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Simple Summary Different types of cells with tumor-initiating cell (TIC) activity contribute to colorectal cancer (CRC) progression and resistance to anti-cancer treatment. In this study, we aimed to understand whether different cell types exist within a patient-derived tumor culture, distinguishable by different patterns of their gene expression. By mRNA sequencing of patient-derived CRC cultures at the single-cell level, we defined expression programs that closely resemble differentiated cell populations of the normal intestine. Here, cell type-associated subpopulations showed differences in functional properties such as cell growth and energy metabolism. Subsequent functional analyses in vitro and in vivo demonstrated that metabolic states are linked to TIC activity in primary CRC cultures. We also show that TIC activity is dependent on oxidative phosphorylation, which may therefore represent a target for novel therapies. Abstract Intra-tumor heterogeneity of tumor-initiating cell (TIC) activity drives colorectal cancer (CRC) progression and therapy resistance. Here, we used single-cell RNA-sequencing of patient-derived CRC models to decipher distinct cell subpopulations based on their transcriptional profiles. Cell type-specific expression modules of stem-like, transit amplifying-like, and differentiated CRC cells resemble differentiation states of normal intestinal epithelial cells. Strikingly, identified subpopulations differ in proliferative activity and metabolic state. In summary, we here show at single-cell resolution that transcriptional heterogeneity identifies functional states during TIC differentiation. Furthermore, identified expression signatures are linked to patient prognosis. Targeting transcriptional states associated to cancer cell differentiation might unravel novel vulnerabilities in human CRC.

Summary Novel treatment options for metastatic colorectal cancer (CRC) are urgently needed to improve patient outcome. Here, we screen a library of non-characterized small molecules against a heterogeneous collection of patient-derived CRC spheroids. By prioritizing compounds with inhibitory activity in a subset of—but not all—spheroid cultures, NCT02 is identified as a candidate with minimal risk of non-specific toxicity. Mechanistically, we show that NCT02 acts as molecular glue that induces ubiquitination of cyclin K (CCNK) and proteasomal degradation of CCNK and its complex partner CDK12. Knockout of CCNK or CDK12 decreases proliferation of CRC cells in vitro and tumor growth in vivo. Interestingly, sensitivity to pharmacological CCNK/CDK12 degradation is associated with TP53 deficiency and consensus molecular subtype 4 in vitro and in patient-derived xenografts. We thus demonstrate the efficacy of targeted CCNK/CDK12 degradation for a CRC subset, highlighting the potential of drug-induced proteolysis for difficult-to-treat types of cancer.

Tumor progression in colorectal cancer (CRC) is driven by a subpopulation of cells with tumor-initiating cell (TIC) activity. In the past, bulk experiments provided strong evidence for functional heterogeneity of the CRC TIC compartment with distinct types of TICs driving CRC progression. However, in these retrospective experiments direct assignment of functional states to individual functionally relevant cell types in CRC has not been possible. We here asked whether functional states of individual cells can be assigned to specific CRC cell subpopulations. Therefore, 4,663 single-cell profiles were generated by single-cell RNA-sequencing (scRNA-seq) of patient-derived CRC spheroid cultures (n=12). Non-negative matrix factorization identified 13 gene expression modules linked to cell states or cell types that resemble differentiation states of normal intestinal epithelial cells. To evaluate their presence in CRC patient tumors, the identified transcriptional programs were applied to publicly available expression data (TCGA cohort; n=459). Clustering of samples based on the contribution of individual expression signatures revealed six patient clusters with significantly different progression-free survival (p=0.043) and a trend towards different overall survival (p=0.12), indicating a relevance for patient outcome. Comparing cell state meta-signature expression in cell type-associated subpopulations revealed differences in functional states, i.e. proliferative and metabolic activity. Interestingly, stem-like cells showed significantly increased oxidative phosphorylation (OXPHOS) and decreased glycolysis scores, suggesting a link between metabolic states and cell differentiation. scRNA-seq of patient-derived organoid and xenograft models as well as primary tumors revealed similar trends. To assess whether metabolic states are linked to TIC activity, CRC spheroid cells were sorted according to mitochondrial membrane potential (MMP), and sphere forming cell (SFC) as well as TIC frequency were assessed by limiting dilutions. Strikingly, MMPhigh cells exhibited a pronounced increase of SFC frequencies in vitro compared to MMPlow cells in 80% of patients (n=5). In addition, TIC frequencies were strongly increased in the MMPhigh compared to the MMPlow subpopulation in vivo (n=2; 1/46,535 vs. 1/211,305 and 1/249 vs. 1/2,089), demonstrating that TIC activity relies on OXPHOS. In line with this, pretreatment with the OXPHOS inhibitor CCCP decreased SFC frequencies (n=3), indicating targetable metabolic vulnerability of CRC spheroid cells. In summary, we here show that transcriptional heterogeneity identifies functional states during CRC TIC differentiation. Targeting context-dependent regulation of tumor cell differentiation might unravel novel vulnerabilities for therapeutic intervention in human CRC. Citation Format: Martina K. Zowada, Stephan M. Tirier, Sebastian M. Dieter, Teresa Krieger, Ava Oberlack, Robert L. Chua, Mario Huerta, Foo Wei Ten, Karin Laaber, Jeongbin Park, Katharina Jechow, Torsten Müller, Mathias Kalxdorf, Mark Kriegsmann, Katharina Kriegsmann, Friederike Herbst, Jeroen Krijgsveld, Martin Schneider, Roland Eils, Hanno Glimm, Christian Conrad, Claudia R. Ball. Transcriptional heterogeneity identifies functional states of tumor-initiating cell differentiation in human colorectal cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1491.

Multifunctionality or multitasking is the capability of some proteins to execute two or more biochemical functions. The objective of this work is to explore the relationship between multifunctional proteins, human diseases and drug targeting. The analysis of the proportion of multitasking proteins from the MultitaskProtDB-II database shows that 78% of the proteins analyzed are involved in human diseases. This percentage is much higher than the 17.9% found in human proteins in general. A similar analysis using drug target databases shows that 48% of these analyzed human multitasking proteins are targets of current drugs, while only 9.8% of the human proteins present in UniProt are specified as drug targets. In almost 50% of these proteins, both the canonical and moonlighting functions are related to the molecular basis of the disease. A procedure to identify multifunctional proteins from disease databases and a method to structurally map the canonical and moonlighting functions of the protein have also been proposed here. Both of the previous percentages suggest that multitasking is not a rare phenomenon in proteins causing human diseases, and that their detailed study might explain some collateral drug effects. This research was supported by Ministerio de Economía y Competitividad of Spain [BFU2013-50176-EXP, BIO2013-48704-R and BIO2017-84166R], by the Centre de Referència de R+D de Biotecnologia de la Generalitat de Catalunya, and from the Comisión Coordinadora del Interior de Uruguay.

Mexico ranks among the 11 major producer countries of minerals worldwide; its open pit and underground systems are 500 years old. This paper presents an overview of the Mexican mining industry from technological development, historical and economic perspectives. The efforts made by mining companies to address issues of environmental management and sustainable development expressed in national and international frameworks, as well as the Mexican environmental regulatory framework for the mining sector, are analyzed. Since, among others, life cycle assessment (LCA) has been recognized as a key topic to promote sustainable development in the Latin American and Caribbean region, this paper also examines the application of LCA in mining. Two life cycle approaches are presented: a national life cycle inventory for base metals, and an integral life cycle model for the management of mining processes.

Principal curves where introduced by Hastie & Stuetzle (1989) as smooth parametric curves passing through the middle of a multidimensional data set. Delicado (2001) defines Principal Curves of Oriented Points, based on the fixed points of a function from ℝp into itself. This definition is nonparametric and smoothing methods are used to find principal curves of a data set. Here we extend this work in two directions. First, we propose a bandwidth choice method based on the Minimum Spanning Tree of the data set. Second, we present an object oriented application that implements the principal curves computation for any dimension in a flexible recursive way. Examples on synthetic and real data are included.

Summary: The microarrays performed by scientific teams grow exponentially. These microarray data could be useful for researchers around the world, but unfortunately they are underused. To fully exploit these data, it is necessary (i) to extract these data from a repository of the high-throughput gene expression data like Gene Expression Omnibus (GEO) and (ii) to make the data from different microarrays comparable with tools easy to use for scientists. We have developed these two solutions in our server, implementing a database of microarray marker genes (Marker Genes Data Base). This database contains the marker genes of all GEO microarray datasets and it is updated monthly with the new microarrays from GEO. Thus, researchers can see whether the marker genes of their microarray are marker genes in other microarrays in the database, expanding the analysis of their microarray to the rest of the public microarrays. This solution helps not only to corroborate the conclusions regarding a researcher's microarray but also to identify the phenotype of different subsets of individuals under investigation, to frame the results with microarray experiments from other species, pathologies or tissues, to search for drugs that promote the transition between the studied phenotypes, to detect undesirable side effects of the treatment applied, etc. Thus, the researcher can quickly add relevant information to his/her studies from all of the previous analyses performed in other studies as long as they have been deposited in public repositories. Availability: Marker-gene database tool: http://ibb.uab.es/mgdb Contact: jcedano@unorte.edu.uy

Organisms simplify the orchestration of gene expression by coregulating genes whose products function together in the cell. The use of clustering methods to obtain sets of coexpressed genes from expression arrays is very common; nevertheless there are no appropriate tools to study the expression networks among these sets of coexpressed genes. The aim of the developed tools is to allow studying the complex expression dependences that exist between sets of coexpressed genes. For this purpose, we start detecting the nonlinear expression relationships between pairs of genes, plus the coexpressed genes. Next, we form networks among sets of coexpressed genes that maintain nonlinear expression dependences between all of them. The expression relationship between the sets of coexpressed genes is defined by the expression relationship between theskeletonsof these sets, where thisskeletonrepresents the coexpressed genes with a well-defined nonlinear expression relationship with theskeletonof the other sets. As a result, we can study the nonlinear expression relationships between a target gene and other sets of coexpressed genes, or start the study from theskeletonof the sets, to study the complex relationships of activation and deactivation between the sets of coexpressed genes that carry out the different cellular processes present in the expression experiments.

Background Glucocorticoids are commonly used as adjuvant treatment for side-effects and have anti-proliferative activity in several tumors but, on the other hand, their proliferative effect has been reported in several studies, some of them involving the spread of cancer. We shall attempt to reconcile these incongruities from the genomic and tissue-physiology perspectives with our findings. Methods An accurate phenotype analysis of microarray data can help to solve multiple paradoxes derived from tumor-progression models. We have developed a new strategy to facilitate the study of interdependences among the phenotypes defined by the sample clusters obtained by common clustering methods (HC, SOTA, SOM, PAM). These interdependences are obtained by the detection of non-linear expression-relationships where each fluctuation in the relationship implies a phenotype change and each relationship typology implies a specific phenotype interdependence. As a result, multiple phenotypic changes are identified together with the genes involved in the phenotype transitions. In this way, we study the phenotypic changes from microarray data that describe common phenotypes in cancer from different tissues, and we cross our results with biomedical databases to relate the glucocorticoid activity to the phenotypic changes. Results 11,244 significant non-linear expression relationships, classified into 11 different typologies, have been detected from the data matrix analyzed. From them, 415 non-linear expression relationships were related to glucocorticoid activity. Studying them, we have found the possible reason for opposite effects of some stressor agents like dexamethasone on tumor progression and it has been confirmed by literature. This hidden reason has resulted in being linked with the type of tumor progression of the tissues. In the first type of tumor progression found, new cells can be stressed during proliferation and stressor agents increase tumor proliferation. In the second type, cell stress and tumor proliferation are antagonists so, therefore, stressor agents stop tumor proliferation in order to stress the cells. The non-linear expression relationships among DUSP6, FERMT2, FKBP5, EGFR, NEDD4L and CITED2 genes are used to synthesize these findings.

In this paper we present several web-based tools to identify conserved patterns in sequences. In particular we present details on the functionality of PROMO version 2.0, a program for the prediction of transcription factor binding site in a single sequence or in a group of related sequences and, of MALGEN, a tool to visualize sequence correspondences among long DNA sequences. The web tools and associated documentation can be accessed at http://www.lsi.upc.es/~alggen (RESEARCH link).

Background Microarray technology is so expensive and powerful that it is essential to extract maximum value from microarray data, specially from large-sample-series microarrays. Our web tools attempt to respond to these researchers' needs by facilitating the possibility to test and formulate from a hypothesis to entire models under a holistic point of view. Results PCOPGene-Net is a web application for facilitating the study of the relationships among gene expressions under microarray conditions, to classify these conditions and to study their effect on expression relationships. Furthermore, the system guides the researcher in the navigation through the microarray data by providing the most suitable genes and information for the researcher's interests at each moment. We achieve all of these by means of the zoom-out operation, the zoom-in operation, the non-continuous analysis and crossing the PCOPGene results with external data-servers. Conclusion PCOPGene-Net helps to identify cellular states and the genes involved in these. All of that is accomplished in a flexible way, guided by the researcher's interests and taking advantage of the ability of our system to relate gene expressions, even when these relationships are non-continuous and cannot be found using linear or non-linear analytical methods. Currently, our tools are used for tumour-progression study from a holistic point of view.

Behavioural fever, defined as an acute change in thermal preference driven by pathogen recognition, has been reported in a variety of invertebrates and ectothermic vertebrates. It has been suggested, but so far not confirmed, that such changes in thermal regime favour the immune response and thus promote survival. Here, we show that zebrafish display behavioural fever that acts to promote extensive and highly specific temperature-dependent changes in the brain transcriptome. The observed coupling of the immune response to fever acts at the gene–environment level to promote a robust, highly specific time-dependent anti-viral response that, under viral infection, increases survival. Fish that are not offered a choice of temperatures and that therefore cannot express behavioural fever show decreased survival under viral challenge. This phenomenon provides an underlying explanation for the varied functional responses observed during systemic fever. Given the effects of behavioural fever on survival and the fact that it exists across considerable phylogenetic space, such immunity–environment interactions are likely to be under strong positive selection. Refereed/Peer-reviewed