A review on methods for diagnosis of breast cancer cells and tissues

Breast cancer has seriously been threatening physical and mental health of women in the world, and its morbidity and mortality also show clearly upward trend in China over time. Through inquiry, we find that survival rate of patients with early‐stage breast cancer is significantly higher than those with middle‐ and late‐stage breast cancer, hence, it is essential to conduct research to quickly diagnose breast cancer. Until now, many methods for diagnosing breast cancer have been developed, mainly based on imaging and molecular biotechnology examination. These methods have great contributions in screening and confirmation of breast cancer. In this review article, we introduce and elaborate the advances of these methods, and then conclude some gold standard diagnostic methods for certain breast cancer patients. We lastly discuss how to choose the most suitable diagnostic methods for breast cancer patients. In general, this article not only summarizes application and development of these diagnostic methods, but also provides the guidance for researchers who work on diagnosis of breast cancer.     

Miro: A molecular switch at the center of mitochondrial regulation

The orchestration of mitochondria within the cell represents a critical aspect of cell biology. At the center of this process is the outer mitochondrial membrane protein, Miro. Miro coordinates diverse cellular processes by regulating connections between organelles and the cytoskeleton that range from mediating contacts between the endoplasmic reticulum and mitochondria to the regulation of both actin and microtubule motor proteins. Recently, a number of cell biological, biochemical, and protein structure studies have helped to characterize the myriad roles played by Miro. In addition to answering questions regarding Miro's function, these studies have opened the door to new avenues in the study of Miro in the cell. This review will focus on summarizing recent findings for Miro's structure, function, and activity while highlighting key questions that remain unanswered.     

The mechanism of Single strand binding protein–RecG binding: Implications for SSB interactome function

The Escherichia coli single‐strand DNA binding protein (SSB) is essential to viability where it functions to regulate SSB interactome function. Here it binds to single‐stranded DNA and to target proteins that comprise the interactome. The region of SSB that links these two essential protein functions is the intrinsically disordered linker. Key to linker function is the presence of three, conserved PXXP motifs that mediate binding to oligosaccharide‐oligonucleotide binding folds (OB‐fold) present in SSB and its interactome partners. Not surprisingly, partner OB‐fold deletions eliminate SSB binding. Furthermore, single point mutations in either the PXXP motifs or, in the RecG OB‐fold, obliterate SSB binding. The data also demonstrate that, and in contrast to the view currently held in the field, the C‐terminal acidic tip of SSB is not required for interactome partner binding. Instead, we propose the tip has two roles. First, and consistent with the proposal of Dixon, to regulate the structure of the C‐terminal domain in a biologically active conformation that prevents linkers from binding to SSB OB‐folds until this interaction is required. Second, as a secondary binding domain. Finally, as OB‐folds are present in SSB and many of its partners, we present the SSB interactome as the first family of OB‐fold genome guardians identified in prokaryotes.     

The intronic promoter of Actin4 mediates high-level transgene expression mainly in the wing and epidermis of silkworms

Transgenic Research - The cytoplasmic actin gene Actin4 (A4) in silkworm (Bombyx mori) was isolated 20 years ago and has a distal promoter upstream of the first exon and a proximal...     

Scale‐dependent effects of neighborhood biodiversity on individual tree productivity in a coniferous and broad‐leaved mixed forest in China

The relationship between biodiversity and productivity has stimulated an increasing body of research over the past decades, and this topic still occupies a central place in ecology. While most studies have focused on biomass production in quadrats or plots, few have investigated the scale‐dependent relationship from an individual plant perspective. We present an analysis of the effects of biodiversity (species diversity and functional diversity) on individual tree growth with a data set of 16,060 growth records from a 30‐ha temperate forest plot using spatially explicit individual tree‐based methods. A significant relationship between species diversity and tree growth was found at the individual tree level in our study. The magnitude and direction of biodiversity effects varies with the spatial scale. We found positive effects of species diversity on tree growth at scales exceeding 9 m. Individual tree growth rates increased when there was a greater diversity of species in the neighborhood of the focal tree, which provides evidence of a niche complementarity effect. At small scales (3–5 m), species diversity had negative effects on tree growth, suggesting that competition is more prevalent than complementarity or facilitation in these close neighborhoods. The results also revealed many confounding factors which influence tree growth, such as elevation and available sun light. We conclude that the use of individual tree‐based methods may lead to a better understanding of the biodiversity‐productivity relationship in forest communities.     

Differential responses of litter decomposition in the air and on the soil surface to shrub encroachment in a graminoid-dominated temperate wetland

Plant and Soil - Graminoid-dominated wetlands have been subjected to widespread shrub encroachment, yet the effect of this shift in species composition on litter decomposition remains unclear,...     

Plant litter quality regulates soil eco-enzymatic stoichiometry and microbial nutrient limitation in a citrus orchard

Plant and Soil - The impacts associated with litter decomposition in intensive cropping on eco-enzymatic stoichiometry and microbial nutrient limitation are still under debate. To evaluate the...     

The C‐degron pathway eliminates mislocalized proteins and products of deubiquitinating enzymes

Protein termini are determinants of protein stability. Proteins bearing degradation signals, or degrons, at their amino‐ or carboxyl‐termini are eliminated by the N‐ or C‐degron pathways, respectively. We aimed to elucidate the function of C‐degron pathways and to unveil how normal proteomes are exempt from C‐degron pathway‐mediated destruction. Our data reveal that C‐degron pathways remove mislocalized cellular proteins and cleavage products of deubiquitinating enzymes. Furthermore, the C‐degron and N‐degron pathways cooperate in protein removal. Proteome analysis revealed a shortfall in normal proteins targeted by C‐degron pathways, but not of defective proteins, suggesting proteolysis‐based immunity as a constraint for protein evolution/selection. Our work highlights the importance of protein termini for protein quality surveillance, and the relationship between the functional proteome and protein degradation pathways.     

Independent component analysis based gene co-expression network inference (ICAnet) to decipher functional modules for better single-cell clustering and batch integration

With the tremendous increase of publicly available single-cell RNA-sequencing (scRNA-seq) datasets, bioinformatics methods based on gene co-expression network are becoming efficient tools for analyzing scRNA-seq data, improving cell type prediction accuracy and in turn facilitating biological discovery. However, the current methods are mainly based on overall co-expression correlation and overlook co-expression that exists in only a subset of cells, thus fail to discover certain rare cell types and sensitive to batch effect. Here, we developed independent component analysis-based gene co-expression network inference (ICAnet) that decomposed scRNA-seq data into a series of independent gene expression components and inferred co-expression modules, which improved cell clustering and rare cell-type discovery. ICAnet showed efficient performance for cell clustering and batch integration using scRNA-seq datasets spanning multiple cells/tissues/donors/library types. It works stably on datasets produced by different library construction strategies and with different sequencing depths and cell numbers. We demonstrated the capability of ICAnet to discover rare cell types in multiple independent scRNA-seq datasets from different sources. Importantly, the identified modules activated in acute myeloid leukemia scRNA-seq datasets have the potential to serve as new diagnostic markers. Thus, ICAnet is a competitive tool for cell clustering and biological interpretations of single-cell RNA-seq data analysis.