A proposed CO2-controlled mechanism of woody plant invasion in grasslands and savannas

We propose that elevated CO₂ may have a significant positive effect on woody plant success and thus favour tree invasion and thickening in grass‐dominated ecosystems. We note that savanna tree biomass is strongly constrained by disturbance, particularly fire, and that elevated CO₂ could act to reduce this constraint. Our argument combines knowledge of tree recovery from injury after grassland fires, with theory about carbon acquisition and carbohydrate storage patterns in C3 woody plants in response to elevated CO₂. We propose simply that elevated CO₂ will tend to favour regrowth of juvenile trees trapped (sometimes for decades) in the ‘topkill’ zone, thus allowing them to escape more readily from periodic fires as CO₂ continues to rise. Little empirical evidence exists to test this hypothesis, even though the process may have important implications for tree/grass codominated ecosystems currently in a dynamic equilibrium.     

Export chaperone SecB uses one surface of interaction for diverse unfolded polypeptide ligands

SecB, a remarkable chaperone involved in protein export, binds diverse ligands rapidly with high affinity and low specificity. Site‐directed spin labeling and electron paramagnetic resonance spectroscopy were used to investigate the surface of interaction on the export chaperone SecB. We examined SecB in complex with the unfolded precursor form of outer membrane protein OmpA as well as with a truncated version of OmpA that includes the transmembrane domain and lacks both the signal peptide and the periplasmic domain. In addition, we studied the binding of SecB to the unfolded mature form of galactose‐binding protein, a soluble periplasmic protein. We have previously used the same strategy to map the binding surface for the precursor of galactose‐binding protein. We show that for all ligands tested the patterns of contact are the same.     

Comparative Network Biology Discovers Protein Complexes That Underline Cellular Differentiation in Anabaena sp.

The filamentous cyanobacterium Anabaena sp. PCC 7120 can differentiate into heterocysts to fix atmospheric nitrogen. During cell differentiation, cellular morphology and gene expression undergo a series of significant changes. To uncover the mechanisms responsible for these alterations, we built protein–protein interaction (PPI) networks for these two cell types by cofractionation coupled with mass spectrometry. We predicted 280 and 215 protein complexes, with 6322 and 2791 high-confidence PPIs in vegetative cells and heterocysts, respectively. Most of the proteins in both types of cells presented similar elution profiles, whereas the elution peaks of 438 proteins showed significant changes. We observed that some well-known complexes recruited new members in heterocysts, such as ribosomes, diflavin flavoprotein, and cytochrome c oxidase. Photosynthetic complexes, including photosystem I, photosystem II, and phycobilisome, remained in both vegetative cells and heterocysts for electron transfer and energy generation. Besides that, PPI data also reveal new functions of proteins. For example, the hypothetical protein Alr4359 was found to interact with FraH and Alr4119 in heterocysts and was located on heterocyst poles, thereby influencing the diazotrophic growth of filaments. The overexpression of Alr4359 suspended heterocyst formation and altered the pigment composition and filament length. This work demonstrates the differences in protein assemblies and provides insight into physiological regulation during cell differentiation.     

Temporal analyses of postnatal liver development and maturation by single-cell transcriptomics

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Essential role of MESP1-RING1A complex in cardiac differentiation

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Deciphering Spatial Protein–Protein Interactions in Brain Using Proximity Labeling

Cellular biomolecular complexes including protein–protein, protein–RNA, and protein–DNA interactions regulate and execute most biological functions. In particular in brain, protein–protein interactions (PPIs) mediate or regulate virtually all nerve cell functions, such as neurotransmission, cell–cell communication, neurogenesis, synaptogenesis, and synaptic plasticity. Perturbations of PPIs in specific subsets of neurons and glia are thought to underly a majority of neurobiological disorders. Therefore, understanding biological functions at a cellular level requires a reasonably complete catalog of all physical interactions between proteins. An enzyme-catalyzed method to biotinylate proximal interacting proteins within 10 to 300 nm of each other is being increasingly used to characterize the spatiotemporal features of complex PPIs in brain. Thus, proximity labeling has emerged recently as a powerful tool to identify proteomes in distinct cell types in brain as well as proteomes and PPIs in structures difficult to isolate, such as the synaptic cleft, axonal projections, or astrocyte–neuron junctions. In this review, we summarize recent advances in proximity labeling methods and their application to neurobiology.     

Targeting of extracellular protein–protein interactions with macrocyclic peptides

Targeting of extracellular protein–protein interactions (PPI) is emerging as a major application for de novo discovered macrocyclic peptides. Modern discovery platforms can routinely identify macrocyclic peptide ligands capable of highly selective modulation of extracellular signaling pathways; amenability to chemical synthesis and natural modularity of peptides additionally provides an avenue for their further structural elaboration, while the challenge of cell internalization can be minimized. Here, we discuss the recent progress in targeting extracellular PPIs with macrocyclic peptides by focusing on a number of recent case studies. We analyze the scope and potential limitations of the discovery systems in identifying functional macrocyclic ligands. We also highlight the recent technical advancements allowing for a more streamlined discovery pipeline and our brief perspective in this field.     

Quantitative Metaproteomics and Activity-based Protein Profiling of Patient Fecal Microbiome Identifies Host and Microbial Serine-type Endopeptidase Activity Associated With Ulcerative Colitis

The gut microbiota plays an important yet incompletely understood role in the induction and propagation of ulcerative colitis (UC). Organism-level efforts to identify UC-associated microbes have revealed the importance of community structure, but less is known about the molecular effectors of disease. We performed 16S rRNA gene sequencing in parallel with label-free data-dependent LC-MS/MS proteomics to characterize the stool microbiomes of healthy (n = 8) and UC (n = 10) patients. Comparisons of taxonomic composition between techniques revealed major differences in community structure partially attributable to the additional detection of host, fungal, viral, and food peptides by metaproteomics. Differential expression analysis of metaproteomic data identified 176 significantly enriched protein groups between healthy and UC patients. Gene ontology analysis revealed several enriched functions with serine-type endopeptidase activity overrepresented in UC patients. Using a biotinylated fluorophosphonate probe and streptavidin-based enrichment, we show that serine endopeptidases are active in patient fecal samples and that additional putative serine hydrolases are detectable by this approach compared with unenriched profiling. Finally, as metaproteomic databases expand, they are expected to asymptotically approach completeness. Using ComPIL and de novo peptide sequencing, we estimate the size of the probable peptide space unidentified (“dark peptidome”) by our large database approach to establish a rough benchmark for database sufficiency. Despite high variability inherent in patient samples, our analysis yielded a catalog of differentially enriched proteins between healthy and UC fecal proteomes. This catalog provides a clinically relevant jumping-off point for further molecular-level studies aimed at identifying the microbial underpinnings of UC.     

陕西省新型城镇化与生态环境协调度研究

城镇化与生态环境之间相互影响、相互促进，两系统间耦合协调状态对于区域绿色可持续发展意义重大。以陕西省2005—2019年数据为例，在构建新型城镇化与生态环境系统评价指标体系的基础上结合耦合协调度模型、相对发展模型及面板向量自回归(PVAR)模型分析新型城镇化系统与生态环境系统间的耦合协调时空规律及互动关系。结果表明：(1)陕西省耦合协调度总体呈现“基本不协调—基本协调—中度协调”的演变规律。(2)各市耦合协调发展状态空间差异明显，2005年、2013年、2019年陕西省10个城市新型城镇化与生态环境耦合协调发展状态存在较大差异性，相对发展状态整体上由新型城镇化滞后状态发展为生态环境滞后状态。(3)脉冲响应图反映出新型城镇化与生态环境两系统间存在明显互动关系，前期生态环境受新型城镇化冲击的负向抑制作用，后期影响逐渐减小，受自身冲击较大，而新型城镇化发展前后期均主要受自身影响。本研究认为新型城镇化发展必须以生态为优先，在提高生态承载力的基础上，加速提升陕西省各区域空间人口等不同维度城镇化发展水平，从而实现高质量可持续的绿色发展目标。