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Chemically identical chlorophyll (Chl) molecules undergo conformational changes when they are embedded in a protein matrix. The conformational changes will modulate their absorption spectra to meet the need for programmed excitation energy transfer or electron transfer. To interpret spectroscopic data using the knowledge of pigment–protein interactions requires a single pigment embedded in one polypeptide matrix. Unfortunately, most of the known photosynthetic systems contain a set of multiple pigments in each protein subunit. This makes it complicated to interpret spectroscopic data using structural data due to the potential overlapping spectra of two or more pigments. Chl–protein interactions have not been systematically studied to answer three fundamental questions: (i) What are the structural characteristics and commonly shared substructures of different types of Chl molecules (e.g., Chl a, b, c, d, and f)? (ii) How many structural groups can Chl molecules be divided into and how are different structural groups influenced by their surrounding environments? (iii) What are the structural characteristics of pigment surrounding environments? Having no clear answers to the unresolved questions is probably due to a lack of computational methods for quantifying conformational changes in individual Chls and individual surrounding amino acids. The first version of the Triangular Spatial Relationship (TSR)-based method was developed for comparing protein 3D structures. The input data for the TSR-based method are experimentally determined 3D structures from the Protein Data Bank (PDB). In this study, we take advantage of the 3D structures of Chl-binding proteins deposited in the PDB and the TSR-based method to systematically investigate the 3D structures of various types of Chls and their protein environments. The key contributions of this study can be summarized as follows: (i) Specific structural characteristics of Chl d and f were identified and are defined using the TSR keys. (ii) Two and three clusters were found for various types of Chls and Chls a, respectively. The signature structures for distinguishing their corresponding two and three clusters were identified. (iii) Histidine residues were used as an example for revealing structural characteristics of Chl-binding sites. This study provides evidence for the three unresolved questions and builds a structural foundation through quantifying Chl conformations as well as structures of their embedded protein environments for future mechanistic understanding of relationships between Chl–protein interactions and their corresponding spectroscopic data. 

Abstract BackgroundAll chemical forms of energy and oxygen on Earth are generated via photosynthesis where light energy is converted into redox energy by two photosystems (PS I and PS II). There is an increasing number of PS I 3D structures deposited in the Protein Data Bank (PDB). The Triangular Spatial Relationship (TSR)-based algorithm converts 3D structures into integers (TSR keys). A comprehensive study was conducted, by taking advantage of the PS I 3D structures and the TSR-based algorithm, to answer three questions: (i) Are electron cofactors including P700, A_-1and A₀, which are chemically identical chlorophylls, structurally different? (ii) There are two electron transfer chains (A and B branches) in PS I. Are the cofactors on both branches structurally different? (iii) Are the amino acids in cofactor binding sites structurally different from those not in cofactor binding sites? ResultsThe key contributions and important findings include: (i) a novel TSR-based method for representing 3D structures of pigments as well as for quantifying pigment structures was developed; (ii) the results revealed that the redox cofactor, P700, are structurally conserved and different from other redox factors. Similar situations were also observed for both A_-1and A₀; (iii) the results demonstrated structural differences between A and B branches for the redox cofactors P700, A_-1, A₀and A₁as well as their cofactor binding sites; (iv) the tryptophan residues close to A₀and A₁are structurally conserved; (v) The TSR-based method outperforms the Root Mean Square Deviation (RMSD) and the Ultrafast Shape Recognition (USR) methods. ConclusionsThe structural analyses of redox cofactors and their binding sites provide a foundation for understanding the unique chemical and physical properties of each redox cofactor in PS I, which are essential for modulating the rate and direction of energy and electron transfers. 

The involvement of the second pair of chlorophylls, termed A-1A and A-1B, in light-induced electron transfer in photosystem I (PSI) is currently debated. Asparagines at PsaA600 and PsaB582 are involved in coordinating the A-1B and A-1A pigments, respectively. Here we have mutated these asparagine residues to methionine in two single mutants and a double mutant in PSI from Synechocystis sp. PCC 6803, which we term NA600M, NB582M, and NA600M/NB582M mutants. (P700+–P700) FTIR difference spectra (DS) at 293 K were obtained for the wild-type and the three mutant PSI samples. The wild-type and mutant FTIR DS differ considerably. This difference indicates that the observed changes in the (P700+–P700) FTIR DS cannot be due to only the PA and PB pigments of P700. Comparison of the wild-type and mutant FTIR DS allows the assignment of different features to both A-1 pigments in the FTIR DS for wild-type PSI and assesses how these features shift upon cation formation and upon mutation. While the exact role the A-1 pigments play in the species we call P700 is unclear, we demonstrate that the vibrational modes of the A-1A and A-1B pigments are modified upon P700+ formation. Previously, we showed that the A-1 pigments contribute to P700 in green algae. In this manuscript, we demonstrate that this is also the case in cyanobacterial PSI. The nature of the mutation-induced changes in algal and cyanobacterial PSI is similar and can be considered within the same framework, suggesting a universality in the nature of P700 in different photosynthetic organisms. 

Photosystem I (PS I) is a photosynthetic pigment–protein complex that absorbs light and uses the absorbed energy to initiate electron transfer. Electron transfer has been shown to occur concurrently along two (A- and B-) branches of reaction center (RC) cofactors. The electron transfer chain originates from a special pair of chlorophyll a molecules (P700), followed by two chlorophylls and one phylloquinone in each branch (denoted as A−1, A0, A1, respectively), converging in a single iron–sulfur complex Fx. While there is a consensus that the ultimate electron donor–acceptor pair is P700+A0−, the involvement of A−1 in electron transfer, as well as the mechanism of the very first step in the charge separation sequence, has been under debate. To resolve this question, multiple groups have targeted electron transfer cofactors by site-directed mutations. In this work, the peripheral hydrogen bonds to keto groups of A0 chlorophylls have been disrupted by mutagenesis. Four mutants were generated: PsaA-Y692F; PsaB-Y667F; PsaB-Y667A; and a double mutant PsaA-Y692F/PsaB-Y667F. Contrary to expectations, but in agreement with density functional theory modeling, the removal of the hydrogen bond by Tyr → Phe substitution was found to have a negligible effect on redox potentials and optical absorption spectra of respective chlorophylls. In contrast, Tyr → Ala substitution was shown to have a fatal effect on the PS I function. It is thus inferred that PsaA-Y692 and PsaB-Y667 residues have primarily structural significance, and their ability to coordinate respective chlorophylls in electron transfer via hydrogen bond plays a minor role. 

Abstract While human activities are known to elicit rapid turnover in species composition through time, the properties of the species that increase or decrease their spatial occupancy underlying this turnover are less clear. Here, we used an extensive dataset of 238 metacommunity time series of multiple taxa spread across the globe to evaluate whether species that are more widespread (large-ranged species) differed in how they changed their site occupancy over the 10–90 years the metacommunities were monitored relative to species that are more narrowly distributed (small-ranged species). We found that on average, large-ranged species tended to increase in occupancy through time, whereas small-ranged species tended to decrease. These relationships were stronger in marine than in terrestrial and freshwater realms. However, in terrestrial regions, the directional changes in occupancy were less extreme in protected areas. Our findings provide evidence for systematic decreases in occupancy of small-ranged species, and that habitat protection could mitigate these losses in the face of environmental change. 

ABSTRACT MotivationHere, we make available a second version of the BioTIME database, which compiles records of abundance estimates for species in sample events of ecological assemblages through time. The updated version expands version 1.0 of the database by doubling the number of studies and includes substantial additional curation to the taxonomic accuracy of the records, as well as the metadata. Moreover, we now provide an R package (BioTIMEr) to facilitate use of the database. Main Types of Variables IncludedThe database is composed of one main data table containing the abundance records and 11 metadata tables. The data are organised in a hierarchy of scales where 11,989,233 records are nested in 1,603,067 sample events, from 553,253 sampling locations, which are nested in 708 studies. A study is defined as a sampling methodology applied to an assemblage for a minimum of 2 years. Spatial Location and GrainSampling locations in BioTIME are distributed across the planet, including marine, terrestrial and freshwater realms. Spatial grain size and extent vary across studies depending on sampling methodology. We recommend gridding of sampling locations into areas of consistent size. Time Period and GrainThe earliest time series in BioTIME start in 1874, and the most recent records are from 2023. Temporal grain and duration vary across studies. We recommend doing sample‐level rarefaction to ensure consistent sampling effort through time before calculating any diversity metric. Major Taxa and Level of MeasurementThe database includes any eukaryotic taxa, with a combined total of 56,400 taxa. Software Formatcsv and. SQL. 

Quaternary climate change reduced and homogenized angiosperm tree diversity across large landscapes worldwide. 

Safeguarding Earth’s tree diversity is a conservation priority due to the importance of trees for biodiversity and ecosystem functions and services such as carbon sequestration. Here, we improve the foundation for effective conservation of global tree diversity by analyzing a recently developed database of tree species covering 46,752 species. We quantify range protection and anthropogenic pressures for each species and develop conservation priorities across taxonomic, phylogenetic, and functional diversity dimensions. We also assess the effectiveness of several influential proposed conservation prioritization frameworks to protect the top 17% and top 50% of tree priority areas. We find that an average of 50.2% of a tree species’ range occurs in 110-km grid cells without any protected areas (PAs), with 6,377 small-range tree species fully unprotected, and that 83% of tree species experience nonnegligible human pressure across their range on average. Protecting high-priority areas for the top 17% and 50% priority thresholds would increase the average protected proportion of each tree species’ range to 65.5% and 82.6%, respectively, leaving many fewer species (2,151 and 2,010) completely unprotected. The priority areas identified for trees match well to the Global 200 Ecoregions framework, revealing that priority areas for trees would in large part also optimize protection for terrestrial biodiversity overall. Based on range estimates for >46,000 tree species, our findings show that a large proportion of tree species receive limited protection by current PAs and are under substantial human pressure. Improved protection of biodiversity overall would also strongly benefit global tree diversity.