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CD4 + T cells play a central role in the adaptive immune response through their capacity to activate, support and control other immune cells. Although these cells have become the focus of intense research, a comprehensive understanding of the underlying regulatory networks that orchestrate CD4 + T cell function and activation is still incomplete. Here, we analyzed a large transcriptomic dataset consisting of 48 different human CD4 + T cell conditions. By performing reverse network engineering, we identified six common denominators of CD4 + T cell functionality (CREB1, E2F3, AHR, STAT1, NFAT5 and NFATC3). Moreover, we also analyzed condition-specific genes which led us to the identification of the transcription factor MEOX1 in T reg cells. Expression of MEOX1 was comparable to FOXP3 in T reg cells and can be upregulated by IL-2. Epigenetic analyses revealed a permissive epigenetic landscape for MEOX1 solely in T reg cells. Knockdown of MEOX1 in T reg cells revealed a profound impact on downstream gene expression programs and T reg cell suppressive capacity. These findings in the context of CD4 + T cells contribute to a better understanding of the transcriptional networks and biological mechanisms controlling CD4 + T cell functionality, which opens new avenues for future therapeutic strategies., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Baßler, Schmidleithner, Shakiba, Elmzzahi, Köhne, Floess, Scholz, Ohkura, Sadlon, Klee, Neubauer, Sakaguchi, Barry, Huehn, Bonaguro, Ulas and Beyer.)

Epigenetic features such as DNA accessibility dictate transcriptional regulation in a cell type- and cell state- specific manner, and mapping this in health vs. disease in clinically relevant material is opening the door to new mechanistic insights and new targets for therapy. Assay for Transposase Accessible Chromatin Sequencing (ATAC-seq) allows chromatin accessibility profiling from low cell input, making it tractable on rare cell populations, such as regulatory T (Treg) cells. However, little is known about the compatibility of the assay with cryopreserved rare cell populations. Here we demonstrate the robustness of an ATAC-seq protocol comparing primary Treg cells recovered from fresh or cryopreserved PBMC samples, in the steady state and in response to stimulation. We extend this method to explore the feasibility of conducting simultaneous quantitation of chromatin accessibility and transcriptome from a single aliquot of 50,000 cryopreserved Treg cells. Profiling of chromatin accessibility and gene expression in parallel within the same pool of cells controls for cellular heterogeneity and is particularly beneficial when constrained by limited input material. Overall, we observed a high correlation of accessibility patterns and transcription factor dynamics between fresh and cryopreserved samples. Furthermore, highly similar transcriptomic profiles were obtained from whole cells and from the supernatants recovered from ATAC-seq reactions. We highlight the feasibility of applying these techniques to profile the epigenomic landscape of cells recovered from cryopreservation biorepositories., (© 2023. The Author(s).)

Background: Genome-wide association studies (GWAS) have enabled the discovery of single nucleotide polymorphisms (SNPs) that are significantly associated with many autoimmune diseases including type 1 diabetes (T1D). However, many of the identified variants lie in non-coding regions, limiting the identification of mechanisms that contribute to autoimmune disease progression. To address this problem, we developed a variant filtering workflow called 3DFAACTS-SNP to link genetic variants to target genes in a cell-specific manner. Here, we use 3DFAACTS-SNP to identify candidate SNPs and target genes associated with the loss of immune tolerance in regulatory T cells (Treg) in T1D., Results: Using 3DFAACTS-SNP, we identified from a list of 1228 previously fine-mapped variants, 36 SNPs with plausible Treg-specific mechanisms of action. The integration of cell type-specific chromosome conformation capture data in 3DFAACTS-SNP identified 266 regulatory regions and 47 candidate target genes that interact with these variant-containing regions in Treg cells. We further demonstrated the utility of the workflow by applying it to three other SNP autoimmune datasets, identifying 16 Treg-centric candidate variants and 60 interacting genes. Finally, we demonstrate the broad utility of 3DFAACTS-SNP for functional annotation of all known common (> 10% allele frequency) variants from the Genome Aggregation Database (gnomAD). We identified 9376 candidate variants and 4968 candidate target genes, generating a list of potential sites for future T1D or other autoimmune disease research., Conclusions: We demonstrate that it is possible to further prioritise variants that contribute to T1D based on regulatory function, and illustrate the power of using cell type-specific multi-omics datasets to determine disease mechanisms. Our workflow can be customised to any cell type for which the individual datasets for functional annotation have been generated, giving broad applicability and utility., (© 2022. The Author(s).)

Evaluation of penicillin and oxacillin susceptibility testing was conducted on 200 Staphylococcus lugdunensis isolates. Disc diffusion with penicillin 1 IU (P1, EUCAST) and penicillin 10 IU (P10, CLSI) was compared with nitrocefin discs (Cefinase) and automated broth microdilution (Vitek 2). Oxacillin susceptibility was extrapolated from cefoxitin (FOX; 30 μg) disc diffusion and compared with Vitek 2 results. The reference methods were blaZ and mecA PCR. Penicillin zone diameter and zone edge correlated with blaZ PCR results in all except two P10-susceptible isolates (very major error [VME]) and one P1-resistant isolate (major error [ME]). A total of 148 isolates were blaZ negative, of which 146 and 149 isolates were susceptible by P1 and P10, respectively. A total of 127 were penicillin susceptible by Vitek 2. Vitek 2 overcalled resistance in 21 blaZ -negative, 20 P1-susceptible, and 22 P10-susceptible isolates (Vitek 2 ME rate, 14.2%). Two mecA -positive isolates were oxacillin resistant by FOX disc and Vitek 2 methods (categorical agreement). However, 18 FOX-susceptible mecA -negative isolates tested resistant by Vitek 2. In conclusion, Vitek 2 overestimated penicillin and oxacillin resistance compared with disc diffusion and PCR results. In our study, disc diffusion with zone edge interpretation was more accurate and specific than automated broth microdilution for S. lugdunensis.

Eukaryotic genomes are highly organised within the nucleus of a cell, allowing widely dispersed regulatory elements such as enhancers to interact with gene promoters through physical contacts in three-dimensional space. Recent chromosome conformation capture methodologies such as Hi-C have enabled the analysis of interacting regions of the genome providing a valuable insight into the three-dimensional organisation of the chromatin in the nucleus, including chromosome compartmentalisation and gene expression. Complicating the analysis of Hi-C data, however, is the massive amount of identified interactions, many of which do not directly drive gene function, thus hindering the identification of potentially biologically functional 3D interactions. In this review, we collate and examine the downstream analysis of Hi-C data with particular focus on methods that prioritise potentially functional interactions. We classify three groups of approaches: structural-based discovery methods, e.g. A/B compartments and topologically associated domains, detection of statistically significant chromatin interactions, and the use of epigenomic data integration to narrow down useful interaction information. Careful use of these three approaches is crucial to successfully identifying potentially functional interactions within the genome., (© 2021. The Author(s).)

Background: Rural/remote blood collection can cause delays in processing, reducing PBMC number, viability, cell composition and function. To mitigate these impacts, blood was stored at 4 °C prior to processing. Viable cell number, viability, immune phenotype, and Interferon-γ (IFN-γ) release were measured. Furthermore, the lowest protective volume of cryopreservation media and cell concentration was investigated., Methods: Blood from 10 individuals was stored for up to 10 days. Flow cytometry and IFN-γ ELISPOT were used to measure immune phenotype and function on thawed PBMC. Additionally, PBMC were cryopreserved in volumes ranging from 500 µL to 25 µL and concentration from 10 × 10 6 cells/mL to 1.67 × 10 6 cells/mL., Results: PBMC viability and viable cell number significantly reduced over time compared with samples processed immediately, except when stored for 24 h at RT. Monocytes and NK cells significantly reduced over time regardless of storage temperature. Samples with >24 h of RT storage had an increased proportion in Low-Density Neutrophils and T cells compared with samples stored at 4 °C. IFN-γ release was reduced after 24 h of storage, however not in samples stored at 4 °C for >24 h. The lowest protective volume identified was 150 µL with the lowest density of 6.67 × 10 6 cells/mL., Conclusion: A sample delay of 24 h at RT does not impact the viability and total viable cell numbers. When long-term delays exist (>4 d) total viable cell number and cell viability losses are reduced in samples stored at 4 °C. Immune phenotype and function are slightly altered after 24 h of storage, further impacts of storage are reduced in samples stored at 4 °C.

There has been much interest in the ability of regulatory T cells (Treg) to switch function in vivo , either as a result of genetic risk of disease or in response to environmental and metabolic cues. The relationship between levels of FOXP3 and functional fitness plays a significant part in this plasticity. There is an emerging role for Treg in tissue repair that may be less dependent on FOXP3, and the molecular mechanisms underpinning this are not fully understood. As a result of detailed, high-resolution functional genomics, the gene regulatory networks and key functional mediators of Treg phenotype downstream of FOXP3 have been mapped, enabling a mechanistic insight into Treg function. This transcription factor-driven programming of T-cell function to generate Treg requires the switching on and off of key genes that form part of the Treg gene regulatory network and raises the possibility that this is reversible. It is plausible that subtle shifts in expression levels of specific genes, including transcription factors and non-coding RNAs, change the regulation of the Treg gene network. The subtle skewing of gene expression initiates changes in function, with the potential to promote chronic disease and/or to license appropriate inflammatory responses. In the case of autoimmunity, there is an underlying genetic risk, and the interplay of genetic and environmental cues is complex and impacts gene regulation networks frequently involving promoters and enhancers, the regulatory elements that control gene expression levels and responsiveness. These promoter-enhancer interactions can operate over long distances and are highly cell type specific. In autoimmunity, the genetic risk can result in changes in these enhancer/promoter interactions, and this mainly impacts genes which are expressed in T cells and hence impacts Treg/conventional T-cell (Tconv) function. Genetic risk may cause the subtle alterations to the responsiveness of gene regulatory networks which are controlled by or control FOXP3 and its target genes, and the application of assays of the 3D organization of chromatin, enabling the connection of non-coding regulatory regions to the genes they control, is revealing the direct impact of environmental/metabolic/genetic risk on T-cell function and is providing mechanistic insight into susceptibility to inflammatory and autoimmune conditions., (Copyright © 2020 Brown, Sadlon, Hope, Wong, Wong, Liu, Withers, Brown, Bandara, Gundsambuu, Pederson, Breen, Robertson, Forrest, Beyer and Barry.)

CD4 + T-cell subsets play a major role in the host response to infection, and a healthy immune system requires a fine balance between reactivity and tolerance. This balance is in part maintained by regulatory T cells (Treg), which promote tolerance, and loss of immune tolerance contributes to autoimmunity. As the T cells which drive immunity are diverse, identifying and understanding how these subsets function requires specific biomarkers. From a human CD4 Tconv/Treg cell genome wide analysis we identified peptidase inhibitor 16 (PI16) as a CD4 subset biomarker and we now show detailed analysis of its distribution, phenotype and links to Treg function in type 1 diabetes. To determine the clinical relevance of Pi16 Treg, we analysed PI16 + Treg cells from type 1 diabetes patient samples. We observed that FOXP3 expression levels declined with disease progression, suggesting loss of functional fitness in these Treg cells in Type 1 diabetes, and in particular the rate of loss of FOXP3 expression was greatest in the PI16+ve Treg. We propose that PI16 has utility as a biomarker of functional human Treg subsets and may be useful for tracking loss of immune function in vivo. The ability to stratify at risk patients so that tailored interventions can be applied would open the door to personalised medicine for Type 1 diabetes., (© 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Regulatory T cells (Treg cells) are important for preventing autoimmunity and maintaining tissue homeostasis, but whether Treg cells can adopt tissue- or immune-context-specific suppressive mechanisms is unclear. Here, we found that the enzyme hydroxyprostaglandin dehydrogenase (HPGD), which catabolizes prostaglandin E 2 (PGE 2 ) into the metabolite 15-keto PGE 2 , was highly expressed in Treg cells, particularly those in visceral adipose tissue (VAT). Nuclear receptor peroxisome proliferator-activated receptor-γ (PPARγ)-induced HPGD expression in VAT Treg cells, and consequential Treg-cell-mediated generation of 15-keto PGE 2 suppressed conventional T cell activation and proliferation. Conditional deletion of Hpgd in mouse Treg cells resulted in the accumulation of functionally impaired Treg cells specifically in VAT, causing local inflammation and systemic insulin resistance. Consistent with this mechanism, humans with type 2 diabetes showed decreased HPGD expression in Treg cells. These data indicate that HPGD-mediated suppression is a tissue- and context-dependent suppressive mechanism used by Treg cells to maintain adipose tissue homeostasis., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Regulatory T cells (Treg) are critical for preventing autoimmunity and curtailing responses of conventional effector T cells (Tconv). The reprogramming of T-cell fate and function to generate Treg requires switching on and off of key gene regulatory networks, which may be initiated by a subtle shift in expression levels of specific genes. This can be achieved by intermediary regulatory processes that include microRNA and long noncoding RNA-based regulation of gene expression. There are well-documented microRNA profiles in Treg and Tconv, and these can operate to either reinforce or reduce expression of a specific set of target genes, including FOXP3 itself. This type of feedforward/feedback regulatory loop is normally stable in the steady state, but can alter in response to local cues or genetic risk. This may go some way to explaining T-cell plasticity. In addition, in chronic inflammation or autoimmunity, altered Treg/Tconv function may be influenced by changes in enhancer-promoter interactions, which are highly cell type-specific. These interactions are impacted by genetic risk based on genome-wide association studies and may cause subtle alterations to the gene regulatory networks controlled by or controlling FOXP3 and its target genes. Recent insights into the 3D organisation of chromatin and the mapping of noncoding regulatory regions to the genes they control are shedding new light on the direct impact of genetic risk on T-cell function and susceptibility to inflammatory and autoimmune conditions.