Integrated transcriptomics uncovers an enhanced association between the prion protein gene expression and vesicle dynamics signatures in glioblastomas.

BACKGROUND: Glioblastoma (GBM) is an aggressive brain tumor that exhibits resistance to current treatment, making the identification of novel therapeutic targets essential. In this context, cellular prion protein (PrPC) stands out as a potential candidate for new therapies. Encoded by the PRNP gene, PrPC can present increased expression levels in GBM, impacting cell proliferation, growth, migration, invasion and stemness. Nevertheless, the exact molecular mechanisms through which PRNP/PrPC modulates key aspects of GBM biology remain elusive. METHODS: To elucidate the implications of PRNP/PrPC in the biology of this cancer, we analyzed publicly available RNA sequencing (RNA-seq) data of patient-derived GBMs from four independent studies. First, we ranked samples profiled by bulk RNA-seq as PRNPhigh and PRNPlow and compared their transcriptomic landscape. Then, we analyzed PRNP+ and PRNP- GBM cells profiled by single-cell RNA-seq to further understand the molecular context within which PRNP/PrPC might function in this tumor. We explored an additional proteomics dataset, applying similar comparative approaches, to corroborate our findings. RESULTS: Functional profiling revealed that vesicular dynamics signatures are strongly correlated with PRNP/PrPC levels in GBM. We found a panel of 73 genes, enriched in vesicle-related pathways, whose expression levels are increased in PRNPhigh/PRNP+ cells across all RNA-seq datasets. Vesicle-associated genes, ANXA1, RAB31, DSTN and SYPL1, were found to be upregulated in vitro in an in-house collection of patient-derived GBM. Moreover, proteome analysis of patient-derived samples reinforces the findings of enhanced vesicle biogenesis, processing and trafficking in PRNPhigh/PRNP+ GBM cells. CONCLUSIONS: Together, our findings shed light on a novel role for PrPC as a potential modulator of vesicle biology in GBM, which is pivotal for intercellular communication and cancer maintenance. We also introduce GBMdiscovery, a novel user-friendly tool that allows the investigation of specific genes in GBM biology.

RUNX1 mutations mitigate quiescence to promote transformation of hematopoietic progenitors in Fanconi anemia.

Many inherited bone marrow failure syndromes (IBMFSs) present a high risk of transformation to myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML). During transformation of IBMFSs, hematopoietic stem and progenitor cells (HSPCs) with poor fitness gain ectopic, dysregulated self-renewal secondary to somatic mutations via undefined mechanisms. Here, in the context of the prototypical IBMFS Fanconi anemia (FA), we performed multiplexed gene editing of mutational hotspots in MDS-associated genes in human induced pluripotent stem cells (iPSCs) followed by hematopoietic differentiation. We observed aberrant self-renewal and impaired differentiation of HSPCs with enrichment of RUNX1 insertions and deletions (indels), generating a model of IBMFS-associated MDS. We observed that compared to the failure state, FA MDS cells show mutant RUNX1-mediated blunting of the G1/S cell cycle checkpoint that is normally activated in FA in response to DNA damage. RUNX1 indels also lead to activation of innate immune signaling, which stabilizes the homologous recombination (HR) effector BRCA1, and this pathway can be targeted to abrogate viability and restore sensitivity to genotoxins in FA MDS. Together, these studies develop a paradigm for modeling clonal evolution in IBMFSs, provide basic understanding of the pathogenesis of MDS, and uncover a therapeutic target in FA-associated MDS.

Lifelong multilineage contribution by embryonic-born blood progenitors.

Haematopoietic stem cells (HSCs) arise in the embryo from the arterial endothelium through a process known as the endothelial-to-haematopoietic transition (EHT)1-4. This process generates hundreds of blood progenitors, of which a fraction go on to become definitive HSCs. It is generally thought that most adult blood is derived from those HSCs, but to what extent other progenitors contribute to adult haematopoiesis is not known. Here we use in situ barcoding and classical fate mapping to assess the developmental and clonal origins of adult blood in mice. Our analysis uncovers an early wave of progenitor specification-independent of traditional HSCs-that begins soon after EHT. These embryonic multipotent progenitors (eMPPs) predominantly drive haematopoiesis in the young adult, have a decreasing yet lifelong contribution over time and are the predominant source of lymphoid output. Putative eMPPs are specified within intra-arterial haematopoietic clusters and represent one fate of the earliest haematopoietic progenitors. Altogether, our results reveal functional heterogeneity during the definitive wave that leads to distinct sources of adult blood.

External signals regulate continuous transcriptional states in hematopoietic stem cells.

Hematopoietic stem cells (HSCs) must ensure adequate blood cell production following distinct external stressors. A comprehensive understanding of in vivo heterogeneity and specificity of HSC responses to external stimuli is currently lacking. We performed single-cell RNA sequencing (scRNA-Seq) on functionally validated mouse HSCs and LSK (Lin-, c-Kit+, Sca1+) progenitors after in vivo pharmacological perturbation of niche signals interferon, granulocyte colony-stimulating factor (G-CSF), and prostaglandin. We identified six HSC states that are characterized by enrichment but not exclusive expression of marker genes. External signals induced rapid transitions between HSC states but transcriptional response varied both between external stimulants and within the HSC population for a given perturbation. In contrast to LSK progenitors, HSCs were characterized by a greater link between molecular signatures at baseline and in response to external stressors. Chromatin analysis of unperturbed HSCs and LSKs by scATAC-Seq suggested some HSC-specific, cell intrinsic predispositions to niche signals. We compiled a comprehensive resource of HSC- and LSK progenitor-specific chromatin and transcriptional features that represent determinants of signal receptiveness and regenerative potential during stress hematopoiesis.

Most organs in the human body are maintained by a type of immature cells known as adult stem cells, which ensure a constant supply of new, mature cells. Adult stem cells monitor their environment through external signalling molecules and replace damaged cells as needed. Stem cell therapy takes advantage of the regenerative ability of immature stem cells and can be helpful for conditions such as blood diseases, autoimmune diseases, neurodegeneration and cancer. For example, hematopoietic stem-cell transplantation is a treatment for some types of cancer and blood disorders, in which stem cells are harvested from the blood or bone marrow and reintroduced into the body, where they can develop into all types of blood cells, including white blood cells, red blood cells and platelets. Hematopoietic stem-cell transplants have been in use for over 30 years, but they remain a highly risky procedure. One of the challenges is that outcomes can vary between patients and many of the factors that can influence the 'regenerative' potential of hematopoietic stem cells, such as external signalling molecules, are not well understood. To fill this gap, Fast et al. analysed which genes are turned on and off in hematopoietic stem cells in response to several external signalling molecules. To do so, three signalling pathways in mice were altered by injecting them with different chemicals. After two hours, the hematopoietic stem cells were purified and the gene expression for each cell was analysed. This revealed that the types of genes and the strength at which they were affected by each chemical was unique. Moreover, hematopoietic stem cells responded rapidly to external signals, with substantial differences in gene expression between individual groups of cells. Contrary to more specialised cells, the external signalling genes in some hematopoietic stem cells were already activated without being injected with external signalling molecules. This suggest that low levels of external signalling molecules released from their microenvironment may prepare stem cells to better respond to future stress or injuries. These results help to better understand stem cells and to evaluate how the signalling state of hematopoietic stem cells affects regeneration, and ultimately improve hematopoietic stem cell transplantation for patients.

LIN28B alters ribosomal dynamics to promote metastasis in MYCN-driven malignancy.

High expression of LIN28B is associated with aggressive malignancy and poor survival. Here, probing MYCN-amplified neuroblastoma as a model system, we showed that LIN28B expression was associated with enhanced cell migration in vitro and invasive and metastatic behavior in murine xenografts. Sequence analysis of the polyribosome fraction of LIN28B-expressing neuroblastoma cells revealed let-7-independent enrichment of transcripts encoding components of the translational and ribosomal apparatus and depletion of transcripts of neuronal developmental programs. We further observed that LIN28B utilizes both its cold shock and zinc finger RNA binding domains to preferentially interact with MYCN-induced transcripts of the ribosomal complex, enhancing their translation. These data demonstrated that LIN28B couples the MYCN-driven transcriptional program to enhanced ribosomal translation, thereby implicating LIN28B as a posttranscriptional driver of the metastatic phenotype.

Evaluating the transcriptional fidelity of cancer models.

BACKGROUND: Cancer researchers use cell lines, patient-derived xenografts, engineered mice, and tumoroids as models to investigate tumor biology and to identify therapies. The generalizability and power of a model derive from the fidelity with which it represents the tumor type under investigation; however, the extent to which this is true is often unclear. The preponderance of models and the ability to readily generate new ones has created a demand for tools that can measure the extent and ways in which cancer models resemble or diverge from native tumors. METHODS: We developed a machine learning-based computational tool, CancerCellNet, that measures the similarity of cancer models to 22 naturally occurring tumor types and 36 subtypes, in a platform and species agnostic manner. We applied this tool to 657 cancer cell lines, 415 patient-derived xenografts, 26 distinct genetically engineered mouse models, and 131 tumoroids. We validated CancerCellNet by application to independent data, and we tested several predictions with immunofluorescence. RESULTS: We have documented the cancer models with the greatest transcriptional fidelity to natural tumors, we have identified cancers underserved by adequate models, and we have found models with annotations that do not match their classification. By comparing models across modalities, we report that, on average, genetically engineered mice and tumoroids have higher transcriptional fidelity than patient-derived xenografts and cell lines in four out of five tumor types. However, several patient-derived xenografts and tumoroids have classification scores that are on par with native tumors, highlighting both their potential as faithful model classes and their heterogeneity. CONCLUSIONS: CancerCellNet enables the rapid assessment of transcriptional fidelity of tumor models. We have made CancerCellNet available as a freely downloadable R package ( https://github.com/pcahan1/cancerCellNet ) and as a web application ( http://www.cahanlab.org/resources/cancerCellNet_web ) that can be applied to new cancer models that allows for direct comparison to the cancer models evaluated here.

An evolutionary recent IFN/IL-6/CEBP axis is linked to monocyte expansion and tuberculosis severity in humans.

Monocyte counts are increased during human tuberculosis (TB) but it has not been determined whether Mycobacterium tuberculosis (Mtb) directly regulates myeloid commitment. We demonstrated that exposure to Mtb directs primary human CD34+ cells to differentiate into monocytes/macrophages. In vitro myeloid conversion did not require type I or type II IFN signaling. In contrast, Mtb enhanced IL-6 responses by CD34+ cell cultures and IL-6R neutralization inhibited myeloid differentiation and decreased mycobacterial growth in vitro. Integrated systems biology analysis of transcriptomic, proteomic and genomic data of large data sets of healthy controls and TB patients established the existence of a myeloid IL-6/IL6R/CEBP gene module associated with disease severity. Furthermore, genetic and functional analysis revealed the IL6/IL6R/CEBP gene module has undergone recent evolutionary selection, including Neanderthal introgression and human pathogen adaptation, connected to systemic monocyte counts. These results suggest Mtb co-opts an evolutionary recent IFN-IL6-CEBP feed-forward loop, increasing myeloid differentiation linked to severe TB in humans.

Haematopoietic stem and progenitor cells from human pluripotent stem cells.

A variety of tissue lineages can be differentiated from pluripotent stem cells by mimicking embryonic development through stepwise exposure to morphogens, or by conversion of one differentiated cell type into another by enforced expression of master transcription factors. Here, to yield functional human haematopoietic stem cells, we perform morphogen-directed differentiation of human pluripotent stem cells into haemogenic endothelium followed by screening of 26 candidate haematopoietic stem-cell-specifying transcription factors for their capacity to promote multi-lineage haematopoietic engraftment in mouse hosts. We recover seven transcription factors (ERG, HOXA5, HOXA9, HOXA10, LCOR, RUNX1 and SPI1) that are sufficient to convert haemogenic endothelium into haematopoietic stem and progenitor cells that engraft myeloid, B and T cells in primary and secondary mouse recipients. Our combined approach of morphogen-driven differentiation and transcription-factor-mediated cell fate conversion produces haematopoietic stem and progenitor cells from pluripotent stem cells and holds promise for modelling haematopoietic disease in humanized mice and for therapeutic strategies in genetic blood disorders.

A network-based phenotype mapping approach to identify genes that modulate drug response phenotypes.

To better address the problem of drug resistance during cancer chemotherapy and explore the possibility of manipulating drug response phenotypes, we developed a network-based phenotype mapping approach (P-Map) to identify gene candidates that upon perturbed can alter sensitivity to drugs. We used basal transcriptomics data from a panel of human lymphoblastoid cell lines (LCL) to infer drug response networks (DRNs) that are responsible for conferring response phenotypes for anthracycline and taxane, two common anticancer agents use in clinics. We further tested selected gene candidates that interact with phenotypic differentially expressed genes (PDEGs), which are up-regulated genes in LCL for a given class of drug response phenotype in triple-negative breast cancer (TNBC) cells. Our results indicate that it is possible to manipulate a drug response phenotype, from resistant to sensitive or vice versa, by perturbing gene candidates in DRNs and suggest plausible mechanisms regulating directionality of drug response sensitivity. More important, the current work highlights a new way to formulate systems-based therapeutic design: supplementing therapeutics that aim to target disease culprits with phenotypic modulators capable of altering DRN properties with the goal to re-sensitize resistant phenotypes.

TGF-ß inhibitors stimulate red blood cell production by enhancing self-renewal of BFU-E erythroid progenitors.

Burst-forming unit erythroid progenitors (BFU-Es) are so named based on their ability to generate in methylcellulose culture large colonies of erythroid cells that consist of "bursts" of smaller erythroid colonies derived from the later colony-forming unit erythroid progenitor erythropoietin (Epo)-dependent progenitors. "Early" BFU-E cells forming large BFU-E colonies presumably have higher capacities for self-renewal than do "late" BFU-Es forming small colonies, but the mechanism underlying this heterogeneity remains unknown. We show that the type III transforming growth factor ß (TGF-ß) receptor (TßRIII) is a marker that distinguishes early and late BFU-Es. Transient elevation of TßRIII expression promotes TGF-ß signaling during the early BFU-E to late BFU-E transition. Blocking TGF-ß signaling using a receptor kinase inhibitor increases early BFU-E cell self-renewal and total erythroblast production, suggesting the usefulness of this type of drug in treating Epo-unresponsive anemias.

NetDecoder: a network biology platform that decodes context-specific biological networks and gene activities.

The sequential chain of interactions altering the binary state of a biomolecule represents the 'information flow' within a cellular network that determines phenotypic properties. Given the lack of computational tools to dissect context-dependent networks and gene activities, we developed NetDecoder, a network biology platform that models context-dependent information flows using pairwise phenotypic comparative analyses of protein-protein interactions. Using breast cancer, dyslipidemia and Alzheimer's disease as case studies, we demonstrate NetDecoder dissects subnetworks to identify key players significantly impacting cell behaviour specific to a given disease context. We further show genes residing in disease-specific subnetworks are enriched in disease-related signalling pathways and information flow profiles, which drive the resulting disease phenotypes. We also devise a novel scoring scheme to quantify key genes-network routers, which influence many genes, key targets, which are influenced by many genes, and high impact genes, which experience a significant change in regulation. We show the robustness of our results against parameter changes. Our network biology platform includes freely available source code (http://www.NetDecoder.org) for researchers to explore genome-wide context-dependent information flow profiles and key genes, given a set of genes of particular interest and transcriptome data. More importantly, NetDecoder will enable researchers to uncover context-dependent drug targets.

Análise computacional para auxílio ao diagnóstico de osteoartrite de coluna lombar baseado em redes neurais artificiais / Computational analysis based on artificial neural networks for aiding in diagnosing osteoarthritis of the lumbar spine

OBJETIVOS: Conhecer as vantagens da utilização das redes neurais artificiais no reconhecimento de padrões em radiografias de coluna lombar na incidência perfil para auxiliar no diagnóstico da osteoartrite primária. MÉTODO: Estudo transversal, descritivo, analítico, de abordagem quantitativa e com ênfase diagnóstica. O conjunto de treinamento foi composto por imagens coletadas no período de janeiro a julho de 2009 de pacientes submetidos a radiografias digitais de coluna lombar na incidência em perfil provenientes de um serviço de radiologia localizado no município de Criciúma (SC). Das 260 imagens coletadas, foram excluídas: as radiografias distorcidas, as patologias que alteram a arquitetura da coluna lombar e os padrões de difícil caracterização, resultando em um total de 206 imagens. O banco de imagens (n = 206) foi subdividido, resultando em 68 radiografias para a etapa de treinamento, 68 para testes e 70 para validação. Foi utilizada uma rede neural híbrida baseada em mapas auto-organizáveis de Kohonen e redes Multilayer Perceptron. RESULTADOS: Após 90 ciclos, foi realizada a validação com o melhor teste, alcançando acurácia de 62,85 por cento, sensibilidade de 65,71 por cento e especificidade de 60 por cento. CONCLUSÃO: Apesar da demonstração de uma eficácia mediana, por se tratar de estudo de caráter inovador, seus valores mostram um futuro promissor da técnica utilizada, com sugestão para trabalhos futuros com abrangência na metodologia de processamento das imagens e ciclos com uma quantidade maior de radiografias.

OBJECTIVE: To ascertain the advantages of applying artificial neural networks to recognize patterns on lumbar column radiographs in order to aid in the process of diagnosing primary osteoarthritis. METHODS: This was a cross-sectional descriptive analytical study with a quantitative approach and an emphasis on diagnosis. The training set was composed of images collected between January and July 2009 from patients who had undergone lateral-view digital radiographs of the lumbar column, which were provided by a radiology clinic located in the municipality of Criciúma (SC). Out of the total of 260 images gathered, those with distortions, those presenting pathological conditions that altered the architecture of the lumbar column and those with patterns that were difficult to characterize were discarded, thus resulting in 206 images. The image data base (n = 206) was then subdivided, resulting in 68 radiographs for the training stage, 68 images for tests and 70 for validation. A hybrid neural network based on Kohonen self-organizing maps and on Multilayer Perceptron networks was used. RESULTS: After 90 cycles, the validation was carried out on the best results, thereby reaching accuracy of 62.85 percent, sensitivity of 65.71 percent and specificity of 60 percent. CONCLUSIONS: Even though the effectiveness shown was moderate, this study is of innovative nature. Hence, the values show that the technique used has a promising future, thus pointing towards further studies covering the image and cycle processing methodology with a larger quantity of radiographs.