a need for methods to analyze these pathways. One class of methods compares pathways semantically in order to discover parts that are evolutionarily conserved between species or to discover intraspecies similarities. Such methods usually require that the topologies of the pathways being compared are known, i.e. that a query pathway is being aligned to a model pathway. However, sometimes the query only consists of an unordered set of gene products. Previous methods for mapping sets of gene products onto known pathways have not been based on semantic comparison of gene products using ontologies or other abstraction hierarchies. Therefore, we here propose an approach that uses a similarity function defined in Gene Ontology (GO) terms to find semantic alignments when comparing paths in biological pathways where the nodes are gene products. A known pathway graph is used as a model, and an evolutionary algorithm (EA) is used to evolve putative paths from a set of experimentally determined gene products. The method uses a measure of GO term similarity to calculate a match score between gene products, and the fitness value of each candidate path alignment is derived from these match scores. A statistical test is used to assess the significance of evolved alignments. The performance of the method has been tested using regulatory pathways for S. cerevisiae and M. musculus.

We assess the robustness of partitional clustering algorithms applied to gene expression data. A number of clusterings are made with identical parameter settings and input data using SOM and  k-means algorithms, which both rely on random initialisation and may produce different clusterings with different seeds. We define a reproducibility index and use it to assess the algorithms. The index is based on the number of pairs of genes consistently clustered together in different clusterings. The effect of noise applied to the original data is also studied. Our results show a lack of robustness for both classes of algorithms, with slightly higher reproducibility for SOM than for k-means.

With the rise of NGS technologies, the transcriptomes of non-model organisms can be reconstructed even with the absence of a reference genome, using de novo assembly tools. There is a wide range of de novo assembly tools frequently being developed, however, there is a still a knowledge gap about the different effects and efficiency of different de novo assembly software types for RNA-seq assembly. This study aims to assemble the transcriptome of two different mussel species, Anodonta anatina and Margaritifera margaritifera, using three different types of genomic assemblers and to evaluate their distinct performances. Here, the transcriptomes have been assembled using whole-genome, single-cell and RNA-seq specific assemblers, and the results have been evaluated and compared using reference-free transcriptome evaluation tools. Whole-genome assemblers are not designed to handle variable transcript expressions and splice variations, and have thus achieved poor performance at assembling the transcriptomes. Single-cell assemblers, however, are designed to assemble genomes with uneven coverage, which make them able to handle variable transcript expressions and have therefore achieved good efficiency at assembling the transcriptomes. Single-cell assembler SPAdes has matched the performance of the well stablished RNA-seq assembler Trinity and the single-cell version of IBDA performed just as well as their RNA version. Overall, the top performing assembler in the study was the RNA version of SPAdes.

Questions have been raised about the genomic complexity of the human genome, since it was reported that it only consisted of 32,000 genes. Alternative splicing is considered the explanation of the enormous difference between the number of genes and the number of proteins. Aligning expressed sequence tags (ESTs) to the genomic sequence has become a popular approach for gene prediction, revealing alternative splice variants. The aim in this thesis is to identify and analyse splice variants of the adhesion family of G protein-coupled receptors using EST data. 75% of the genes in the data set of 33 sequences were found to have a total of 51 splice variants. About half of the variants were considered functional.

Human pluripotent stem cell-derived hepatocytes (hPSC-HEP) display many properties of mature hepatocytes, including expression of important genes of the drug metabolizing machinery, glycogen storage, and production of multiple serum proteins. To this date, hPSC-HEP do not, however, fully recapitulate the complete functionality of in vivo mature hepatocytes. In this study, we applied versatile bioinformatic algorithms, including functional annotation and pathway enrichment analyses, transcription factor binding-site enrichment, and similarity and correlation analyses, to datasets collected from different stages during hPSC-HEP differentiation and compared these to developmental stages and tissues from fetal and adult human liver. Our results demonstrate a high level of similarity between the in vitro differentiation of hPSC-HEP and in vivo hepatogenesis. Importantly, the transcriptional correlation of hPSC-HEP with adult liver (AL) tissues was higher than with fetal liver (FL) tissues (0.83 and 0.70, respectively). Functional data revealed mature features of hPSC-HEP including cytochrome P450 enzymes activities and albumin secretion. Moreover, hPSC-HEP showed expression of many genes involved in drug absorption, distribution, metabolism, and excretion. Despite the high similarities observed, we identified differences of specific pathways and regulatory players by analyzing the gene expression between hPSC-HEP and AL. These findings will aid future intervention and improvement of in vitro hepatocyte differentiation protocol in order to generate hepatocytes displaying the complete functionality of mature hepatocytes. Finally, on the transcriptional level, our results show stronger correlation and higher similarity of hPSC-HEP to AL than to FL. In addition, potential targets for further functional improvement of hPSC-HEP were also identified.