THADA regulates the organismal balance between energy storage and heat production

Human susceptibility to obesity is mainly genetic, yet the underlying evolutionary drivers causing variation from person to person are not clear. One theory rationalizes that populations that have adapted to warmer climates have reduced their metabolic rates, thereby increasing their propensity to store energy. We uncover here the function of a gene that supports this theory. THADA is one of the genes most strongly selected during evolution as humans settled in different climates. We report here that THADA knockout flies are obese, hyperphagic, have reduced energy production, and are sensitive to the cold. THADA binds the sarco/ER Ca2+ ATPase (SERCA) and acts on it as an uncoupler. Reducing SERCA activity in THADA mutant flies rescues their obesity, pinpointing SERCA as a key effector of THADA function. In sum, this identifies THADA as a regulator of the balance between energy consumption and energy storage, which was selected during human evolution

Pharmacoproteomic characterisation of human colon and rectal cancer

Most molecular cancer therapies act on protein targets but data on the proteome status of patients and cellular models for proteome-guided pre-clinical drug sensitivity studies are only beginning to emerge. Here, we profiled the proteomes of 65 colorectal cancer (CRC) cell lines to a depth of > 10,000 proteins using mass spectrometry. Integration with proteomes of 90 CRC patients and matched transcriptomics data defined integrated CRC subtypes, highlighting cell lines representative of each tumour subtype. Modelling the responses of 52 CRC cell lines to 577 drugs as a function of proteome profiles enabled predicting drug sensitivity for cell lines and patients. Among many novel associations, MERTK was identified as a predictive marker for resistance towards MEK1/2 inhibitors and immunohistochemistry of 1,074 CRC tumours confirmed MERTK as a prognostic survival marker. We provide the proteomic and pharmacological data as a resource to the community to, for example, facilitate the design of innovative prospective clinical trials. © 2017 The Authors. Published under the terms of the CC BY 4.0 licens

A fitness assay for comparing RNAi effects across multiple C. elegans genotypes

<p>Abstract</p> <p>Background</p> <p>RNAi technology by feeding of <it>E. coli </it>containing dsRNA in <it>C. elegans </it>has significantly contributed to further our understanding of many different fields, including genetics, molecular biology, developmental biology and functional genomics. Most of this research has been carried out in a single genotype or genetic background. However, RNAi effects in one genotype do not reveal the allelic effects that segregate in natural populations and contribute to phenotypic variation.</p> <p>Results</p> <p>Here we present a method that allows for rapidly comparing RNAi effects among diverse genotypes at an improved high throughput rate. It is based on assessing the fitness of a population of worms by measuring the rate at which <it>E. coli </it>is consumed. Critically, we demonstrate the analytical power of this method by QTL mapping the loss of RNAi sensitivity (in the germline) in a recombinant inbred population derived from a cross between Bristol and a natural isolate from Hawaii. Hawaii has lost RNAi sensitivity in the germline. We found that polymorphisms in <it>ppw-1 </it>contribute to this loss of RNAi sensitivity, but that other loci are also likely to be important.</p> <p>Conclusions</p> <p>In summary, we have established a fast method that improves the throughput of RNAi in liquid, that generates quantitative data, that is easy to implement in most laboratories, and importantly that enables QTL mapping using RNAi.</p

Pharmacoproteomic characterisation of human colon and rectal cancer

Colorectal cancer (CRC) is one of the top three most common cancers and among the top four causes of cancer-related deaths worldwide (Torre et al., 2015). CRC patients are well characterised on the transcriptome and proteome level, but proteomics data on representative cell lines as model systems for pre-clinical drug sensitivity studies lag behind. Here, label-free quantitative mass spectrometry was used to characterise the kinomes and full proteomes of 65 CRC cell lines, collectively termed the CRC65 cell line panel. This data was integrated with proteomics data on patient samples, as well as public transcriptome and drug sensitivity datasets, which were reanalysed from raw data in order to unify and streamline the data processing. Protein/mRNA ratios were constant across these datasets, enabling linear prediction of protein abundance from mRNA abundance after appropriate adjustment, which was used for mRNA-guided missing value imputation. An exploration of secondary imputation methods prompted the development of a complementary method for minimum-guided missing value imputation. Combining the proteomics datasets on cell lines and patients led to the discovery of integrated proteomic subtypes of CRC and enabled the identification of representative cell lines for each of them. Modelling publicly available dose-response data generated by four large-scale drug sensitivity studies as a function of kinome/full proteome profiles fuelled the prediction of drug sensitivity for cell lines and patients, allowed the identification of drugs differentially effective between the different integrated proteomic subtypes and revealed MERTK as a predictive biomarker for resistance towards MEK1/2 inhibitors. This predictive role of MERTK was subsequently confirmed using in vitro experiments, while immunohistochemistry of TMAs from 1,074 tumours generated as part of the QUASAR2 clinical trial unveiled that MERTK is also a prognostic biomarker in CRC. This dataset will be made available to the scientific community to facilitate the design of prospective clinical studies.</p

Pharmacoproteomic characterisation of human colon and rectal cancer

Colorectal cancer (CRC) is one of the top three most common cancers and among the top four causes of cancer-related deaths worldwide (Torre et al., 2015). CRC patients are well characterised on the transcriptome and proteome level, but proteomics data on representative cell lines as model systems for pre-clinical drug sensitivity studies lag behind. Here, label-free quantitative mass spectrometry was used to characterise the kinomes and full proteomes of 65 CRC cell lines, collectively termed the CRC65 cell line panel. This data was integrated with proteomics data on patient samples, as well as public transcriptome and drug sensitivity datasets, which were reanalysed from raw data in order to unify and streamline the data processing. Protein/mRNA ratios were constant across these datasets, enabling linear prediction of protein abundance from mRNA abundance after appropriate adjustment, which was used for mRNA-guided missing value imputation. An exploration of secondary imputation methods prompted the development of a complementary method for minimum-guided missing value imputation. Combining the proteomics datasets on cell lines and patients led to the discovery of integrated proteomic subtypes of CRC and enabled the identification of representative cell lines for each of them. Modelling publicly available dose-response data generated by four large-scale drug sensitivity studies as a function of kinome/full proteome profiles fuelled the prediction of drug sensitivity for cell lines and patients, allowed the identification of drugs differentially effective between the different integrated proteomic subtypes and revealed MERTK as a predictive biomarker for resistance towards MEK1/2 inhibitors. This predictive role of MERTK was subsequently confirmed using in vitro experiments, while immunohistochemistry of TMAs from 1,074 tumours generated as part of the QUASAR2 clinical trial unveiled that MERTK is also a prognostic biomarker in CRC. This dataset will be made available to the scientific community to facilitate the design of prospective clinical studies.</p

Optimized Enrichment of Phosphoproteomes by Fe-IMAC Column Chromatography

Phosphorylation is among the most important post-translational modifications of proteins and has numerous regulatory functions across all domains of life. However, phosphorylation is often substoichiometric, requiring selective and sensitive methods to enrich phosphorylated peptides from complex cellular digests. Various methods have been devised for this purpose and we have recently described a Fe-IMAC HPLC column chromatography setup which is capable of comprehensive, reproducible, and selective enrichment of phosphopeptides out of complex peptide mixtures. In contrast to other formats such as StageTips or batch incubations using TiO2 or Ti-IMAC beads, Fe-IMAC HPLC columns do not suffer from issues regarding incomplete phosphopeptide binding or elution and enrichment efficiency scales linearly with the amount of starting material. Here, we provide a step-by-step protocol for the entire phosphopeptide enrichment procedure including sample preparation (lysis, digestion, desalting), Fe-IMAC column chromatography (column setup, operation, charging), measurement by LC-MS/MS (nHPLC gradient, MS parameters) and data analysis (MaxQuant). To increase throughput, we have optimized several key steps such as the gradient time of the Fe-IMAC separation (15 min per enrichment), the number of consecutive enrichments possible between two chargings (>20) and the column recharging itself (90 %) identification of more than 10,000 unique phosphopeptides from 1 mg of HeLa digest within 2 h of measurement time (Q Exactive Plus)

moCluster: Identifying Joint Patterns Across Multiple Omics Data Sets

Increasingly, multiple omics approaches are being applied to understand the complexity of biological systems. Yet, computational approaches that enable the efficient integration of such data are not well developed. Here, we describe a novel algorithm, termed moCluster, which discovers joint patterns among multiple omics data. The method first employs a multiblock multivariate analysis to define a set of latent variables representing joint patterns across input data sets, which is further passed to an ordinary clustering algorithm in order to discover joint clusters. Using simulated data, we show that moCluster’s performance is not compromised by issues present in iCluster/iCluster+ (notably, the nondeterministic solution) and that it operates 100× to 1000× faster than iCluster/iCluster+. We used moCluster to cluster proteomic and transcriptomic data from the NCI-60 cell line panel. The resulting cluster model revealed different phenotypes across cellular subtypes, such as doubling time and drug response. Applying moCluster to methylation, mRNA, and protein data from a large study on colorectal cancer patients identified four molecular subtypes, including one characterized by microsatellite instability and high expression of genes/proteins involved in immunity, such as PDL1, a target of multiple drugs currently in development. The other three subtypes have not been discovered before using single data sets, which clearly illustrates the molecular complexity of oncogenesis and the need for holistic, multidata analysis strategies

Phosphoproteome Profiling Reveals Molecular Mechanisms of Growth-Factor-Mediated Kinase Inhibitor Resistance in EGFR-Overexpressing Cancer Cells

Although substantial progress has been made regarding the use of molecularly targeted cancer therapies, resistance almost invariably develops and presents a major clinical challenge. The tumor microenvironment can rescue cancer cells from kinase inhibitors by growth-factor-mediated induction of pro-survival pathways. Here we show that epidermal growth factor receptor (EGFR) inhibition by Gefitinib is counteracted by growth factors, notably FGF2, and we assessed the global molecular consequences of this resistance at the proteome and phosphoproteome level in A431 cells. Tandem mass tag peptide labeling and quantitative mass spectrometry allowed the identification and quantification of 22 000 phosphopeptides and 8800 proteins in biological triplicates without missing values. The data show that FGF2 protects the cells from the antiproliferative effect of Gefitinib and largely prevents reprogramming of the proteome and phosphoproteome. Simultaneous EGFR/FGFR or EGFR/GSG2 (Haspin) inhibition overcomes this resistance, and the phosphoproteomic experiments further prioritized the RAS/MEK/ERK as well as the PI3K/mTOR axis for combination treatment. Consequently, the MEK inhibitor Trametinib prevented FGF2-mediated survival of EGFR inhibitor-resistant cells when used in combination with Gefitinib. Surprisingly, the PI3K/mTOR inhibitor Omipalisib reversed resistance mediated by all four growth factors tested, making it an interesting candidate for mitigating the effects of the tumor microenvironment

Optimized Enrichment of Phosphoproteomes by Fe-IMAC Column Chromatography

Phosphorylation is among the most important post-translational modifications of proteins and has numerous regulatory functions across all domains of life. However, phosphorylation is often substoichiometric, requiring selective and sensitive methods to enrich phosphorylated peptides from complex cellular digests. Various methods have been devised for this purpose and we have recently described a Fe-IMAC HPLC column chromatography setup which is capable of comprehensive, reproducible, and selective enrichment of phosphopeptides out of complex peptide mixtures. In contrast to other formats such as StageTips or batch incubations using TiO2 or Ti-IMAC beads, Fe-IMAC HPLC columns do not suffer from issues regarding incomplete phosphopeptide binding or elution and enrichment efficiency scales linearly with the amount of starting material. Here, we provide a step-by-step protocol for the entire phosphopeptide enrichment procedure including sample preparation (lysis, digestion, desalting), Fe-IMAC column chromatography (column setup, operation, charging), measurement by LC-MS/MS (nHPLC gradient, MS parameters) and data analysis (MaxQuant). To increase throughput, we have optimized several key steps such as the gradient time of the Fe-IMAC separation (15 min per enrichment), the number of consecutive enrichments possible between two chargings (>20) and the column recharging itself (90 %) identification of more than 10,000 unique phosphopeptides from 1 mg of HeLa digest within 2 h of measurement time (Q Exactive Plus)