Adipose mitochondrial metabolism controls body growth by modulating systemic cytokine and insulin signaling.

Animals must adapt their growth to fluctuations in nutrient availability to ensure proper development. These adaptations often rely on specific nutrient-sensing tissues that control whole-body physiology through inter-organ communication. While the signaling mechanisms that underlie this communication are well studied, the contributions of metabolic alterations in nutrient-sensing tissues are less clear. Here, we show how the reprogramming of adipose mitochondria controls whole-body growth in Drosophila larvae. We find that dietary nutrients alter fat-body mitochondrial morphology to lower their bioenergetic activity, leading to rewiring of fat-body glucose metabolism. Strikingly, we find that genetic reduction of mitochondrial bioenergetics just in the fat body is sufficient to accelerate body growth and development. These growth effects are caused by inhibition of the fat-derived secreted peptides ImpL2 and tumor necrosis factor alpha (TNF-α)/Eiger, leading to enhanced systemic insulin signaling. Our work reveals how reprogramming of mitochondrial metabolism in one nutrient-sensing tissue can couple nutrient availability to whole-body growth.

Metaproteomics method to determine carbon sources and assimilation pathways of species in microbial communities.

Measurements of stable carbon isotope ratios (δ13C) are widely used in biology to address questions regarding food sources and metabolic pathways used by organisms. The analysis of these so-called stable isotope fingerprints (SIFs) for microbes involved in biogeochemical cycling and microbiota of plants and animals has led to major discoveries in environmental microbiology. Currently, obtaining SIFs for microbial communities is challenging as the available methods either only provide low taxonomic resolution, such as the use of lipid biomarkers, or are limited in throughput, such as nanoscale secondary ion MS imaging of single cells. Here we present "direct protein-SIF" and the Calis-p software package (https://sourceforge.net/projects/calis-p/), which enable high-throughput measurements of accurate δ13C values for individual species within a microbial community. We benchmark the method using 20 pure culture microorganisms and show that the method reproducibly provides SIF values consistent with gold-standard bulk measurements performed with an isotope ratio mass spectrometer. Using mock community samples, we demonstrate that SIF values can also be obtained for individual species within a microbial community. Finally, a case study of an obligate bacteria-animal symbiosis shows that direct protein-SIF confirms previous physiological hypotheses and can provide unexpected insights into the symbionts' metabolism. This confirms the usefulness of this approach to accurately determine δ13C values for different species in microbial community samples.

Assessing species biomass contributions in microbial communities via metaproteomics.

Microbial community structure can be analyzed by quantifying cell numbers or by quantifying biomass for individual populations. Methods for quantifying cell numbers are already available (e.g., fluorescence in situ hybridization, 16-S rRNA gene amplicon sequencing), yet high-throughput methods for assessing community structure in terms of biomass are lacking. Here we present metaproteomics-based methods for assessing microbial community structure using protein abundance as a measure for biomass contributions of individual populations. We optimize the accuracy and sensitivity of the method using artificially assembled microbial communities and show that it is less prone to some of the biases found in sequencing-based methods. We apply the method to communities from two different environments, microbial mats from two alkaline soda lakes, and saliva from multiple individuals. We show that assessment of species biomass contributions adds an important dimension to the analysis of microbial community structure.

Fast and Simple Analysis of MiSeq Amplicon Sequencing Data with MetaAmp.

Microbial community profiling by barcoded 16S rRNA gene amplicon sequencing currently has many applications in microbial ecology. The low costs of the parallel sequencing of multiplexed samples, combined with the relative ease of data processing and interpretation (compared to shotgun metagenomes) have made this an entry-level approach. Here we present the MetaAmp pipeline for processing of SSU rRNA gene and other non-coding or protein-coding amplicon sequencing data by investigators that are inexperienced with bioinformatics procedures. It accepts single-end or paired-end sequences in fasta or fastq format from various sequencing platforms. It includes read quality control, and merging of forward and reverse reads of paired-end reads. It makes use of UPARSE, Mothur, and the SILVA database for clustering, removal of chimeric reads, taxonomic classification, and generation of diversity metrics. The pipeline has been validated with a mock community of known composition. MetaAmp provides a convenient web interface as well as command line interface. It is freely available at: http://ebg.ucalgary.ca/metaamp. Since its launch 2 years ago, MetaAmp has been used >2,800 times, by many users worldwide.