scDrugPrio: a framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases

Drug repurposing; Immune-mediated inflammatory disease; Single-cell RNA sequencingReutilización de medicamentos; Enfermedad inflamatoria inmunomediada; Secuenciación de ARN unicelularReutilització de medicaments; Malaltia inflamatòria immunomediada; Seqüenciació d'ARN unicel·lularBackground Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs. Methods Here, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs. Results scDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive in vitro, in vivo, and in silico studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohn’s disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohn’s disease patients. The analysis showed great variations in drug predictions between patients, for example, assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment. Conclusions We propose a computational framework, scDrugPrio, for drug prioritisation based on scRNA-seq of IMID disease. Application to individual patients indicates scDrugPrio’s potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package (https://github.com/SDTC-CPMed/scDrugPrio).Open access funding provided by Karolinska Institute. This work was supported by the DocTIS project, which has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement N° 848028; Swedish Cancer Society CAN 2017/411; Cocozza Foundation; National Natural Science Foundation of China 82171791, US National Institutes of Health grants HL155107 and HL155096; American Heart Association grant 957729; European Union’s Horizon 2021 Research and Innovation Programme grant 101057619 and Mag-Tarmfonden (grant 1–23). The computations were partially enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC) at Linköping University partially funded by the Swedish Research Council through grant agreement no. 2018–05973

Digital twins to personalize medicine

Personalized medicine requires the integration and processing of vast amounts of data. Here, we propose a solution to this challenge that is based on constructing Digital Twins. These are high-resolution models of individual patients that are computationally treated with thousands of drugs to find the drug that is optimal for the patient

Der Gebrauch kausativer Konstruktionen mit lassen, bei denen Menschen das Subjekt darstellen : anhand von Beispielen aus Thomas Manns'Doktor Faustus'(1. Teil)

Background: The use of intravenous lipid emulsions in preterm infants has been limited by concerns regarding impaired lipid tolerance. As a result, the time of initiation of parenteral lipid infusion to very-low-birth-weight (VLBW) infants varies widely among different neonatal intensive care units. However, lipids provide energy for protein synthesis and supply essential fatty acids that are necessary for central nervous system development. Objective: The objective was to summarize the effects of initiation of lipids within the first 2 d of life and the effects of different lipid compositions on growth and morbidities in VLBW infants. Design: A systematic review and meta-analysis of publications identified in a search of PubMed, EMBASE, and Cochrane databases was undertaken. Randomized controlled studies were eligible if information on growth was available. Results: The search yielded 14 studies. No differences were observed in growth or morbidity with early lipid initiation. We found a weak favorable association of non-purely soybean-based emulsions with the incidence of sepsis (RR: 0.75; 95% CI: 0.56, 1.00). Conclusions: The initiation of lipids within the first 2 d of life in VLBW infants appears to be safe and well tolerated; however, beneficial effects on growth could not be shown for this treatment nor for the type of lipid emulsion. Emulsions that are not purely soybean oil-based might be associated with a lower incidence of sepsis. Large-scale randomized controlled trials in preterm infants are warranted to determine whether early initiation of lipids and lipid emulsions that are not purely soybean oil-based results in improved long-term outcomes. Am J Clin Nutr 2012;96:255-6

Association of Variants in the SPTLC1 Gene With Juvenile Amyotrophic Lateral Sclerosis

Importance: Juvenile amyotrophic lateral sclerosis (ALS) is a rare form of ALS characterized by age of symptom onset less than 25 years and a variable presentation.Objective: To identify the genetic variants associated with juvenile ALS.Design, Setting, and Participants: In this multicenter family-based genetic study, trio whole-exome sequencing was performed to identify the disease-associated gene in a case series of unrelated patients diagnosed with juvenile ALS and severe growth retardation. The patients and their family members were enrolled at academic hospitals and a government research facility between March 1, 2016, and March 13, 2020, and were observed until October 1, 2020. Whole-exome sequencing was also performed in a series of patients with juvenile ALS. A total of 66 patients with juvenile ALS and 6258 adult patients with ALS participated in the study. Patients were selected for the study based on their diagnosis, and all eligible participants were enrolled in the study. None of the participants had a family history of neurological disorders, suggesting de novo variants as the underlying genetic mechanism.Main Outcomes and Measures: De novo variants present only in the index case and not in unaffected family members.Results: Trio whole-exome sequencing was performed in 3 patients diagnosed with juvenile ALS and their parents. An additional 63 patients with juvenile ALS and 6258 adult patients with ALS were subsequently screened for variants in the SPTLC1 gene. De novo variants in SPTLC1 (p.Ala20Ser in 2 patients and p.Ser331Tyr in 1 patient) were identified in 3 unrelated patients diagnosed with juvenile ALS and failure to thrive. A fourth variant (p.Leu39del) was identified in a patient with juvenile ALS where parental DNA was unavailable. Variants in this gene have been previously shown to be associated with autosomal-dominant hereditary sensory autonomic neuropathy, type 1A, by disrupting an essential enzyme complex in the sphingolipid synthesis pathway.Conclusions and Relevance: These data broaden the phenotype associated with SPTLC1 and suggest that patients presenting with juvenile ALS should be screened for variants in this gene.</p

Genome-wide Analyses Identify KIF5A as a Novel ALS Gene

To identify novel genes associated with ALS, we undertook two lines of investigation. We carried out a genome-wide association study comparing 20,806 ALS cases and 59,804 controls. Independently, we performed a rare variant burden analysis comparing 1,138 index familial ALS cases and 19,494 controls. Through both approaches, we identified kinesin family member 5A (KIF5A) as a novel gene associated with ALS. Interestingly, mutations predominantly in the N-terminal motor domain of KIF5A are causative for two neurodegenerative diseases: hereditary spastic paraplegia (SPG10) and Charcot-Marie-Tooth type 2 (CMT2). In contrast, ALS-associated mutations are primarily located at the C-terminal cargo-binding tail domain and patients harboring loss-of-function mutations displayed an extended survival relative to typical ALS cases. Taken together, these results broaden the phenotype spectrum resulting from mutations in KIF5A and strengthen the role of cytoskeletal defects in the pathogenesis of ALS.Peer reviewe

Genome-wide structural variant analysis identifies risk loci for non-Alzheimer’s dementias

We characterized the role of structural variants, a largely unexplored type of genetic variation, in two non-Alzheimer’s dementias, namely Lewy body dementia (LBD) and frontotemporal dementia (FTD)/amyotrophic lateral sclerosis (ALS). To do this, we applied an advanced structural variant calling pipeline (GATK-SV) to short-read whole-genome sequence data from 5,213 European-ancestry cases and 4,132 controls. We discovered, replicated, and validated a deletion in TPCN1 as a novel risk locus for LBD and detected the known structural variants at the C9orf72 and MAPT loci as associated with FTD/ALS. We also identified rare pathogenic structural variants in both LBD and FTD/ALS. Finally, we assembled a catalog of structural variants that can be mined for new insights into the pathogenesis of these understudied forms of dementia

The association of the genes HvNAM1 and HvNAM2 with grain protein content in Nordic barley

In barley, the GPC (Grain Protein Content) has proved to be of great importance for both feed, food and beer production. When it comes to feed and food, a high GPC is desirable since it indicates good nutritional values, while in beer production a low and stable GPC is needed to avoid beer chill haze. In previous studies a decrease in the GPC has been seen in different accessions of barley developed at different time periods during the last 100 years. The gene family HvNAM, including the genes HvNAM1 and HvNAM2, has in previous studies been shown to be important for the remobilization of nutrients towards the grains during the senescence and thus also for the GPC. In this study, 40 Nordic accessions from different improvement groups from the end of the 19th century until today have been analyzed for polymorphism in those genes. Statistical analyses has been conducted to investigate if there are any associations between the polymorph nucleotide positions and the nutritional values of grain protein, iron and zinc contents. However, no such associations were found. Instead some correlations could be seen between the nutrient content and thousand grain weight, a relative measurement of the grain size. In conclusion, since no polymorphisms were found to be associated to the nutritional value there might instead be a correlation between the gene expression and the nutritional value. Future work should thus focus on the gene expression of HvNAM1 and HvNAM2 in Nordic accessions of barley

Digital Twins : High Resolution Disease Models for Optimized Diagnosis and Treatment

To study immune-mediated diseases, which can affect the expression of thousands of genes among many different cell types and organs, is a daunting challenge. However, for effective diagnosis and therapeutic treatment it is relevant to understand the regulatory functions of disease. In this thesis, we hypothesized that regulatory functions in complex diseases can be effectively prioritized based on so called digital twins, which are based on high-resolution single cell data in combination with network theories. More specifically, we tested if digital twins could be used on a patient-group level to prioritize cell types, genes, and/or organs based on their regulatory function in the disease progression. If this hypothesis is true, potential biomarkers and therapeutic targets can be identified for optimized diagnosis and treatment. The long-term goal is to construct digital twins for personalized medicine, to predict the optimal treatment strategies for the individual patients. Although, this is a very ambitious goal which could not be reached through this thesis, relevant steps towards it have been reached. First, we tested if high-resolution disease models based on single cell RNAsequencing (scRNA-seq) data could be used in combination with network theories, to predict and prevent disease. For this aim, we used a mouse model of antigeninduced arthritis (AIA). Based on the cell type specific genes in AIA joint, we identified a multi-cellular disease model (MCDM), including predicted cell-cell interactions. Analyzing this model, Granulocytes were identified as most central in AIA joint. The results from this centrality analysis correlated with GWAS enrichment among the cell type specific genes, as well as with the centrality analyses based on human RA, supporting our results relevance for human disease. A drug, bezafibrate, was further identified which mainly targeted shared disease modules over the central and GWAS enriched CD4+ T cells in nine of 13 analyzed human diseases. Bezafibrate treatment of our AIA mouse model resulted in a decrease in arthritis severity score as well as a decrease in T cell proliferation into the joint. Since blood is an easily available source of data, it is of interest to know it’s potential usefulness when constructing digital twins. To test if samples taken from blood are representative of the inflamed organ, we performed a meta-analysis of different samples from blood and joint of patients with rheumatoid arthritis, as well as from joint and blood Granulocytes of our AIA mouse model. Based on differentially expressed genes (DEGs) between sick and healthy samples from each dataset, we performed pathway analyses and predicted potential biomarkers and upstream regulators (URs). Comparing the lists of pathways, biomarkers, and URs between the datasets from different subsets of blood samples showed low or no similarities. However, the datasets of human bulk or mouse single cell data collected from synovial fluid or full joint showed high similarities. Furthermore, the top shared enriched pathways, predicted biomarkers, and URs from both human and mouse were to a higher degree connected to known functions of autoimmune diseases or rheumatoid arthritis, compared to the respective results from samples taken from blood. These findings indicate that inflammatory mechanisms in cells in blood and inflamed organs differ greatly, which may have important diagnostic and therapeutic implications. We next analyzed if digital twins could be used to identify the early regulatory mechanisms that are also present at the late time points. For this, we used an in vitro time series model of seasonal allergic rhinitis. Samples were taken before allergen stimulation, as well as at 12 hours, 1 day, 2 days, 3 days, 5 days, and 7 days after allergen stimulation, for scRNA-seq and MCDM construction. Multi-directional interactions including all cell types were found at all time points, even before allergen stimulation, which complicated the identification of one key regulatory cell type or gene. Instead, we found that the regulatory genes could be ranked based on their overall downstream effect over all the time points. Our top-ranked regulatory gene, PDGFB, targeted most of the cell types at all the time points, while a previously known early regulator and drug target in allergy, IL4, targeted only five cell type and time point combinations. Validation studies further showed that neutralization of PDGF-BB on allergen-stimulated PBMC from SAR patients were more effective compared to neutralization of IL-4. Finally, we tested if a digital twin including data from multiple organs could be used to understand the systemic interactional changes due to disease. For this aim, we used a systemic mouse model of arthritis, namely collagen induced arthritis (CIA). We first analyzed ten different organs, based on which we prioritized five organs with the highest number of DEGs between CIA and healthy mice, namely joint, lung, muscle, skin, and spleen. Although only joint showed signs of inflammation, many DEGs were identified in all five organs. Those changes were organized into a multi-organ multi-cellular disease model, which indicated an on/off switch of pro-/anti-inflammatory functions in joint and muscle respectively. Validation studies in human immune-mediated inflammatory diseases supported this on/off switch, where pro-inflammatory functions were mainly found in inflamed organs, while anti-inflammatory functions were found in non-inflamed organs. In conclusion, this thesis supports the potential of using high-resolution disease models for digital twin construction. Such digital twins could then be used to prioritize cell types and genes, for further prediction of diagnostic markers and therapeutic targets. Even though the identification of one key regulatory function was complicated due to multidirectional interactions, the genes could be ranked based on their relative downstream effect. For reproducible results, we found that digital twins should ideally be based on data from locally inflamed organs, while systemic models and models covering different disease stages could be useful to understand the disease progression.Studier av immunologiska sjukdomar kompliceras av förändrade uttrycknivåer hos tusentals gener, bland många olika celltyper och organ. För effektiv diagnostik och behandling är det därför viktigt att förstå överordnade, reglerande funktioner bakom sjukdomstillståndet. I denna avhandling testar vi hypotesen att reglerande funktioner hos komplexa sjukdomar effektivt kan prioriteras genom s.k. digitala tvillingar, vilka baserats på högupplösta data av enskilda celler i kombination med nätverks-teorier. Mer specifikt testar vi om digitala tvillingar på patientgrupps-nivå kan användas för prioritering av celltyper, gener, och/eller organ utifrån deras reglerande funktioner i sjukdomsförloppet. Om denna hypotes stämmer kan potentiella biomarkörer och målgener för läkemedel identifieras, för optimerad diagnostik och behandling. Det långsiktiga målet är att konstruera digitala tvillingar på patient-nivå, för en individuellt anpassad diagnos och behandling. Trots att detta är ett stort mål som inte kunde nås genom denna avhandling har flera viktiga delmål uppnåtts. Först testade vi huruvida högupplösta sjukdomsmodeller baserade på data från enskilda celler kan användas i kombination med nätverksteorier för att förstå och förhindra sjukdomsprocesser. För detta använde vi en musmodell, där lokal inflammation inducerats i leden genom injektion av antigen, även kallad ”antigeninduced artritis” (AIA). Baserat på celltyps-specifika gener från leden konstruerade vi en multicellulära sjukdomsmodell (MCDM), vilken inkluderade predikterade cell-cell-interaktioner. Genom vidare analyser av modellen identifierades de mest centrala celltyperna för sjukdomen. Resultatens relevans bekräftades, då de mest centrala celltyperna var berikade för gener tidigare associerade med reumatism, samt då centralitet-analyser av reumatism hos människa gav liknande resultat. Ett läkemedel, bezafibrate, identifierades, vilket huvudsakligen angrep gemensamma sjukdomsmoduler i centrala T celler över flera sjukdomar. Valideringsstudier där AIA möss behandlades med bezafibrate resulterade i en minskning av symptom. Då blodprover utgör en lättillgänglig datakälla är det av intresse att veta dess potential för konstruktion av digitala tvillingar. För att testa huruvida data från blod representerar den lokala sjukdomsbilden i inflammerade organ analyserade vi en samling publika data från blod och ledvätska hos patienter med reumatism, samt data från blod och led hos vår AIA musmodell. Baserat på differentialuttryckta gener (DEGs) mellan sjuka och friska individer från varje datauppsättning utförde vi nätverksanalyser och förutspådde potentiella biomarkörer och uppströmsregulatorer (URs). Jämförelser av gennätverk, biomarkörer och URs mellan olika datauppsättningar från blod visade låga eller inga likheter, medan datauppsättningarna från ledvätska eller hel led visade stora likheter. Dessutom kunde de mest berikade nätverken, predikterade biomarkörer och URs från led-data i både människa och mus i högre grad kopplas till kända funktioner hos autoimmuna sjukdomar eller reumatism jämfört med respektive resultat från blod-data. Dessa resultat tyder på att inflammatoriska mekanismer i blodceller och inflammerade organ är mycket olika, vilket kan ha både diagnostisk och terapeutisk betydelse. Därefter testade vi huruvida digitala tvillingar kunde användas för att identifiera tidiga regleringsmekanismerna vilka också återfinns i senare sjukdomsstadier. För detta använde vi en sjukdomsmodell där vi i provrör inducerade allergisk reaktion i blod från pollenallergiker. Analys och konstruktion av MCDMs gjordes före allergenstimulering, samt 12 timmar, 1 dag, 2 dagar, 3 dagar, 5 dagar, och 7 dagar efter stimulering. Vidare analyser visade hur alla celltyper interagerade i ett komplext nätverk vid alla analyserade tidpunkter, även före stimulering. Detta komplicerade identifieringen av en enskild tidigt reglerande celltyp eller gen. I stället såg vi att gener kunde rangordnas baserat på deras relativa effektgrad över alla tidpunkter. Den högst rankade genen, PDGFB, var en predikterad UR för nästan alla celltyper vid alla tidpunkter, till skillnad från IL4, som är ett känt mål för läkemedel vid allergi, vilken enbart var predikterad UR för fem olika celltyper och tidpunkter. Valideringsstudier bekräftade att neutralisering av PDGFB-kodande protein (PDGFF-BB) på allergenstimulerade blodprover från pollenallergiker var mer effektivt än neutralisering av IL4-kodande protein (IL-4). Slutligen testade vi huruvida en digital tvilling över flera organ kunde användas för att förstå ett systemiskt sjukdomsförlopp, dvs. en sjukdom som påverkar flera organ. För detta använde vi en systemisk musmodell av artrit, nämligen kollageninducerad artrit (CIA). Vi analyserade först tio olika organ, utifrån vilka vi prioriterade fem organ med högsta antal DEGs mellan CIA och friska möss, nämligen led, lunga, muskel, hud och mjälte. Trots att endast leden visade tecken på inflammation kunde många DEGs identifieras i alla fem organ. Dessa förändringar organiserades i en multiorgan multicellulär sjukdomsmodell, som indikerade ett av/på skifte mellan pro-/antiinflammatoriska funktioner i muskel respektive led. Valideringsstudier av immunologiska sjukdomar hos människa stödde detta av/på skifte, där proinflammatoriska funktioner huvudsakligen identifierades i inflammerade organ, medan antiinflammatoriska funktioner identifierades i ickeinflammerade organ. Sammanfattningsvis stöder denna avhandling användandet av högupplösta sjukdomsmodeller för konstruktion av digitala tvillingar. Dessa digitala tvillingar kan användas för att prioritera celltyper och gener, för identifiering av diagnostiska markörer och potentiell behandling. Trots att det visade sig komplicerat att identifiera en enskild regulatorisk funktion på grund av komplexa interaktionsmönster kunde gener rangordnas baserat på deras relativa nedströms effekt. För reproducerbara resultat fann vi att digitala tvillingar helst bör baseras på data från inflammerade organ, medan systemiska modeller och modeller som täcker olika sjukdomsstadier kan vara användbara för att bättre förstå sjukdomsförloppet

The association of the genes HvNAM1 and HvNAM2 with grain protein content in Nordic barley

In barley, the GPC (Grain Protein Content) has proved to be of great importance for both feed, food and beer production. When it comes to feed and food, a high GPC is desirable since it indicates good nutritional values, while in beer production a low and stable GPC is needed to avoid beer chill haze. In previous studies a decrease in the GPC has been seen in different accessions of barley developed at different time periods during the last 100 years. The gene family HvNAM, including the genes HvNAM1 and HvNAM2, has in previous studies been shown to be important for the remobilization of nutrients towards the grains during the senescence and thus also for the GPC. In this study, 40 Nordic accessions from different improvement groups from the end of the 19th century until today have been analyzed for polymorphism in those genes. Statistical analyses has been conducted to investigate if there are any associations between the polymorph nucleotide positions and the nutritional values of grain protein, iron and zinc contents. However, no such associations were found. Instead some correlations could be seen between the nutrient content and thousand grain weight, a relative measurement of the grain size. In conclusion, since no polymorphisms were found to be associated to the nutritional value there might instead be a correlation between the gene expression and the nutritional value. Future work should thus focus on the gene expression of HvNAM1 and HvNAM2 in Nordic accessions of barley