Biology subjects

Larsson, O.

Publications and source records attributed to Larsson, O..

4 recordsLinked to original sources

bioRxiv2017

Translational and HIF1α-dependent metabolic reprograming underpin oncometabolomeplasticity and synergy between oncogenic kinase inhibitors and biguanides.

There is heightened interest to devise therapies that target the oncometabolome. We show that kinase inhibitors (KIs) and biguanides synergistically target melanoma, leukemia, and breast, colon and renal cancer cells, but not non-transformed cells. Metabolic profiling confirmed opposing effects of KIs and biguanides on glycolysis, but this was insufficient to explain the observed synergy between the drugs. Rather, we define a critical role for the synthesis of non-essential amino acids (NEAA) aspartate, asparagine and serine as well as reductive glutamine metabolism, in determining the sensitivity of cancer cells to KI - biguanide combinations. The mTORC1/4E-BP axis regulates aspartate, asparagine and serine synthesis by modulating translation of mRNAs encoding PC, ASNS, PHGDH and PSAT1. Ablation of 4E-BP1 and 2 results in a dramatic increase in serine, aspartate and asparagine levels and a substantial decrease in sensitivity of breast cancer and melanoma cells to KI - biguanide combinations. In turn, efficacy of KI - biguanide combinations is impeded by HIF1 and sustained reductive glutamine metabolism. These findings identify hitherto unappreciated translational reprograming of NEAA synthesis and HIF1-dependent stimulation of reductive glutamine metabolism as critical metabolic vulnerabilities of cancer that underpin synergy between KIs and biguanides.

cancer biology↗

bioRxiv2017

Genome-wide analysis of differential translation and differential translational buffering using anota2seq

mRNA translation plays an evolutionarily conserved role in homeostasis and when dysregulated results in various disorders. Optimal and universally applicable analytical methods to study transcriptome-wide changes in translational efficiency are therefore critical for understanding the complex role of translation regulation under physiological and pathological conditions. Techniques used to interrogate translatomes, including polysome- and ribosome-profiling, require adjustment for changes in total mRNA levels to capture bona fide alterations in translational efficiency. Herein, we present the anota2seq algorithm for such analysis using data from ribosome- or polysome-profiling quantified by DNA-microarrays or RNA sequencing, which outperforms current methods for identification of changes in translational efficiency. In contrast to available analytical methods, anota2seq also allows capture of an underappreciated mode for regulation of gene expression whereby translation acts as a buffering mechanism which maintains constant protein levels despite fluctuations in mRNA levels (\"translational buffering\"). Application of anota2seq shows that insulin affects gene expression at multiple levels, in a largely mTOR-dependent manner. Moreover, insulin induces levels of a subset of mRNAs independently of mTOR that undergo translational buffering upon mTOR inhibition. Thus, the universal anota2seq algorithm allows efficient and hitherto unprecedented interrogation of translatomes and enables studies of translational buffering which represents an unexplored mechanism for regulating of gene expression.

bioinformatics↗

bioRxiv2017

Normalization of Mass Spectrometry Data (NOMAD)

MotivationiTRAQ reagent-based mass spectrometry (MS) is a commonly used technology for identification and quantification of proteins in biological samples. Such studies are often performed over multiple MS runs, potentially resulting in introduction of MS run bias that could affect downstream analysis. iTRAQ MS data have therefore commonly been normalized using a reference sample which is included in each MS run. We show, however, that such normalization does not efficiently remove systematic MS run bias. A linear model approach was previously proposed to improve on the reference normalization approach but does not computationally scale to larger data. Here we describe the NOMAD (normalization of mass spectrometry data) R package which implements a computationally efficient ANOVA normalization approach with protein assembly functionality.\n\nResultsNOMAD provides the same advantages as the linear regression solution but is more computationally efficient which allows superior scaling to larger sample sizes. Moreover, NOMAD efficiently removes bias which allows for valid across MS run comparisons.\n\nAvailabilityThe NOMAD Bioconductor package: www.bioconductor.org\n\nContactola.larsson@ki.se; carl.murie@ki.se

bioinformatics↗

bioRxiv2017

Polysome-profiling in small tissue samples

Polysome-profiling is commonly used to study genome wide patterns of translational efficiency, i.e. the translatome. The standard approach for collecting efficiently translated polysome-associated RNA results in laborious extraction of RNA from a large volume spread across multiple fractions. This property makes polysome-profiling inconvenient for larger experimental designs or samples with low RNA amounts such as primary cells or frozen tissues. To address this we optimized a non-linear sucrose gradient which reproducibly enriches for mRNAs associated with >3 ribosomes in only one or two fractions, thereby reducing sample handling 5-10 fold. The technique can be applied to cells and frozen tissue samples from biobanks, and generates RNA with a quality reflecting the starting material. When coupled with smart-seq2, a single-cell RNA sequencing technique, translatomes from small tissue samples can be obtained. Translatomes acquired using optimized non-linear gradients are very similar to those obtained when applying linear gradients. Polysome-profiling using optimized nonlinear gradients in HCT-116 cells with or without p53 identified a translatome associated with p53 status under serum starvation. Thus, here we present a polysome-profiling technique applicable to larger studies, primary cells where RNA amount is low and frozen tissue samples.

molecular biology↗