Next generation organoids for biomedical research and applications

Organoids are in vitro cultures of miniature fetal or adult organ-like structures. Their potentials for use in tissue and organ replacement, disease modeling, toxicology studies, and drug discovery are tremendous. Currently, major challenges facing human organoid technology include (i) improving the range of cellular heterogeneity for a particular organoid system, (ii) mimicking the native micro- and matrix-environment encountered by cells within organoids, and (iii) developing robust protocols for the in vitro maturation of organoids that remain mostly fetal-like in cultures. To tackle these challenges, we advocate the principle of reverse engineering that replicates the inner workings of in vivo systems with the goal of achieving functionality and maturation of the resulting organoid structures with the input of minimal intrinsic (cellular) and environmental (matrix and niche) constituents. Here, we present an overview of organoid technology development in several systems that employ cell materials derived from fetal and adult tissues and pluripotent stem cell cultures. We focus on key studies that exploit the self-organizing property of embryonic progenitors and the role of designer matrices and cell-free scaffolds in assisting organoid formation. We further explore the relationship between adult stem cells, niche factors, and other current developments that aim to enhance robust organoid maturation. From these works, we propose a standardized pipeline for the development of future protocols that would help generate more physiologically relevant human organoids for various biomedical applications.     

A Plausible Microtubule-Based Mechanism for Cell Division Orientation in Plant Embryogenesis

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Disruption of the E. coli LptC dimerization interface and characterization of lipopolysaccharide and LptA binding to monomeric LptC

Lipopolysaccharide (LPS) is an essential element of nearly all Gram‐negative bacterial outer membranes and serves to protect the cell from adverse environmental stresses. Seven members of the lipopolysaccharide transport (Lpt) protein family function together to transport LPS from the inner membrane (IM) to the outer leaflet of the outer membrane of bacteria such as Escherichia coli. Each of these proteins has a solved crystal structure, including LptC, which is a largely periplasmic protein that is associated with the IM LptB₂FG complex and anchored to the membrane by an N‐terminal helix. LptC directly binds LPS and is hypothesized to be involved in the transfer of LPS to another periplasmic protein, LptA. Purified and in solution, LptC forms a dimer. Here, point mutations designed to disrupt formation of the dimer are characterized using site‐directed spin labeling double electron electron resonance (DEER) spectroscopy, light scattering, circular dichroism, and computational modeling. The computational studies reveal the molecular interactions that drive dimerization of LptC and elucidate how the disruptive mutations change this interaction, while the DEER and light scattering studies identify which mutants disrupt the dimer. And, using electron paramagnetic resonance spectroscopy and comparing the results to the previous quantitative characterization of the interactions between dimeric LptC and LPS and LptA, the functional consequences of monomeric LptC were also determined. These results indicate that disruption of the dimer does not affect LPS or LptA binding and that monomeric LptC binds LPS and LptA at levels similar to dimeric LptC.     

Invited review: Helping dairy farmers to improve economic performance utilizing data-driving decision support tools

The objective of this review paper is to describe the development and application of a suite of more than 40 computerized dairy farm decision support tools contained at the University of Wisconsin-Madison (UW) Dairy Management website http://DairyMGT.info. These data-driven decision support tools are aimed to help dairy farmers improve their decision-making, environmental stewardship and economic performance. Dairy farm systems are highly dynamic in which changing market conditions and prices, evolving policies and environmental restrictions together with every time more variable climate conditions determine performance. Dairy farm systems are also highly integrated with heavily interrelated components such as the dairy herd, soils, crops, weather and management. Under these premises, it is critical to evaluate a dairy farm following a dynamic integrated system approach. For this approach, it is crucial to use meaningful data records, which are every time more available. These data records should be used within decision support tools for optimal decision-making and economic performance. Decision support tools in the UW-Dairy Management website (http://DairyMGT.info) had been developed using combination and adaptation of multiple methods together with empirical techniques always with the primary goal for these tools to be: (1) highly user-friendly, (2) using the latest software and computer technologies, (3) farm and user specific, (4) grounded on the best scientific information available, (5) remaining relevant throughout time and (6) providing fast, concrete and simple answers to complex farmers’ questions. DairyMGT.info is a translational innovative research website in various areas of dairy farm management that include nutrition, reproduction, calf and heifer management, replacement, price risk and environment. This paper discusses the development and application of 20 selected (http://DairyMGT.info) decision support tools.     

Which species,how many,and from where: Integrating habitat suitability,population genomics,and abundance estimates into species reintroduction planning

Extirpated organisms are reintroduced into their former ranges worldwide to combat species declines and biodiversity losses. The growing field of reintroduction biology provides guiding principles for reestablishing populations, though criticisms remain regarding limited integration of initial planning, modeling frameworks, interdisciplinary collaborations, and multispecies approaches. We used an interdisciplinary, multispecies, quantitative framework to plan reintroductions of three fish species into Abrams Creek, Great Smoky Mountains National Park, USA. We first assessed the appropriateness of habitat at reintroduction sites for banded sculpin (Cottus carolinae), greenside darter (Etheostoma blennioides), and mottled sculpin (Cottus bairdii) using species distribution modeling. Next, we evaluated the relative suitability of nine potential source stock sites using population genomics, abundance estimates, and multiple‐criteria decision analysis (MCDA) based on known correlates of reintroduction success. Species distribution modeling identified mottled sculpin as a poor candidate, but banded sculpin and greenside darter as suitable candidates for reintroduction based on species‐habitat relationships and habitats available in Abrams Creek. Genotyping by sequencing revealed acceptable levels of genetic diversity at all candidate source stock sites, identified population clusters, and allowed for estimating the number of fish that should be included in translocations. Finally, MCDA highlighted priorities among candidate source stock sites that were most likely to yield successful reintroductions based on differential weightings of habitat assessment, population genomics, and the number of fish available for translocation. Our integrative approach represents a unification of multiple recent advancements in the field of reintroduction biology and highlights the benefit of shifting away from simply choosing nearby populations for translocation to an information‐based science with strong a priori planning coupled with several suggested posteriori monitoring objectives. Our framework can be applied to optimize reintroduction successes for a multitude of organisms and advances in the science of reintroduction biology by simultaneously addressing a variety of past criticisms of the field.     

Increasing drought and diminishing benefits of elevated carbon dioxide for soybean yields across the US Midwest

Elevated atmospheric CO₂ concentrations ([CO₂]) are expected to increase C3 crop yield through the CO₂ fertilization effect (CFE) by stimulating photosynthesis and by reducing stomatal conductance and transpiration. The latter effect is widely believed to lead to greater benefits in dry rather than wet conditions, although some recent experimental evidence challenges this view. Here we used a process‐based crop model, the Agricultural Production Systems sIMulator (APSIM), to quantify the contemporary and future CFE on soybean in one of its primary production area of the US Midwest. APSIM accurately reproduced experimental data from the Soybean Free‐Air CO₂ Enrichment site showing that the CFE declined with increasing drought stress. This resulted from greater radiation use efficiency (RUE) and above‐ground biomass production at elevated [CO₂] that outpaced gains in transpiration efficiency (TE). Using an ensemble of eight climate model projections, we found that drought frequency in the US Midwest is projected to increase from once every 5 years currently to once every other year by 2050. In addition to directly driving yield loss, greater drought also significantly limited the benefit from rising [CO₂]. This study provides a link between localized experiments and regional‐scale modeling to highlight that increased drought frequency and severity pose a formidable challenge to maintaining soybean yield progress that is not offset by rising [CO₂] as previously anticipated. Evaluating the relative sensitivity of RUE and TE to elevated [CO₂] will be an important target for future modeling and experimental studies of climate change impacts and adaptation in C3 crops.     

How will climate novelty influence ecological forecasts? Using the Quaternary to assess future reliability

Future climates are projected to be highly novel relative to recent climates. Climate novelty challenges models that correlate ecological patterns to climate variables and then use these relationships to forecast ecological responses to future climate change. Here, we quantify the magnitude and ecological significance of future climate novelty by comparing it to novel climates over the past 21,000 years in North America. We then use relationships between model performance and climate novelty derived from the fossil pollen record from eastern North America to estimate the expected decrease in predictive skill of ecological forecasting models as future climate novelty increases. We show that, in the high emissions scenario (RCP 8.5) and by late 21st century, future climate novelty is similar to or higher than peak levels of climate novelty over the last 21,000 years. The accuracy of ecological forecasting models is projected to decline steadily over the coming decades in response to increasing climate novelty, although models that incorporate co‐occurrences among species may retain somewhat higher predictive skill. In addition to quantifying future climate novelty in the context of late Quaternary climate change, this work underscores the challenges of making reliable forecasts to an increasingly novel future, while highlighting the need to assess potential avenues for improvement, such as increased reliance on geological analogs for future novel climates and improving existing models by pooling data through time and incorporating assemblage‐level information.     

Dramatic loss of seagrass habitat under projected climate change in the Mediterranean Sea

Although climate warming is affecting most marine ecosystems, the Mediterranean is showing earlier impacts. Foundation seagrasses are already experiencing a well‐documented regression in the Mediterranean which could be aggravated by climate change. Here, we forecast distributions of two seagrasses and contrast predicted loss with discrete regions identified on the basis of extant genetic diversity. Under the worst‐case scenario, Posidonia oceanica might lose 75% of suitable habitat by 2050 and is at risk of functional extinction by 2100, whereas Cymodocea nodosa would lose only 46.5% in that scenario as losses are compensated with gained and stable areas in the Atlantic. Besides, we predict that erosion of present genetic diversity and vicariant processes can happen, as all Mediterranean genetic regions could decrease considerably in extension in future warming scenarios. The functional extinction of Posidonia oceanica would have important ecological impacts and may also lead to the release of the massive carbon stocks these ecosystems stored over millennia.     

The role of protected areas in land use/land cover change and the carbon cycle in the conterminous United States

Protected areas (PAs) cover about 22% of the conterminous United States. Understanding their role on historical land use and land cover change (LULCC) and on the carbon cycle is essential to provide guidance for environmental policies. In this study, we compiled historical LULCC and PAs data to explore these interactions within the terrestrial ecosystem model (TEM). We found that intensive LULCC occurred in the conterminous United States from 1700 to 2005. More than 3 million km² of forest, grassland and shrublands were converted into agricultural lands, which caused 10,607 Tg C release from land ecosystems to atmosphere. PAs had experienced little LULCC as they were generally established in the 20th century after most of the agricultural expansion had occurred. PAs initially acted as a carbon source due to land use legacies, but their accumulated carbon budget switched to a carbon sink in the 1960s, sequestering an estimated 1,642 Tg C over 1700–2005, or 13.4% of carbon losses in non‐PAs. We also find that PAs maintain larger carbon stocks and continue sequestering carbon in recent years (2001–2005), but at a lower rate due to increased heterotrophic respiration as well as lower productivity associated to aging ecosystems. It is essential to continue efforts to maintain resilient, biodiverse ecosystems and avoid large‐scale disturbances that would release large amounts of carbon in PAs.     

Projecting the future of an alpine ungulate under climate change scenarios

Climate change represents a primary threat to species persistence and biodiversity at a global scale. Cold adapted alpine species are especially sensitive to climate change and can offer key “early warning signs” about deleterious effects of predicted change. Among mountain ungulates, survival, a key determinant of demographic performance, may be influenced by future climate in complex, and possibly opposing ways. Demographic data collected from 447 mountain goats in 10 coastal Alaska, USA, populations over a 37‐year time span indicated that survival is highest during low snowfall winters and cool summers. However, general circulation models (GCMs) predict future increase in summer temperature and decline in winter snowfall. To disentangle how these opposing climate‐driven effects influence mountain goat populations, we developed an age‐structured population model to project mountain goat population trajectories for 10 different GCM/emissions scenarios relevant for coastal Alaska. Projected increases in summer temperature had stronger negative effects on population trajectories than the positive demographic effects of reduced winter snowfall. In 5 of the 10 GCM/representative concentration pathway (RCP) scenarios, the net effect of projected climate change was extinction over a 70‐year time window (2015–2085); smaller initial populations were more likely to go extinct faster than larger populations. Using a resource selection modeling approach, we determined that distributional shifts to higher elevation (i.e., “thermoneutral”) summer range was unlikely to be a viable behavioral adaptation strategy; due to the conical shape of mountains, summer range was expected to decline by 17%–86% for 7 of the 10 GCM/RCP scenarios. Projected declines of mountain goat populations are driven by climate‐linked bottom‐up mechanisms and may have wide ranging implications for alpine ecosystems. These analyses elucidate how projected climate change can negatively alter population dynamics of a sentinel alpine species and provide insight into how demographic modeling can be used to assess risk to species persistence.