The microRNA miR-31 inhibits CD8+ T cell function in chronic viral infection.

During infection, antigen-specific T cells undergo tightly regulated developmental transitions controlled by transcriptional and post-transcriptional regulation of gene expression. We found that the microRNA miR-31 was strongly induced by activation of the T cell antigen receptor (TCR) in a pathway involving calcium and activation of the transcription factor NFAT. During chronic infection with lymphocytic choriomeningitis virus (LCMV) clone 13, miR-31-deficent mice recovered from clinical disease, while wild-type mice continued to show signs of disease. This disease phenotype was explained by the presence of larger numbers of cytokine-secreting LCMV-specific CD8+ T cells in miR-31-deficent mice than in wild-type mice. Mechanistically, miR-31 increased the sensitivity of T cells to type I interferons, which interfered with effector T cell function and increased the expression of several proteins related to T cell dysfunction during chronic infection. These studies identify miR-31 as an important regulator of T cell exhaustion in chronic infection.

The transcription factor NFAT promotes exhaustion of activated CD8⁺ T cells.

During persistent antigen stimulation, CD8(+) T cells show a gradual decrease in effector function, referred to as exhaustion, which impairs responses in the setting of tumors and infections. Here we demonstrate that the transcription factor NFAT controls the program of T cell exhaustion. When expressed in cells, an engineered form of NFAT1 unable to interact with AP-1 transcription factors diminished T cell receptor (TCR) signaling, increased the expression of inhibitory cell surface receptors, and interfered with the ability of CD8(+) T cells to protect against Listeria infection and attenuate tumor growth in vivo. We defined the genomic regions occupied by endogenous and engineered NFAT1 in primary CD8(+) T cells and showed that genes directly induced by the engineered NFAT1 overlapped with genes expressed in exhausted CD8(+) T cells in vivo. Our data show that NFAT promotes T cell anergy and exhaustion by binding at sites that do not require cooperation with AP-1.

High-definition spatial transcriptomics for in situ tissue profiling.

Spatial and molecular characteristics determine tissue function, yet high-resolution methods to capture both concurrently are lacking. Here, we developed high-definition spatial transcriptomics, which captures RNA from histological tissue sections on a dense, spatially barcoded bead array. Each experiment recovers several hundred thousand transcript-coupled spatial barcodes at 2-µm resolution, as demonstrated in mouse brain and primary breast cancer. This opens the way to high-resolution spatial analysis of cells and tissues.

Tumor Tolerance-Promoting Function of Regulatory T Cells Is Optimized by CD28, but Strictly Dependent on Calcineurin.

Regulatory T cells (Treg) restrain immune responses against malignant tumors, but their global depletion in cancer patients will likely be limited by systemic autoimmune toxicity. Instead, approaches to "tune" their activities may allow for preferential targeting of tumor-reactive Treg. Although Ag recognition regulates Treg function, the roles of individual TCR-dependent signaling pathways in enabling Treg to promote tumor tolerance are not well characterized. In this study, we examined in mouse tumor models the role of calcineurin, a key mediator of TCR signaling, and the role of the costimulatory receptor CD28 in the differentiation of resting central Treg into effector Treg endowed with tumor tropism. We find that calcineurin, although largely dispensable for suppressive activity in vitro, is essential for upregulation of ICOS and CTLA-4 in Treg, as well as for expression of chemokine receptors driving their accumulation in tumors. In contrast, CD28 is not critical, but optimizes the formation of tumor-homing Treg and their fitness in tumor tissue. Accordingly, although deletion of either CnB or CD28 strongly impairs Treg-mediated tumor tolerance, lack of CnB has an even more pronounced impact than lack of CD28. Hence, our studies reveal distinct roles for what has classically been defined as signal 1 and signal 2 of conventional T cell activation in the context of Treg-mediated tumor tolerance.

Temporal probabilistic modeling of bacterial compositions derived from 16S rRNA sequencing.

Motivation: The number of microbial and metagenomic studies has increased drastically due to advancements in next-generation sequencing-based measurement techniques. Statistical analysis and the validity of conclusions drawn from (time series) 16S rRNA and other metagenomic sequencing data is hampered by the presence of significant amount of noise and missing data (sampling zeros). Accounting uncertainty in microbiome data is often challenging due to the difficulty of obtaining biological replicates. Additionally, the compositional nature of current amplicon and metagenomic data differs from many other biological data types adding another challenge to the data analysis. Results: To address these challenges in human microbiome research, we introduce a novel probabilistic approach to explicitly model overdispersion and sampling zeros by considering the temporal correlation between nearby time points using Gaussian Processes. The proposed Temporal Gaussian Process Model for Compositional Data Analysis (TGP-CODA) shows superior modeling performance compared to commonly used Dirichlet-multinomial, multinomial and non-parametric regression models on real and synthetic data. We demonstrate that the nonreplicative nature of human gut microbiota studies can be partially overcome by our method with proper experimental design of dense temporal sampling. We also show that different modeling approaches have a strong impact on ecological interpretation of the data, such as stationarity, persistence and environmental noise models. Availability and implementation: A Stan implementation of the proposed method is available under MIT license at https://github.com/tare/GPMicrobiome. Contact: taijo@flatironinstitute.org or rb113@nyu.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Modulation of TET2 expression and 5-methylcytosine oxidation by the CXXC domain protein IDAX.

TET (ten-eleven-translocation) proteins are Fe(ii)- and α-ketoglutarate-dependent dioxygenases that modify the methylation status of DNA by successively oxidizing 5-methylcytosine to 5-hydroxymethylcytosine, 5-formylcytosine and 5-carboxycytosine, potential intermediates in the active erasure of DNA-methylation marks. Here we show that IDAX (also known as CXXC4), a reported inhibitor of Wnt signalling that has been implicated in malignant renal cell carcinoma and colonic villous adenoma, regulates TET2 protein expression. IDAX was originally encoded within an ancestral TET2 gene that underwent a chromosomal gene inversion during evolution, thus separating the TET2 CXXC domain from the catalytic domain. The IDAX CXXC domain binds DNA sequences containing unmethylated CpG dinucleotides, localizes to promoters and CpG islands in genomic DNA and interacts directly with the catalytic domain of TET2. Unexpectedly, IDAX expression results in caspase activation and TET2 protein downregulation, in a manner that depends on DNA binding through the IDAX CXXC domain, suggesting that IDAX recruits TET2 to DNA before degradation. IDAX depletion prevents TET2 downregulation in differentiating mouse embryonic stem cells, and short hairpin RNA against IDAX increases TET2 protein expression in the human monocytic cell line U937. Notably, we find that the expression and activity of TET3 is also regulated through its CXXC domain. Taken together, these results establish the separate and linked CXXC domains of TET2 and TET3, respectively, as previously unknown regulators of caspase activation and TET enzymatic activity.

LuxGLM: a probabilistic covariate model for quantification of DNA methylation modifications with complex experimental designs.

MOTIVATION: 5-methylcytosine (5mC) is a widely studied epigenetic modification of DNA. The ten-eleven translocation (TET) dioxygenases oxidize 5mC into oxidized methylcytosines (oxi-mCs): 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC) and 5-carboxylcytosine (5caC). DNA methylation modifications have multiple functions. For example, 5mC is shown to be associated with diseases and oxi-mC species are reported to have a role in active DNA demethylation through 5mC oxidation and DNA repair, among others, but the detailed mechanisms are poorly understood. Bisulphite sequencing and its various derivatives can be used to gain information about all methylation modifications at single nucleotide resolution. Analysis of bisulphite based sequencing data is complicated due to the convoluted read-outs and experiment-specific variation in biochemistry. Moreover, statistical analysis is often complicated by various confounding effects. How to analyse 5mC and oxi-mC data sets with arbitrary and complex experimental designs is an open and important problem. RESULTS: We propose the first method to quantify oxi-mC species with arbitrary covariate structures from bisulphite based sequencing data. Our probabilistic modeling framework combines a previously proposed hierarchical generative model for oxi-mC-seq data and a general linear model component to account for confounding effects. We show that our method provides accurate methylation level estimates and accurate detection of differential methylation when compared with existing methods. Analysis of novel and published data gave insights into to the demethylation of the forkhead box P3 (Foxp3) locus during the induced T regulatory cell differentiation. We also demonstrate how our covariate model accurately predicts methylation levels of the Foxp3 locus. Collectively, LuxGLM method improves the analysis of DNA methylation modifications, particularly for oxi-mC species. AVAILABILITY AND IMPLEMENTATION: An implementation of the proposed method is available under MIT license at https://github.org/tare/LuxGLM/ CONTACT: taijo@simonsfoundation.org or harri.lahdesmaki@aalto.fi SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Dissecting the dynamic changes of 5-hydroxymethylcytosine in T-cell development and differentiation.

The discovery of Ten Eleven Translocation proteins, enzymes that oxidize 5-methylcytosine (5mC) in DNA, has revealed novel mechanisms for the regulation of DNA methylation. We have mapped 5-hydroxymethylcytosine (5hmC) at different stages of T-cell development in the thymus and T-cell differentiation in the periphery. We show that 5hmC is enriched in the gene body of highly expressed genes at all developmental stages and that its presence correlates positively with gene expression. Further emphasizing the connection with gene expression, we find that 5hmC is enriched in active thymus-specific enhancers and that genes encoding key transcriptional regulators display high intragenic 5hmC levels in precursor cells at those developmental stages where they exert a positive effect. Our data constitute a valuable resource that will facilitate detailed analysis of the role of 5hmC in T-cell development and differentiation.

Biophysically Motivated Regulatory Network Inference: Progress and Prospects.

Thanks to the confluence of genomic technology and computational developments, the possibility of network inference methods that automatically learn large comprehensive models of cellular regulation is closer than ever. This perspective focuses on enumerating the elements of computational strategies that, when coupled to appropriate experimental designs, can lead to accurate large-scale models of chromatin state and transcriptional regulatory structure and dynamics. We highlight 4 research questions that require further investigation in order to make progress in network inference: (1) using overall constraints on network structure such as sparsity, (2) use of informative priors and data integration to constrain individual model parameters, (3) estimation of latent regulatory factor activity under varying cell conditions, and (4) new methods for learning and modeling regulatory factor interactions. We conclude that methods combining advances in these 4 categories of required effort with new genomic technologies will result in biophysically motivated dynamic genome-wide regulatory network models for several of the best-studied organisms and cell types.

MicroRNA-directed program of cytotoxic CD8+ T-cell differentiation.

Acquisition of effector properties is a key step in the generation of cytotoxic T lymphocytes (CTLs). Here we show that inflammatory signals regulate Dicer expression in CTLs, and that deletion or depletion of Dicer in mouse or human activated CD8(+) T cells causes up-regulation of perforin, granzymes, and effector cytokines. Genome-wide analysis of microRNA (miR, miRNA) changes induced by exposure of differentiating CTLs to IL-2 and inflammatory signals identifies miR-139 and miR-150 as components of an miRNA network that controls perforin, eomesodermin, and IL-2Rα expression in differentiating CTLs and whose activity is modulated by IL-2, inflammation, and antigenic stimulation. Overall, our data show that strong IL-2R and inflammatory signals act through Dicer and miRNAs to control the cytolytic program and other aspects of effector CTL differentiation.

Methods for time series analysis of RNA-seq data with application to human Th17 cell differentiation.

MOTIVATION: Gene expression profiling using RNA-seq is a powerful technique for screening RNA species' landscapes and their dynamics in an unbiased way. While several advanced methods exist for differential expression analysis of RNA-seq data, proper tools to anal.yze RNA-seq time-course have not been proposed. RESULTS: In this study, we use RNA-seq to measure gene expression during the early human T helper 17 (Th17) cell differentiation and T-: cell activation (Th0). To quantify Th17-: specific gene expression dynamics, we present a novel statistical methodology, DyNB, for analyzing time-course RNA-seq data. We use non-parametric Gaussian processes to model temporal correlation in gene expression and combine that with negative binomial likelihood for the count data. To account for experiment-: specific biases in gene expression dynamics, such as differences in cell differentiation efficiencies, we propose a method to rescale the dynamics between replicated measurements. We develop an MCMC sampling method to make inference of differential expression dynamics between conditions. DyNB identifies several known and novel genes involved in Th17 differentiation. Analysis of differentiation efficiencies revealed consistent patterns in gene expression dynamics between different cultures. We use qRT-PCR to validate differential expression and differentiation efficiencies for selected genes. Comparison of the results with those obtained via traditional timepoint-: wise analysis shows that time-course analysis together with time rescaling between cultures identifies differentially expressed genes which would not otherwise be detected. AVAILABILITY: An implementation of the proposed computational methods will be available at http://research.ics.aalto.fi/csb/software/

Heterogeneous nuclear ribonucleoprotein L-like (hnRNPLL) and elongation factor, RNA polymerase II, 2 (ELL2) are regulators of mRNA processing in plasma cells.

B cells and plasma cells possess distinct RNA processing environments that respectively promote the expression of membrane-associated Ig by B cells versus the secretion of Ig by plasma cells. Through a combination of transcriptional profiling and screening using a lentiviral short-hairpin RNA interference library, we show that both the splicing factor hnRNPLL and the transcription elongation factor ELL2 modulate the ratio of secreted versus membrane-encoding Ighg2b transcripts in MPC11 plasmacytoma cell lines. hnRNPLL and ELL2 are both highly expressed in primary plasma cells relative to B cells, but hnRNPLL binds Ighg2b mRNA transcripts and promotes an increase in levels of the membrane-encoding Ighg2b isoform at the expense of the secreted Ighg2b isoform, whereas ELL2 counteracts this effect and drives Ig secretion by increasing the frequency of the secreted Ighg2b isoform. As in T cells, hnRNPLL also alters the splicing pattern of mRNA encoding the adhesion receptor CD44, promoting exon inclusion, and decreasing the overall level of CD44 expression. Further characterization of ELL2-dependent transcription by RNA-Seq revealed that ∼12% of transcripts expressed by plasma cells were differentially processed because of the activities of ELL2, including B-cell maturation antigen BCMA, a receptor with a defined role in plasma cell survival. Taken together, our data identify hnRNPLL and ELL2 as regulators of pre-mRNA processing in plasma cells.

Sorad: a systems biology approach to predict and modulate dynamic signaling pathway response from phosphoproteome time-course measurements.

MOTIVATION: Signaling networks mediate responses to different stimuli using a multitude of feed-forward, feedback and cross-talk mechanisms, and malfunctions in these mechanisms have an important role in various diseases. To understand a disease and to help discover novel therapeutic approaches, we have to reveal the molecular mechanisms underlying signal transduction and use that information to design targeted perturbations. RESULTS: We have pursued this direction by developing an efficient computational approach, Sorad, which can estimate the structure of signal transduction networks and the associated continuous signaling dynamics from phosphoprotein time-course measurements. Further, Sorad can identify experimental conditions that modulate the signaling toward a desired response. We have analyzed comprehensive phosphoprotein time-course data from a human hepatocellular liver carcinoma cell line and demonstrate here that Sorad provides more accurate predictions of phosphoprotein responses to given stimuli than previously presented methods and, importantly, that Sorad can estimate experimental conditions to achieve a desired signaling response. Because Sorad is data driven, it has a high potential to generate novel hypotheses for further research. Our analysis of the hepatocellular liver carcinoma data predict a regulatory connection where AKT activity is dependent on IKK in TGFα stimulated cells, which is supported by the original data but not included in the original model. AVAILABILITY: An implementation of the proposed computational methods will be available at http://research.ics.aalto.fi/csb/software/. CONTACT: tarmo.aijo@aalto.fi or harri.lahdesmaki@aalto.fi SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identification of early gene expression changes during human Th17 cell differentiation.

Th17 cells play an essential role in the pathogenesis of autoimmune and inflammatory diseases. Most of our current understanding on Th17 cell differentiation relies on studies carried out in mice, whereas the molecular mechanisms controlling human Th17 cell differentiation are less well defined. In this study, we identified gene expression changes characterizing early stages of human Th17 cell differentiation through genome-wide gene expression profiling. CD4(+) cells isolated from umbilical cord blood were used to determine detailed kinetics of gene expression after initiation of Th17 differentiation with IL1ß, IL6, and TGFß. The differential expression of selected candidate genes was further validated at protein level and analyzed for specificity in initiation of Th17 compared with initiation of other Th subsets, namely Th1, Th2, and iTreg. This first genome-wide profiling of transcriptomics during the induction of human Th17 differentiation provides a starting point for defining gene regulatory networks and identifying new candidates regulating Th17 differentiation in humans.

Improving the Reliability of Peer Review Without a Gold Standard.

Peer review plays a crucial role in accreditation and credentialing processes as it can identify outliers and foster a peer learning approach, facilitating error analysis and knowledge sharing. However, traditional peer review methods may fall short in effectively addressing the interpretive variability among reviewing and primary reading radiologists, hindering scalability and effectiveness. Reducing this variability is key to enhancing the reliability of results and instilling confidence in the review process. In this paper, we propose a novel statistical approach called "Bayesian Inter-Reviewer Agreement Rate" (BIRAR) that integrates radiologist variability. By doing so, BIRAR aims to enhance the accuracy and consistency of peer review assessments, providing physicians involved in quality improvement and peer learning programs with valuable and reliable insights. A computer simulation was designed to assign predefined interpretive error rates to hypothetical interpreting and peer-reviewing radiologists. The Monte Carlo simulation then sampled (100 samples per experiment) the data that would be generated by peer reviews. The performances of BIRAR and four other peer review methods for measuring interpretive error rates were then evaluated, including a method that uses a gold standard diagnosis. Application of the BIRAR method resulted in 93% and 79% higher relative accuracy and 43% and 66% lower relative variability, compared to "Single/Standard" and "Majority Panel" peer review methods, respectively. Accuracy was defined by the median difference of Monte Carlo simulations between measured and pre-defined "actual" interpretive error rates. Variability was defined by the 95% CI around the median difference of Monte Carlo simulations between measured and pre-defined "actual" interpretive error rates. BIRAR is a practical and scalable peer review method that produces more accurate and less variable assessments of interpretive quality by accounting for variability within the group's radiologists, implicitly applying a standard derived from the level of consensus within the group across various types of interpretive findings.

Deciphering the TET3 interactome in primary thymic developing T cells.

Ten-eleven translocation (TET) proteins are DNA dioxygenases that mediate active DNA demethylation. TET3 is the most highly expressed TET protein in thymic developing T cells. TET3, either independently or in cooperation with TET1 or TET2, has been implicated in T cell lineage specification by regulating DNA demethylation. However, TET-deficient mice exhibit complex phenotypes, suggesting that TET3 exerts multifaceted roles, potentially by interacting with other proteins. We performed liquid chromatography with tandem mass spectrometry in primary developing T cells to identify TET3 interacting partners in endogenous, in vivo conditions. We discover TET3 interacting partners. Our data establish that TET3 participates in a plethora of fundamental biological processes, such as transcriptional regulation, RNA polymerase elongation, splicing, DNA repair, and DNA replication. This resource brings in the spotlight emerging functions of TET3 and sets the stage for systematic studies to dissect the precise mechanistic contributions of TET3 in shaping T cell biology.

Tissue and cellular spatiotemporal dynamics in colon aging.

Tissue structure and molecular circuitry in the colon can be profoundly impacted by systemic age-related effects, but many of the underlying molecular cues remain unclear. Here, we built a cellular and spatial atlas of the colon across three anatomical regions and 11 age groups, encompassing ~1,500 mouse gut tissues profiled by spatial transcriptomics and ~400,000 single nucleus RNA-seq profiles. We developed a new computational framework, cSplotch, which learns a hierarchical Bayesian model of spatially resolved cellular expression associated with age, tissue region, and sex, by leveraging histological features to share information across tissue samples and data modalities. Using this model, we identified cellular and molecular gradients along the adult colonic tract and across the main crypt axis, and multicellular programs associated with aging in the large intestine. Our multi-modal framework for the investigation of cell and tissue organization can aid in the understanding of cellular roles in tissue-level pathology.

An integrative computational systems biology approach identifies differentially regulated dynamic transcriptome signatures which drive the initiation of human T helper cell differentiation.

BACKGROUND: A proper balance between different T helper (Th) cell subsets is necessary for normal functioning of the adaptive immune system. Revealing key genes and pathways driving the differentiation to distinct Th cell lineages provides important insight into underlying molecular mechanisms and new opportunities for modulating the immune response. Previous computational methods to quantify and visualize kinetic differential expression data of three or more lineages to identify reciprocally regulated genes have relied on clustering approaches and regression methods which have time as a factor, but have lacked methods which explicitly model temporal behavior. RESULTS: We studied transcriptional dynamics of human umbilical cord blood T helper cells cultured in absence and presence of cytokines promoting Th1 or Th2 differentiation. To identify genes that exhibit distinct lineage commitment dynamics and are specific for initiating differentiation to different Th cell subsets, we developed a novel computational methodology (LIGAP) allowing integrative analysis and visualization of multiple lineages over whole time-course profiles. Applying LIGAP to time-course data from multiple Th cell lineages, we identified and experimentally validated several differentially regulated Th cell subset specific genes as well as reciprocally regulated genes. Combining differentially regulated transcriptional profiles with transcription factor binding site and pathway information, we identified previously known and new putative transcriptional mechanisms involved in Th cell subset differentiation. All differentially regulated genes among the lineages together with an implementation of LIGAP are provided as an open-source resource. CONCLUSIONS: The LIGAP method is widely applicable to quantify differential time-course dynamics of many types of datasets and generalizes to any number of conditions. It summarizes all the time-course measurements together with the associated uncertainty for visualization and manual assessment purposes. Here we identified novel human Th subset specific transcripts as well as regulatory mechanisms important for the initiation of the Th cell subset differentiation.

Protocol to isolate mature thymic T cell subsets using fluorescence-activated cell sorting for assessing gene expression by RNA-seq and transcription factor binding across the genome by CUT&RUN.

Here, we describe steps to isolate mature thymic T cell subsets, namely CD4 single positive (SP), CD8 SP, and invariant natural killer T (iNKT) cells starting from murine total thymocytes using fluorescence-activated cell sorting. We detail protocols to study gene expression by RNA-seq and assess binding of transcription factors across the genome using CUT&RUN. This approach deciphers the molecular principles that govern T cell lineage specification and function. This protocol works well with limited starting material. For complete details on the use and execution of this protocol, please refer to Äijö et al. (2022).1.