Analysis of transcription factors and chromatin modifications at the genome-wide level provides insights into gene regulatory processes, such as transcription, cell differentiation and cellular response. Chromatin immunoprecipitation is the most popular and powerful approach for mapping chromatin, and other enzyme-tethering techniques have recently become available for living cells. Among these, Cleavage Under Targets and Tagmentation (CUT&Tag) is a relatively novel chromatin profiling method that has rapidly gained popularity in the field of epigenetics since 2019. It has also been widely adapted to map chromatin modifications and TFs in different species, illustrating the association of these chromatin epitopes with various physiological and pathological processes. Scalable single-cell CUT&Tag can be combined with distinct platforms to distinguish cellular identity, epigenetic features and even spatial chromatin profiling. In addition, CUT&Tag has been developed as a strategy for joint profiling of the epigenome, transcriptome or proteome on the same sample. In this review, we will mainly consolidate the applications of CUT&Tag and its derivatives on different platforms, give a detailed explanation of the pros and cons of this technique as well as the potential development trends and applications in the future.

Genetic mutations in TP53 contribute to human malignancies through various means. To date, there have been a variety of therapeutic strategies targeting p53, including gene therapy to restore normal p53 function, mutant p53 rescue, inhibiting the MDM2-p53 interaction, p53-based vaccines, and a number of other approaches. This review focuses on the functions of TP53 and discusses the aberrant roles of mutant p53 in various types of cancer. Recombinant human p53 adenovirus, trademarked as Gendicine, which is the first anti-tumor gene therapy drug, has made tremendous progress in cancer gene therapy. We herein discuss the biological mechanisms by which Gendicine exerts its effects and describe the clinical responses reported in clinical trials. Notably, the clinical studies suggest that the combination of Gendicine with chemotherapy and/or radiotherapy may produce more pronounced efficacy in slowing tumor growth and progression than gene therapy/chemotherapy alone. Finally, we summarize the methods of administration of recombinant human p53 adenovirus for different cancer types to provide a reference for future clinical trials.

The interactions between proteins are important for the majority of biological functions and the interacting proteins are usually assembled into a complex. Knowing a set of protein complexes of a cell (complexome) is, therefore, essential for a better understanding and global view of cell functions. To visualize and identify the protein complexome of E. coliK-12 under normal native conditions on a proteome-wide scale, we developed an integrated proteomic platform with the combination of 2-D native/SDS-PAGE-based proteomics with co-immunoprecipitation, far-Western blotting, His-tag affinity purification and functional analysis, and used it to investigate the E. coli cytosolic complexome. A total of 24 distinct heteromeric and 8 homomeric protein complexes were identified. These complexes mainly contributed to glycolysis/gluconeogenesis, bioinformation processing, and cellular processes. Of the 24 hetereomeric complexes, 16 were reported for the first time, and 2 known complexes contained novel components that have not been reported previously based on DIP database search. Among them, RpoC-RpsA-Tig-GroL was found to be involved in transcriptional and co-translational folding, and EF-G-TufA-Tsf-RpsA linked a protein synthesis site with protein translational elongation factors. This systematic proteome analysis provides new insights into E. coli molecular systems biology.

Protein function prediction is one of the most challenging problems in the post-genomic era. Previous prediction methods using protein-protein interaction networks relied on the neighborhoods or the connected paths to known proteins. Still new algorithm is required to increase the accuracy. In this paper, we propose a novel protein function prediction approach on the basis of frequent pattern mining in graph data. A protein-protein interaction network is represented as an unweighted, undirected graph with nodes denoting proteins and edges denoting interactions between proteins. Each node is labeled with a set of corresponding protein functions. The function prediction method is processed in three steps, neighbor finding, pattern finding and function annotation. Using our approach we predict protein functions on a core set of protein-protein interaction data from DIP (Database of Interacting Proteins) and function annotation data from FunCat of MIPS (the Munich Information Center for Protein Sequences). The experimental results show better performance in prediction accuracy than existing neighbor counting methods.