A proximal enhancer regulates RORA expression during early human Th17 cell differentiation.

Gene regulatory elements, such as enhancers, greatly influence cell identity by tuning the transcriptional activity of specific cell types. Dynamics of enhancer landscape during early human Th17 cell differentiation remains incompletely understood. Leveraging ATAC-seq-based profiling of chromatin accessibility and comprehensive analysis of key histone marks, we identified a repertoire of enhancers that potentially exert control over the fate specification of Th17 cells. We found 23 SNPs associated with autoimmune diseases within Th17-enhancers that precisely overlapped with the binding sites of transcription factors actively engaged in T-cell functions. Among the Th17-specific enhancers, we identified an enhancer in the intron of RORA and demonstrated that this enhancer positively regulates RORA transcription. Moreover, CRISPR-Cas9-mediated deletion of a transcription factor binding site-rich region within the identified RORA enhancer confirmed its role in regulating RORA transcription. These findings provide insights into the potential mechanism by which the RORA enhancer orchestrates Th17 differentiation.

Long noncoding RNA LIRIL2R modulates FOXP3 levels and suppressive function of human CD4+ regulatory T cells by regulating IL2RA.

Regulatory T cells (Tregs) are central in controlling immune responses, and dysregulation of their function can lead to autoimmune disorders or cancer. Despite extensive studies on Tregs, the basis of epigenetic regulation of human Treg development and function is incompletely understood. Long intergenic noncoding RNAs (lincRNA)s are important for shaping and maintaining the epigenetic landscape in different cell types. In this study, we identified a gene on the chromosome 6p25.3 locus, encoding a lincRNA, that was up-regulated during early differentiation of human Tregs. The lincRNA regulated the expression of interleukin-2 receptor alpha (IL2RA), and we named it the lincRNA regulator of IL2RA (LIRIL2R). Through transcriptomics, epigenomics, and proteomics analysis of LIRIL2R-deficient Tregs, coupled with global profiling of LIRIL2R binding sites using chromatin isolation by RNA purification, followed by sequencing, we identified IL2RA as a target of LIRIL2R. This nuclear lincRNA binds upstream of the IL2RA locus and regulates its epigenetic landscape and transcription. CRISPR-mediated deletion of the LIRIL2R-bound region at the IL2RA locus resulted in reduced IL2RA expression. Notably, LIRIL2R deficiency led to reduced expression of Treg-signature genes (e.g., FOXP3, CTLA4, and PDCD1), upregulation of genes associated with effector T cells (e.g., SATB1 and GATA3), and loss of Treg-mediated suppression.

PIM kinases regulate early human Th17 cell differentiation.

The serine/threonine-specific Moloney murine leukemia virus (PIM) kinase family (i.e., PIM1, PIM2, and PIM3) has been extensively studied in tumorigenesis. PIM kinases are downstream of several cytokine signaling pathways that drive immune-mediated diseases. Uncontrolled T helper 17 (Th17) cell activation has been associated with the pathogenesis of autoimmunity. However, the detailed molecular function of PIMs in human Th17 cell regulation has yet to be studied. In the present study, we comprehensively investigated how the three PIMs simultaneously alter transcriptional gene regulation during early human Th17 cell differentiation. By combining PIM triple knockdown with bulk and scRNA-seq approaches, we found that PIM deficiency promotes the early expression of key Th17-related genes while suppressing Th1-lineage genes. Further, PIMs modulate Th cell signaling, potentially via STAT1 and STAT3. Overall, our study highlights the inhibitory role of PIMs in human Th17 cell differentiation, thereby suggesting their association with autoimmune phenotypes.

Serum Insights: Leveraging the Power of miRNA Profiling as an Early Diagnostic Tool for Non-Small Cell Lung Cancer.

Non-small cell lung cancer is the predominant form of lung cancer and is associated with a poor prognosis. MiRNAs implicated in cancer initiation and progression can be easily detected in liquid biopsy samples and have the potential to serve as non-invasive biomarkers. In this study, we employed next-generation sequencing to globally profile miRNAs in serum samples from 71 early-stage NSCLC patients and 47 non-cancerous pulmonary condition patients. Preliminary analysis of differentially expressed miRNAs revealed 28 upregulated miRNAs in NSCLC compared to the control group. Functional enrichment analyses unveiled their involvement in NSCLC signaling pathways. Subsequently, we developed a gradient-boosting decision tree classifier based on 2588 miRNAs, which demonstrated high accuracy (0.837), sensitivity (0.806), and specificity (0.859) in effectively distinguishing NSCLC from non-cancerous individuals. Shapley Additive exPlanations analysis improved the model metrics by identifying the top 15 miRNAs with the strongest discriminatory value, yielding an AUC of 0.96 ± 0.04, accuracy of 0.896, sensitivity of 0.884, and specificity of 0.903. Our study establishes the potential utility of a non-invasive serum miRNA signature as a supportive tool for early detection of NSCLC while also shedding light on dysregulated miRNAs in NSCLC biology. For enhanced credibility and understanding, further validation in an independent cohort of patients is warranted.

Cell-connectivity-guided trajectory inference from single-cell data.

MOTIVATION: Single-cell RNA-sequencing enables cell-level investigation of cell differentiation, which can be modelled using trajectory inference methods. While tremendous effort has been put into designing these methods, inferring accurate trajectories automatically remains difficult. Therefore, the standard approach involves testing different trajectory inference methods and picking the trajectory giving the most biologically sensible model. As the default parameters are often suboptimal, their tuning requires methodological expertise. RESULTS: We introduce Totem, an open-source, easy-to-use R package designed to facilitate inference of tree-shaped trajectories from single-cell data. Totem generates a large number of clustering results, estimates their topologies as minimum spanning trees, and uses them to measure the connectivity of the cells. Besides automatic selection of an appropriate trajectory, cell connectivity enables to visually pinpoint branching points and milestones relevant to the trajectory. Furthermore, testing different trajectories with Totem is fast, easy, and does not require in-depth methodological knowledge. AVAILABILITY AND IMPLEMENTATION: Totem is available as an R package at https://github.com/elolab/Totem.

Elevated 18:0 lysophosphatidylcholine contributes to the development of pain in tissue injury.

ABSTRACT: Tissue injuries, including burns, are major causes of death and morbidity worldwide. These injuries result in the release of intracellular molecules and subsequent inflammatory reactions, changing the tissues' chemical milieu and leading to the development of persistent pain through activating pain-sensing primary sensory neurons. However, the majority of pain-inducing agents in injured tissues are unknown. Here, we report that, amongst other important metabolite changes, lysophosphatidylcholines (LPCs) including 18:0 LPC exhibit significant and consistent local burn injury-induced changes in concentration. 18:0 LPC induces immediate pain and the development of hypersensitivities to mechanical and heat stimuli through molecules including the transient receptor potential ion channel, vanilloid subfamily, member 1, and member 2 at least partly via increasing lateral pressure in the membrane. As levels of LPCs including 18:0 LPC increase in other tissue injuries, our data reveal a novel role for these lipids in injury-associated pain. These findings have high potential to improve patient care.

Spectrum and Density of Gamma and X-ray Induced Mutations in a Non-Model Rice Cultivar.

Physical mutagens are a powerful tool used for genetic research and breeding for over eight decades. Yet, when compared to chemical mutagens, data sets on the effect of different mutagens and dosages on the spectrum and density of induced mutations remain lacking. To address this, we investigated the landscape of mutations induced by gamma and X-ray radiation in the most widely cultivated crop species: rice. A mutant population of a tropical upland rice, Oryza sativa L., was generated and propagated via self-fertilization for seven generations. Five dosages ranging from 75 Gy to 600 Gy in both X-ray and gamma-irradiated material were applied. In the process of a forward genetic screens, 11 unique rice mutant lines showing phenotypic variation were selected for mutation analysis via whole-genome sequencing. Thousands of candidate mutations were recovered in each mutant with single base substitutions being the most common, followed by small indels and structural variants. Higher dosages resulted in a higher accumulation of mutations in gamma-irradiated material, but not in X-ray-treated plants. The in vivo role of all annotated rice genes is yet to be directly investigated. The ability to induce a high density of single nucleotide and structural variants through mutagenesis will likely remain an important approach for functional genomics and breeding.

COVID-19-specific transcriptomic signature detectable in blood across multiple cohorts.

The coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spreading across the world despite vast global vaccination efforts. Consequently, many studies have looked for potential human host factors and immune mechanisms associated with the disease. However, most studies have focused on comparing COVID-19 patients to healthy controls, while fewer have elucidated the specific host factors distinguishing COVID-19 from other infections. To discover genes specifically related to COVID-19, we reanalyzed transcriptome data from nine independent cohort studies, covering multiple infections, including COVID-19, influenza, seasonal coronaviruses, and bacterial pneumonia. The identified COVID-19-specific signature consisted of 149 genes, involving many signals previously associated with the disease, such as induction of a strong immunoglobulin response and hemostasis, as well as dysregulation of cell cycle-related processes. Additionally, potential new gene candidates related to COVID-19 were discovered. To facilitate exploration of the signature with respect to disease severity, disease progression, and different cell types, we also offer an online tool for easy visualization of the selected genes across multiple datasets at both bulk and single-cell levels.

Long Intergenic Noncoding RNA MIAT as a Regulator of Human Th17 Cell Differentiation.

T helper 17 (Th17) cells protect against fungal and bacterial infections and are implicated in autoimmunity. Several long intergenic noncoding RNAs (lincRNA) are induced during Th17 differentiation, however, their contribution to Th17 differentiation is poorly understood. We aimed to characterize the function of the lincRNA Myocardial Infarction Associated Transcript (MIAT) during early human Th17 cell differentiation. We found MIAT to be upregulated early after induction of human Th17 cell differentiation along with an increase in the chromatin accessibility at the gene locus. STAT3, a key regulator of Th17 differentiation, directly bound to the MIAT promoter and induced its expression during the early stages of Th17 cell differentiation. MIAT resides in the nucleus and regulates the expression of several key Th17 genes, including IL17A, IL17F, CCR6 and CXCL13, possibly by altering the chromatin accessibility of key loci, including IL17A locus. Further, MIAT regulates the expression of protein kinase C alpha (PKCα), an upstream regulator of IL17A. A reanalysis of published single-cell RNA-seq data showed that MIAT was expressed in T cells from the synovium of RA patients. Our results demonstrate that MIAT contributes to human Th17 differentiation by upregulating several genes implicated in Th17 differentiation. High MIAT expression in T cells of RA patient synovia suggests a possible role of MIAT in Th17 mediated autoimmune pathologies.

Benchmarking methods for detecting differential states between conditions from multi-subject single-cell RNA-seq data.

Single-cell RNA-sequencing (scRNA-seq) enables researchers to quantify transcriptomes of thousands of cells simultaneously and study transcriptomic changes between cells. scRNA-seq datasets increasingly include multisubject, multicondition experiments to investigate cell-type-specific differential states (DS) between conditions. This can be performed by first identifying the cell types in all the subjects and then by performing a DS analysis between the conditions within each cell type. Naïve single-cell DS analysis methods that treat cells statistically independent are subject to false positives in the presence of variation between biological replicates, an issue known as the pseudoreplicate bias. While several methods have already been introduced to carry out the statistical testing in multisubject scRNA-seq analysis, comparisons that include all these methods are currently lacking. Here, we performed a comprehensive comparison of 18 methods for the identification of DS changes between conditions from multisubject scRNA-seq data. Our results suggest that the pseudobulk methods performed generally best. Both pseudobulks and mixed models that model the subjects as a random effect were superior compared with the naïve single-cell methods that do not model the subjects in any way. While the naïve models achieved higher sensitivity than the pseudobulk methods and the mixed models, they were subject to a high number of false positives. In addition, accounting for subjects through latent variable modeling did not improve the performance of the naïve methods.

A systematic comparison of FOSL1, FOSL2 and BATF-mediated transcriptional regulation during early human Th17 differentiation.

Th17 cells are essential for protection against extracellular pathogens, but their aberrant activity can cause autoimmunity. Molecular mechanisms that dictate Th17 cell-differentiation have been extensively studied using mouse models. However, species-specific differences underscore the need to validate these findings in human. Here, we characterized the human-specific roles of three AP-1 transcription factors, FOSL1, FOSL2 and BATF, during early stages of Th17 differentiation. Our results demonstrate that FOSL1 and FOSL2 co-repress Th17 fate-specification, whereas BATF promotes the Th17 lineage. Strikingly, FOSL1 was found to play different roles in human and mouse. Genome-wide binding analysis indicated that FOSL1, FOSL2 and BATF share occupancy over regulatory regions of genes involved in Th17 lineage commitment. These AP-1 factors also share their protein interacting partners, which suggests mechanisms for their functional interplay. Our study further reveals that the genomic binding sites of FOSL1, FOSL2 and BATF harbour hundreds of autoimmune disease-linked SNPs. We show that many of these SNPs alter the ability of these transcription factors to bind DNA. Our findings thus provide critical insights into AP-1-mediated regulation of human Th17-fate and associated pathologies.

Early DNA methylation changes in children developing beta cell autoimmunity at a young age.

AIMS/HYPOTHESIS: Type 1 diabetes is a chronic autoimmune disease of complex aetiology, including a potential role for epigenetic regulation. Previous epigenomic studies focused mainly on clinically diagnosed individuals. The aim of the study was to assess early DNA methylation changes associated with type 1 diabetes already before the diagnosis or even before the appearance of autoantibodies. METHODS: Reduced representation bisulphite sequencing (RRBS) was applied to study DNA methylation in purified CD4+ T cell, CD8+ T cell and CD4-CD8- cell fractions of 226 peripheral blood mononuclear cell samples longitudinally collected from seven type 1 diabetes-specific autoantibody-positive individuals and control individuals matched for age, sex, HLA risk and place of birth. We also explored correlations between DNA methylation and gene expression using RNA sequencing data from the same samples. Technical validation of RRBS results was performed using pyrosequencing. RESULTS: We identified 79, 56 and 45 differentially methylated regions in CD4+ T cells, CD8+ T cells and CD4-CD8- cell fractions, respectively, between type 1 diabetes-specific autoantibody-positive individuals and control participants. The analysis of pre-seroconversion samples identified DNA methylation signatures at the very early stage of disease, including differential methylation at the promoter of IRF5 in CD4+ T cells. Further, we validated RRBS results using pyrosequencing at the following CpG sites: chr19:18118304 in the promoter of ARRDC2; chr21:47307815 in the intron of PCBP3; and chr14:81128398 in the intergenic region near TRAF3 in CD4+ T cells. CONCLUSIONS/INTERPRETATION: These preliminary results provide novel insights into cell type-specific differential epigenetic regulation of genes, which may contribute to type 1 diabetes pathogenesis at the very early stage of disease development. Should these findings be validated, they may serve as a potential signature useful for disease prediction and management.

scShaper: an ensemble method for fast and accurate linear trajectory inference from single-cell RNA-seq data.

MOTIVATION: Computational models are needed to infer a representation of the cells, i.e. a trajectory, from single-cell RNA-sequencing data that model cell differentiation during a dynamic process. Although many trajectory inference methods exist, their performance varies greatly depending on the dataset and hence there is a need to establish more accurate, better generalizable methods. RESULTS: We introduce scShaper, a new trajectory inference method that enables accurate linear trajectory inference. The ensemble approach of scShaper generates a continuous smooth pseudotime based on a set of discrete pseudotimes. We demonstrate that scShaper is able to infer accurate trajectories for a variety of trigonometric trajectories, including many for which the commonly used principal curves method fails. A comprehensive benchmarking with state-of-the-art methods revealed that scShaper achieved superior accuracy of the cell ordering and, in particular, the differentially expressed genes. Moreover, scShaper is a fast method with few hyperparameters, making it a promising alternative to the principal curves method for linear pseudotemporal ordering. AVAILABILITY AND IMPLEMENTATION: scShaper is available as an R package at https://github.com/elolab/scshaper. The test data are available at https://doi.org/10.5281/zenodo.5734488. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The Trithorax group protein ASH1 requires a combination of BAH domain and AT hooks, but not the SET domain, for mitotic chromatin binding and survival.

The Drosophila Trithorax group (TrxG) protein ASH1 remains associated with mitotic chromatin through mechanisms that are poorly understood. ASH1 dimethylates histone H3 at lysine 36 via its SET domain. Here, we identify domains of the TrxG protein ASH1 that are required for mitotic chromatin attachment in living Drosophila. Quantitative live imaging demonstrates that ASH1 requires AT hooks and the BAH domain but not the SET domain for full chromatin binding in metaphase, and that none of these domains are essential for interphase binding. Genetic experiments show that disruptions of the AT hooks and the BAH domain together, but not deletion of the SET domain alone, are lethal. Transcriptional profiling demonstrates that intact ASH1 AT hooks and the BAH domain are required to maintain expression levels of a specific set of genes, including several involved in cell identity and survival. This study identifies in vivo roles for specific ASH1 domains in mitotic binding, gene regulation, and survival that are distinct from its functions as a histone methyltransferase.

Differential ATAC-seq and ChIP-seq peak detection using ROTS.

Changes in cellular chromatin states fine-tune transcriptional output and ultimately lead to phenotypic changes. Here we propose a novel application of our reproducibility-optimized test statistics (ROTS) to detect differential chromatin states (ATAC-seq) or differential chromatin modification states (ChIP-seq) between conditions. We compare the performance of ROTS to existing and widely used methods for ATAC-seq and ChIP-seq data using both synthetic and real datasets. Our results show that ROTS outperformed other commonly used methods when analyzing ATAC-seq data. ROTS also displayed the most accurate detection of small differences when modeling with synthetic data. We observed that two-step methods that require the use of a separate peak caller often more accurately called enrichment borders, whereas one-step methods without a separate peak calling step were more versatile in calling sub-peaks. The top ranked differential regions detected by the methods had marked correlation with transcriptional differences of the closest genes. Overall, our study provides evidence that ROTS is a useful addition to the available differential peak detection methods to study chromatin and performs especially well when applied to study differential chromatin states in ATAC-seq data.

ILoReg: a tool for high-resolution cell population identification from single-cell RNA-seq data.

MOTIVATION: Single-cell RNA-seq allows researchers to identify cell populations based on unsupervised clustering of the transcriptome. However, subpopulations can have only subtle transcriptomic differences and the high dimensionality of the data makes their identification challenging. RESULTS: We introduce ILoReg, an R package implementing a new cell population identification method that improves identification of cell populations with subtle differences through a probabilistic feature extraction step that is applied before clustering and visualization. The feature extraction is performed using a novel machine learning algorithm, called iterative clustering projection (ICP), that uses logistic regression and clustering similarity comparison to iteratively cluster data. Remarkably, ICP also manages to integrate feature selection with the clustering through L1-regularization, enabling the identification of genes that are differentially expressed between cell populations. By combining solutions of multiple ICP runs into a single consensus solution, ILoReg creates a representation that enables investigating cell populations with a high resolution. In particular, we show that the visualization of ILoReg allows segregation of immune and pancreatic cell populations in a more pronounced manner compared with current state-of-the-art methods. AVAILABILITY AND IMPLEMENTATION: ILoReg is available as an R package at https://bioconductor.org/packages/ILoReg. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Reproducibility-optimized detection of differential DNA methylation.

Aim: DNA methylation is a key epigenetic mechanism regulating gene expression. Identifying differentially methylated regions is integral to DNA methylation analysis and there is a need for robust tools reliably detecting regions with significant differences in their methylation status. Materials & methods: We present here a reproducibility-optimized test statistic (ROTS) for detection of differential DNA methylation from high-throughput sequencing or array-based data. Results: Using both simulated and real data, we demonstrate the ability of ROTS to identify differential methylation between sample groups. Conclusion: Compared with state-of-the-art methods, ROTS shows competitive sensitivity and specificity in detecting consistently differentially methylated regions.

Leptin and fractalkine: novel subcutaneous cytokines in burn injury.

Burn injury is a pathology underpinned by progressive and aberrant inflammation. It is a major clinical challenge to survival and quality of life. Although the complex local and disseminating pathological processes of a burn injury ultimately stem from local tissue damage, to date relatively few studies have attempted to characterise the local inflammatory mediator profile. Here, cytokine content and associated transcriptional changes were measured in rat skin for three hours immediately following induction of a scald-type (60°C, 2 min) burn injury model. Leptin (P=0.0002) and fractalkine (P=0.0478) concentrations were significantly elevated post-burn above pre-burn and control site values, coinciding with the development of burn site oedema and differential expression of leptin mRNA (P=0.0004). Further, gene sequencing enrichment analysis indicated cytokine-cytokine receptor interaction (P=1.45×10-6). Subsequent behavioural studies demonstrated that, following subcutaneous injection into the dorsum of the paw, both leptin and fractalkine induced mechanical allodynia, heat hyperalgesia and the recruitment of macrophages. This is the first report of leptin elevation specifically at the burn site, and the first report of fractalkine elevation in any tissue post-burn which, together with the functional findings, calls for exploration of the influence of these cytokines on pain, inflammation and burn wound progression. In addition, targeting these signalling molecules represents a therapeutic potential as early formative mediators of these pathological processes.

Evaluation of Transcriptomic Regulations behind Metabolic Syndrome in Obese and Lean Subjects.

Multiple mechanisms have been suggested to confer to the pathophysiology of metabolic syndrome (MetS), however despite great interest from the scientific community, the exact contribution of each of MetS risk factors still remains unclear. The present study aimed to investigate molecular signatures in peripheral blood of individuals affected by MetS and different degrees of obesity. Metabolic health of 1204 individuals from 1000PLUS cohort was assessed, and 32 subjects were recruited to four study groups: MetS lean, MetS obese, "healthy obese", and healthy lean. Whole-blood transcriptome next generation sequencing with functional data analysis were carried out. MetS obese and MetS lean study participants showed the upregulation of genes involved in inflammation and coagulation processes: granulocyte adhesion and diapedesis (p < 0.0001, p = 0.0063), prothrombin activation pathway (p = 0.0032, p = 0.0091), coagulation system (p = 0.0010, p = 0.0155). The results for "healthy obese" indicate enrichment in molecules associated with protein synthesis (p < 0.0001), mitochondrial dysfunction (p < 0.0001), and oxidative phosphorylation (p < 0.0001). Our results suggest that MetS is related to the state of inflammation and vascular system changes independent of excess body weight. Furthermore, "healthy obese", despite not fulfilling the criteria for MetS diagnosis, seems to display an intermediate state with a lower degree of metabolic abnormalities, before they proceed to a full blown MetS.

Estrogen Signaling Drives Ciliogenesis in Human Endometrial Organoids.

The human endometrium is the inner lining of the uterus consisting of stromal and epithelial (secretory and ciliated) cells. It undergoes a hormonally regulated monthly cycle of growth, differentiation, and desquamation. However, how these cyclic changes control the balance between secretory and ciliated cells remains unclear. Here, we established endometrial organoids to investigate the estrogen (E2)-driven control of cell fate decisions in human endometrial epithelium. We demonstrate that they preserve the structure, expression patterns, secretory properties, and E2 responsiveness of their tissue of origin. Next, we show that the induction of ciliated cells is orchestrated by the coordinated action of E2 and NOTCH signaling. Although E2 is the primary driver, inhibition of NOTCH signaling provides a permissive environment. However, inhibition of NOTCH alone is not sufficient to trigger ciliogenesis. Overall, we provide insights into endometrial biology and propose endometrial organoids as a robust and powerful model for studying ciliogenesis in vitro.