Assessing the future global distribution of land ecosystems as determined by climate change and cropland incursion.

The geographic distribution of natural ecosystems is affected by both climate and cropland. Discussions of future land use/land cover usually focus on how cropland expands and displaces natural vegetation especially as climate change impacts become stronger. Less commonly considered is the direct influence of climate change on natural ecosystems simultaneously with cropland incursion. We combine a natural vegetation model responsive to climate with a cropland allocation algorithm to assess the relative importance of climate change compared to cropland incursion. Globally, the model indicates that climate change drives larger gains and losses than cropland incursion. For example, in the Amazonian rainforests, more than one sixth of the forest area could be lost due to climate change with cropland playing virtually no role. Our findings suggest that policies to protect specific ecosystems may be undercut by climate change and that localized analyses that fully account for the impacts of a changing climate on natural vegetation and agriculture are necessary to formulate policies that preserve natural ecosystems over the long term. Supplementary Information: The online version contains supplementary material available at 10.1007/s10584-023-03584-3.

Climate smart agriculture and global food-crop production.

Most business-as-usual scenarios for farming under changing climate regimes project that the agriculture sector will be significantly impacted from increased temperatures and shifting precipitation patterns. Perhaps ironically, agricultural production contributes substantially to the problem with yearly greenhouse gas (GHG) emissions of about 11% of total anthropogenic GHG emissions, not including land use change. It is partly because of this tension that Climate Smart Agriculture (CSA) has attracted interest given its promise to increase agricultural productivity under a changing climate while reducing emissions. Considerable resources have been mobilized to promote CSA globally even though the potential effects of its widespread adoption have not yet been studied. Here we show that a subset of agronomic practices that are often included under the rubric of CSA can contribute to increasing agricultural production under unfavorable climate regimes while contributing to the reduction of GHG. However, for CSA to make a significant impact important investments and coordination are required and its principles must be implemented widely across the entire sector.

Combining the effects of increased atmospheric carbon dioxide on protein, iron, and zinc availability and projected climate change on global diets: a modelling study.

BACKGROUND: Increasing atmospheric concentrations of carbon dioxide (CO2) affect global nutrition via effects on agricultural productivity and nutrient content of food crops. We combined these effects with economic projections to estimate net changes in nutrient availability between 2010 and 2050. METHODS: In this modelling study, we used the International Model for Policy Analysis of Agricultural Commodities and Trade to project per capita availability of protein, iron, and zinc in 2050. We used estimated changes in productivity of individual agricultural commodities to model effects on production, trade, prices, and consumption under moderate and high greenhouse gas emission scenarios. Two independent sources of data, which used different methodologies to determine the effect of increased atmospheric CO2 on different key crops, were combined with the modelled food supply results to estimate future nutrient availability. FINDINGS: Although technological change, market responses, and the effects of CO2 fertilisation on yield are projected to increase global availability of dietary protein, iron, and zinc, these increases are moderated by negative effects of climate change affecting productivity and carbon penalties on nutrient content. The carbon nutrient penalty results in decreases in the global availability of dietary protein of 4·1%, iron of 2·8%, and zinc of 2·5% as calculated using one dataset, and decreases in global availability of dietary protein of 2·9%, iron of 3·9%, and zinc of 3·4% using the other dataset. The combined effects of projected increases in atmospheric CO2 (ie, carbon nutrient penalty, CO2 fertilisation, and climate effects on productivity) will decrease growth in the global availability of nutrients by 19·5% for protein, 14·4% for iron, and 14·6% for zinc relative to expected technology and market gains by 2050. The many countries that currently have high levels of nutrient deficiency would continue to be disproportionately affected. INTERPRETATION: This approach is an improvement in estimating future global food security by simultaneously projecting climate change effects on crop productivity and changes in nutrient content under increased concentrations of CO2, which accounts for a much larger effect on nutrient availability than CO2 fertilisation. Regardless of the scenario used to project future consumption patterns, the net effect of increasing concentrations of atmospheric CO2 will slow progress in decreasing global nutrient deficiencies. FUNDING: US Environmental Protection Agency, Consultative Group on International Agricultural Research (CIGAR) Research Program on Policies, Institutions and Markets (PIM), and the CGIAR Research Program on Climate Change and Food Security (CCAFS).

Gaps between fruit and vegetable production, demand, and recommended consumption at global and national levels: an integrated modelling study.

BACKGROUND: Current diets are detrimental to both human and planetary health and shifting towards more balanced, predominantly plant-based diets is seen as crucial to improving both. Low fruit and vegetable consumption is itself a major nutritional problem. We aim to better quantify the gap between future fruit and vegetable supply and recommended consumption levels by exploring the interactions between supply and demand in more than 150 countries from 1961 to 2050. METHODS: In this global analysis, we use the International Model for Policy Analysis of Agricultural Commodities and Trade, which simulates the global agricultural sector, to explore the role of insufficient production of fruits and vegetables and the effects of food waste and public policy in achieving recommended fruit and vegetable consumption. First, we estimate the average historical (1961-2010) and future (2010-50) national consumption levels needed to meet WHO targets (a minimum target of 400 g/person per day or age-specific recommendations of 330-600 g/person per day) using population pyramids; for future consumption, we use projections from the Shared Socioeconomic Pathways (SSPs), a set of global socioeconomic scenarios characterised by varied assumptions on economic and population growth. We then simulate future fruit and vegetable production and demand to 2050 under three such scenarios (SSP1-3) to assess the potential impacts of economic, demographic, and technological change on consumer and producer behaviour. We then explore the potential effects of food waste applying various waste assumptions (0-33% waste). Finally, we apply two policy analysis frameworks (the NOURISHING framework and the Nuffield ladder) to assess the current state of public policy designed to achieve healthy diets. FINDINGS: Historically, fruit and vegetable availability has consistently been insufficient to supply recommended consumption levels. By 2015, 81 countries representing 55% of the global population had average fruit and vegetable availability above WHO's minimum target. Under more stringent age-specific recommendations, only 40 countries representing 36% of the global population had adequate availability. Although economic growth will help to increase fruit and vegetable availability in the future, particularly in lower-income countries, this alone will be insufficient. Even under the most optimistic socioeconomic scenarios (excluding food waste), many countries fail to achieve sufficient fruit and vegetable availability to meet even the minimum recommended target. Sub-Saharan Africa is a particular region of concern, with projections suggesting, by 2050, between 0·8 and 1·9 billion people could live in countries with average fruit and vegetable availability below 400 g/person per day. Food waste is a serious obstacle that could erode projected gains. Assuming 33% waste and socioeconomic trends similar to historical patterns, the global average availability in 2050 falls below age-specific recommendations, increasing the number of people living in countries with insufficient supply of fruits and vegetables by 1·5 billion compared with a zero waste scenario. INTERPRETATION: Increasing fruit and vegetable consumption is an important component of a shift towards healthier and more sustainable diets. Economic modelling suggests that even under optimistic socioeconomic scenarios future supply will be insufficient to achieve recommended levels in many countries. Consequently, systematic public policy targeting the constraints to producing and consuming fruits and vegetables will be needed. This will require a portfolio of interventions and investments that focus on increasing fruit and vegetable production, developing technologies and practices to reduce waste without increasing the consumer cost, and increasing existing efforts to educate consumers on healthy diets. FUNDING: The Commonwealth Scientific and Industrial Research Organisation; Climate Change, Agriculture and Food Security (CGIAR) Research Program on Climate Change, Agriculture and Food Security; CGIAR Research Program on Policy, Institutions, and Markets; Bill & Melinda Gates Foundation; and Johns Hopkins University.

Agricultural investments and hunger in Africa modeling potential contributions to SDG2 - Zero Hunger.

We use IFPRI's IMPACT framework of linked biophysical and structural economic models to examine developments in global agricultural production systems, climate change, and food security. Building on related work on how increased investment in agricultural research, resource management, and infrastructure can address the challenges of meeting future food demand, we explore the costs and implications of these investments for reducing hunger in Africa by 2030. This analysis is coupled with a new investment estimation model, based on the perpetual inventory methodology (PIM), which allows for a better assessment of the costs of achieving projected agricultural improvements. We find that climate change will continue to slow projected reductions in hunger in the coming decades-increasing the number of people at risk of hunger in 2030 by 16 million in Africa compared to a scenario without climate change. Investments to increase agricultural productivity can offset the adverse impacts of climate change and help reduce the share of people at risk of hunger in 2030 to five percent or less in Northern, Western, and Southern Africa, but the share is projected to remain at ten percent or more in Eastern and Central Africa. Investments in Africa to achieve these results are estimated to cost about 15 billion USD per year between 2015 and 2030, as part of a larger package of investments costing around 52 billion USD in developing countries.

Credentialing preclinical pediatric xenograft models using gene expression and tissue microarray analysis.

Human tumor xenografts have been used extensively for rapid screening of the efficacy of anticancer drugs for the past 35 years. The selection of appropriate xenograft models for drug testing has been largely empirical and has not incorporated a similarity to the tumor type of origin at the molecular level. This study is the first comprehensive analysis of the transcriptome of a large set of pediatric xenografts, which are currently used for preclinical drug testing. Suitable models representing the tumor type of origin were identified. It was found that the characteristic expression patterns of the primary tumors were maintained in the corresponding xenografts for the majority of samples. Because a prerequisite for developing rationally designed drugs is that the target is expressed at the protein level, we developed tissue arrays from these xenografts and corroborated that high mRNA levels yielded high protein levels for two tested genes. The web database and availability of tissue arrays will allow for the rapid confirmation of the expression of potential targets at both the mRNA and the protein level for molecularly targeted agents. The database will facilitate the identification of tumor markers predictive of response to tested agents as well as the discovery of new molecular targets.

BBC3 mediates fenretinide-induced cell death in neuroblastoma.

Fenretinide (4-HPR) is a synthetic retinoid whose apoptosis-inducing effects have been demonstrated in many tumor types. The precise mechanism of its apoptotic action is not fully understood. To further study the mechanism by which 4-HPR exerts its biological effects in neuroblastoma (NB) and to identify the genes that contribute to the induction of apoptosis, we determined the sensitivity of eight NB cell lines to 4-HPR. Additionally, cDNA microarray analysis was performed on a 4-HPR-sensitive cell line to investigate the temporal changes in gene expression, primarily focusing on the induction of proapoptotic genes. BBC3, a transcriptionally regulated proapoptotic member of the BCL2 family, was the most highly induced proapoptotic gene. Western analysis confirmed the induction of BBC3 protein by 4-HPR. Furthermore, the induction of BBC3 was associated with the sensitivity to this agent in the cell lines tested. Finally we demonstrated that BBC3 alone is sufficient to induce cell death in the 4-HPR-sensitive and resistant NB cell lines, and that siRNA against BBC3 significantly decreases apoptosis induced by 4-HPR. Our results indicate that BBC3 mediates cell death in NB cells in response to 4-HPR.

Database of mRNA gene expression profiles of multiple human organs.

Genome-wide expression profiling of normal tissue may facilitate our understanding of the etiology of diseased organs and augment the development of new targeted therapeutics. Here, we have developed a high-density gene expression database of 18,927 unique genes for 158 normal human samples from 19 different organs of 30 different individuals using DNA microarrays. We report four main findings. First, despite very diverse sample parameters (e.g., age, ethnicity, sex, and postmortem interval), the expression profiles belonging to the same organs cluster together, demonstrating internal stability of the database. Second, the gene expression profiles reflect major organ-specific functions on the molecular level, indicating consistency of our database with known biology. Third, we demonstrate that any small (i.e., n approximately 100), randomly selected subset of genes can approximately reproduce the hierarchical clustering of the full data set, suggesting that the observed differential expression of >90% of the probed genes is of biological origin. Fourth, we demonstrate a potential application of this database to cancer research by identifying 19 tumor-specific genes in neuroblastoma. The selected genes are relatively underexpressed in all of the organs examined and belong to therapeutically relevant pathways, making them potential novel diagnostic markers and targets for therapy. We expect this database will be of utility for developing rationally designed molecularly targeted therapeutics in diseases such as cancer, as well as for exploring the functions of genes.

Prediction of clinical outcome using gene expression profiling and artificial neural networks for patients with neuroblastoma.

Currently, patients with neuroblastoma are classified into risk groups (e.g., according to the Children's Oncology Group risk-stratification) to guide physicians in the choice of the most appropriate therapy. Despite this careful stratification, the survival rate for patients with high-risk neuroblastoma remains <30%, and it is not possible to predict which of these high-risk patients will survive or succumb to the disease. Therefore, we have performed gene expression profiling using cDNA microarrays containing 42,578 clones and used artificial neural networks to develop an accurate predictor of survival for each individual patient with neuroblastoma. Using principal component analysis we found that neuroblastoma tumors exhibited inherent prognostic specific gene expression profiles. Subsequent artificial neural network-based prognosis prediction using expression levels of all 37,920 good-quality clones achieved 88% accuracy. Moreover, using an artificial neural network-based gene minimization strategy in a separate analysis we identified 19 genes, including 2 prognostic markers reported previously, MYCN and CD44, which correctly predicted outcome for 98% of these patients. In addition, these 19 predictor genes were able to additionally partition Children's Oncology Group-stratified high-risk patients into two subgroups according to their survival status (P = 0.0005). Our findings provide evidence of a gene expression signature that can predict prognosis independent of currently known risk factors and could assist physicians in the individual management of patients with high-risk neuroblastoma.

cDNA array-CGH profiling identifies genomic alterations specific to stage and MYCN-amplification in neuroblastoma.

BACKGROUND: Recurrent non-random genomic alterations are the hallmarks of cancer and the characterization of these imbalances is critical to our understanding of tumorigenesis and cancer progression. RESULTS: We performed array-comparative genomic hybridization (A-CGH) on cDNA microarrays containing 42,000 elements in neuroblastoma (NB). We found that only two chromosomes (2p and 12q) had gene amplifications and all were in the MYCN amplified samples. There were 6 independent non-contiguous amplicons (10.4-69.4 Mb) on chromosome 2, and the largest contiguous region was 1.7 Mb bounded by NAG and an EST (clone: 757451); the smallest region was 27 Kb including an EST (clone: 241343), NCYM, and MYCN. Using a probabilistic approach to identify single copy number changes, we systemically investigated the genomic alterations occurring in Stage 1 and Stage 4 NBs with and without MYCN amplification (stage 1-, 4-, and 4+). We have not found genomic alterations universally present in all (100%) three subgroups of NBs. However we identified both common and unique patterns of genomic imbalance in NB including gain of 7q32, 17q21, 17q23-24 and loss of 3p21 were common to all three categories. Finally we confirm that the most frequent specific changes in Stage 4+ tumors were the loss of 1p36 with gain of 2p24-25 and they had fewer genomic alterations compared to either stage 1 or 4-, indicating that for this subgroup of poor risk NB requires a smaller number of genomic changes are required to develop the malignant phenotype. CONCLUSIONS: cDNA A-CGH analysis is an efficient method for the detection and characterization of amplicons. Furthermore we were able to detect single copy number changes using our probabilistic approach and identified genomic alterations specific to stage and MYCN amplification.