The RAINFOR database: monitoring forest biomass and dynamics

Problem: Data from over 100 permanent sample plots which have been studied for 10–20 years need a suitable system for storage which allows simple data manipulation and retrieval for analysis. Methods: A relational database linking tree records, taxonomic nomenclature and corresponding environmental data has been built in MS Access as part of the RAINFOR project. Conclusion: The database allows flexible and long‐term use of a large amount of data: more than 100 tree plots across Amazonia, incorporating over 80 000 records of individual trees and over 300 000 total records of tree diameter measurements from successive censuses. The database is designed to enable linkages to existing soil, floristic or plant‐trait databases. This database will be a useful tool for exploring the impact of environmental factors on forest structure and dynamics at local to continental scales, and long term changes in forest ecology. As an early example of its potential, we explore the impact of different methodological assumptions on estimates of tropical forest biomass and carbon storage.     

Structure-wise discrimination of adenine and guanine by proteins on the basis of their nonbonded interactions

We have analyzed the nonbonded interactions of the structurally similar moieties, adenine and guanine forming complexes with proteins. The results comprise (a) the amino acid–ligand atom preferences, (b) solvent accessibility of ligand atoms before and after complex formation with proteins, and (c) preferred amino acid residue atoms involved in the interactions. We have observed that the amino acid preferences involved in the hydrogen bonding interactions vary for adenine and guanine. The structural variation between the purine atoms is clearly reflected by their burial tendency in the solvent environment. Correlation of the mean amino acid preference values show the variation that exists between adenine and guanine preferences of all the amino acid residues. All our observations provide evidence for the discriminating nature of the proteins in recognizing adenine and guanine.     

Multiomic Metabolic Enrichment Network Analysis Reveals Metabolite–Protein Physical Interaction Subnetworks Altered in Cancer

Metabolism is recognized as an important driver of cancer progression and other complex diseases, but global metabolite profiling remains a challenge. Protein expression profiling is often a poor proxy since existing pathway enrichment models provide an incomplete mapping between the proteome and metabolism. To overcome these gaps, we introduce multiomic metabolic enrichment network analysis (MOMENTA), an integrative multiomic data analysis framework for more accurately deducing metabolic pathway changes from proteomics data alone in a gene set analysis context by leveraging protein interaction networks to extend annotated metabolic models. We apply MOMENTA to proteomic data from diverse cancer cell lines and human tumors to demonstrate its utility at revealing variation in metabolic pathway activity across cancer types, which we verify using independent metabolomics measurements. The novel metabolic networks we uncover in breast cancer and other tumors are linked to clinical outcomes, underscoring the pathophysiological relevance of the findings.     

Structure-based protein design with deep learning

Since the first revelation of proteins functioning as macromolecular machines through their three dimensional structures, researchers have been intrigued by the marvelous ways the biochemical processes are carried out by proteins. The aspiration to understand protein structures has fueled extensive efforts across different scientific disciplines. In recent years, it has been demonstrated that proteins with new functionality or shapes can be designed via structure-based modeling methods, and the design strategies have combined all available information — but largely piece-by-piece — from sequence derived statistics to the detailed atomic-level modeling of chemical interactions. Despite the significant progress, incorporating data-derived approaches through the use of deep learning methods can be a game changer. In this review, we summarize current progress, compare the arc of developing the deep learning approaches with the conventional methods, and describe the motivation and concepts behind current strategies that may lead to potential future opportunities.     

A comparative Proteomics Analysis Identified Differentially Expressed Proteins in Pancreatic Cancer–Associated Stellate Cell Small Extracellular Vesicles

Human pancreatic stellate cells (HPSCs) are an essential stromal component and mediators of pancreatic ductal adenocarcinoma (PDAC) progression. Small extracellular vesicles (sEVs) are membrane-enclosed nanoparticles involved in cell-to-cell communications and are released from stromal cells within PDAC. A detailed comparison of sEVs from normal pancreatic stellate cells (HPaStec) and from PDAC-associated stellate cells (HPSCs) remains a gap in our current knowledge regarding stellate cells and PDAC. We hypothesized there would be differences in sEVs secretion and protein expression that might contribute to PDAC biology. To test this hypothesis, we isolated sEVs using ultracentrifugation followed by characterization by electron microscopy and Nanoparticle Tracking Analysis. We report here our initial observations. First, HPSC cells derived from PDAC tumors secrete a higher volume of sEVs when compared to normal pancreatic stellate cells (HPaStec). Although our data revealed that both normal and tumor-derived sEVs demonstrated no significant biological effect on cancer cells, we observed efficient uptake of sEVs by both normal and cancer epithelial cells. Additionally, intact membrane-associated proteins on sEVs were essential for efficient uptake. We then compared sEV proteins isolated from HPSCs and HPaStecs cells using liquid chromatography–tandem mass spectrometry. Most of the 1481 protein groups identified were shared with the exosome database, ExoCarta. Eighty-seven protein groups were differentially expressed (selected by 2-fold difference and adjusted p value ≤0.05) between HPSC and HPaStec sEVs. Of note, HPSC sEVs contained dramatically more CSE1L (chromosome segregation 1–like protein), a described marker of poor prognosis in patients with pancreatic cancer. Based on our results, we have demonstrated unique populations of sEVs originating from stromal cells with PDAC and suggest that these are significant to cancer biology. Further studies should be undertaken to gain a deeper understanding that could drive novel therapy.     

Dynamic and Reversible Aggregation of the Human CAP Superfamily Member GAPR-1 in Protein Inclusions in Saccharomyces cerevisiae

Many proteins that can assemble into higher order structures termed amyloids can also concentrate into cytoplasmic inclusions via liquid–liquid phase separation. Here, we study the assembly of human Golgi-Associated plant Pathogenesis Related protein 1 (GAPR-1), an amyloidogenic protein of the Cysteine-rich secretory proteins, Antigen 5, and Pathogenesis-related 1 proteins (CAP) protein superfamily, into cytosolic inclusions in Saccharomyces cerevisiae. Overexpression of GAPR-1-GFP results in the formation GAPR-1 oligomers and fluorescent inclusions in yeast cytosol. These cytosolic inclusions are dynamic and reversible organelles that gradually increase during time of overexpression and decrease after promoter shut-off. Inclusion formation is, however, a regulated process that is influenced by factors other than protein expression levels. We identified N-myristoylation of GAPR-1 as an important determinant at early stages of inclusion formation. In addition, mutations in the conserved metal-binding site (His54 and His103) enhanced inclusion formation, suggesting that these residues prevent uncontrolled protein sequestration. In agreement with this, we find that addition of Zn²⁺ metal ions enhances inclusion formation. Furthermore, Zn²⁺ reduces GAPR-1 protein degradation, which indicates stabilization of GAPR-1 in inclusions. We propose that the properties underlying both the amyloidogenic properties and the reversible sequestration of GAPR-1 into inclusions play a role in the biological function of GAPR-1 and other CAP family members.     

Kinetic and proteolytic identification of heat-induced conformational changes in the urea herbicide binding site of isolated Phaseolus vulgaris chloroplast thylakoids

Kinetic parameters of 3-(3, 4-dichlorophenyl)-1, 1-dimethyl urea (DCMU)-induced inhibition of electron transport in chloroplast thylakoids isolated from Phaseolus vulgaris L. cv. Oregon 1604 were determined from analysis of a convergent, parallel electrical circuit. Through this analogue, the apparent affinity of the purported binding site for DCMU (K₁) and the relative amount of DCMU-insensitive electron transport (v^max₁/v_o) were obtained using a reiterative non-linear least squares curve-fitting procedure. Exposure of thylakoids to heat caused a gradual increase in K₁ (or decrease in the affinity of the thylakoid for DCMU) with an apparent activation energy of 134 kJ mol⁻¹. Tryptic susceptibility of a protein region regulating K₁ also decreased gradually with exposure to 45°C, suggesting that the heat-induced increase in K₁ might be due to a protein conformational change. On the other hand, thylakoid exposure to 45°C resulted in a rapid (<5 min) irreversible increase in v^max_I/v_o, which was also the apparent result of a conformational change in a region of the protein which regulates this function. These results are suggestive of the existence of differential thermal sensitivities of proteins within the thylakoids and, perhaps, of different regions within a single membrane protein.     

Role of actin in the cAMP-dependent activation of sodium/glucose cotransporter in renal epithelial cells

We examined whether actin filaments are involved in the cAMP-dependent activation of a high affinity sodium/glucose cotransporter (SGLT1) using epithelial expression systems. The expression of enhanced green fluorescent protein-tagged SGLT1 (EGFP-SGLT1) in Madin-Darby canine kidney (MDCK) cells was revealed by Western blotting and confocal laser microscopy. 8-Br-cAMP, a membrane permeable cAMP analog, enhanced [¹⁴C]-α-methyl glucopyranoside ([¹⁴C]-AMG) uptake. Both basal and 8-Br-cAMP-elicited [¹⁴C]-AMG uptakes were inhibited by N-(2{[3-(4-bromophenyl)-2-propenyl]-amino}-ethyl)-5-isoquinolinesulfonamide (H-89), a protein kinase A inhibitor, and cytochalasin D, an actin filament formation inhibitor. Furthermore, cytochalasin D inhibited the distribution of EGFP-SGLT1 at the apical surface. These results suggest that the EGFP-SGLT1 protein is functionally expressed in the apical membrane of MDCK cells, and is up-regulated by a cAMP-dependent pathway requiring intact actin filaments.     

Structural Decomposition Analysis of Raw Material Consumption

The aim of this article is to quantify the drivers for the changes in raw material consumption (domestic material consumption expressed in the form of all materials extracted and used in the production phase) in terms of technology, which refers to the concept of sustainable production; the product structure of final demand, which refers to the concept of sustainable consumption; and the volume of final demand, which is related to economic growth. We also aim to determine to what extent the technological development and a shift in product structure of the final demand compensate for the growth in final consumption volume. Therefore, we apply structural decomposition analysis (SDA) to the change in raw material consumption (RMC) of the Czech Republic between 2000 and 2007. To present the study in a broader context, we also show other material flow indicators for the Czech Republic for 2000 and 2007. Our findings of SDA show that final demand structure has a very limited effect on the change in material flows. The rapid change in final demand volume was not compensated for crude oil, metal ores, construction materials, food crops, and timber. For the material category of non‐iron metal ores, even the change in technology contributes to an increase in material flows. The largest relative increases are reported for non‐iron metal ores (38%) and construction materials (30%). The main changes in material flows related to the Czech Republic are driven by exports and enabled by imports, the main source of these increased material flows. This emphasizes the increasing role of international trade.     

A study of the occurrence of regulator secondary structures in globular proteins

The distribution of regular secondary structures, viz. α-helices and β-strands, along the length of over 70 properties whose secondary structural details have been reported, has been analysed. The occurrence of these regular structures tends to be a maximum at the N- and C-termini. Our analysis suggests that both these free ends could possibly serve as nucleating centers for secondary structures and could play an important role in the folding of proteins.