CamTrapAsia: A dataset of tropical forest vertebrate communities from 239 camera trapping studies.

Information on tropical Asian vertebrates has traditionally been sparse, particularly when it comes to cryptic species inhabiting the dense forests of the region. Vertebrate populations are declining globally due to land-use change and hunting, the latter frequently referred as "defaunation." This is especially true in tropical Asia where there is extensive land-use change and high human densities. Robust monitoring requires that large volumes of vertebrate population data be made available for use by the scientific and applied communities. Camera traps have emerged as an effective, non-invasive, widespread, and common approach to surveying vertebrates in their natural habitats. However, camera-derived datasets remain scattered across a wide array of sources, including published scientific literature, gray literature, and unpublished works, making it challenging for researchers to harness the full potential of cameras for ecology, conservation, and management. In response, we collated and standardized observations from 239 camera trap studies conducted in tropical Asia. There were 278,260 independent records of 371 distinct species, comprising 232 mammals, 132 birds, and seven reptiles. The total trapping effort accumulated in this data paper consisted of 876,606 trap nights, distributed among Indonesia, Singapore, Malaysia, Bhutan, Thailand, Myanmar, Cambodia, Laos, Vietnam, Nepal, and far eastern India. The relatively standardized deployment methods in the region provide a consistent, reliable, and rich count data set relative to other large-scale pressence-only data sets, such as the Global Biodiversity Information Facility (GBIF) or citizen science repositories (e.g., iNaturalist), and is thus most similar to eBird. To facilitate the use of these data, we also provide mammalian species trait information and 13 environmental covariates calculated at three spatial scales around the camera survey centroids (within 10-, 20-, and 30-km buffers). We will update the dataset to include broader coverage of temperate Asia and add newer surveys and covariates as they become available. This dataset unlocks immense opportunities for single-species ecological or conservation studies as well as applied ecology, community ecology, and macroecology investigations. The data are fully available to the public for utilization and research. Please cite this data paper when utilizing the data.

Characterizing the Conformational Free-Energy Landscape of RNA Stem-Loops Using Single-Molecule Field-Effect Transistors.

We have developed and used single-molecule field-effect transistors (smFETs) to characterize the conformational free-energy landscape of RNA stem-loops. Stem-loops are one of the most common RNA structural motifs and serve as building blocks for the formation of complex RNA structures. Given their prevalence and integral role in RNA folding, the kinetics of stem-loop (un)folding has been extensively characterized using both experimental and computational approaches. Interestingly, these studies have reported vastly disparate timescales of (un)folding, which has been interpreted as evidence that (un)folding of even simple stem-loops occurs on a highly rugged conformational energy landscape. Because smFETs do not rely on fluorophore reporters of conformation or mechanical (un)folding forces, they provide a unique approach that has allowed us to directly monitor tens of thousands of (un)folding events of individual stem-loops at a 200 µs time resolution. Our results show that under our experimental conditions, stem-loops (un)fold over a 1-200 ms timescale during which they transition between ensembles of unfolded and folded conformations, the latter of which is composed of at least two sub-populations. The 1-200 ms timescale of (un)folding we observe here indicates that smFETs report on complete (un)folding trajectories in which unfolded conformations of the RNA spend long periods of time wandering the free-energy landscape before sampling one of several misfolded conformations or the natively folded conformation. Our findings highlight the extremely rugged landscape on which even the simplest RNA structural elements fold and demonstrate that smFETs are a unique and powerful approach for characterizing the conformational free-energy of RNA.

How do terrestrial wildlife communities respond to small-scale Acacia plantations embedded in harvested tropical forest?

To offset the declining timber supply by shifting towards more sustainable forestry practices, industrial tree plantations are expanding in tropical production forests. The conversion of natural forests to tree plantation is generally associated with loss of biodiversity and shifts towards more generalist and disturbance tolerant communities, but effects of mixed-landuse landscapes integrating natural and plantation forests remain little understood. Using camera traps, we surveyed the medium-to-large bodied terrestrial wildlife community across two mixed-landuse forest management areas in Sarawak, Malaysia Borneo which include areas dedicated to logging of natural forests and adjacent planted Acacia forests. We analyzed data from a 25-wildlife species community using a Bayesian community occupancy model to assess species richness and species-specific occurrence responses to Acacia plantations at a broad scale, and to remote-sensed local habitat conditions within the different forest landuse types. All species were estimated to occur in both landuse types, but species-level percent area occupied and predicted average local species richness were slightly higher in the natural forest management areas compared to licensed planted forest management areas. Similarly, occupancy-based species diversity profiles and defaunation indices for both a full community and only threatened and endemic species suggested the diversity and occurrence were slightly higher in the natural forest management areas. At the local scale, forest quality was the most prominent predictor of species occurrence. These associations with forest quality varied among species but were predominantly positive. Our results highlight the ability of a mixed-landuse landscape with small-scale Acacia plantations embedded in natural forests to retain terrestrial wildlife communities while providing an alternate source of timber. Nonetheless, there was a tendency towards reduced biodiversity in planted forests, which would likely be more pronounced in plantations that are larger or embedded in a less natural matrix.

An empirical energy landscape reveals mechanism of proteasome in polypeptide translocation.

The ring-like ATPase complexes in the AAA+ family perform diverse cellular functions that require coordination between the conformational transitions of their individual ATPase subunits (Erzberger and Berger, 2006; Puchades et al., 2020). How the energy from ATP hydrolysis is captured to perform mechanical work by these coordinated movements is unknown. In this study, we developed a novel approach for delineating the nucleotide-dependent free-energy landscape (FEL) of the proteasome's heterohexameric ATPase complex based on complementary structural and kinetic measurements. We used the FEL to simulate the dynamics of the proteasome and quantitatively evaluated the predicted structural and kinetic properties. The FEL model predictions are consistent with a wide range of experimental observations in this and previous studies and suggested novel mechanistic features of the proteasomal ATPases. We find that the cooperative movements of the ATPase subunits result from the design of the ATPase hexamer entailing a unique free-energy minimum for each nucleotide-binding status. ATP hydrolysis dictates the direction of substrate translocation by triggering an energy-dissipating conformational transition of the ATPase complex.

In cells, many biological processes are carried out by large complexes made up of different proteins. These macromolecules act like miniature machines, flexing and moving their various parts to perform their cellular roles. One such complex is the 26S proteasome, which is responsible for recycling other proteins in the cell. The proteasome consists of approximately 31 subunits, including a ring of six ATPase enzymes that provide the complex with the energy it needs to mechanically unfold proteins. To understand how the proteasome and other large complexes work, researchers need to be able to monitor how their structure changes over time. These dynamics are challenging to probe directly with experiments, but can be assessed using computer simulations which track the movement of individual molecules and atoms. However, currently available computer systems do not have enough power to simulate the dynamics of large protein assemblies, like the 26S proteasome: for example, it would take longer than a thousand years to model how each atom in the complex moves over a timescale in which a biological change would happen (roughly 100ms). Here, Fang, Hon et al. have developed a new approach to simulate the structural dynamics of the proteasome's ring of ATPase enzymes. Different known structures of the proteasome were used to identify the range of possible movements and shapes the complex can make. Fang, Hon et al. then used this data to calculate the energy level of each structure ­ also known as the 'free energy landscape' ­ and the rate of transition between them. This made it possible to simulate how the different ATPase enzymes move within the ring under a wide range of conditions. The simulated ATPase movements predicted how the proteasome machine would behave during various tasks, including degrading other proteins. Fan, Hon et al. carefully examined these predictions and found that they were consistent with experimental observations, validating their new simulation method. This work demonstrates the feasibility of simulating the actions of a large protein complex based on its free energy landscape. The results offer important insights into the functional mechanics of the 26S proteasome and related protein machines. Further work may help to simplify this process so the approach can be used to investigate the dynamics of other protein assemblies.

Bayesian-Estimated Hierarchical HMMs Enable Robust Analysis of Single-Molecule Kinetic Heterogeneity.

Single-molecule kinetic experiments allow the reaction trajectories of individual biomolecules to be directly observed, eliminating the effects of population averaging and providing a powerful approach for elucidating the kinetic mechanisms of biomolecular processes. A major challenge to the analysis and interpretation of these experiments, however, is the kinetic heterogeneity that almost universally complicates the recorded single-molecule signal versus time trajectories (i.e., signal trajectories). Such heterogeneity manifests as changes and/or differences in the transition rates that are observed within individual signal trajectories or across a population of signal trajectories. Because characterizing kinetic heterogeneity can provide critical mechanistic information, we have developed a computational method that effectively and comprehensively enables such analysis. To this end, we have developed a computational algorithm and software program, hFRET, that uses the variational approximation for Bayesian inference to estimate the parameters of a hierarchical hidden Markov model, thereby enabling robust identification and characterization of kinetic heterogeneity. Using simulated signal trajectories, we demonstrate the ability of hFRET to accurately and precisely characterize kinetic heterogeneity. In addition, we use hFRET to analyze experimentally recorded signal trajectories reporting on the conformational dynamics of ribosomal pre-translocation (PRE) complexes. The results of our analyses demonstrate that PRE complexes exhibit kinetic heterogeneity, reveal the physical origins of this heterogeneity, and allow us to expand the current model of PRE complex dynamics. The methods described here can be applied to signal trajectories generated using any type of signal and can be easily extended to the analysis of signal trajectories exhibiting more complex kinetic behaviors. Moreover, variations of our approach can be easily developed to integrate kinetic data obtained from different experimental constructs and/or from molecular dynamics simulations of a biomolecule of interest.

Single-molecule methods for measuring ubiquitination and protein stability.

The ubiquitin-proteasome system (UPS) contributes to changes in cell state and homeostatic maintenance in humans by modulating the stability of about a third of human proteins. For example, cell-cycle regulation requires a central ubiquitin ligase, the anaphase-promoting complex/cyclosome (APC/C), which starts a ubiquitination cascade leading to the degradation of multiple targets. This targeted degradation is mediated by the 26S proteasome, a 2.5-MDa protein complex, which recognizes and degrades ubiquitinated proteins at rates partially controlled by the variations in ubiquitin chain topology. Substrate selectivity of ubiquitin ligases such as the APC/C and of the 26S proteasome from pools of near-identical targets reflects highly regulated kinetic mechanisms. Single-molecule techniques are powerful tools that allow distinction between differential substrate affinities and identification of reaction intermediates in complex mixtures. Here we describe fluorescence-based single-molecule imaging of in vitro ubiquitination reactions catalyzed by the APC/C and ubiquitin-dependent degradation reactions catalyzed by the 26S proteasome.

Kinetic characterization of the inhibition of protein tyrosine phosphatase-1B by Vanadyl (VO2+) chelates.

Protein tyrosine phosphatases (PTPases) are a prominent focus of drug design studies because of their roles in homeostasis and disorders of metabolism. These studies have met with little success because (1) virtually all inhibitors hitherto exhibit only competitive behavior and (2) a consensus sequence H/V-C-X5-R-S/T characterizes the active sites of PTPases, leading to low specificity of active site directed inhibitors. With protein tyrosine phosphatase-1B (PTP1B) identifed as the target enzyme of the vanadyl (VO2+) chelate bis(acetylacetonato)oxidovanadium(IV) [VO(acac)2] in 3T3-L1 adipocytes [Ou et al. J Biol Inorg Chem 10: 874-886, 2005], we compared the inhibition of PTP1B by VO(acac)2 with other VO2+-chelates, namely, bis(2-ethyl-maltolato)oxidovanadium(IV) [VO(Et-malto)2] and bis(3-hydroxy-2-methyl-4(1H)pyridinonato)oxidovanadium(IV) [VO(mpp)2] under steady-state conditions, using the soluble portion of the recombinant human enzyme (residues 1-321). Our results differed from those of previous investigations because we compared inhibition in the presence of the nonspecific substrate p-nitrophenylphosphate and the phosphotyrosine-containing undecapeptide DADEpYLIPQQG mimicking residues 988-998 of the epidermal growth factor receptor, a relevant, natural substrate. While VO(Et-malto)2 acts only as a noncompetitive inhibitor in the presence of either subtrate, VO(acac)2 exhibits classical uncompetitive inhibition in the presence of DADEpYLIPQQG but only apparent competitive inhibition with p-nitrophenylphosphate as substrate. Because uncompetitive inhibitors are more potent pharmacologically than competitive inhibitors, structural characterization of the site of uncompetitive binding of VO(acac)2 may provide a new direction for design of inhibitors for therapeutic purposes. Our results suggest also that the true behavior of other inhibitors may have been masked when assayed with only p-nitrophenylphosphate as substrate.

Single-Molecule Reaction Chemistry in Patterned Nanowells.

A new approach to synthetic chemistry is performed in ultraminiaturized, nanofabricated reaction chambers. Using lithographically defined nanowells, we achieve single-point covalent chemistry on hundreds of individual carbon nanotube transistors, providing robust statistics and unprecedented spatial resolution in adduct position. Each device acts as a sensor to detect, in real-time and through quantized changes in conductance, single-point functionalization of the nanotube as well as consecutive chemical reactions, molecular interactions, and molecular conformational changes occurring on the resulting single-molecule probe. In particular, we use a set of sequential bioconjugation reactions to tether a single-strand of DNA to the device and record its repeated, reversible folding into a G-quadruplex structure. The stable covalent tether allows us to measure the same molecule in different solutions, revealing the characteristic increased stability of the G-quadruplex structure in the presence of potassium ions (K(+)) versus sodium ions (Na(+)). Nanowell-confined reaction chemistry on carbon nanotube devices offers a versatile method to isolate and monitor individual molecules during successive chemical reactions over an extended period of time.

Connecting science, policy, and implementation for landscape-scale habitat connectivity.

We examined the links between the science and policy of habitat corridors to better understand how corridors can be implemented effectively. As a case study, we focused on a suite of landscape-scale connectivity plans in tropical and subtropical Asia (Malaysia, Singapore, and Bhutan). The process of corridor designation may be more efficient if the scientific determination of optimal corridor locations and arrangement is synchronized in time with political buy-in and establishment of policies to create corridors. Land tenure and the intactness of existing habitat in the region are also important to consider because optimal connectivity strategies may be very different if there are few, versus many, political jurisdictions (including commercial and traditional land tenures) and intact versus degraded habitat between patches. Novel financing mechanisms for corridors include bed taxes, payments for ecosystem services, and strategic forest certifications. Gaps in knowledge of effective corridor design include an understanding of how corridors, particularly those managed by local communities, can be protected from degradation and unsustainable hunting. There is a critical need for quantitative, data-driven models that can be used to prioritize potential corridors or multicorridor networks based on their relative contributions to long-term metacommunity persistence.