Nutritional, environmental and economic impacts of ultra-processed food consumption in Australia.

OBJECTIVE: To quantify the full life cycle impacts of ultra-processed foods (UPF) for key environmental, economic and nutritional indicators to identify trade-offs between UPF contribution to broad-scope sustainability. DESIGN: Using 24-h dietary recalls along with an input-output database for the Australian economy, dietary environmental and economic impacts were quantified in this national representative cross-sectional analysis. Food items were classified into non-UPF and UPF using the NOVA system, and dietary energy contribution from non-UPF and UPF fractions in diets was estimated. Thereafter, associations between nutritional, environmental and economic impacts of non-UPF and UPF fractions of diets were examined using a multi-dimensional nutritional geometry representation. SETTING: National Nutrition and Physical Activity Survey 2011-2012 of Australia. PARTICIPANTS: Respondents (n 5344) aged > 18 years with 1 d of 24-h dietary recall data excluding respondents with missing values and outlier data points and under reporters. RESULTS: Australian diets rich in UPF were associated with reduced nutritional quality, high greenhouse gas emissions, energy use, and increased employment and income associated with the food supply chains. The environmental and economic impacts associated with the UPF portion of diets become more distinct when the diets are standardised to average protein recommendation. CONCLUSION: Increased consumption of UPF has socio-economic benefits, but this comes with adverse effects on the environment and public health. Consideration of such trade-offs is important in identifying policy and other mechanisms regarding UPF for establishing healthy and sustainable food systems.

Illuminating the dark phosphoproteome.

Protein phosphorylation is a major regulator of protein function and biological outcomes. This was first recognized through functional biochemical experiments, and in the past decade, major technological advances in mass spectrometry have enabled the study of protein phosphorylation on a global scale. This rapidly growing field of phosphoproteomics has revealed that more than 100,000 distinct phosphorylation events occur in human cells, which likely affect the function of every protein. Phosphoproteomics has improved the understanding of the function of even the most well-characterized protein kinases by revealing new downstream substrates and biology. However, current biochemical and bioinformatic approaches have only identified kinases for less than 5% of the phosphoproteome, and functional assignments of phosphosites are almost negligible. Notably, our understanding of the relationship between kinases and their substrates follows a power law distribution, with almost 90% of phosphorylation sites currently assigned to the top 20% of kinases. In addition, more than 150 kinases do not have a single known substrate. Despite a small group of kinases dominating biomedical research, the number of substrates assigned to a kinase does not correlate with disease relevance as determined by pathogenic human mutation prevalence and mouse model phenotypes. Improving our understanding of the substrates targeted by all kinases and functionally annotating the phosphoproteome will be broadly beneficial. Advances in phosphoproteomics technologies, combined with functional screening approaches, should make it feasible to illuminate the connectivity and functionality of the entire phosphoproteome, providing enormous opportunities for discovering new biology, therapeutic targets, and possibly diagnostics.

ORTI: An Open-Access Repository of Transcriptional Interactions for Interrogating Mammalian Gene Expression Data.

Transcription factors (TFs) play a fundamental role in coordinating biological processes in response to stimuli. Consequently, we often seek to determine the key TFs and their regulated target genes (TGs) amidst gene expression data. This requires a knowledge-base of TF-TG interactions, which would enable us to determine the topology of the transcriptional network and predict novel regulatory interactions. To address this, we generated an Open-access Repository of Transcriptional Interactions, ORTI, by integrating available TF-TG interaction databases. These databases rely on different types of experimental evidence, including low-throughput assays, high-throughput screens, and bioinformatics predictions. We have subsequently categorised TF-TG interactions in ORTI according to the quality of this evidence. To demonstrate its capabilities, we applied ORTI to gene expression data and identified modulated TFs using an enrichment analysis. Combining this with pairwise TF-TG interactions enabled us to visualise temporal regulation of a transcriptional network. Additionally, ORTI enables the prediction of novel TF-TG interactions, based on how well candidate genes co-express with known TGs of the target TF. By filtering out known TF-TG interactions that are unlikely to occur within the experimental context, this analysis predicts context-specific TF-TG interactions. We show that this can be applied to experimental designs of varying complexities. In conclusion, ORTI is a rich and publicly available database of experimentally validated mammalian transcriptional interactions which is accompanied with tools that can identify and predict transcriptional interactions, serving as a useful resource for unravelling the topology of transcriptional networks.