This article reviews the current state-of-the-art concerning the functions of the signal processing protein PII in cyanobacteria and plants, with a special focus on evolutionary aspects. We start out with a general introduction to PII proteins, their distribution, and their evolution. We also discuss PII-like proteins and domains, in particular, the similarity between ATP-phosphoribosyltransferase (ATP-PRT) and its PII-like domain and the complex between N-acetyl-L-glutamate kinase (NAGK) and its PII activator protein from oxygenic phototrophs. The structural basis of the function of PII as an ATP/ADP/ 2-oxoglutarate signal processor is described for Synechococcus elongatus PII. In both cyanobacteria and plants, a major target of PII regulation is NAGK, which catalyzes the committed step of arginine biosynthesis. The common principles of NAGK regulation by PII are outlined. Based on the observation that PII proteins from cyanobacteria and plants can functionally replace each other, the hypothesis that PII-dependent NAGK control was under selective pressure during the evolution of plastids of Chloroplastida and Rhodophyta is tested by bioinformatics approaches. It is noteworthy that two lineages of heterokont algae, diatoms and brown algae, also possess NAGK, albeit lacking PII; their NAGK however appears to have descended from an alphaproteobacterium and not from a cyanobacterium as in plants. We end this article by coming to the conclusion that during the evolution of plastids, PII lost its function in coordinating gene expression through the PipX-NtcA network but preserved its role in nitrogen (arginine) storage metabolism, and subsequently took over the fine-tuned regulation of carbon (fatty acid) storage metabolism, which is important in certain developmental stages of plants.
Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful technique for investigating biological heterogeneity at the single-cell level in human systems and model organisms. Recent advances in scRNA-seq have enabled the pooling of cells from multiple samples into single libraries, thereby increasing sample throughput while reducing technical batch effects, library preparation time, and the overall cost. However, a comparative analysis of scRNA-seq methods with and without sample multiplexing is lacking. In this study, we benchmarked methods from two representative platforms: Parse Biosciences (Parse; with sample multiplexing) and 10X Genomics (10x; without sample multiplexing). By using peripheral blood mononuclear cells (PBMCs) obtained from two healthy individuals, we demonstrate that demultiplexed scRNA-seq data obtained from Parse showed similar cell type frequencies compared to 10X data where samples are not multiplexed. Despite a relatively lower library and cell capture efficiencies, Parse can detect rare cell types (e.g. plasmablasts and dendritic cells) which is likely due to its relatively higher sensitivity in gene detection. Moreover, comparative analysis of transcript quantification between the two platforms revealed platform-specific distributions of gene length and GC content. These results offer guidance for researchers in designing high-throughput scRNA-seq studies.
Systemic sclerosis (SSc), a complex multi-systemic disease characterized by immune dysregulation, vasculopathy and fibrosis, is associated with high mortality. Its pathogenesis is only partially understood. The heterogenous pathological processes that define SSc and its stages present a challenge to targeting appropriate treatment, with differing treatment outcomes of SSc patients despite similar initial clinical presentations. Timing of the appropriate treatments targeted at the underlying disease process is critical. For example, immunomodulatory treatments may be used for patients in a predominantly inflammatory phase, anti-fibrotic treatments for those in the fibrotic phase, or combination therapies for those in the fibro-inflammatory phase. In advancing personalized care through precision medicine, groups of patients with similar disease characteristics and shared pathological processes may be identified through molecular stratification. This would improve current clinical sub-setting systems and guide personalization of therapies. In this review, we will provide updates in SSc clinical and molecular stratification in relation to patient outcomes and treatment responses. Promises of molecular stratification through advances in high-dimensional tools, including omic-based stratification (transcriptomics, genomics, epigenomics, proteomics, cytomics, microbiomics) and machine learning will be discussed. Innovative and more granular stratification systems that integrate molecular characteristics to clinical phenotypes would potentially improve therapeutic approaches through personalized medicine and lead to better patient outcomes.
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