Autonomous and self-sustained oscillator circuits mediating the periodic induction of specific target genes are minimal genetic time-keeping devices found in the central and peripheral circadian clocks. They have attracted significant attention because of their intriguing dynamics and their importance in controlling critical repair, metabolic and signalling pathways. The precise molecular mechanism and expression dynamics of this mammalian circadian clock are still not fully understood. Here we describe a synthetic mammalian oscillator based on an auto-regulated sense-antisense transcription control circuit encoding a positive and a time-delayed negative feedback loop, enabling autonomous, self-sustained and tunable oscillatory gene expression. After detailed systems design with experimental analyses and mathematical modelling, we monitored oscillating concentrations of green fluorescent protein with tunable frequency and amplitude by time-lapse microscopy in real time in individual Chinese hamster ovary cells. The synthetic mammalian clock may provide an insight into the dynamics of natural periodic processes and foster advances in the design of prosthetic networks in future gene and cell therapies.
Summary: Structural and physiochemical descriptors extracted from sequence data have been widely used to represent sequences and predict structural, functional, expression and interaction profiles of proteins and peptides as well as DNAs/RNAs. Here, we present iFeature, a versatile Python-based toolkit for generating various numerical feature representation schemes for both protein and peptide sequences. iFeature is capable of calculating and extracting a comprehensive spectrum of 18 major sequence encoding schemes that encompass 53 different types of feature descriptors. It also allows users to extract specific amino acid properties from the AAindex database. Furthermore, iFeature integrates 12 different types of commonly used feature clustering, selection and dimensionality reduction algorithms, greatly facilitating training, analysis and benchmarking of machine-learning models. The functionality of iFeature is made freely available via an online web server and a stand-alone toolkit. Availability and implementation:
SUMMARY Interleukin 2 (IL-2) promotes Foxp3+-regulatory T (Treg) cell responses, but inhibits T follicular helper (TFH) cell development. However, it is not clear how IL-2 affects T follicular regulatory (TFR) cells, a cell type with properties of both Treg and TFH cells. Using an influenza infection model, we demonstrated that high IL-2 concentrations at the peak of the infection prevented TFR cell development by a Blimp-1–dependent mechanism. However, once the immune response resolved, some Treg cells down-regulated CD25, up-regulated Bcl-6 and differentiated into TFR cells, which then migrated into the B cell follicles to prevent the expansion of self-reactive B cell clones. Thus, unlike its effects on conventional Treg cells, IL-2 inhibits TFR cell responses.
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.
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