Microenvironmental reorganization in brain tumors following radiotherapy and recurrence revealed by hyperplexed immunofluorescence imaging.

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.

NIPMAP: niche-phenotype mapping of multiplex histology data by community ecology.

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.

Microscopy-based phenotypic monitoring of MDA-MB-231 spheroids allows the evaluation of phenotype-directed therapy.

Breast cancer (BC) is the most commonly diagnosed cancer among women. Prognosis has improved over the years, to a large extent, owing to personalized therapy informed by molecular profiling of hormone receptors. However, there is a need for new therapeutic approaches for a subgroup of BCs lacking molecular markers, the Triple Negative Breast Cancer (TNBC) subgroup. TNBC is the most aggressive type of BC, lacks an effective standard of care, shows high levels of resistance and relapse is often inevitable. High resistance to therapy has been hypothesized to be associated with high intratumoral phenotypic heterogeneity. To characterize and treat this phenotypic heterogeneity, we optimized a whole-mount staining and image analysis protocol for three-dimensions (3D) spheroids. Applying this protocol to TNBC spheroids located in the outer region of the spheroid the cells with selected phenotypes: dividing, migrating, and high mitochondrial mass phenotypes. To evaluate the relevance of phenotype-based targeting these cell populations were targeted with Paclitaxel, Trametinib, and Everolimus, respectively, in a dose-dependent manner. Single agents cannot specifically target all phenotypes at the same time. Therefore, we combined drugs that should target independent phenotype. With this rationale we observed that combining Trametinib and Everolimus achieves the highest cytotoxicity at lower doses from all the tested combinations. These findings suggest a rational approach to design treatments can be evaluated in spheroids prior to pre-clinical models and potentially reduce adverse effects.

Controls for Phylogeny and Robust Analysis in Pareto Task Inference.

Understanding the tradeoffs faced by organisms is a major goal of evolutionary biology. One of the main approaches for identifying these tradeoffs is Pareto task inference (ParTI). Two recent papers claim that results obtained in ParTI studies are spurious due to phylogenetic dependence (Mikami T, Iwasaki W. 2021. The flipping t-ratio test: phylogenetically informed assessment of the Pareto theory for phenotypic evolution. Methods Ecol Evol. 12(4):696-706) or hypothetical p-hacking and population-structure concerns (Sun M, Zhang J. 2021. Rampant false detection of adaptive phenotypic optimization by ParTI-based Pareto front inference. Mol Biol Evol. 38(4):1653-1664). Here, we show that these claims are baseless. We present a new method to control for phylogenetic dependence, called SibSwap, and show that published ParTI inference is robust to phylogenetic dependence. We show how researchers avoided p-hacking by testing for the robustness of preprocessing choices. We also provide new methods to control for population structure and detail the experimental tests of ParTI in systems ranging from ammonites to cancer gene expression. The methods presented here may help to improve future ParTI studies.

Tumour heterogeneity and the evolutionary trade-offs of cancer.

Tumours vary in gene expression programmes and genetic alterations. Understanding this diversity and its biological meaning requires a theoretical framework, which could in turn guide the development of more accurate prognosis and therapy. Here, we review the theory of multi-task evolution of cancer, which is based upon the premise that tumours evolve in the host and face selection trade-offs between multiple biological functions. This theory can help identify the major biological tasks that cancer cells perform and the trade-offs between these tasks. It introduces the concept of specialist tumours, which focus on one task, and generalist tumours, which perform several tasks. Specialist tumours are suggested to be sensitive to therapy targeting their main task. Driver mutations tune gene expression towards specific tasks in a tissue-dependent manner and thus help to determine whether a tumour is specialist or generalist. We discuss potential applications of the theory of multi-task evolution to interpret the spatial organization of tumours and intratumour heterogeneity.

Tumor diversity and the trade-off between universal cancer tasks.

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.

Central dogma rates and the trade-off between precision and economy in gene expression.

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.

An active ß-lactamase is a part of an orchestrated cell wall stress resistance network of Bacillus subtilis and related rhizosphere species.

A hallmark of the Gram-positive bacteria, such as the soil-dwelling bacterium Bacillus subtilis, is their cell wall. Here, we report that d-leucine and flavomycin, biofilm inhibitors targeting the cell wall, activate the ß-lactamase PenP. This ß-lactamase contributes to ampicillin resistance in B. subtilis under all conditions tested. In contrast, both Spo0A, a master regulator of nutritional stress, and the general cell wall stress response, differentially contribute to ß-lactam resistance under different conditions. To test whether ß-lactam resistance and ß-lactamase genes are widespread in other Bacilli, we isolated Bacillus species from undisturbed soils, and found that their genomes can encode up to five ß-lactamases with differentiated activity spectra. Surprisingly, the activity of environmental ß-lactamases and PenP, as well as the general stress response, resulted in a similarly reduced lag phase of the culture in the presence of ß-lactam antibiotics, with little or no impact on the logarithmic growth rate. The length of the lag phase may determine the outcome of the competition between ß-lactams and ß-lactamases producers. Overall, our work suggests that antibiotic resistance genes in B. subtilis and related species are ancient and widespread, and could be selected by interspecies competition in undisturbed soils.

Massively Parallel Interrogation of the Effects of Gene Expression Levels on Fitness.

Data of gene expression levels across individuals, cell types, and disease states is expanding, yet our understanding of how expression levels impact phenotype is limited. Here, we present a massively parallel system for assaying the effect of gene expression levels on fitness in Saccharomyces cerevisiae by systematically altering the expression level of ∼100 genes at ∼100 distinct levels spanning a 500-fold range at high resolution. We show that the relationship between expression levels and growth is gene and environment specific and provides information on the function, stoichiometry, and interactions of genes. Wild-type expression levels in some conditions are not optimal for growth, and genes whose fitness is greatly affected by small changes in expression level tend to exhibit lower cell-to-cell variability in expression. Our study addresses a fundamental gap in understanding the functional significance of gene expression regulation and offers a framework for evaluating the phenotypic effects of expression variation.

miR-184 Regulates Pancreatic ß-Cell Function According to Glucose Metabolism.

In response to fasting or hyperglycemia, the pancreatic ß-cell alters its output of secreted insulin; however, the pathways governing this adaptive response are not entirely established. Although the precise role of microRNAs (miRNAs) is also unclear, a recurring theme emphasizes their function in cellular stress responses. We recently showed that miR-184, an abundant miRNA in the ß-cell, regulates compensatory proliferation and secretion during insulin resistance. Consistent with previous studies showing miR-184 suppresses insulin release, expression of this miRNA was increased in islets after fasting, demonstrating an active role in the ß-cell as glucose levels lower and the insulin demand ceases. Additionally, miR-184 was negatively regulated upon the administration of a sucrose-rich diet in Drosophila, demonstrating strong conservation of this pathway through evolution. Furthermore, miR-184 and its target Argonaute2 remained inversely correlated as concentrations of extracellular glucose increased, underlining a functional relationship between this miRNA and its targets. Lastly, restoration of Argonaute2 in the presence of miR-184 rescued suppression of miR-375-targeted genes, suggesting these genes act in a coordinated manner during changes in the metabolic context. Together, these results highlight the adaptive role of miR-184 according to glucose metabolism and suggest the regulatory role of this miRNA in energy homeostasis is highly conserved.

Geometry of the Gene Expression Space of Individual Cells.

There is a revolution in the ability to analyze gene expression of single cells in a tissue. To understand this data we must comprehend how cells are distributed in a high-dimensional gene expression space. One open question is whether cell types form discrete clusters or whether gene expression forms a continuum of states. If such a continuum exists, what is its geometry? Recent theory on evolutionary trade-offs suggests that cells that need to perform multiple tasks are arranged in a polygon or polyhedron (line, triangle, tetrahedron and so on, generally called polytopes) in gene expression space, whose vertices are the expression profiles optimal for each task. Here, we analyze single-cell data from human and mouse tissues profiled using a variety of single-cell technologies. We fit the data to shapes with different numbers of vertices, compute their statistical significance, and infer their tasks. We find cases in which single cells fill out a continuum of expression states within a polyhedron. This occurs in intestinal progenitor cells, which fill out a tetrahedron in gene expression space. The four vertices of this tetrahedron are each enriched with genes for a specific task related to stemness and early differentiation. A polyhedral continuum of states is also found in spleen dendritic cells, known to perform multiple immune tasks: cells fill out a tetrahedron whose vertices correspond to key tasks related to maturation, pathogen sensing and communication with lymphocytes. A mixture of continuum-like distributions and discrete clusters is found in other cell types, including bone marrow and differentiated intestinal crypt cells. This approach can be used to understand the geometry and biological tasks of a wide range of single-cell datasets. The present results suggest that the concept of cell type may be expanded. In addition to discreet clusters in gene-expression space, we suggest a new possibility: a continuum of states within a polyhedron, in which the vertices represent specialists at key tasks.

Inferring biological tasks using Pareto analysis of high-dimensional data.

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.

Identification and consequences of miRNA-target interactions--beyond repression of gene expression.

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.

Linear superposition and prediction of bacterial promoter activity dynamics in complex conditions.

Bacteria often face complex environments. We asked how gene expression in complex conditions relates to expression in simpler conditions. To address this, we obtained accurate promoter activity dynamical measurements on 94 genes in E. coli in environments made up of all possible combinations of four nutrients and stresses. We find that the dynamics across conditions is well described by two principal component curves specific to each promoter. As a result, the promoter activity dynamics in a combination of conditions is a weighted average of the dynamics in each condition alone. The weights tend to sum up to approximately one. This weighted-average property, called linear superposition, allows predicting the promoter activity dynamics in a combination of conditions based on measurements of pairs of conditions. If these findings apply more generally, they can vastly reduce the number of experiments needed to understand how E. coli responds to the combinatorially huge space of possible environments.

MicroRNA-7a regulates pancreatic ß cell function.

Dysfunctional microRNA (miRNA) networks contribute to inappropriate responses following pathological stress and are the underlying cause of several disease conditions. In pancreatic ß cells, miRNAs have been largely unstudied and little is known about how specific miRNAs regulate glucose-stimulated insulin secretion (GSIS) or impact the adaptation of ß cell function to metabolic stress. In this study, we determined that miR-7 is a negative regulator of GSIS in ß cells. Using Mir7a2 deficient mice, we revealed that miR-7a2 regulates ß cell function by directly regulating genes that control late stages of insulin granule fusion with the plasma membrane and ternary SNARE complex activity. Transgenic mice overexpressing miR-7a in ß cells developed diabetes due to impaired insulin secretion and ß cell dedifferentiation. Interestingly, perturbation of miR-7a expression in ß cells did not affect proliferation and apoptosis, indicating that miR-7 is dispensable for the maintenance of endocrine ß cell mass. Furthermore, we found that miR-7a levels are decreased in obese/diabetic mouse models and human islets from obese and moderately diabetic individuals with compensated ß cell function. Our results reveal an interconnecting miR-7 genomic circuit that regulates insulin granule exocytosis in pancreatic ß cells and support a role for miR-7 in the adaptation of pancreatic ß cell function in obesity and type 2 diabetes.

MicroRNA binding sites in the coding region of mRNAs: extending the repertoire of post-transcriptional gene regulation.

It is well established that microRNAs (miRNAs) induce mRNA degradation by binding to 3' untranslated regions (UTRs). The functionality of sites in the coding domain sequence (CDS), on the other hand, remains under discussion. Such sites have limited impact on target mRNA abundance and recent work suggests that miRNAs bind in the CDS to inhibit translation. What then could be the regulatory benefits of translation inhibition through CDS targeting compared to mRNA degradation following 3' UTR binding? We propose that these domain-dependent effects serve to diversify the functional repertoire of post-transcriptional gene expression control. Possible regulatory benefits may include tuning the time-scale and magnitude of post-transcriptional regulation, regulating protein abundance depending on or independently of the cellular state, and regulation of the protein abundance of alternative splice variants. Finally, we review emerging evidence that these ideas may generalize to RNA-binding proteins beyond miRNAs and Argonaute proteins.

Argonaute2 mediates compensatory expansion of the pancreatic ß cell.

Pancreatic ß cells adapt to compensate for increased metabolic demand during insulin resistance. Although the microRNA pathway has an essential role in ß cell proliferation, the extent of its contribution is unclear. Here, we report that miR-184 is silenced in the pancreatic islets of insulin-resistant mouse models and type 2 diabetic human subjects. Reduction of miR-184 promotes the expression of its target Argonaute2 (Ago2), a component of the microRNA-induced silencing complex. Moreover, restoration of miR-184 in leptin-deficient ob/ob mice decreased Ago2 and prevented compensatory ß cell expansion. Loss of Ago2 during insulin resistance blocked ß cell growth and relieved the regulation of miR-375-targeted genes, including the growth suppressor Cadm1. Lastly, administration of a ketogenic diet to ob/ob mice rescued insulin sensitivity and miR-184 expression and restored Ago2 and ß cell mass. This study identifies the targeting of Ago2 by miR-184 as an essential component of the compensatory response to regulate proliferation according to insulin sensitivity.

Timescales and bottlenecks in miRNA-dependent gene regulation.

MiRNAs are post-transcriptional regulators that contribute to the establishment and maintenance of gene expression patterns. Although their biogenesis and decay appear to be under complex control, the implications of miRNA expression dynamics for the processes that they regulate are not well understood. We derived a mathematical model of miRNA-mediated gene regulation, inferred its parameters from experimental data sets, and found that the model describes well time-dependent changes in mRNA, protein and ribosome density levels measured upon miRNA transfection and induction. The inferred parameters indicate that the timescale of miRNA-dependent regulation is slower than initially thought. Delays in miRNA loading into Argonaute proteins and the slow decay of proteins relative to mRNAs can explain the typically small changes in protein levels observed upon miRNA transfection. For miRNAs to regulate protein expression on the timescale of a day, as miRNAs involved in cell-cycle regulation do, accelerated miRNA turnover is necessary.

Analysis of CDS-located miRNA target sites suggests that they can effectively inhibit translation.

Most of what is presently known about how miRNAs regulate gene expression comes from studies that characterized the regulatory effect of miRNA binding sites located in the 3' untranslated regions (UTR) of mRNAs. In recent years, there has been increasing evidence that miRNAs also bind in the coding region (CDS), but the implication of these interactions remains obscure because they have a smaller impact on mRNA stability compared with miRNA-target interactions that involve 3' UTRs. Here we show that miRNA-complementary sites that are located in both CDS and 3'-UTRs are under selection pressure and share the same sequence and structure properties. Analyzing recently published data of ribosome-protected fragment profiles upon miRNA transfection from the perspective of the location of miRNA-complementary sites, we find that sites located in the CDS are most potent in inhibiting translation, while sites located in the 3' UTR are more efficient at triggering mRNA degradation. Our study suggests that miRNAs may combine targeting of CDS and 3' UTR to flexibly tune the time scale and magnitude of their post-transcriptional regulatory effects.

A biophysical miRNA-mRNA interaction model infers canonical and noncanonical targets.

We introduce a biophysical model of miRNA-target interaction and infer its parameters from Argonaute 2 cross-linking and immunoprecipitation data. We show that a substantial fraction of human miRNA target sites are noncanonical and that predicted target-site affinity correlates well with the extent of target destabilization. Our model provides a rigorous biophysical approach to miRNA target identification beyond ad hoc miRNA seed-based methods.