Assay of Transposase Accessible Chromatin sequencing (ATAC-seq) is widely used in studying chromatin biology, but a comprehensive review of the analysis tools has not been completed yet. Here, we discuss the major steps in ATAC-seq data analysis, including pre-analysis (quality check and alignment), core analysis (peak calling), and advanced analysis (peak differential analysis and annotation, motif enrichment, footprinting, and nucleosome position analysis). We also review the reconstruction of transcriptional regulatory networks with multiomics data and highlight the current challenges of each step. Finally, we describe the potential of single-cell ATAC-seq and highlight the necessity of developing ATAC-seq specific analysis tools to obtain biologically meaningful insights.

Age-associated decreases in primary CD8 T cell responses occur, in part, due to direct effects on naive CD8 T cells to reduce intrinsic functionality, but the precise nature of this defect remains undefined. Aging also causes accumulation of antigen-naive but semi-differentiated "virtual memory" (T) cells, but their contribution to age-related functional decline is unclear. Here, we show that T cells are poorly proliferative in aged mice and humans, despite being highly proliferative in young individuals, while conventional naive T cells (T cells) retain proliferative capacity in both aged mice and humans. Adoptive transfer experiments in mice illustrated that naive CD8 T cells can acquire a proliferative defect imposed by the aged environment but age-related proliferative dysfunction could not be rescued by a young environment. Molecular analyses demonstrate that aged T cells exhibit a profile consistent with senescence, marking an observation of senescence in an antigenically naive T cell population.

To fight infections, macrophages undergo a metabolic shift whereby increased glycolysis fuels antimicrobial inflammation and killing of pathogens. Here we demonstrate that the pathogen Candida albicans turns this metabolic reprogramming into an Achilles' heel for macrophages. During Candida-macrophage interactions intertwined metabolic shifts occur, with concomitant upregulation of glycolysis in both host and pathogen setting up glucose competition. Candida thrives on multiple carbon sources, but infected macrophages are metabolically trapped in glycolysis and depend on glucose for viability: Candida exploits this limitation by depleting glucose, triggering rapid macrophage death. Using pharmacological or genetic means to modulate glucose metabolism of host and/or pathogen, we show that Candida infection perturbs host glucose homeostasis in the murine candidemia model and demonstrate that glucose supplementation improves host outcomes. Our results support the importance of maintaining glucose homeostasis for immune cell survival during Candida challenge and for host survival in systemic infection.

With the explosive growth of biological sequences generated in the post-genomic era, one of the most challenging problems in bioinformatics and computational biology is to computationally characterize sequences, structures and functions in an efficient, accurate and high-throughput manner. A number of online web servers and stand-alone tools have been developed to address this to date; however, all these tools have their limitations and drawbacks in terms of their effectiveness, user-friendliness and capacity. Here, we present iLearn, a comprehensive and versatile Python-based toolkit, integrating the functionality of feature extraction, clustering, normalization, selection, dimensionality reduction, predictor construction, best descriptor/model selection, ensemble learning and results visualization for DNA, RNA and protein sequences. iLearn was designed for users that only want to upload their data set and select the functions they need calculated from it, while all necessary procedures and optimal settings are completed automatically by the software. iLearn includes a variety of descriptors for DNA, RNA and proteins, and four feature output formats are supported so as to facilitate direct output usage or communication with other computational tools. In total, iLearn encompasses 16 different types of feature clustering, selection, normalization and dimensionality reduction algorithms, and five commonly used machine-learning algorithms, thereby greatly facilitating feature analysis and predictor construction. iLearn is made freely available via an online web server and a stand-alone toolkit.