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Steady-state protein abundance is set by four rates: transcription, translation, mRNA decay and protein decay. A given protein abundance can be obtained from infinitely many combinations of these rates. This raises the question of whether the natural rates for each gene result from historical accidents, or are there rules that give certain combinations a selective advantage? We address this question using high-throughput measurements in rapidly growing cells from diverse organisms to find that about half of the rate combinations do not exist: genes that combine high transcription with low translation are strongly depleted. This depletion is due to a trade-off between precision and economy: high transcription decreases stochastic fluctuations but increases transcription costs. Our theory quantitatively explains which rate combinations are missing, and predicts the curvature of the fitness function for each gene. It may guide the design of gene circuits with desired expression levels and noise. The same protein abundance can be achieved by many combinations of transcription, translation and degradation rates. Here, the authors find that genes combining high transcription with low translation rate are rare due to a trade-off between the cost of transcription and expression noise.

The comparison of cross-linking and immunoprecipitation (CLIP) and photoactivatable ribonucleoside–enhanced CLIP (PAR-CLIP) protocols shows specific biases of each method in enriching subsets of binding sites of RNA-binding proteins and shows ways around these biases. Cross-linking and immunoprecipitation (CLIP) is increasingly used to map transcriptome-wide binding sites of RNA-binding proteins. We developed a method for CLIP data analysis, and applied it to compare CLIP with photoactivatable ribonucleoside–enhanced CLIP (PAR-CLIP) and to uncover how differences in cross-linking and ribonuclease digestion affect the identified sites. We found only small differences in accuracies of these methods in identifying binding sites of HuR, which binds low-complexity sequences, and Argonaute 2, which has a complex binding specificity. We found that cross-link–induced mutations led to single-nucleotide resolution for both PAR-CLIP and CLIP. Our results confirm the expectation from original CLIP publications that RNA-binding proteins do not protect their binding sites sufficiently under the denaturing conditions used during the CLIP procedure, and we show that extensive digestion with sequence-specific RNases strongly biases the recovered binding sites. This bias can be substantially reduced by milder nuclease digestion conditions.

Recent advances have enabled powerful methods to sort tumors into prognosis and treatment groups. We are still missing, however, a general theoretical framework to understand the vast diversity of tumor gene expression and mutations. Here we present a framework based on multi-task evolution theory, using the fact that tumors need to perform multiple tasks that contribute to their fitness. We find that trade-offs between tasks constrain tumor gene-expression to a continuum bounded by a polyhedron whose vertices are gene-expression profiles, each specializing in one task. We find five universal cancer tasks across tissue-types: cell-division, biomass and energy, lipogenesis, immune-interaction and invasion and tissue-remodeling. Tumors that specialize in a task are sensitive to drugs that interfere with this task. Driver, but not passenger, mutations tune gene-expression towards specialization in specific tasks. This approach can integrate additional types of molecular data into a framework of tumor diversity grounded in evolutionary theory. The evolutionary trade-offs faced by tumors are poorly understood, yet could contribute to the molecular diversity of cancer. Here, the authors analyze the diversity of genetic alterations, genes expressed and drug sensitivities among solid tumors to explore what evolutionary trade-offs may explain the molecular diversity of tumors.

The tumor microenvironment plays a crucial role in determining response to treatment. This involves a series of interconnected changes in the cellular landscape, spatial organization, and extracellular matrix composition. However, assessing these alterations simultaneously is challenging from a spatial perspective, due to the limitations of current high-dimensional imaging techniques and the extent of intratumoral heterogeneity over large lesion areas. In this study, we introduce a spatial proteomic workflow termed Hyperplexed Immunofluorescence Imaging (HIFI) that overcomes these limitations. HIFI allows for the simultaneous analysis of > 45 markers in fragile tissue sections at high magnification, using a cost-effective high-throughput workflow. We integrate HIFI with machine learning feature detection, graph-based network analysis, and cluster-based neighborhood analysis to analyze the microenvironment response to radiation therapy in a preclinical model of glioblastoma, and compare this response to a mouse model of breast-to-brain metastasis. Here we show that glioblastomas undergo extensive spatial reorganization of immune cell populations and structural architecture in response to treatment, while brain metastases show no comparable reorganization. Our integrated spatial analyses reveal highly divergent responses to radiation therapy between brain tumor models, despite equivalent radiotherapy benefit. Improved imaging techniques are required to help advance our understanding of the complex role of the tumour microenvironment (TME). Here, the authors develop a high-throughput, highly multiplexed tissue visualisation workflow and demonstrate its utility by characterising the response of the TME to radiotherapy in preclinical models of glioblastoma.

A new target for diabetes and obesity drugs A microRNA (miRNA) microarray analysis of the livers of two mouse models of obesity show that the expression of miRNA-103 and miRNA-107 is upregulated in obesity. Silencing these miRNAs improves glucose homeostasis and insulin sensitivity. Turning them on in liver or fat is sufficient to impair glucose homeostasis. Caveolin-1, a protein involved in regulation of insulin signalling, is a direct target gene of the miRNAs. These findings show that these miRNAs regulate insulin sensitivity and identify a new potential target for the treatment of type 2 diabetes and obesity. Defects in insulin signalling are among the most common and earliest defects that predispose an individual to the development of type 2 diabetes 1 , 2 , 3 . MicroRNAs have been identified as a new class of regulatory molecules that influence many biological functions, including metabolism 4 , 5 . However, the direct regulation of insulin sensitivity by microRNAs in vivo has not been demonstrated. Here we show that the expression of microRNAs 103 and 107 (miR-103/107) is upregulated in obese mice. Silencing of miR-103/107 leads to improved glucose homeostasis and insulin sensitivity. In contrast, gain of miR-103/107 function in either liver or fat is sufficient to induce impaired glucose homeostasis. We identify caveolin-1, a critical regulator of the insulin receptor, as a direct target gene of miR-103/107. We demonstrate that caveolin-1 is upregulated upon miR-103/107 inactivation in adipocytes and that this is concomitant with stabilization of the insulin receptor, enhanced insulin signalling, decreased adipocyte size and enhanced insulin-stimulated glucose uptake. These findings demonstrate the central importance of miR-103/107 to insulin sensitivity and identify a new target for the treatment of type 2 diabetes and obesity.

Altered growth and development of the endocrine pancreas is a frequent cause of the hyperglycemia associated with diabetes. Here we show that microRNA-375 (miR-375), which is highly expressed in pancreatic islets, is required for normal glucose homeostasis. Mice lacking miR-375 (375KO) are hyperglycemic, exhibit increased total pancreatic α-cell numbers, fasting and fed plasma glucagon levels, and increased gluconeogenesis and hepatic glucose output. Furthermore, pancreatic β-cell mass is decreased in 375KO mice as a result of impaired proliferation. In contrast, pancreatic islets of obese mice (ob/ob), a model of increased β-cell mass, exhibit increased expression of miR-375. Genetic deletion of miR-375 from these animals (375/ob) profoundly diminished the proliferative capacity of the endocrine pancreas and resulted in a severely diabetic state. Bioinformatic analysis of transcript data from 375KO islets revealed that miR-375 regulates a cluster of genes controlling cellular growth and proliferation. These data provide evidence that miR-375 is essential for normal glucose homeostasis, α- and β-cell turnover, and adaptive β-cell expansion in response to increasing insulin demand in insulin resistance.

Key Points Experimental approaches such as genetic screening, mRNA expression profiling, and Argonaute crosslinking and immunoprecipitation aim to identify microRNA (miRNA) targets and/or individual binding sites within them. Computational analyses of the resultant high-throughput data reveal the miRNA targets with high accuracy and resolution. Factors that define functional miRNA interaction sites include miRNA 'seed' complementarity, structural accessibility, and sequence and positional biases. These factors support modulatory interactions with RNA-binding proteins. miRNA seed families and clusters of co-expressed miRNAs are prevalent and may contribute to regulation of individual pathways across tissues and developmental stages. 'Non-canonical' miRNA-binding sites seem to be prevalent, and their functionality should be further investigated. Outcomes of miRNA–target interactions include repression and increased precision of target gene expression, as well as induction of correlations in the expression levels of different targets. Computational modelling of miRNA–target interactions has provided insights into their consequences on target expression. This Review discusses the main experimental approaches for microRNA (miRNA) target identification, as well as the modulators and the consequences of miRNA–target interactions. It also highlights the role of computational modelling in furthering the conceptual understanding of miRNA functions in gene regulatory networks. Comparative genomics analyses and high-throughput experimental studies indicate that a microRNA (miRNA) binds to hundreds of sites across the transcriptome. Although the knockout of components of the miRNA biogenesis pathway has profound phenotypic consequences, most predicted miRNA targets undergo small changes at the mRNA and protein levels when the expression of the miRNA is perturbed. Alternatively, miRNAs can establish thresholds in and increase the coherence of the expression of their target genes, as well as reduce the cell-to-cell variability in target gene expression. Here, we review the recent progress in identifying miRNA targets and the emerging paradigms of how miRNAs shape the dynamics of target gene expression.

Combining 3D cultures such as tumor spheroids and organoids with spatial omics holds great potential for tissue biology and cancer research. Yet, this potential is presently limited by technical and financial challenges of spatial omics methods and 3D cultures. To address this, we combine dye diffusion, the Smart-seq3xpress protocol for deep single-cell gene expression profiling, and dedicated probabilistic inference methods into diffusion Smart-seq3 (Smart-seq3D), to reveal the transcriptome of single cells along with their position along the core-periphery axis of spheroids. Applying Smart-seq3D to triple-negative breast tumor spheroids identifies thousands of spatial genes and reveals continuous, ungated spatial gene expression. Spatial gene and pathway expression patterns suggest biologies specific to spheroid regions, which we validate by immunostainings and pharmacological interventions. We use the Smart-seq3D data to test evolutionary principles of spatial tumor heterogeneity. Finally, we characterize aspects of tumor heterogeneity captured by 3D spheroids that are missing from 2D cultures but found in tumors in vivo . Smart-seq3D can offer a cost-efficient approach to explore how cells adapt their transcriptome to different micro-environments, reveal spatial determinants of drug resistance and could serve to characterize spatial interactions between cancer and stromal/immune cells in 3D co-cultures.

Advances in multiplex histology allow surveying millions of cells, dozens of cell types, and up to thousands of phenotypes within the spatial context of tissue sections. This leads to a combinatorial challenge in (a) summarizing the cellular and phenotypic architecture of tissues and (b) identifying phenotypes with interesting spatial architecture. To address this, we combine ideas from community ecology and machine learning into niche-phenotype mapping (NIPMAP). NIPMAP takes advantage of geometric constraints on local cellular composition imposed by the niche structure of tissues in order to automatically segment tissue sections into niches and their interfaces. Projecting phenotypes on niches and their interfaces identifies previously-reported and previously-unreported spatially-driven phenotypes, concisely summarizes the phenotypic architecture of tissues, and reveals fundamental properties of tissue architecture. NIPMAP is applicable to both protein and RNA multiplex histology of healthy and diseased tissue. An open-source R/Python package implements NIPMAP. Multiplex histology faces the challenge of integrating tissue architecture with the identification of relevant spatial cellular phenotypes. Using community ecology principles, the authors propose NIPMAP, a tool for niche-phenotype mapping of multiplex histology data.

Pareto task inference (ParTI) computes a polytype that encloses a data set and determines the enrichment of features around the vertices (archetypes) of the polytype, which allows the identification of the task the archetype represents. We present the Pareto task inference method (ParTI; http://www.weizmann.ac.il/mcb/UriAlon/download/ParTI ) for inferring biological tasks from high-dimensional biological data. Data are described as a polytope, and features maximally enriched closest to the vertices (or archetypes) allow identification of the tasks the vertices represent. We demonstrate that human breast tumors and mouse tissues are well described by tetrahedrons in gene expression space, with specific tumor types and biological functions enriched at each of the vertices, suggesting four key tasks.