Excessive consumption of sucrose, in the form of sugar-sweetened beverages, has been implicated in the pathogenesis of metabolic dysfunction-associated fatty liver disease (MAFLD) and other related metabolic syndromes. The c-Jun N-terminal kinase (JNK) pathway plays a crucial role in response to dietary stressors, and it was demonstrated that the inhibition of the JNK pathway could potentially be used in the treatment of MAFLD. However, the intricate mechanisms underlying these interventions remain incompletely understood given their multifaceted effects across multiple tissues. In this study, we challenged rats with sucrose-sweetened water and investigated the potential effects of JNK inhibition by employing network analysis based on the transcriptome profiling obtained from hepatic and extrahepatic tissues, including visceral white adipose tissue, skeletal muscle, and brain. Our data demonstrate that JNK inhibition by JNK-IN-5A effectively reduces the circulating triglyceride accumulation and inflammation in rats subjected to sucrose consumption. Coexpression analysis and genome-scale metabolic modeling reveal that sucrose overconsumption primarily induces transcriptional dysfunction related to fatty acid and oxidative metabolism in the liver and adipose tissues, which are largely rectified after JNK inhibition at a clinically relevant dose. Skeletal muscle exhibited minimal transcriptional changes to sucrose overconsumption but underwent substantial metabolic adaptation following the JNK inhibition. Overall, our data provides novel insights into the molecular basis by which JNK inhibition exerts its metabolic effect in the metabolically active tissues. Furthermore, our findings underpin the critical role of extrahepatic metabolism in the development of diet-induced steatosis, offering valuable guidance for future studies focused on JNK-targeting for effective treatment of MAFLD.

Chronic diseases such as metabolic, renal, or liver disorders involve complex interactions of genes, cell types, and tissues. This doctoral thesis leverages systems biology by integrating transcriptomics with other omics data to map biological interactions and identify novel therapeutic targets. By viewing gene perturbations as interconnected networks rather than isolated factors, the research uncovers key drivers of disease and matches them with potential interventions. A combination of bulk and single-cell RNA sequencing is used: bulk RNA-seq provides a broad view of tissue-level changes, while single-cell RNA-seq pinpoints changes in specific cell populations. Together, these approaches enable more precise identification of drug targets for chronic diseases and facilitate drug repositioning to expedite therapy development.The thesis is structured into three key sections. The first part (Paper I) integrates transcriptomic, proteomic and lipidomic data, exploring PKLR as a druggable target of non-alcoholic fatty liver disease (NAFLD). This study investigates whether small-molecule inhibitors of PKLR expression could serve as therapeutic agents, offering a drug repurposing strategy to mitigate disease progression. The second part (Papers II–IV) relies on gene co-expression network, and leverages both bulk and single cell transcriptomics to discover disease-associated molecular drivers of hepatocellular carcinoma (HCC) and chronic kidney disease (CKD), respectively. These studies illustrate how single cell data can locate key molecular targets in diverse cell types within tissues, and help to understand molecular mechanism of these diseases.In the final section (Paper V), a whole-body single-cell gene expression atlas is introduced, providing a foundational reference for human biology. This resource enhances the systems biology toolkit, enabling rapid contextualization of newly identified disease genes and drug targets. Researchers can determine tissue and cell-type specificity, facilitating a clearer understanding of therapeutic strategies for chronic diseases.Overall, this thesis underscores the power of systems biology in deciphering disease mechanisms and advancing precision medicine. The integration of multi-omics data with network analysis fosters a holistic understanding of chronic diseases, leading to effective and targeted treatments. Beyond identifying therapeutic targets, the research contributes a lasting resource in form of the single-cell gene expression atlas, bridging molecular discoveries withIclinical applications. These insights accelerate the development of novel, data- driven therapies for complex diseases, advancing translational medicine.

Kroniska sjukdomar såsom metaboliska, njur och leverpåverkande sådana involverar komplexa interaktioner mellan gener, celltyper och vävnader. Denna doktorsavhandling utnyttjar systembiologi genom att integrera transkriptomik med andra omikdata för att kartlägga biologiska sammanhang och identifiera nya terapeutiska mål. Genom att betrakta genpåverkan som sammanlänkade nätverk snarare än isolerade faktorer avslöjar forskningen viktiga drivkrafter bakom sjukdomar och matchar dem med potentiella interventioner. En kombination av bulk- och enkelcells RNA-sekvensering används: bulk RNA-seq ger en bred överblick av vävnadsnivåförändringar, medan enkelcells RNA-seq fångar förändringar i specifika cellpopulationer. Tillsammans möjliggör dessa metoder en mer exakt identifiering av läkemedelsmål för kroniska sjukdomar och underlättar läkemedelsåteranvändning för att påskynda utvecklingen av nya behandlingarAvhandlingen är uppdelad i trenyckelkapitel. Det första (Paper I) integrerar transkriptomiska, proteomiska och lipidomiska data och undersöker PKLR som ett läkemedelsmål för icke-alkoholrelaterad fettlever (NAFLD). Denna studie undersöker om småmolekylinhibitorer av PKLR-uttryck kan fungera som terapeutiska medel och erbjuda en strategi åt läkemedelsåteranvändning för att bromsa sjukdomens utveckling. Det andra kapitlet (Papers II–IV) baseras på gensamuttrycksnätverk och utnyttjar både bulk- och enkelcells transkriptomik för att upptäcka sjukdomsrelaterade molekylära drivkrafter för hepatocellulärt karcinom (HCC) och kronisk njursjukdom (CKD), respektive. Dessa studier illustrerar hur enskilda celldata kan lokalisera viktiga molekylära mål i olika celltyper inom vävnader och hjälpa till att förstå de molekylära mekanismerna bakom dessa sjukdomar.I det sista kapitlet (Paper V) introduceras en helkroppstäckande, cellupplöst, genuttrycksatlas, vilket ger ett grundläggande referensverk för mänsklig biologi. Denna resurs förbättrar verktygslådan inom systembiologi och möjliggör snabb kontextualisering av nyidentifierade sjukdomsgener och läkemedelsmål. Forskare kan bestämma vävnads- och celltypsspecificitet, vilket underlättar en tydligare förståelse bakom terapeutiska strategier för kroniska sjukdomar.Sammanfattningsvis betonar denna avhandling systembiologins kraft i att förstå sjukdomsmekanismer och driva precisionmedicin framåt. Integreringen av multi-omikdata med nätverksanalys främjar en holistisk förståelse av kroniska sjukdomar, vilket leder till effektiva och målinriktade behandlingar. Förutom att identifiera terapeutiska mål, bidrar forskningen med en varaktig resurs i form av en enkelcellupplöst genuttrycksatlas, som kopplar sammanIIImolekylära upptäckter med kliniska tillämpningar. Dessa insikter påskyndar utvecklingen av nya, datadrivna behandlingar för komplexa sjukdomar och främjar translationell medicin.

Background: Cancer is one of the leading causes of mortality worldwide, highlighting the urgent need for a deeper molecular understanding and the development of personalized treatments. The present study aims to establish a solid association between gene expression and patient survival outcomes to enhance the utility of the Human Pathology Atlas for cancer research. Methods: In this updated analysis, we examined the expression profiles of 6918 patients across 21 cancer types. We integrated data from 10 independent cancer cohorts, creating a cross-validated, reliable collection of prognostic genes. We applied systems biology approach to identify the association between gene expression profiles and patient survival outcomes. We further constructed prognostic regulatory networks for kidney renal clear cell carcinoma (KIRC) and liver hepatocellular carcinoma (LIHC), which elucidate the molecular underpinnings associated with patient survival in these cancers. Findings: We observed that gene expression during the transition from normal to tumorous tissue exhibited diverse shifting patterns in their original tissue locations. Significant correlations between gene expression and patient survival outcomes were identified in KIRC and LIHC among the major cancer types. Additionally, the prognostic regulatory network established for these two cancers showed the indicative capabilities of the Human Pathology Atlas and provides actionable insights for cancer research. Interpretation: The updated Human Pathology Atlas provides a significant foundation for precision oncology and the formulation of personalized treatment strategies. These findings deepen our understanding of cancer biology and have the potential to advance targeted therapeutic approaches in clinical practice. Funding: The Knut and Alice Wallenberg Foundation ( 72110), the China Scholarship Council (Grant No. 202006940003).

Introduction: Macrophages and T cells play crucial roles in liver physiology, but their functional diversity in hepatocellular carcinoma (HCC) remains largely unknown. Methods: Two bulk RNA-sequencing (RNA-seq) cohorts for HCC were analyzed using gene co-expression network analysis. Key gene modules and networks were mapped to single-cell RNA-sequencing (scRNA-seq) data of HCC. Cell type fraction of bulk RNA-seq data was estimated by deconvolution approach using single-cell RNA-sequencing data as a reference. Survival analysis was carried out to estimate the prognosis of different immune cell types in bulk RNA-seq cohorts. Cell-cell interaction analysis was performed to identify potential links between immune cell types in HCC. Results: In this study, we analyzed RNA-seq data from two large-scale HCC cohorts, revealing a major and consensus gene co-expression cluster with significant implications for immunosuppression. Notably, these genes exhibited higher enrichment in liver macrophages than T cells, as confirmed by scRNA-seq data from HCC patients. Integrative analysis of bulk and single-cell RNA-seq data pinpointed SPP1+ macrophages as an unfavorable cell type, while VCAN+ macrophages, C1QA+ macrophages, and CD8+ T cells were associated with a more favorable prognosis for HCC patients. Subsequent scRNA-seq investigations and in vitro experiments elucidated that SPP1, predominantly secreted by SPP1+ macrophages, inhibits CD8+ T cell proliferation. Finally, targeting SPP1 in tumor-associated macrophages through inhibition led to a shift towards a favorable phenotype. Discussion: This study underpins the potential of SPP1 as a translational target in immunotherapy for HCC.

Excessive consumption of sucrose, in the form of sugar-sweetened beverages, has been implicated in the pathogenesis of metabolic dysfunction-associated fatty liver disease (MAFLD) and other related metabolic syndromes. The c-Jun N-terminal kinase (JNK) pathway plays a crucial role in response to dietary stressors, and it was demonstrated that the inhibition of the JNK pathway could potentially be used in the treatment of MAFLD. However, the intricate mechanisms underlying these interventions remain incompletely understood given their multifaceted effects across multiple tissues. In this study, we challenged rats with sucrose-sweetened water and investigated the potential effects of JNK inhibition by employing network analysis based on the transcriptome profiling obtained from hepatic and extrahepatic tissues, including visceral white adipose tissue, skeletal muscle, and brain. Our data demonstrate that JNK inhibition by JNK-IN-5A effectively reduces the circulating triglyceride accumulation and inflammation in rats subjected to sucrose consumption. Coexpression analysis and genome-scale metabolic modelling reveal that sucrose overconsumption primarily induces transcriptional dysfunction related to fatty acid and oxidative metabolism in the liver and adipose tissues, which are largely rectified after JNK inhibition at a clinically relevant dose. Skeletal muscle exhibited minimal transcriptional changes to sucrose overconsumption but underwent substantial metabolic adaptation following the JNK inhibition. Overall, our data provides novel insights into the molecular basis by which JNK inhibition exerts its metabolic effect in the metabolically active tissues. Furthermore, our findings underpin the critical role of extrahepatic metabolism in the development of diet-induced steatosis, offering valuable guidance for future studies focused on JNK-targeting for effective treatment of MAFLD.

MOTIVATION: Many approaches in systems biology have been applied in drug repositioning due to the increased availability of the omics data and computational biology tools. Using a multi-omics integrated network, which contains information of various biological interactions, could offer a more comprehensive inspective and interpretation for the drug mechanism of action (MoA). RESULTS: We developed a computational pipeline for dissecting the hidden MoAs of drugs (Open MoA). Our pipeline computes confidence scores to edges that represent connections between genes/proteins in the integrated network. The interactions showing the highest confidence score could indicate potential drug targets and infer the underlying molecular MoAs. Open MoA was also validated by testing some well-established targets. Additionally, we applied Open MoA to reveal the MoA of a repositioned drug (JNK-IN-5A) that modulates the PKLR expression in HepG2 cells and found STAT1 is the key transcription factor. Overall, Open MoA represents a first-generation tool that could be utilized for predicting the potential MoA of repurposed drugs and dissecting de novo targets for developing effective treatments. AVAILABILITY AND IMPLEMENTATION: Source code is available at https://github.com/XinmengLiao/Open_MoA.

Cell lines are valuable resources as model for human biology and translational medicine. It is thus important to explore the concordance between the expression in various cell lines vis-à-vis human native and disease tissues. In this study, we investigate the expression of all human protein-coding genes in more than 1,000 human cell lines representing 27 cancer types by a genome-wide transcriptomics analysis. The cell line gene expression is compared with the corresponding profiles in various tissues, organs, single-cell types and cancers. Here, we present the expression for each cell line and give guidance for the most appropriate cell line for a given experimental study. In addition, we explore the cancer-related pathway and cytokine activity of the cell lines to aid human biology studies and drug development projects. All data are presented in an open access cell line section of the Human Protein Atlas to facilitate the exploration of all human protein-coding genes across these cell lines.

Simple Summary Hepatocellular carcinoma (HCC) is the most common malignancy of liver cancer. However, treatment of HCC is still severely limited due to limitation of drug therapy. We aimed to screen more possible target genes and candidate drugs for HCC, exploring the possibility of drug treatments from systems biological perspective. We identified ten candidate target genes, which are hub genes in HCC co-expression networks, which also possess significant prognostic value in two independent HCC cohorts. The rationality of these target genes was well demonstrated through variety analyses of patient expression profiles. We then screened candidate drugs for target genes and finally identified withaferin-a and mitoxantrone as the candidate drug for HCC treatment. The drug effectiveness was validated in in vitro model and computational analysis, providing more evidence for our drug repositioning method and results. Hepatocellular carcinoma (HCC) is a malignant liver cancer that continues to increase deaths worldwide owing to limited therapies and treatments. Computational drug repurposing is a promising strategy to discover potential indications of existing drugs. In this study, we present a systematic drug repositioning method based on comprehensive integration of molecular signatures in liver cancer tissue and cell lines. First, we identify robust prognostic genes and two gene co-expression modules enriched in unfavorable prognostic genes based on two independent HCC cohorts, which showed great consistency in functional and network topology. Then, we screen 10 genes as potential target genes for HCC on the bias of network topology analysis in these two modules. Further, we perform a drug repositioning method by integrating the shRNA and drug perturbation of liver cancer cell lines and identifying potential drugs for every target gene. Finally, we evaluate the effects of the candidate drugs through an in vitro model and observe that two identified drugs inhibited the protein levels of their corresponding target genes and cell migration, also showing great binding affinity in protein docking analysis. Our study demonstrates the usefulness and efficiency of network-based drug repositioning approach to discover potential drugs for cancer treatment and precision medicine approach.

Background Non-alcoholic fatty liver disease (NAFLD) encompasses a wide spectrum of liver pathologies. However, no medical treatment has been approved for the treatment of NAFLD. In our previous study, we found that PKLR could be a potential target for treatment of NALFD. Here, we investigated the effect of PKLR in in vivo model and performed drug repositioning to identify a drug candidate for treatment of NAFLD. Methods Tissue samples from liver, muscle, white adipose and heart were obtained from control and PKLR knock-out mice fed with chow and high sucrose diets. Lipidomics as well as transcriptomics analyses were conducted using these tissue samples. In addition, a computational drug repositioning analysis was performed and drug candidates were identified. The drug candidates were both tested in in vitro and in vivo models to evaluate their toxicity and efficacy. Findings The Pklr KO reversed the increased hepatic triglyceride level in mice fed with high sucrose diet and partly recovered the transcriptomic changes in the liver as well as in other three tissues. Both liver and white adipose tissues exhibited dysregulated circadian transcriptomic profiles, and these dysregulations were reversed by hepatic knockout of Pklr. In addition, 10 small molecule drug candidates were identified as potential inhibitor of PKLR using our drug repositioning pipeline, and two of them significantly inhibited both the PKLR expression and triglyceride level in in vitro model. Finally, the two selected small molecule drugs were evaluated in in vivo rat models and we found that these drugs attenuate the hepatic steatosis without side effect on other tissues. Interpretation In conclusion, our study provided biological insights about the critical role of PKLR in NAFLD progression and proposed a treatment strategy for NAFLD patients, which has been validated in preclinical studies. 

Metabolic dysfunction-associated fatty liver disease (MAFLD) is a complex disease involving alterations in multiple biological processes regulated by the interactions between obesity, genetic background, and environmental factors including the microbiome. To decipher hepatic steatosis (HS) pathogenesis by excluding critical confounding factors including genetic variants and diabetes, 56 heterogenous MAFLD patients are characterized by generating multiomics data including oral and gut metagenomics as well as plasma metabolomics and inflammatory proteomics data. The dysbiosis in the oral and gut microbiome is explored and the host–microbiome interactions based on global metabolic and inflammatory processes are revealed. These multiomics data are integrated using the biological network and HS's key features are identified using multiomics data. HS is finally predicted using these key features and findings are validated in a follow-up cohort, where 22 subjects with varying degree of HS are characterized.