Climate, history, society over the last millennium in southeast Africa

Climate variability has been causally linked to the transformation of society in pre-industrial southeast Africa. A growing critique, however, challenges the simplicity of ideas that identify climate as an agent of past societal change; arguing instead that the value of historical climate–society research lies in understanding human vulnerability and resilience, as well as how past societies framed, responded and adapted to climatic phenomena. We work across this divide to present the first critical analysis of climate–society relationships in southeast Africa over the last millennium. To achieve this, we review the now considerable body of scholarship on the role of climate in regional societal transformation, and bring forward new perspectives on climate–society interactions across three areas and periods using the theoretical frameworks of vulnerability and resilience. We find that recent advances in paleoclimatology and archaeology give weight to the suggestion that responses to climate variability played an important part in early state formation in the Limpopo valley (1000–1300), though evidence remains insufficient to clarify similar debates concerning Great Zimbabwe (1300–1450/1520). Written and oral evidence from the Zambezi-Save (1500–1830) and KwaZulu-Natal areas (1760–1828) nevertheless reveals a plurality of past responses to climate variability. These were underpinned by the organization of food systems, the role of climate-related ritual and political power, social networks, and livelihood assets and capabilities, as well as the nature of climate variability itself. To conclude, we identify new lines of research on climate, history and society, and discuss how these can more directly inform contemporary African climate adaptation challenges

Treatment of organic resources before soil incorporation in semi-arid regions improves resilience to El Niño, and increases crop production and economic returns

We are grateful for support from the DFID-NERC El Niño programme in project NE P004830, “Building Resilience in Ethiopia’s Awassa region to Drought (BREAD)”, the ESRC NEXUS programme in project IEAS/POO2501/1, “Improving organic resource use in rural Ethiopia (IPORE)”, and the NERC ESPA programme in project NEK0104251 “Alternative carbon investments in ecosystems for poverty alleviation (ALTER)”. We are also grateful to Anke Fischer (James Hutton Insitute) for her comments on the paper.Peer reviewedPublisher PD

Differential epigenetic reprogramming in response to specific endocrine therapies promotes cholesterol biosynthesis and cellular invasion

Endocrine therapies target the activation of the oestrogen receptor alpha (ERα) via distinct mechanisms, but it is not clear whether breast cancer cells can adapt to treatment using drug-specific mechanisms. Here we demonstrate that resistance emerges via drug-specific epigenetic reprogramming. Resistant cells display a spectrum of phenotypical changes with invasive phenotypes evolving in lines resistant to the aromatase inhibitor (AI). Orthogonal genomics analysis of reprogrammed regulatory regions identifies individual drug-induced epigenetic states involving large topologically associating domains (TADs) and the activation of super-enhancers. AI-resistant cells activate endogenous cholesterol biosynthesis (CB) through stable epigenetic activation in vitro and in vivo. Mechanistically, CB sparks the constitutive activation of oestrogen receptors alpha (ERα) in AI-resistant cells, partly via the biosynthesis of 27-hydroxycholesterol. By targeting CB using statins, ERα binding is reduced and cell invasion is prevented. Epigenomic-led stratification can predict resistance to AI in a subset of ERα-positive patients

Genomic prediction of complex human traits: relatedness, trait architecture and predictive meta-models

We explore the prediction of individuals' phenotypes for complex traits using genomic data. We compare several widely used prediction models, including Ridge Regression, LASSO and Elastic Nets estimated from cohort data, and polygenic risk scores constructed using published summary statistics from genome-wide association meta-analyses (GWAMA). We evaluate the interplay between relatedness, trait architecture and optimal marker density, by predicting height, body mass index (BMI) and high-density lipoprotein level (HDL) in two data cohorts, originating from Croatia and Scotland. We empirically demonstrate that dense models are better when all genetic effects are small (height and BMI) and target individuals are related to the training samples, while sparse models predict better in unrelated individuals and when some effects have moderate size (HDL). For HDL sparse models achieved good across-cohort prediction, performing similarly to the GWAMA risk score and to models trained within the same cohort, which indicates that, for predicting traits with moderately sized effects, large sample sizes and familial structure become less important, though still potentially useful. Finally, we propose a novel ensemble of whole-genome predictors with GWAMA risk scores and demonstrate that the resulting meta-model achieves higher prediction accuracy than either model on its own. We conclude that although current genomic predictors are not accurate enough for diagnostic purposes, performance can be improved without requiring access to large-scale individual-level data. Our methodologically simple meta-model is a means of performing predictive meta-analysis for optimizing genomic predictions and can be easily extended to incorporate multiple population-level summary statistics or other domain knowledge