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

BACKGROUND: 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 ).

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

Background: 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. Conclusion: 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).

A dynamic single cell-based framework for digital twins to prioritize disease genes and drug targets.

BACKGROUND: Medical digital twins are computational disease models for drug discovery and treatment. Unresolved problems include how to organize and prioritize between disease-associated changes in digital twins, on cellulome- and genome-wide scales. We present a dynamic framework that can be used to model such changes and thereby prioritize upstream regulators (URs) for biomarker- and drug discovery. METHODS: We started with seasonal allergic rhinitis (SAR) as a disease model, by analyses of in vitro allergen-stimulated peripheral blood mononuclear cells (PBMC) from SAR patients. Time-series a single-cell RNA-sequencing (scRNA-seq) data of these cells were used to construct multicellular network models (MNMs) at each time point of molecular interactions between cell types. We hypothesized that predicted molecular interactions between cell types in the MNMs could be traced to find an UR gene, at an early time point. We performed bioinformatic and functional studies of the MNMs to develop a scalable framework to prioritize UR genes. This framework was tested on a single-cell and bulk-profiling data from SAR and other inflammatory diseases. RESULTS: Our scRNA-seq-based time-series MNMs of SAR showed thousands of differentially expressed genes (DEGs) across multiple cell types, which varied between time points. Instead of a single-UR gene in each MNM, we found multiple URs dispersed across the cell types. Thus, at each time point, the MNMs formed multi-directional networks. The absence of linear hierarchies and time-dependent variations in MNMs complicated the prioritization of URs. For example, the expression and functions of Th2 cytokines, which are approved drug targets in allergies, varied across cell types, and time points. Our analyses of bulk- and single-cell data from other inflammatory diseases also revealed multi-directional networks that showed stage-dependent variations. We therefore developed a quantitative approach to prioritize URs: we ranked the URs based on their predicted effects on downstream target cells. Experimental and bioinformatic analyses supported that this kind of ranking is a tractable approach for prioritizing URs. CONCLUSIONS: We present a scalable framework for modeling dynamic changes in digital twins, on cellulome- and genome-wide scales, to prioritize UR genes for biomarker and drug discovery.

Modelling maternal and perinatal risk factors to predict poorly controlled childhood asthma.

Asthma is the most common non-communicable pulmonary condition, affecting prepubertal boys more often than girls. This study explored how maternal and perinatal risk factors are linked to poorly controlled childhood asthma in a sex dependent manner. This single centre study was performed at a metropolitan teaching hospital in Western Sydney, Australia, using electronical obstetric records from 2000 to 2017 and electronical pediatric records from 2007 to 2018. The data of 1694 children with complete entries were retrospectively analysed. Risk factors for multiple hospital admission for asthma were selected by backward-eliminated Poisson regression modelling. Selection stability of these parameters was independently confirmed using approximated exhaustive search. Sex-specific regression models indicated that most notably parity (RR[95%CI] for parity = 3; 1.85[1.22-2.81]), birth length z-score (1.45[1.23-1.70]) and birth weight z-score (0.77[0.65-0.90]) contributed to multiple asthma admissions in girls, while boys were affected most prominently by maternal BMI (e.g. BMI 35-39.9; 1.92[1.38-2.67]) and threatened preterm labor (1.68[1.10-2.58]). Allergic status was a risk factors for both boys and girls (1.47[1.18-1.83] and 1.46[1.13-1.89]). Applying ROC analysis, the predictive modelling of risk factors for hospital admissions showed an incremental increase with an AUC of 0.84 and 0.75 for girls and boys respectively for >3 hospital admissions. Multiple hospital admissions for asthma are associated with maternal and perinatal risk factors in a sex and birth order dependent manner. Hence, prospective risk stratification studies aiming to improve childhood asthma control are warranted to test the clinical utility of these parameters. Furthermore, the influence of the early in utero environment on male-female differences in other communicable and non-communicable respiratory conditions should be considered.

Firstborn sex defines early childhood growth of subsequent siblings.

Animal studies have shown that maternal resource allocation can be sex-biased in order to maximize reproductive success, yet this basic concept has not been investigated in humans. In this study, we explored relationships between maternal factors, offspring sex and prenatal and postnatal weight gain. Sex-specific regression models not only indicated that maternal ethnicity impacted male (n = 2456) and female (n = 1871) childrens postnatal weight gain differently but also that parity and mode of feeding influenced weight velocity of female (ß ± s.e. = -0.31 ± 0.11 kg, p = 0.005; ß ± s.e. = -0.37 ± 0.11 kg, p < 0.001) but not male offspring. Collectively, our findings imply that maternal resource allocation to consecutive offspring increases after a male firstborn. The absence of this finding in formula fed children suggests that this observation could be mediated by breast milk. Our results warrant further mechanistic and epidemiological studies to elucidate the role of breastfeeding on the programming of infant growth as well as of metabolic and cardiovascular diseases, with potential implications for tailoring infant formulae according to sex and birth order.

Analysis of maternal and perinatal determinants of allergic sensitization in childhood.

BACKGROUND: Non-communicable diseases, such as allergies, are influenced by both genetic and epigenetic factors. Perinatal determinants conceivably modify the epigenetic makeup of the developing fetal immune system preventing or predisposing the development of allergies. The aim of this study therefore was to identify independent perinatal factors associated with allergic sensitization in childhood. METHODS: In a single center retrospective case-cohort study electronic obstetric medical records and available skin prick testing results of children were analyzed. For the analysis 286 skin prick test positive (sensitized) children [median (IQR): 3.47 (1.70-7.34) years] were compared with data from all remaining live births in the obstetric cohort (n = 66,583). RESULTS: Sensitized children more frequently had a mother born in Asia (19.1% vs. 10.2%; P < 10-6). Applying backward elimination logistic regression, seven out of 23 initially entered perinatal factors remained in the model. High maternal age (> 35 years; OR: 1.912; P < 0.001), male offspring sex (OR: 1.423; P < 0.01) and assisted conception (OR: 1.771; P < 0.05) increased the risk for allergic sensitization. In contrast, maternal smoking (OR: 0.469; P < 0.005), increasing parity (OR: 0.881; P < 0.05), maternal pre-pregnancy overweight (OR: 0.742; P < 0.005) and preterm birth (OR: 0.484; P < 0.05) decreased the risk for allergic sensitization. CONCLUSIONS: In addition to supporting previous findings, this study is first to report an increased risk of allergic sensitization after assisted conception. Beyond this finding's potential implementation in preventative strategies, exploration of this association could further pathophysiological understanding of allergic disease.

Meta-Analysis of Expression Profiling Data Indicates Need for Combinatorial Biomarkers in Pediatric Ulcerative Colitis.

BACKGROUND: Unbiased studies using different genome-wide methods have identified a great number of candidate biomarkers for diagnosis and treatment response in pediatric ulcerative colitis (UC). However, clinical translation has been proven difficult. Here, we hypothesized that one reason could be differences between inflammatory responses in an inflamed gut and in peripheral blood cells. METHODS: We performed meta-analysis of gene expression microarray data from intestinal biopsies and whole blood cells (WBC) from pediatric patients with UC and healthy controls in order to identify overlapping pathways, predicted upstream regulators, and potential biomarkers. RESULTS: Analyses of profiling datasets from colonic biopsies showed good agreement between different studies regarding pathways and predicted upstream regulators. The most activated predicted upstream regulators included TNF, which is known to have a key pathogenic and therapeutic role in pediatric UC. Despite this, the expression levels of TNF were increased in neither colonic biopsies nor WBC. A potential explanation was increased expression of TNFR2, one of the membrane-bound receptors of TNF in the inflamed colon. Further analyses showed a similar pattern of complex relations between the expression levels of the regulators and their receptors. We also found limited overlap between pathways and predicted upstream regulators in colonic biopsies and WBC. An extended search including all differentially expressed genes that overlapped between colonic biopsies and WBC only resulted in identification of three potential biomarkers involved in the regulation of intestinal inflammation. However, two had been previously proposed in adult inflammatory bowel diseases (IBD), namely, MMP9 and PROK2. CONCLUSIONS: Our findings indicate that biomarker identification in pediatric UC is complicated by the involvement of multiple pathways, each of which includes many different types of genes in the blood or inflamed intestine. Therefore, further studies for identification of combinatorial biomarkers are warranted. Our study may provide candidate biomarkers for such studies.

Correction to: A validated single-cell-based strategy to identify diagnostic and therapeutic targets in complex diseases.

An amendment to this paper has been published and can be accessed via the original article.

Bulk and single cell transcriptomic data indicate that a dichotomy between inflammatory pathways in peripheral blood and arthritic joints complicates biomarker discovery.

BACKGROUND: Unbiased studies using different genome-wide methods have identified several novel biomarkers for diagnosis and treatment response in Rheumatoid Arthritis (RA). However, clinical translation has proven difficult. Here, we hypothesized that one reason could be that inflammatory responses in peripheral blood are different from those in the arthritic joint. METHODS: We performed meta-analysis of gene expression microarray data from synovium, whole blood cells (WBC), peripheral blood mononuclear cells (PBMC), and CD4+ T cells from patients with RA and healthy controls in order to identify overlapping pathways, upstream regulators and potential biomarkers. We also analyzed single cell RNA-sequencing (scRNA-seq) data from peripheral blood and whole joints from a mouse model of antigen-induced arthritis. RESULTS: Analyses of two profiling data sets from synovium from RA patients and healthy controls all showed significant activation of pathways with known pathogenic relevance, such as the Th1 pathway, the role of NFAT in regulation of the immune response, dendritic cell maturation, iCOS-iCOSL signaling in T helper cells, Fcγ receptor-mediated phagocytosis, interferon signaling, Cdc42 signaling, and cytotoxic T lymphocyte-mediated apoptosis. The most activated upstream regulators included TNF, an important drug target, as well as IFN-gamma and CD40LG, all of which are known to play important pathogenic roles in RA. The differentially expressed genes from synovium included several potential biomarkers, such as CCL5, CCL13, CCL18, CX3CL1, CXCL6, CXCL9, CXCL10, CXCL13, IL15, IL32, IL1RN, SPP1, and TNFSF11. By contrast, microarray studies of WBC, PBMC and CD4+ T cells showed variable pathways and limited pathway overlap with synovium. Similarly, scRNA-seq data from a mouse model of arthritis did not support that inflammatory responses in peripheral blood reflect those in the arthritic joints. These data showed pathway overlap between mouse joint cells and synovium from patients with RA, but not with cells in peripheral blood. CONCLUSIONS: Our findings indicate a dichotomy between gene expression changes, pathways, upstream regulators and biomarkers in synovium and cell types in peripheral blood, which complicates identification of biomarkers in blood.

HIV chromatin is a preferred target for drugs that bind in the DNA minor groove.

The HIV genome is rich in A but not G or U and deficient in C. This nucleotide bias controls HIV phenotype by determining the highly unusual composition of all major HIV proteins. The bias is also responsible for the high frequency of narrow DNA minor groove sites in the double-stranded HIV genome as compared to cellular protein coding sequences and the bulk of the human genome. Since drugs that bind in the DNA minor groove disrupt nucleosomes on sequences that contain closely spaced oligo-A tracts which are prevalent in HIV DNA because of its bias, it was of interest to determine if these drugs exert this selective inhibitory effect on HIV chromatin. To test this possibility, nucleosomes were reconstituted onto five double-stranded DNA fragments from the HIV-1 pol gene in the presence and in the absence of several minor groove binding drugs (MGBDs). The results demonstrated that the MGBDs inhibited the assembly of nucleosomes onto all of the HIV-1 segments in a manner that was proportional to the A-bias, but had no detectable effect on the formation of nucleosomes on control cloned fragments or genomic DNA from chicken and human. Nucleosomes preassembled onto HIV DNA were also preferentially destabilized by the drugs as evidenced by enhanced nuclease accessibility in physiological ionic strength and by the preferential loss of the histone octamer in hyper-physiological salt solutions. The drugs also selectively disrupted HIV-containing nucleosomes in yeast as revealed by enhanced nuclease accessibility of the in vivo assembled HIV chromatin and reductions in superhelical densities of plasmid chromatin containing HIV sequences. A comparison of these results to the density of A-tracts in the HIV genome indicates that a large fraction of the nucleosomes that make up HIV chromatin should be preferred in vitro targets for the MGBDs. These results show that the MGBDs preferentially disrupt HIV-1 chromatin in vitro and in vivo and raise the possibility that non-toxic derivatives of certain MGBDs might serve as a novel class of anti-HIV agents.

An algorithm-based meta-analysis of genome- and proteome-wide data identifies a combination of potential plasma biomarkers for colorectal cancer.

Screening programs for colorectal cancer (CRC) often rely on detection of blood in stools, which is unspecific and leads to a large number of colonoscopies of healthy subjects. Painstaking research has led to the identification of a large number of different types of biomarkers, few of which are in general clinical use. Here, we searched for highly accurate combinations of biomarkers by meta-analyses of genome- and proteome-wide data from CRC tumors. We focused on secreted proteins identified by the Human Protein Atlas and used our recently described algorithms to find optimal combinations of proteins. We identified nine proteins, three of which had been previously identified as potential biomarkers for CRC, namely CEACAM5, LCN2 and TRIM28. The remaining proteins were PLOD1, MAD1L1, P4HA1, GNS, C12orf10 and P3H1. We analyzed these proteins in plasma from 80 patients with newly diagnosed CRC and 80 healthy controls. A combination of four of these proteins, TRIM28, PLOD1, CEACAM5 and P4HA1, separated a training set consisting of 90% patients and 90% of the controls with high accuracy, which was verified in a test set consisting of the remaining 10%. Further studies are warranted to test our algorithms and proteins for early CRC diagnosis.

A validated single-cell-based strategy to identify diagnostic and therapeutic targets in complex diseases.

BACKGROUND: Genomic medicine has paved the way for identifying biomarkers and therapeutically actionable targets for complex diseases, but is complicated by the involvement of thousands of variably expressed genes across multiple cell types. Single-cell RNA-sequencing study (scRNA-seq) allows the characterization of such complex changes in whole organs. METHODS: The study is based on applying network tools to organize and analyze scRNA-seq data from a mouse model of arthritis and human rheumatoid arthritis, in order to find diagnostic biomarkers and therapeutic targets. Diagnostic validation studies were performed using expression profiling data and potential protein biomarkers from prospective clinical studies of 13 diseases. A candidate drug was examined by a treatment study of a mouse model of arthritis, using phenotypic, immunohistochemical, and cellular analyses as read-outs. RESULTS: We performed the first systematic analysis of pathways, potential biomarkers, and drug targets in scRNA-seq data from a complex disease, starting with inflamed joints and lymph nodes from a mouse model of arthritis. We found the involvement of hundreds of pathways, biomarkers, and drug targets that differed greatly between cell types. Analyses of scRNA-seq and GWAS data from human rheumatoid arthritis (RA) supported a similar dispersion of pathogenic mechanisms in different cell types. Thus, systems-level approaches to prioritize biomarkers and drugs are needed. Here, we present a prioritization strategy that is based on constructing network models of disease-associated cell types and interactions using scRNA-seq data from our mouse model of arthritis, as well as human RA, which we term multicellular disease models (MCDMs). We find that the network centrality of MCDM cell types correlates with the enrichment of genes harboring genetic variants associated with RA and thus could potentially be used to prioritize cell types and genes for diagnostics and therapeutics. We validated this hypothesis in a large-scale study of patients with 13 different autoimmune, allergic, infectious, malignant, endocrine, metabolic, and cardiovascular diseases, as well as a therapeutic study of the mouse arthritis model. CONCLUSIONS: Overall, our results support that our strategy has the potential to help prioritize diagnostic and therapeutic targets in human disease.

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.