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It has been predicted that plant breeding will focus on developing heat-resistant, drought-tolerant, and flood-resistant crops to ensure food security under climate challenges. The on-going climate change can cause sudden heavy rainfalls leading to waterlogging (flooded/ponded/saturated soils), which negatively influences crop growth. The consequence of waterlogging in crop plants is oxygen deficiency (hypoxia) or complete absence (anoxia), which inhibits root respiration, rate of photosynthesis and CO2 assimilation. Germinating seeds/emerging seedlings are very sensitive to waterlogging, as their level of metabolism is high. Herein, one sensitive and one tolerant maize inbred line grown on Knop nutritive solution were submitted to waterlogging stress by keeping the 5-day-old seedlings 2 cm below the solution surface for 24h, 72h and seven days. RT-qPCR analysis was conducted on the control (optimal saturation) and treated samples in order to determine differential expression of Zmpdc1 and Zmpdc3 (involved in metabolism maintenance under anaerobic conditions), XET (involved in root aerenchyma development), ATPSchl (involved in sulfur metabolism), FS2chl and FS2cyBa (involved in PSII function) genes. Student’s t-test analysis revealed statistically significant differences between the sensitive and the tolerant genotype for FS2cyBA after 24 h, XET after 72h and seven days, Zmpdc1 for all three time points and Zmpdc3 after 24h and 72h, indicating their involvement in waterlogging tolerance. Further on, more in-depth studies will be conducted in order to reveal molecular and genetic data favorable for breeding tolerant maize genotypes.

Somatic embryogenesis (SE) is a powerful model system for studying embryo development and an important method for scaling up availability of elite and climate-adapted genetic material of Norway spruce (Picea abies L. Karst). However, there are several steps during the development of the somatic embryo (Sem) that are suboptimal compared to zygotic embryo (Zem) development. These differences are poorly understood and result in substantial yield losses during plant production, which limits cost-effective large-scale production of SE plants. This study presents a comprehensive data resource profiling gene expression during zygotic and somatic embryo development to support studies aiming to advance understanding of gene regulatory programmes controlling embryo development. Transcriptome expression patterns were analysed during zygotic embryogenesis (ZE) in Norway spruce, including separated samples of the female gametophytes and Zem, and at multiple stages during SE. Expression data from eight developmental stages of SE, starting with pro-embryogenic masses (PEMs) up until germination, revealed extensive modulation of the transcriptome between the early and mid-stage maturing embryos and at the transition of desiccated embryos to germination. Comparative analysis of gene expression changes during ZE and SE identified differences in the pattern of gene expression changes and functional enrichment of these provided insight into the associated biological processes. Orthologs of transcription factors known to regulate embryo development in angiosperms were differentially regulated during Zem and Sem development and in the different zygotic embryo tissues, providing clues to the differences in development observed between Zem and Sem. This resource represents the most comprehensive dataset available for exploring embryo development in conifers.

Complex regulation of gene expression is fundamental for the development and function of the human brain. The brain exhibits the highest levels of splicing activity and the RNA modification N6-methyladenosine (m6A) in human tissues. The expression of alternatively spliced RNA isoforms is critical for cellular specificity and development and the dysregulation of isoform expression and m6A modification processes is implicated in various neuropsychiatric and neurodegenerative disorders. Therefore, it is essential to profile the expression and modification landscape at the isoform level to elucidate the underlying pathology of complex neurological disorders. However, the short-read sequencing methods to study expression changes and m6A modifications only provide gene-level resolution. Long- read direct RNA sequencing (DRS) with Oxford Nanopore Technologies is a new method in the transcriptomics field that may address many of the remaining questions about the roles of RNA isoforms and gene regulation. We applied DRS in multiple studies with a focus on isoform quantification and isoform-level profiling of m6A modifications. DRS does not require any amplification or fragmentation steps and quantifies genes and isoforms while also characterising RNA modifications and polyA tail lengths of each isoform. However, DRS is an emerging technology and many previous transcriptomics methods have not yet been tested on DRS data. We performed DRS of human neuroblastoma cell lines and established the ability of DRS to detect differential isoform expression between synthetic RNA controls and human RNA populations. We developed and benchmarked NanoCount, a novel isoform quantification tool specifically designed for DRS data that we demonstrate outperforms other tools. DRS was subsequently applied to post-mortem human brain samples from three distinct regions: prefrontal cortex, caudate nucleus, and cerebellum. We found >15k differentially expressed isoforms between the brain regions, and

The model moss species Physcomitrium patens has long been used for studying divergence of land plants spanning from bryophytes to angiosperms. In addition to its phylogenetic relationships, the limited number of differential tissues, and comparable morphology to the earliest embryophytes provide a system to represent basic plant architecture. Based on plant-fungal interactions today, it is hypothesized these kingdoms have a long-standing relationship, predating plant terrestrialization. Mortierellaceae have origins diverging from other land fungi paralleling bryophyte divergence, are related to arbuscular mycorrhizal fungi but are free-living, observed to interact with plants, and can be found in moss microbiomes globally. Due to their parallel origins, we assess here how two Mortierellaceae species, Linnemannia elongata and Benniella erionia, interact with P. patens in coculture. We also assess how Mollicute-related or Burkholderia-related endobacterial symbionts (MRE or BRE) of these fungi impact plant response. Coculture interactions are investigated through high-throughput phenomics, microscopy, RNA-sequencing, differential expression profiling, gene ontology enrichment, and comparisons among 99 other P. patens transcriptomic studies. Here we present new high-throughput approaches for measuring P. patens growth, identify novel expression of over 800 genes that are not expressed on traditional agar media, identify subtle interactions between P. patens and Mortierellaceae, and observe changes to plant-fungal interactions dependent on whether MRE or BRE are present. Our study provides insights into how plants and fungal partners may have interacted based on their communications observed today as well as identifying L. elongata and B. erionia as modern fungal endophytes with P. patens.

Intraspecific genetic variation in foundation species such as aspen (Populus tremuloides Michx.) shapes their impact on forest structure and function. Identifying genes underlying ecologically important traits is key to understanding that impact. Previous studies, using single-locus genome-wide association (GWA) analyses to identify candidate genes, have identified fewer genes than anticipated for highly heritable quantitative traits. Mounting evidence suggests that polygenic control of quantitative traits is largely responsible for this “missing heritability” phenomenon. Our research characterized the genetic architecture of 30 ecologically important traits using a common garden of aspen through genomic and transcriptomic analyses. A multilocus association model revealed that most traits displayed a highly polygenic architecture, with most variation explained by loci with small effects (likely below the detection levels of single-locus GWA methods). Consistent with a polygenic architecture, our single-locus GWA analyses found only 38 significant SNPs in 22 genes across 15 traits. Next, we used differential expression analysis on a subset of aspen genets with divergent concentrations of salicinoid phenolic glycosides (key defense traits). This complementary method to traditional GWA discovered 1243 differentially expressed genes for a polygenic trait. Soft clustering analysis revealed three gene clusters (241 candidate genes) involved in secondary metabolite biosynthesis and regulation. Our work reveals that ecologically important traits governing higher-order community- and ecosystem-level attributes of a foundation forest tree species have complex underlying genetic structures and will require methods beyond traditional GWA analyses to unravel.

Transcriptome reconstruction is an important component in the bioinformatical part of transcriptome studies. When a reference genome is missing, highly fragmented or incomplete, a de novo transcriptome assembly is the transcriptome reconstruction approach of choice, since in such situations, a simple alignment (or mapping) would not necessarily give all theinformation concerning splice junctions, isoforms or even the full extent of the gene. Several methods for de novo transcriptome assembly have been suggested, but many of these methods lack sufficient ability to recover isoforms or are memory intense, which requires themethods to be executed on computing clusters. One species, whose published reference genome is highly fragmented, is the Norway spruce (Picea abies (L.) H. Karst.) – a conifer, very important for Swedish forestry ande conomy, but with a long juvenile phase and irregular cone setting, the demand of cultivated seeds is larger than the supply. Thus, there is a desire to understand the molecular biology behind the cone setting in P. abies, not least regarding gene expression and its regulation. This doctoral dissertation addresses these problems by describing the biological background in general, followed by an introduction to theoretical computational problems relatedto the methods applied for transcriptome reconstruction, which then are described in depth themselves, as is P. abies. Paper I uses a novel de novo assembler to detect connections between scaffolds in the P. abies genome, and also studies P. abies var acrocona, a mutant with shorter juvenile phase and more regular cone setting than the wild type, in order to detect how cone setting is initiated. By means of allele-specific expression analysis, this study detects a SNP ina miRNA binding site on a novel gene, a mutation which is coherent with the acroconaphenotype. Paper II and paper III both introduce one novel de novo transcriptome assembler each: Paper II describes the assembly method app, Transkriptomrekonstruktion är en viktig beståndsdel i den bioinformatiska avdelningen av transkriptomstudier. När ett referensgenom saknas, är kraftigt fragmenterat eller ofullständigt, så måste sammansättningen av transkriptomet ske de novo, ty i dessa situationer skulle inte en vanlig inpassning (eller mappning) nödvändigtvis ge all information om splitsningsplatser, isoformer eller ens genens fullständiga omfattning. Flertalet metoder för sammansättning av transkriptom de novo har föreslagits, men många av dessa saknar förmåga att återskapa isoformer eller är minnesintensiva, vilket kräver att metoderna exekveras på datorkluster. En art, vars referensgenom är kraftigt fragmenterat, är rödgran (Picea abies (L.) H. Karst.) - ett barrträd som är mycket viktigt för svenskt skogsbruk och svensk ekonomi, men med en lång uppväxtsfas och oregelbunden kottsättning så är efterfrågan på förädlade fröer större än utbudet. Således finns ett intresse att förstå kottsättningens bakomliggande molekylärbiologi, inte minst med avseenede på vilka gener som uttrycks och hur de regleras. Denna doktorsavhandling behandlar dessa problem medelst beskrivande av den allmänna biologiska bakgrunden, följt av en introduktion av problem från teoretisk datalogi, vilka är relaterade till metoder för sammansättning av transkriptom, som i sin tur själva är beskrivna i detalj därefter, liksom också rödgran är. Artikel I tillämpar en ny sammasättningsmetod för att upptäcka kopplingar mellan olika fragment i grangenomet, men studerar även en mutant: P. abies var acrocona (kottegran), vilken har kortare uppväxtsfas och mer regelbunden kottsättning än vildtypen, för att avgöra hur kottsättning initieras. Med hjälp av allelspecifik uttrycksanalys hittar denna stuide en SNP i ett bindningsställe för miRNA i en tidigare ostuderad gen, vilken visar sig sammanhängande med kottegranens fenotyp. Artikel II och artikel III presenterar varsin ny metod för de novo sammansättning av transkriptom: Artikel II bes, QC 2023-11-10

Intraspecific genetic variation in foundation species such as aspen (Populus tremuloides Michx.) shapes their impact on forest structure and function. Identifying genes underlying ecologically important traits is key to understanding that impact. Previous studies, using single-locus genome-wide association (GWA) analyses to identify candidate genes, have identified fewer genes than anticipated for highly heritable quantitative traits. Mounting evidence suggests that polygenic control of quantitative traits is largely responsible for this “missing heritability” phenomenon. Our research characterized the genetic architecture of 30 ecologically important traits using a common garden of aspen through genomic and transcriptomic analyses. A multilocus association model revealed that most traits displayed a highly polygenic architecture, with most variation explained by loci with small effects (likely below the detection levels of single-locus GWA methods). Consistent with a polygenic architecture, our single-locus GWA analyses found only 38 significant SNPs in 22 genes across 15 traits. Next, we used differential expression analysis on a subset of aspen genets with divergent concentrations of salicinoid phenolic glycosides (key defense traits). This complementary method to traditional GWA discovered 1243 differentially expressed genes for a polygenic trait. Soft clustering analysis revealed three gene clusters (241 candidate genes) involved in secondary metabolite biosynthesis and regulation. Our work reveals that ecologically important traits governing higher-order community- and ecosystem-level attributes of a foundation forest tree species have complex underlying genetic structures and will require methods beyond traditional GWA analyses to unravel.

Intraspecific genetic variation in foundation species such as aspen (Populus tremuloides Michx.) shapes their impact on forest structure and function. Identifying genes underlying ecologically important traits is key to understanding that impact. Previous studies, using single-locus genome-wide association (GWA) analyses to identify candidate genes, have identified fewer genes than anticipated for highly heritable quantitative traits. Mounting evidence suggests that polygenic control of quantitative traits is largely responsible for this “missing heritability” phenomenon. Our research characterized the genetic architecture of 30 ecologically important traits using a common garden of aspen through genomic and transcriptomic analyses. A multilocus association model revealed that most traits displayed a highly polygenic architecture, with most variation explained by loci with small effects (likely below the detection levels of single-locus GWA methods). Consistent with a polygenic architecture, our single-locus GWA analyses found only 38 significant SNPs in 22 genes across 15 traits. Next, we used differential expression analysis on a subset of aspen genets with divergent concentrations of salicinoid phenolic glycosides (key defense traits). This complementary method to traditional GWA discovered 1243 differentially expressed genes for a polygenic trait. Soft clustering analysis revealed three gene clusters (241 candidate genes) involved in secondary metabolite biosynthesis and regulation. Our work reveals that ecologically important traits governing higher-order community- and ecosystem-level attributes of a foundation forest tree species have complex underlying genetic structures and will require methods beyond traditional GWA analyses to unravel.

Intraspecific genetic variation in foundation species such as aspen (Populus tremuloides Michx.) shapes their impact on forest structure and function. Identifying genes underlying ecologically important traits is key to understanding that impact. Previous studies, using single-locus genome-wide association (GWA) analyses to identify candidate genes, have identified fewer genes than anticipated for highly heritable quantitative traits. Mounting evidence suggests that polygenic control of quantitative traits is largely responsible for this “missing heritability” phenomenon. Our research characterized the genetic architecture of 30 ecologically important traits using a common garden of aspen through genomic and transcriptomic analyses. A multilocus association model revealed that most traits displayed a highly polygenic architecture, with most variation explained by loci with small effects (likely below the detection levels of single-locus GWA methods). Consistent with a polygenic architecture, our single-locus GWA analyses found only 38 significant SNPs in 22 genes across 15 traits. Next, we used differential expression analysis on a subset of aspen genets with divergent concentrations of salicinoid phenolic glycosides (key defense traits). This complementary method to traditional GWA discovered 1243 differentially expressed genes for a polygenic trait. Soft clustering analysis revealed three gene clusters (241 candidate genes) involved in secondary metabolite biosynthesis and regulation. Our work reveals that ecologically important traits governing higher-order community- and ecosystem-level attributes of a foundation forest tree species have complex underlying genetic structures and will require methods beyond traditional GWA analyses to unravel.

Intraspecific genetic variation in foundation species such as aspen (Populus tremuloides Michx.) shapes their impact on forest structure and function. Identifying genes underlying ecologically important traits is key to understanding that impact. Previous studies, using single-locus genome-wide association (GWA) analyses to identify candidate genes, have identified fewer genes than anticipated for highly heritable quantitative traits. Mounting evidence suggests that polygenic control of quantitative traits is largely responsible for this “missing heritability” phenomenon. Our research characterized the genetic architecture of 30 ecologically important traits using a common garden of aspen through genomic and transcriptomic analyses. A multilocus association model revealed that most traits displayed a highly polygenic architecture, with most variation explained by loci with small effects (likely below the detection levels of single-locus GWA methods). Consistent with a polygenic architecture, our single-locus GWA analyses found only 38 significant SNPs in 22 genes across 15 traits. Next, we used differential expression analysis on a subset of aspen genets with divergent concentrations of salicinoid phenolic glycosides (key defense traits). This complementary method to traditional GWA discovered 1243 differentially expressed genes for a polygenic trait. Soft clustering analysis revealed three gene clusters (241 candidate genes) involved in secondary metabolite biosynthesis and regulation. Our work reveals that ecologically important traits governing higher-order community- and ecosystem-level attributes of a foundation forest tree species have complex underlying genetic structures and will require methods beyond traditional GWA analyses to unravel.

Intraspecific genetic variation in foundation species such as aspen (Populus tremuloides Michx.) shapes their impact on forest structure and function. Identifying genes underlying ecologically important traits is key to understanding that impact. Previous studies, using single-locus genome-wide association (GWA) analyses to identify candidate genes, have identified fewer genes than anticipated for highly heritable quantitative traits. Mounting evidence suggests that polygenic control of quantitative traits is largely responsible for this “missing heritability” phenomenon. Our research characterized the genetic architecture of 30 ecologically important traits using a common garden of aspen through genomic and transcriptomic analyses. A multilocus association model revealed that most traits displayed a highly polygenic architecture, with most variation explained by loci with small effects (likely below the detection levels of single-locus GWA methods). Consistent with a polygenic architecture, our single-locus GWA analyses found only 38 significant SNPs in 22 genes across 15 traits. Next, we used differential expression analysis on a subset of aspen genets with divergent concentrations of salicinoid phenolic glycosides (key defense traits). This complementary method to traditional GWA discovered 1243 differentially expressed genes for a polygenic trait. Soft clustering analysis revealed three gene clusters (241 candidate genes) involved in secondary metabolite biosynthesis and regulation. Our work reveals that ecologically important traits governing higher-order community- and ecosystem-level attributes of a foundation forest tree species have complex underlying genetic structures and will require methods beyond traditional GWA analyses to unravel.

The widespread availability of high-dimensional biological data has made the simultaneous screening of many biological characteristics a central problem in computational and high-dimensional biology. As the dimensionality of datasets continues to grow, so too does the complexity of identifying biomarkers linked to exposure patterns. The statistical analysis of such data often relies upon parametric modeling assumptions motivated by convenience, inviting opportunities for model misspecification. While estimation frameworks incorporating flexible, data adaptive regression strategies can mitigate this, their standard variance estimators are often unstable in high-dimensional settings, resulting in inflated Type-I error even after standard multiple testing corrections. We adapt a shrinkage approach compatible with parametric modeling strategies to semiparametric variance estimators of a family of efficient, asymptotically linear estimators of causal effects, defined by counterfactual exposure contrasts. Augmenting the inferential stability of these estimators in high-dimensional settings yields a data adaptive approach for robustly uncovering stable causal associations, even when sample sizes are limited. Our generalized variance estimator is evaluated against appropriate alternatives in numerical experiments, and an open source R/Bioconductor package, biotmle, is introduced. The proposal is demonstrated in an analysis of high-dimensional DNA methylation data from an observational study on the epigenetic effects of tobacco smoking.

Graphene oxide has become a very appealing nanomaterial during the last years for many different applications, but its possible impact in different biological systems remains unclear. Here, an assessment to understand the toxicity of different commercial graphene oxide nanomaterials on the unicellular fungal model organism Saccharomyces cerevisiae was performed. For this task, an RNA purification protocol was optimized to avoid the high nucleic acid absorption capacity of graphene oxide. The developed protocol is based on a sorbitol gradient separation process for the isolation of adequate ribonucleic acid levels (in concentration and purity) from yeast cultures exposed to the carbon derived nanomaterial. To pinpoint potential toxicity mechanisms and pathways, the transcriptome of S. cerevisiae exposed to 160 mg L–1 of monolayer graphene oxide (GO) and graphene oxide nanocolloids (GOC) was studied and compared. Both graphene oxide products induced expression changes in a common group of genes (104), many of them related to iron homeostasis, starvation and stress response, amino acid metabolism and formate catabolism. Also, a high number of genes were only differentially expressed in either GO (236) or GOC (1077) exposures, indicating that different commercial products can induce specific changes in the physiological state of the fungus., This work was supported by the European Union’s H2020 Research and Innovation Programme under the Marie Skłodowska-Curie Grant Agreement Nos. 691095 and 734873 and Junta de Castilla y Leon-FEDER under Grant Nos. BU079U16 and UBU-16-B.

Autoimmuna sjukdomar är en samling komplexa, kroniska, inflammatoriska sjukdomstillstånd som kännetecknas av dysreglering av immunsystemet, vilket resulterar i inflammation och skada av vävnader, celler och organ. Dessa sjukdomar har en betydande inverkan på individens livskvalitet och bidrar ofta till ökad dödsrisk där komorbiditeter föreligger. Emellertid medför den varierande symptombilden för olika autoimmuna sjukdomar betydande utmaningar för att uppnå noggrann diagnos, prognos och utvärdering av behandling. Det finns därför ett påtagligt behov av att upptäcka nya biomarkörer. I denna studie utfördes en omfattande analys av 944 plasmaprover med hjälp av OlinkR Explore-plattformen, vilket genererade data för 1463 unika proteiner. Baserat på uttrycksdata identifierades proteiner förknippade med de sex utvalda autoimmuna sjukdomarna multipel skleros, myosit, reumatoid artrit, systemisk skleros, Sjögrens sjukdom och systemisk lupus erythematosus samt några av deras definierade subgrupper. Dessa potentiella biomarkörer kommer eventuellt att underlätta tidig diagnos, sjukdomsdifferentiering och prognos. Flertalet av dessa proteiner har ännu aldrig kopplats till de här specifika sjukdomarna i litteraturen, särskilt inte från plasmaprover, vilket ger spännande nya perspektiv för biomarkörsutveckling. Det är dock av största vikt att genomföra robusta valideringsstudier i oberoende kohorter. Sammanfattningsvis belyser våra resultat den potentiella brukbarheten hos dessa proteomiska plasmabiomarkörer för att förbättra tidig sjukdomsdetektering, karakterisering av subgrupper och sjukdomsdifferentiering att stimulera. Förhoppningsvis kan dessa resultat stimulera till vidare forskning inom området för biomarkörer och potentiella framsteg inom individbaserad medicin., Autoimmune diseases are complex, chronic, inflammatory conditions characterized by dysregulation of the immune system, resulting in inflammation and damage to various tissues, cells and organs. These diseases significantly impact individuals’ quality of life and often contribute to increased mortality risk in the presence of comorbidities. However, due to the diverse array of symptoms associated with different autoimmune diseases, accurate diagnosis, prognosis, and treatment evaluation pose significant challenges. Thus, there is a pressing need for the discovery of novel biomarkers. In this study, a comprehensive analysis of 944 plasma samples using the OlinkR Explore platform was conducted, generating data on 1463 unique proteins. Based on the expression data, associated proteins were identified for six selected autoimmune diseases, namely multiple sclerosis, myositis, rheumatoid arthritis, systemic sclerosis, Sjögren’s syndrome, and systemic lupus erythematosus, as well as some of their defined subgroups. These are prospective biomarkers and have the potential to aid in early diagnosis, therapeutic intervention, subgroup identification, disease differentiation, and disease prognosis. Notably, some of these proteins have not been previously associated with the specific diseases in the existing literature, especially not in plasma samples, thereby offering intriguing new perspectives for biomarker development. However, it is of great importance to conduct robust validation studies in independent cohorts to confirm the outcomes of this study. In summary, our findings highlight the potential utility of these proteomic plasma biomarkers in improving the early detection, subgroup characterization, and disease differentiation of autoimmune diseases. The identification of these proteins will hopefully stimulate further investigation in the field of biomarker research and potential advancements in personalized medicine.

Autoimmuna sjukdomar är en samling komplexa, kroniska, inflammatoriska sjukdomstillstånd som kännetecknas av dysreglering av immunsystemet, vilket resulterar i inflammation och skada av vävnader, celler och organ. Dessa sjukdomar har en betydande inverkan på individens livskvalitet och bidrar ofta till ökad dödsrisk där komorbiditeter föreligger. Emellertid medför den varierande symptombilden för olika autoimmuna sjukdomar betydande utmaningar för att uppnå noggrann diagnos, prognos och utvärdering av behandling. Det finns därför ett påtagligt behov av att upptäcka nya biomarkörer. I denna studie utfördes en omfattande analys av 944 plasmaprover med hjälp av OlinkR Explore-plattformen, vilket genererade data för 1463 unika proteiner. Baserat på uttrycksdata identifierades proteiner förknippade med de sex utvalda autoimmuna sjukdomarna multipel skleros, myosit, reumatoid artrit, systemisk skleros, Sjögrens sjukdom och systemisk lupus erythematosus samt några av deras definierade subgrupper. Dessa potentiella biomarkörer kommer eventuellt att underlätta tidig diagnos, sjukdomsdifferentiering och prognos. Flertalet av dessa proteiner har ännu aldrig kopplats till de här specifika sjukdomarna i litteraturen, särskilt inte från plasmaprover, vilket ger spännande nya perspektiv för biomarkörsutveckling. Det är dock av största vikt att genomföra robusta valideringsstudier i oberoende kohorter. Sammanfattningsvis belyser våra resultat den potentiella brukbarheten hos dessa proteomiska plasmabiomarkörer för att förbättra tidig sjukdomsdetektering, karakterisering av subgrupper och sjukdomsdifferentiering att stimulera. Förhoppningsvis kan dessa resultat stimulera till vidare forskning inom området för biomarkörer och potentiella framsteg inom individbaserad medicin., Autoimmune diseases are complex, chronic, inflammatory conditions characterized by dysregulation of the immune system, resulting in inflammation and damage to various tissues, cells and organs. These diseases significantly impact individuals’ quality of life and often contribute to increased mortality risk in the presence of comorbidities. However, due to the diverse array of symptoms associated with different autoimmune diseases, accurate diagnosis, prognosis, and treatment evaluation pose significant challenges. Thus, there is a pressing need for the discovery of novel biomarkers. In this study, a comprehensive analysis of 944 plasma samples using the OlinkR Explore platform was conducted, generating data on 1463 unique proteins. Based on the expression data, associated proteins were identified for six selected autoimmune diseases, namely multiple sclerosis, myositis, rheumatoid arthritis, systemic sclerosis, Sjögren’s syndrome, and systemic lupus erythematosus, as well as some of their defined subgroups. These are prospective biomarkers and have the potential to aid in early diagnosis, therapeutic intervention, subgroup identification, disease differentiation, and disease prognosis. Notably, some of these proteins have not been previously associated with the specific diseases in the existing literature, especially not in plasma samples, thereby offering intriguing new perspectives for biomarker development. However, it is of great importance to conduct robust validation studies in independent cohorts to confirm the outcomes of this study. In summary, our findings highlight the potential utility of these proteomic plasma biomarkers in improving the early detection, subgroup characterization, and disease differentiation of autoimmune diseases. The identification of these proteins will hopefully stimulate further investigation in the field of biomarker research and potential advancements in personalized medicine.

Whole-genome duplication has shaped the evolution of angiosperms and other organisms, and is important for many crops. Structural reorganization of chromosomes and repatterning of gene expression are frequently observed in allopolyploids, with physiological and ecological consequences. Recurrent origins from different parental populations are widespread among polyploids, resulting in an array of lineages that provide excellent models to uncover mechanisms of adaptation to divergent environments in early phases of polyploid evolution. We integrate here transcriptomic and ecophysiological comparative studies to show that sibling allopolyploid marsh orchid species (Dactylorhiza, Orchidaceae) occur in different habitats (low nutrient fens vs. meadows with mesic soils) and are characterized by a complex suite of intertwined, pronounced ecophysiological differences between them. We uncover distinct features in leaf elemental chemistry, light-harvesting, photoprotection, nutrient transport and stomata activity of the two sibling allopolyploids, which appear to match their specific ecologies, in particular soil chemistry differences at their native sites. We argue that the phenotypic divergence between the sibling allopolyploids has a clear genetic basis, generating ecological barriers that maintain distinct, independent lineages, despite pervasive interspecific gene flow. This suggests that recurrent origins of polyploids bring about a long-term potential to trigger and maintain functional and ecological diversity in marsh orchids and other groups.

Transcriptome reconstruction is an important component in the bioinformatical part of transcriptome studies. When a reference genome is missing, highly fragmented or incomplete, a de novo transcriptome assembly is the transcriptome reconstruction approach of choice, since in such situations, a simple alignment (or mapping) would not necessarily give all theinformation concerning splice junctions, isoforms or even the full extent of the gene. Several methods for de novo transcriptome assembly have been suggested, but many of these methods lack sufficient ability to recover isoforms or are memory intense, which requires themethods to be executed on computing clusters. One species, whose published reference genome is highly fragmented, is the Norway spruce (Picea abies (L.) H. Karst.) – a conifer, very important for Swedish forestry ande conomy, but with a long juvenile phase and irregular cone setting, the demand of cultivated seeds is larger than the supply. Thus, there is a desire to understand the molecular biology behind the cone setting in P. abies, not least regarding gene expression and its regulation. This doctoral dissertation addresses these problems by describing the biological background in general, followed by an introduction to theoretical computational problems relatedto the methods applied for transcriptome reconstruction, which then are described in depth themselves, as is P. abies. Paper I uses a novel de novo assembler to detect connections between scaffolds in the P. abies genome, and also studies P. abies var acrocona, a mutant with shorter juvenile phase and more regular cone setting than the wild type, in order to detect how cone setting is initiated. By means of allele-specific expression analysis, this study detects a SNP ina miRNA binding site on a novel gene, a mutation which is coherent with the acroconaphenotype. Paper II and paper III both introduce one novel de novo transcriptome assembler each: Paper II describes the assembly method app, Transkriptomrekonstruktion är en viktig beståndsdel i den bioinformatiska avdelningen av transkriptomstudier. När ett referensgenom saknas, är kraftigt fragmenterat eller ofullständigt, så måste sammansättningen av transkriptomet ske de novo, ty i dessa situationer skulle inte en vanlig inpassning (eller mappning) nödvändigtvis ge all information om splitsningsplatser, isoformer eller ens genens fullständiga omfattning. Flertalet metoder för sammansättning av transkriptom de novo har föreslagits, men många av dessa saknar förmåga att återskapa isoformer eller är minnesintensiva, vilket kräver att metoderna exekveras på datorkluster. En art, vars referensgenom är kraftigt fragmenterat, är rödgran (Picea abies (L.) H. Karst.) - ett barrträd som är mycket viktigt för svenskt skogsbruk och svensk ekonomi, men med en lång uppväxtsfas och oregelbunden kottsättning så är efterfrågan på förädlade fröer större än utbudet. Således finns ett intresse att förstå kottsättningens bakomliggande molekylärbiologi, inte minst med avseenede på vilka gener som uttrycks och hur de regleras. Denna doktorsavhandling behandlar dessa problem medelst beskrivande av den allmänna biologiska bakgrunden, följt av en introduktion av problem från teoretisk datalogi, vilka är relaterade till metoder för sammansättning av transkriptom, som i sin tur själva är beskrivna i detalj därefter, liksom också rödgran är. Artikel I tillämpar en ny sammasättningsmetod för att upptäcka kopplingar mellan olika fragment i grangenomet, men studerar även en mutant: P. abies var acrocona (kottegran), vilken har kortare uppväxtsfas och mer regelbunden kottsättning än vildtypen, för att avgöra hur kottsättning initieras. Med hjälp av allelspecifik uttrycksanalys hittar denna stuide en SNP i ett bindningsställe för miRNA i en tidigare ostuderad gen, vilken visar sig sammanhängande med kottegranens fenotyp. Artikel II och artikel III presenterar varsin ny metod för de novo sammansättning av transkriptom: Artikel II bes, QC 2023-11-10

Intraspecific genetic variation in foundation species such as aspen (Populus tremuloides Michx.) shapes their impact on forest structure and function. Identifying genes underlying ecologically important traits is key to understanding that impact. Previous studies, using single-locus genome-wide association (GWA) analyses to identify candidate genes, have identified fewer genes than anticipated for highly heritable quantitative traits. Mounting evidence suggests that polygenic control of quantitative traits is largely responsible for this “missing heritability” phenomenon. Our research characterized the genetic architecture of 30 ecologically important traits using a common garden of aspen through genomic and transcriptomic analyses. A multilocus association model revealed that most traits displayed a highly polygenic architecture, with most variation explained by loci with small effects (likely below the detection levels of single-locus GWA methods). Consistent with a polygenic architecture, our single-locus GWA analyses found only 38 significant SNPs in 22 genes across 15 traits. Next, we used differential expression analysis on a subset of aspen genets with divergent concentrations of salicinoid phenolic glycosides (key defense traits). This complementary method to traditional GWA discovered 1243 differentially expressed genes for a polygenic trait. Soft clustering analysis revealed three gene clusters (241 candidate genes) involved in secondary metabolite biosynthesis and regulation. Our work reveals that ecologically important traits governing higher-order community- and ecosystem-level attributes of a foundation forest tree species have complex underlying genetic structures and will require methods beyond traditional GWA analyses to unravel.

Whole-genome duplication has shaped the evolution of angiosperms and other organisms, and is important for many crops. Structural reorganization of chromosomes and repatterning of gene expression are frequently observed in allopolyploids, with physiological and ecological consequences. Recurrent origins from different parental populations are widespread among polyploids, resulting in an array of lineages that provide excellent models to uncover mechanisms of adaptation to divergent environments in early phases of polyploid evolution. We integrate here transcriptomic and ecophysiological comparative studies to show that sibling allopolyploid marsh orchid species (Dactylorhiza, Orchidaceae) occur in different habitats (low nutrient fens vs. meadows with mesic soils) and are characterized by a complex suite of intertwined, pronounced ecophysiological differences between them. We uncover distinct features in leaf elemental chemistry, light-harvesting, photoprotection, nutrient transport and stomata activity of the two sibling allopolyploids, which appear to match their specific ecologies, in particular soil chemistry differences at their native sites. We argue that the phenotypic divergence between the sibling allopolyploids has a clear genetic basis, generating ecological barriers that maintain distinct, independent lineages, despite pervasive interspecific gene flow. This suggests that recurrent origins of polyploids bring about a long-term potential to trigger and maintain functional and ecological diversity in marsh orchids and other groups.

Intraspecific genetic variation in foundation species such as aspen (Populus tremuloides Michx.) shapes their impact on forest structure and function. Identifying genes underlying ecologically important traits is key to understanding that impact. Previous studies, using single-locus genome-wide association (GWA) analyses to identify candidate genes, have identified fewer genes than anticipated for highly heritable quantitative traits. Mounting evidence suggests that polygenic control of quantitative traits is largely responsible for this “missing heritability” phenomenon. Our research characterized the genetic architecture of 30 ecologically important traits using a common garden of aspen through genomic and transcriptomic analyses. A multilocus association model revealed that most traits displayed a highly polygenic architecture, with most variation explained by loci with small effects (likely below the detection levels of single-locus GWA methods). Consistent with a polygenic architecture, our single-locus GWA analyses found only 38 significant SNPs in 22 genes across 15 traits. Next, we used differential expression analysis on a subset of aspen genets with divergent concentrations of salicinoid phenolic glycosides (key defense traits). This complementary method to traditional GWA discovered 1243 differentially expressed genes for a polygenic trait. Soft clustering analysis revealed three gene clusters (241 candidate genes) involved in secondary metabolite biosynthesis and regulation. Our work reveals that ecologically important traits governing higher-order community- and ecosystem-level attributes of a foundation forest tree species have complex underlying genetic structures and will require methods beyond traditional GWA analyses to unravel.

Intraspecific genetic variation in foundation species such as aspen (Populus tremuloides Michx.) shapes their impact on forest structure and function. Identifying genes underlying ecologically important traits is key to understanding that impact. Previous studies, using single-locus genome-wide association (GWA) analyses to identify candidate genes, have identified fewer genes than anticipated for highly heritable quantitative traits. Mounting evidence suggests that polygenic control of quantitative traits is largely responsible for this “missing heritability” phenomenon. Our research characterized the genetic architecture of 30 ecologically important traits using a common garden of aspen through genomic and transcriptomic analyses. A multilocus association model revealed that most traits displayed a highly polygenic architecture, with most variation explained by loci with small effects (likely below the detection levels of single-locus GWA methods). Consistent with a polygenic architecture, our single-locus GWA analyses found only 38 significant SNPs in 22 genes across 15 traits. Next, we used differential expression analysis on a subset of aspen genets with divergent concentrations of salicinoid phenolic glycosides (key defense traits). This complementary method to traditional GWA discovered 1243 differentially expressed genes for a polygenic trait. Soft clustering analysis revealed three gene clusters (241 candidate genes) involved in secondary metabolite biosynthesis and regulation. Our work reveals that ecologically important traits governing higher-order community- and ecosystem-level attributes of a foundation forest tree species have complex underlying genetic structures and will require methods beyond traditional GWA analyses to unravel.

Motivation The identification of predictive biomarker signatures from omics and multi-omics data for clinical applications is an active area of research. Recent developments in assay technologies and machine learning (ML) methods have led to significant improvements in predictive performance. However, most high-performing ML methods suffer from complex architectures and lack interpretability.Results We present the application of a novel symbolic-regression-based algorithm, the QLattice, on a selection of clinical omics datasets. This approach generates parsimonious high-performing models that can both predict disease outcomes and reveal putative disease mechanisms, demonstrating the importance of selecting maximally relevant and minimally redundant features in omics-based machine-learning applications. The simplicity and high-predictive power of these biomarker signatures make them attractive tools for high-stakes applications in areas such as primary care, clinical decision-making and patient stratification.

Fruit development has been central in the evolution and domestication of flowering plants. In common bean (Phaseolus vulgaris), the principal global grain legume staple, two main production categories are distinguished by fibre deposition in pods: dry beans, with fibrous, stringy pods; and stringless snap/green beans, with reduced fibre deposition, which frequently revert to the ancestral stringy state. Here, we identify genetic and developmental patterns associated with pod fibre deposition. Transcriptional, anatomical, epigenetic and genetic regulation of pod strings were explored through RNA-seq, RT-qPCR, fluorescence microscopy, bisulfite sequencing and whole-genome sequencing. Overexpression of the INDEHISCENT ('PvIND') orthologue was observed in stringless types compared with isogenic stringy lines, associated with overspecification of weak dehiscence-zone cells throughout the pod vascular sheath. No differences in DNA methylation were correlated with this phenotype. Nonstringy varieties showed a tandemly direct duplicated PvIND and a Ty1-copia retrotransposon inserted between the two repeats. These sequence features are lost during pod reversion and are predictive of pod phenotype in diverse materials, supporting their role in PvIND overexpression and reversible string phenotype. Our results give insight into reversible gain-of-function mutations and possible genetic solutions to the reversion problem, of considerable economic value for green bean production.

The venom duct transcriptomes and proteomes of the cryptic cone snail species Virroconus ebraeus and Virroconus judaeus were obtained and compared. The most abundant and shared conotoxin precursor superfamilies in both species were M, O1, and O2. Additionally, three new putative conotoxin precursor superfamilies (Virro01-03) with cysteine pattern types VI/VII and XVI were identified. The most expressed conotoxin precursor superfamilies were SF-mi2 and M in V. ebraeus, and Cerm03 and M in V. judaeus. Up to 16 conotoxin precursor superfamilies and hormones were differentially expressed between both species, and clustered into two distinct sets, which could represent adaptations of each species to different diets. Finally, we predicted, with machine learning algorithms, the 3D structure model of selected venom proteins including the differentially expressed Cerm03 and SF-mi2, an insulin type 3, a Gastridium geographus GVIA-like conotoxin, and an ortholog to the Pionoconus magus omega-conotoxin MVIIA (Ziconotide).

Non-alcoholic fatty liver disease (NAFLD) is a fast growing epidemic, which remains grossly underdiagnosed due to the lack of affordable and practical diagnostic tools in the primary health care setting. Here, we utilize dual-tissue RNA-seq data in subcutaneous adipose tissue and liver paired with liver histology-based NAFLD diagnosis from a cohort of obese individuals to discover serum biomarker candidates (SBCs) for obesity-related NAFLD. We hypothesize that in some obese individuals, obesity and its accompanying low-grade inflammation compromise the key functions of subcutaneous adipose tissue, preventing efficient adipogenesis and storage of fat into the subcutaneous fat depot, and thus driving ectopic fat accumulation into the liver. To identify subcutaneous adipose tissue-origin SBCs for the three primary NAFLD histology traits, steatosis, fibrosis, and non-alcoholic steatohepatitis (NASH), we scan for genes that are transcriptome-wide significantly differentially expressed (DE) for these NAFLD traits in subcutaneous adipose tissue but not in the liver, encode proteins secreted to serum, and show preferential expression in subcutaneous adipose tissue over the liver. Using a best subsets analysis, we identify the secreted adipokines CCDC80, SOD3, and COL6A2 as the key SBCs, the adipose expression of which explains the most significant amount of variance in steatosis, fibrosis, and NASH among the SBCs. We also show, by knockdown in human preadipocytes during adipogenesis, that the fibrosis and NASH SBCs, CCDC80 and SOD3, modulate the crucial adipogenesis genes, SREBF1 and LEP, emphasizing their premise as indicators for adipose-tissue origin, obesity-related NAFLD. Our results have a great translational potential to improve the diagnosis of obesity-related NAFLD by providing a blood panel of SBCs.

This works aims to provide value to three types of readers. First, for students in statistics, psychology, and the social sciences, I provide a summary and review of three classical statistical methods: factor analysis, principal component analysis (PCA), and probabilistic PCA (PPCA), all of which fall under the category of linear factor models. These methods are widely used in many fields, including psychology, education, and computational biology, and are the cornerstones of many new, more complicated methods. However, most available materials about them are either decades old (and very long and use old-style notations) or cursory. This work provides current coverage of them that is in-depth yet concise.Second, for new computational biologists who are unfamiliar with differential gene expression (DE) analysis and quantitative trait locus (QTL) analysis — in particular, expression quantitative trait locus (eQTL) analysis — I provide an introduction to DE analysis and eQTL analysis from a statistical perspective, with an emphasis on DE and eQTL analysis with hidden covariates. I avoid unnecessary jargon and aim for this material to be accessible to those without much background in biology.Third, for computational biologists and geneticists who need to work with newly developed computational methods such as surrogate variable analysis (SVA), probabilistic estimation of expression residuals (PEER), and hidden covariates with prior (HCP), I document these methods in a unified framework and explore their connections to classical methods such as factor analysis and PCA. To the best of our knowledge, such precise and in-depth review of SVA, PEER, and HCP is currently not available elsewhere in the literature.In short, this work aspires to be a useful reference manual for students and researchers working with linear factor models or newly developed methods for capturing hidden covariates in DE or eQTL analysis.

The performance in physical activity is determined not only by physiological processes such as age, body composition, gender and degree of training, but also by the genomics and even epigenetic events occurring during the training programs. In this context, using bioinformatics resources, we aimed to analyse the expression of genes associated with muscle function in vastus lateral samples. We used data from DNA microarray experiments reported in NCBI's GEO DataSet database under the series number GSE117070. Differential expression was calculated using the Z-ratio equation. We also used the software Cytoscape 3.6 to build a protein-protein interaction network with over-expressed genes. We found that seven genes out of the 397 genes analysed in the 41 individuals subjected to aerobic exercise with an increase in training intensity through the percentage of VO2max, were over-expressed based on the statistical approach. The Protein-Protein Interaction (PPI) network showed 477 nodes, two connected components, 17 multi-edge node pairs and an average number of neighbours of 2.092. The node with the highest number of interactions was TPM1 with 150. GO categories of biological processes most relevant of the network included indispensable processes for muscle function and contraction such as polymerization of actin filaments and ATP synthesis from electron transport chain.

Alzheimer's Disease (AD) is a complex neurodegenerative disease that gravely affects patients and imposes an immense burden on caregivers. Apolipoprotein E4 (APOE4) has been identified as the most common genetic risk factor for AD, yet the molecular mechanisms connecting APOE4 to AD are not well understood. Past transcriptomic analyses in AD have revealed APOE genotype-specific transcriptomic differences; however, these differences have not been explored at a single-cell level. To elucidate more complex APOE genotype-specific disease-relevant changes masked by the bulk analysis, we leverage the first two single-nucleus RNA sequencing AD datasets from human brain samples, including nearly 55,000 cells from the prefrontal and entorhinal cortices. In each brain region, we performed a case versus control APOE genotype-stratified differential gene expression analysis and pathway network enrichment in astrocytes, microglia, neurons, oligodendrocytes, and oligodendrocyte progenitor cells. We observed more global transcriptomic changes in APOE4 positive AD cells and identified differences across APOE genotypes primarily in glial cell types. Our findings highlight the differential transcriptomic perturbations of APOE isoforms at a single-cell level in AD pathogenesis and have implications for precision medicine development in the diagnosis and treatment of AD.

This works aims to provide value to three types of readers. First, for students in statistics, psychology, and the social sciences, I provide a summary and review of three classical statistical methods: factor analysis, principal component analysis (PCA), and probabilistic PCA (PPCA), all of which fall under the category of linear factor models. These methods are widely used in many fields, including psychology, education, and computational biology, and are the cornerstones of many new, more complicated methods. However, most available materials about them are either decades old (and very long and use old-style notations) or cursory. This work provides current coverage of them that is in-depth yet concise.Second, for new computational biologists who are unfamiliar with differential gene expression (DE) analysis and quantitative trait locus (QTL) analysis — in particular, expression quantitative trait locus (eQTL) analysis — I provide an introduction to DE analysis and eQTL analysis from a statistical perspective, with an emphasis on DE and eQTL analysis with hidden covariates. I avoid unnecessary jargon and aim for this material to be accessible to those without much background in biology.Third, for computational biologists and geneticists who need to work with newly developed computational methods such as surrogate variable analysis (SVA), probabilistic estimation of expression residuals (PEER), and hidden covariates with prior (HCP), I document these methods in a unified framework and explore their connections to classical methods such as factor analysis and PCA. To the best of our knowledge, such precise and in-depth review of SVA, PEER, and HCP is currently not available elsewhere in the literature.In short, this work aspires to be a useful reference manual for students and researchers working with linear factor models or newly developed methods for capturing hidden covariates in DE or eQTL analysis.

Non-alcoholic fatty liver disease (NAFLD) is a fast growing epidemic, which remains grossly underdiagnosed due to the lack of affordable and practical diagnostic tools in the primary health care setting. Here, we utilize dual-tissue RNA-seq data in subcutaneous adipose tissue and liver paired with liver histology-based NAFLD diagnosis from a cohort of obese individuals to discover serum biomarker candidates (SBCs) for obesity-related NAFLD. We hypothesize that in some obese individuals, obesity and its accompanying low-grade inflammation compromise the key functions of subcutaneous adipose tissue, preventing efficient adipogenesis and storage of fat into the subcutaneous fat depot, and thus driving ectopic fat accumulation into the liver. To identify subcutaneous adipose tissue-origin SBCs for the three primary NAFLD histology traits, steatosis, fibrosis, and non-alcoholic steatohepatitis (NASH), we scan for genes that are transcriptome-wide significantly differentially expressed (DE) for these NAFLD traits in subcutaneous adipose tissue but not in the liver, encode proteins secreted to serum, and show preferential expression in subcutaneous adipose tissue over the liver. Using a best subsets analysis, we identify the secreted adipokines CCDC80, SOD3, and COL6A2 as the key SBCs, the adipose expression of which explains the most significant amount of variance in steatosis, fibrosis, and NASH among the SBCs. We also show, by knockdown in human preadipocytes during adipogenesis, that the fibrosis and NASH SBCs, CCDC80 and SOD3, modulate the crucial adipogenesis genes, SREBF1 and LEP, emphasizing their premise as indicators for adipose-tissue origin, obesity-related NAFLD. Our results have a great translational potential to improve the diagnosis of obesity-related NAFLD by providing a blood panel of SBCs.

Background & aimsLiver disease carries significant healthcare burden and frequently requires a combination of blood tests, imaging, and invasive liver biopsy to diagnose. Distinguishing between inflammatory liver diseases, which may have similar clinical presentations, is particularly challenging. In this study, we implemented a machine learning pipeline for the identification of diagnostic gene expression biomarkers across several alcohol-associated and non-alcohol-associated liver diseases, using either liver tissue or blood-based samples.MethodsWe collected peripheral blood mononuclear cells (PBMCs) and liver tissue samples from participants with alcohol-associated hepatitis (AH), alcohol-associated cirrhosis (AC), non-alcohol-associated fatty liver disease, chronic HCV infection, and healthy controls. We performed RNA sequencing (RNA-seq) on 137 PBMC samples and 67 liver tissue samples. Using gene expression data, we implemented a machine learning feature selection and classification pipeline to identify diagnostic biomarkers which distinguish between the liver disease groups. The liver tissue results were validated using a public independent RNA-seq dataset. The biomarkers were computationally validated for biological relevance using pathway analysis tools.ResultsUtilizing liver tissue RNA-seq data, we distinguished between AH, AC, and healthy conditions with overall accuracies of 90% in our dataset, and 82% in the independent dataset, with 33 genes. Distinguishing 4 liver conditions and healthy controls yielded 91% overall accuracy in our liver tissue dataset with 39 genes, and 75% overall accuracy in our PBMC dataset with 75 genes.ConclusionsOur machine learning pipeline was effective at identifying a small set of diagnostic gene biomarkers and classifying several liver diseases using RNA-seq data from liver tissue and PBMCs. The methodologies implemented and genes identified in this study may facilitate future efforts toward a liquid biopsy diagnostic fo

Alzheimer's Disease (AD) is a complex neurodegenerative disease that gravely affects patients and imposes an immense burden on caregivers. Apolipoprotein E4 (APOE4) has been identified as the most common genetic risk factor for AD, yet the molecular mechanisms connecting APOE4 to AD are not well understood. Past transcriptomic analyses in AD have revealed APOE genotype-specific transcriptomic differences; however, these differences have not been explored at a single-cell level. To elucidate more complex APOE genotype-specific disease-relevant changes masked by the bulk analysis, we leverage the first two single-nucleus RNA sequencing AD datasets from human brain samples, including nearly 55,000 cells from the prefrontal and entorhinal cortices. In each brain region, we performed a case versus control APOE genotype-stratified differential gene expression analysis and pathway network enrichment in astrocytes, microglia, neurons, oligodendrocytes, and oligodendrocyte progenitor cells. We observed more global transcriptomic changes in APOE4 positive AD cells and identified differences across APOE genotypes primarily in glial cell types. Our findings highlight the differential transcriptomic perturbations of APOE isoforms at a single-cell level in AD pathogenesis and have implications for precision medicine development in the diagnosis and treatment of AD.

Results from the discovery study. Proteins selected to be verified in the whole Phase 1 are highlighted in grey. Proteins in bold but not highlighted were already tested in the whole phase 1 as part of a previous published work.

The performance in physical activity is determined not only by physiological processes such as age, body composition, gender and degree of training, but also by the genomics and even epigenetic events occurring during the training programs. In this context, using bioinformatics resources, we aimed to analyse the expression of genes associated with muscle function in vastus lateral samples. We used data from DNA microarray experiments reported in NCBI's GEO DataSet database under the series number GSE117070. Differential expression was calculated using the Z-ratio equation. We also used the software Cytoscape 3.6 to build a protein-protein interaction network with over-expressed genes. We found that seven genes out of the 397 genes analysed in the 41 individuals subjected to aerobic exercise with an increase in training intensity through the percentage of VO2max, were over-expressed based on the statistical approach. The Protein-Protein Interaction (PPI) network showed 477 nodes, two connected components, 17 multi-edge node pairs and an average number of neighbours of 2.092. The node with the highest number of interactions was TPM1 with 150. GO categories of biological processes most relevant of the network included indispensable processes for muscle function and contraction such as polymerization of actin filaments and ATP synthesis from electron transport chain.

Motivation The identification of predictive biomarker signatures from omics and multi-omics data for clinical applications is an active area of research. Recent developments in assay technologies and machine learning (ML) methods have led to significant improvements in predictive performance. However, most high-performing ML methods suffer from complex architectures and lack interpretability.Results We present the application of a novel symbolic-regression-based algorithm, the QLattice, on a selection of clinical omics datasets. This approach generates parsimonious high-performing models that can both predict disease outcomes and reveal putative disease mechanisms, demonstrating the importance of selecting maximally relevant and minimally redundant features in omics-based machine-learning applications. The simplicity and high-predictive power of these biomarker signatures make them attractive tools for high-stakes applications in areas such as primary care, clinical decision-making and patient stratification.

BACKGROUND: Experimental studies indicate that neuroendocrine pathways might play a role in progression of breast cancer. We aim to test the hypothesis that somatic mutations in the genes of neuroendocrine pathways influence breast cancer prognosis, through dysregulated gene expression in tumor tissue. METHODS: We conducted an extreme case-control study including 208 breast cancer patients with poor invasive disease-free survival (iDFS) and 208 patients with favorable iDFS who were individually matched on molecular subtype from the Breast Cancer Cohort at West China Hospital (WCH; N = 192) and The Cancer Genome Atlas (TCGA; N = 224). Whole exome sequencing and RNA sequencing of tumor and paired normal breast tissues were performed. Adrenergic, glucocorticoid, dopaminergic, serotonergic, and cholinergic pathways were assessed for differences in mutation burden and gene expression in relation to breast cancer iDFS using the logistic regression and global test, respectively. RESULTS: In the pooled analysis, presence of any somatic mutation (odds ratio = 1.66, 95% CI: 1.07-2.58) of the glucocorticoid pathway was associated with poor iDFS and a two-fold increase of tumor mutation burden was associated with 17% elevated odds (95% CI: 2-35%), after adjustment for cohort membership, age, menopausal status, molecular subtype, and tumor stage. Differential expression of genes in the glucocorticoid pathway in tumor tissue (P = 0.028), but not normal tissue (P = 0.701), was associated with poor iDFS. Somatic mutation of the adrenergic and cholinergic pathways was significantly associated with iDFS in WCH, but not in TCGA. CONCLUSION: Glucocorticoid pathway may play a role in breast cancer prognosis through differential mutations and expression. Further characterization of its functional role may open new avenues for the development of novel therapeutic targets for breast cancer., Funding agencies:China Scholarship Council 201806240005 National Natural Science Foundation of China (NSFC) 8187111500

BackgroundBoth RNA-Seq and sample freeze-thaw are ubiquitous. However, knowledge about the impact of freeze-thaw on downstream analyses is limited. The lack of common quality metrics that are sufficiently sensitive to freeze-thaw and RNA degradation, e.g. the RNA Integrity Score, makes such assessments challenging.ResultsHere we quantify the impact of repeated freeze-thaw cycles on the reliability of RNA-Seq by examining poly(A)-enriched and ribosomal RNA depleted RNA-seq from frozen leukocytes drawn from a toddler Autism cohort. To do so, we estimate the relative noise, or percentage of random counts, separating technical replicates. Using this approach we measured noise associated with RIN and freeze-thaw cycles. As expected, RIN does not fully capture sample degradation due to freeze-thaw. We further examined differential expression results and found that three freeze-thaws should extinguish the differential expression reproducibility of similar experiments. Freeze-thaw also resulted in a 3' shift in the read coverage distribution along the gene body of poly(A)-enriched samples compared to ribosomal RNA depleted samples, suggesting that library preparation may exacerbate freeze-thaw-induced sample degradation.ConclusionThe use of poly(A)-enrichment for RNA sequencing is pervasive in library preparation of frozen tissue, and thus, it is important during experimental design and data analysis to consider the impact of repeated freeze-thaw cycles on reproducibility.

Fungi are ecologically outstanding decomposers of lignocellulose. Fungal lignocellulose degradation is prominent in saprotrophic Ascomycota and Basidiomycota of the subkingdom Dikarya. Despite ascomycetes dominating the Dikarya inventory of aquatic environments, genome and transcriptome data relating to enzymes involved in lignocellulose decay remain limited to terrestrial representatives of these phyla. We sequenced the genome of an exclusively aquatic ascomycete (the aquatic hyphomycete Clavariopsis aquatica), documented the presence of genes for the modification of lignocellulose and its constituents, and compared differential gene expression between C. aquatica cultivated on lignocellulosic and sugar-rich substrates. We identified potential peroxidases, laccases, and cytochrome P450 monooxygenases, several of which were differentially expressed when experimentally grown on different substrates. Additionally, we found indications for the regulation of pathways for cellulose and hemicellulose degradation. Our results suggest that C. aquatica is able to modify lignin to some extent, detoxify aromatic lignin constituents, or both. Such characteristics would be expected to facilitate the use of carbohydrate components of lignocellulose as carbon and energy sources.

22q11.2 reciprocal copy number variants (CNVs) offer a powerful quasi-experimental "reverse-genetics" paradigm to elucidate how gene dosage (i.e., deletions and duplications) disrupts the transcriptome to cause further downstream effects. Clinical profiles of 22q11.2 CNV carriers indicate that disrupted gene expression causes alterations in neuroanatomy, cognitive function, and psychiatric disease risk. However, interpreting transcriptomic signal in bulk tissue requires careful consideration of potential changes in cell composition. We first characterized transcriptomic dysregulation in peripheral blood from reciprocal 22q11.2 CNV carriers using differential expression analysis and weighted gene co-expression network analysis (WGCNA) to identify modules of co-expressed genes. We also assessed for group differences in cell composition and re-characterized transcriptomic differences after accounting for cell type proportions and medication usage. Finally, to explore whether CNV-related transcriptomic changes relate to downstream phenotypes associated with 22q11.2 CNVs, we tested for associations of gene expression with neuroimaging measures and behavioral traits, including IQ and psychosis or ASD diagnosis. 22q11.2 deletion carriers (22qDel) showed widespread expression changes at the individual gene as well as module eigengene level compared to 22q11.2 duplication carriers (22qDup) and controls. 22qDup showed increased expression of 5 genes within the 22q11.2 locus, and CDH6 located outside of the locus. Downregulated modules in 22qDel implicated altered immune and inflammatory processes. Celltype deconvolution analyses revealed significant differences between CNV and control groups in T-cell, mast cell, and macrophage proportions; differential expression of individual genes between groups was substantially attenuated after adjusting for cell composition. Individual gene, module eigengene, and cell proportions were not significantly associated with psychiatric or neu

Traumatic brain injury (TBI) causes acute and lasting impacts on the brain, driving pathology along anatomical, cellular, and behavioral dimensions. Rodent models offer an opportunity to study the temporal progression of disease from injury to recovery. Transcriptomic and epigenomic analysis were applied to evaluate gene expression in ipsilateral hippocampus at 1 and 14 days after sham (n = 2 and 4, respectively per time point) and moderate lateral fluid percussion injury (n = 4 per time point). This enabled the identification of dynamic changes and differential gene expression (differentially expressed genes; DEGs) modules linked to underlying epigenetic response. We observed acute signatures associated with cell death, astrocytosis, and neurotransmission that largely recovered by 2 weeks. Inflammation and immune signatures segregated into upregulated modules with distinct expression trajectories and functions. Whereas most down-regulated genes recovered by 14 days, two modules with delayed and persistent changes were associated with cholesterol metabolism, amyloid beta clearance, and neurodegeneration. Differential expression was paralleled by changes in histone H3 lysine residue 4 trimethylation at the promoters of DEGs at 1 day post-TBI, with the strongest changes observed for inflammation and immune response genes. These results demonstrate how integrated genomics analysis in the pre-clinical setting has the potential to identify stage-specific biomarkers for injury and/or recovery. Though limited in scope here, our general strategy has the potential to capture pathological signatures over time and evaluate treatment efficacy at the systems level.

Fungi are ecologically outstanding decomposers of lignocellulose. Fungal lignocellulose degradation is prominent in saprotrophic Ascomycota and Basidiomycota of the subkingdom Dikarya. Despite ascomycetes dominating the Dikarya inventory of aquatic environments, genome and transcriptome data relating to enzymes involved in lignocellulose decay remain limited to terrestrial representatives of these phyla. We sequenced the genome of an exclusively aquatic ascomycete (the aquatic hyphomycete Clavariopsis aquatica), documented the presence of genes for the modification of lignocellulose and its constituents, and compared differential gene expression between C. aquatica cultivated on lignocellulosic and sugar-rich substrates. We identified potential peroxidases, laccases, and cytochrome P450 monooxygenases, several of which were differentially expressed when experimentally grown on different substrates. Additionally, we found indications for the regulation of pathways for cellulose and hemicellulose degradation. Our results suggest that C. aquatica is able to modify lignin to some extent, detoxify aromatic lignin constituents, or both. Such characteristics would be expected to facilitate the use of carbohydrate components of lignocellulose as carbon and energy sources.

BackgroundBoth RNA-Seq and sample freeze-thaw are ubiquitous. However, knowledge about the impact of freeze-thaw on downstream analyses is limited. The lack of common quality metrics that are sufficiently sensitive to freeze-thaw and RNA degradation, e.g. the RNA Integrity Score, makes such assessments challenging.ResultsHere we quantify the impact of repeated freeze-thaw cycles on the reliability of RNA-Seq by examining poly(A)-enriched and ribosomal RNA depleted RNA-seq from frozen leukocytes drawn from a toddler Autism cohort. To do so, we estimate the relative noise, or percentage of random counts, separating technical replicates. Using this approach we measured noise associated with RIN and freeze-thaw cycles. As expected, RIN does not fully capture sample degradation due to freeze-thaw. We further examined differential expression results and found that three freeze-thaws should extinguish the differential expression reproducibility of similar experiments. Freeze-thaw also resulted in a 3' shift in the read coverage distribution along the gene body of poly(A)-enriched samples compared to ribosomal RNA depleted samples, suggesting that library preparation may exacerbate freeze-thaw-induced sample degradation.ConclusionThe use of poly(A)-enrichment for RNA sequencing is pervasive in library preparation of frozen tissue, and thus, it is important during experimental design and data analysis to consider the impact of repeated freeze-thaw cycles on reproducibility.

Traumatic brain injury (TBI) causes acute and lasting impacts on the brain, driving pathology along anatomical, cellular, and behavioral dimensions. Rodent models offer an opportunity to study the temporal progression of disease from injury to recovery. Transcriptomic and epigenomic analysis were applied to evaluate gene expression in ipsilateral hippocampus at 1 and 14 days after sham (n = 2 and 4, respectively per time point) and moderate lateral fluid percussion injury (n = 4 per time point). This enabled the identification of dynamic changes and differential gene expression (differentially expressed genes; DEGs) modules linked to underlying epigenetic response. We observed acute signatures associated with cell death, astrocytosis, and neurotransmission that largely recovered by 2 weeks. Inflammation and immune signatures segregated into upregulated modules with distinct expression trajectories and functions. Whereas most down-regulated genes recovered by 14 days, two modules with delayed and persistent changes were associated with cholesterol metabolism, amyloid beta clearance, and neurodegeneration. Differential expression was paralleled by changes in histone H3 lysine residue 4 trimethylation at the promoters of DEGs at 1 day post-TBI, with the strongest changes observed for inflammation and immune response genes. These results demonstrate how integrated genomics analysis in the pre-clinical setting has the potential to identify stage-specific biomarkers for injury and/or recovery. Though limited in scope here, our general strategy has the potential to capture pathological signatures over time and evaluate treatment efficacy at the systems level.

22q11.2 reciprocal copy number variants (CNVs) offer a powerful quasi-experimental "reverse-genetics" paradigm to elucidate how gene dosage (i.e., deletions and duplications) disrupts the transcriptome to cause further downstream effects. Clinical profiles of 22q11.2 CNV carriers indicate that disrupted gene expression causes alterations in neuroanatomy, cognitive function, and psychiatric disease risk. However, interpreting transcriptomic signal in bulk tissue requires careful consideration of potential changes in cell composition. We first characterized transcriptomic dysregulation in peripheral blood from reciprocal 22q11.2 CNV carriers using differential expression analysis and weighted gene co-expression network analysis (WGCNA) to identify modules of co-expressed genes. We also assessed for group differences in cell composition and re-characterized transcriptomic differences after accounting for cell type proportions and medication usage. Finally, to explore whether CNV-related transcriptomic changes relate to downstream phenotypes associated with 22q11.2 CNVs, we tested for associations of gene expression with neuroimaging measures and behavioral traits, including IQ and psychosis or ASD diagnosis. 22q11.2 deletion carriers (22qDel) showed widespread expression changes at the individual gene as well as module eigengene level compared to 22q11.2 duplication carriers (22qDup) and controls. 22qDup showed increased expression of 5 genes within the 22q11.2 locus, and CDH6 located outside of the locus. Downregulated modules in 22qDel implicated altered immune and inflammatory processes. Celltype deconvolution analyses revealed significant differences between CNV and control groups in T-cell, mast cell, and macrophage proportions; differential expression of individual genes between groups was substantially attenuated after adjusting for cell composition. Individual gene, module eigengene, and cell proportions were not significantly associated with psychiatric or neu

Fungi are ecologically outstanding decomposers of lignocellulose. Fungal lignocellulose degradation is prominent in saprotrophic Ascomycota and Basidiomycota of the subkingdom Dikarya. Despite ascomycetes dominating the Dikarya inventory of aquatic environments, genome and transcriptome data relating to enzymes involved in lignocellulose decay remain limited to terrestrial representatives of these phyla. We sequenced the genome of an exclusively aquatic ascomycete (the aquatic hyphomycete Clavariopsis aquatica), documented the presence of genes for the modification of lignocellulose and its constituents, and compared differential gene expression between C. aquatica cultivated on lignocellulosic and sugar-rich substrates. We identified potential peroxidases, laccases, and cytochrome P450 monooxygenases, several of which were differentially expressed when experimentally grown on different substrates. Additionally, we found indications for the regulation of pathways for cellulose and hemicellulose degradation. Our results suggest that C. aquatica is able to modify lignin to some extent, detoxify aromatic lignin constituents, or both. Such characteristics would be expected to facilitate the use of carbohydrate components of lignocellulose as carbon and energy sources.

Pines are the dominant conifers in Mediterranean forests. As long-lived sessile organisms that seasonally have to cope with drought periods, they have developed a variety of adaptive responses. However, during last decades, highly intense and long-lasting drought events could have contributed to decay and mortality of the most susceptible trees. Among conifer species, Pinus pinaster Ait. shows remarkable ability to adapt to different environments. Previous molecular analysis of a full-sib family designed to study drought response led us to find active transcriptional activity of stress-responding genes even without water deprivation in tolerant genotypes. To improve our knowledge about communication between above- and below-ground organs of maritime pine, we have analyzed four graft-type constructions using two siblings as rootstocks and their progenitors, Gal 1056 and Oria 6, as scions. Transcriptomic profiles of needles from both scions were modified by the rootstock they were grafted on. However, the most significant differential gene expression was observed in drought-sensitive Gal 1056, while in drought-tolerant Oria 6, differential gene expression was very much lower. Furthermore, both scions grafted onto drought-tolerant rootstocks showed activation of genes involved in tolerance to abiotic stress, and is most remarkable in Oria 6 grafts where higher accumulation of transcripts involved in phytohormone action, transcriptional regulation, photosynthesis and signaling has been found. Additionally, processes, such as those related to secondary metabolism, were mainly associated with the scion genotype. This study provides pioneering information about rootstock effects on scion gene expression in conifers.

Despite significant advancements in breast cancer (BC) research, clinicians lack robust serological protein markers for accurate diagnostics and tumor stratification. Tumor interstitial fluid (TIF) accumulates aberrantly externalized proteins within the local tumor space, which can potentially gain access to the circulatory system. As such, TIF may represent a valuable starting point for identifying relevant tumor-specific serological biomarkers. The aim of the study was to perform comprehensive proteomic profiling of TIF to identify proteins associated with BC tumor status and subtype. A liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis of 35 TIFs of three main subtypes: luminal (19), Her2 (4), and triple-negative (TNBC) (12) resulted in the identification of > 8800 proteins. Unsupervised hierarchical clustering segregated the TIF proteome into two major clusters, luminal and TNBC/Her2 subgroups. High-grade tumors enriched with tumor infiltrating lymphocytes (TILs) were also stratified from low-grade tumors. A consensus analysis approach, including differential abundance analysis, selection operator regression, and random forest returned a minimal set of 24 proteins associated with BC subtypes, receptor status, and TIL scoring. Among them, a panel of 10 proteins, AGR3, BCAM, CELSR1, MIEN1, NAT1, PIP4K2B, SEC23B, THTPA, TMEM51, and ULBP2, was found to stratify the tumor subtype-specific TIFs. In particular, upregulation of BCAM and CELSR1 differentiates luminal subtypes, while upregulation of MIEN1 differentiates Her2 subtypes. Immunohistochemistry analysis showed a direct correlation between protein abundance in TIFs and intratumor expression levels for all 10 proteins. Sensitivity and specificity were estimated for this protein panel by using an independent, comprehensive breast tumor proteome dataset. The results of this analysis strongly support our data, with eight of the proteins potentially representing biomarkers for stratification of