Multi-omics analysis suggests enhanced epileptogenesis in the Cornu Ammonis 3 of the pilocarpine model of mesial temporal lobe epilepsy.

Mesial temporal lobe epilepsy (MTLE) is a chronic neurological disorder characterized by the occurrence of seizures, and histopathological abnormalities in the mesial temporal lobe structures, mainly hippocampal sclerosis (HS). We used a multi-omics approach to determine the profile of transcript and protein expression in the dorsal and ventral hippocampal dentate gyrus (DG) and Cornu Ammonis 3 (CA3) in an animal model of MTLE induced by pilocarpine. We performed label-free proteomics and RNAseq from laser-microdissected tissue isolated from pilocarpine-induced Wistar rats. We divided the DG and CA3 into dorsal and ventral areas and analyzed them separately. We performed a data integration analysis and evaluated enriched signaling pathways, as well as the integrated networks generated based on the gene ontology processes. Our results indicate differences in the transcriptomic and proteomic profiles among the DG and the CA3 subfields of the hippocampus. Moreover, our data suggest that epileptogenesis is enhanced in the CA3 region when compared to the DG, with most abnormalities in transcript and protein levels occurring in the CA3. Furthermore, our results show that the epileptogenesis in the pilocarpine model involves predominantly abnormal regulation of excitatory neuronal mechanisms mediated by N-methyl D-aspartate (NMDA) receptors, changes in the serotonin signaling, and neuronal activity controlled by calcium/calmodulin-dependent protein kinase (CaMK) regulation and leucine-rich repeat kinase 2 (LRRK2)/WNT signaling pathways.

The LMO2 oncogene regulates DNA replication in hematopoietic cells.

Oncogenic transcription factors are commonly activated in acute leukemias and subvert normal gene expression networks to reprogram hematopoietic progenitors into preleukemic stem cells, as exemplified by LIM-only 2 (LMO2) in T-cell acute lymphoblastic leukemia (T-ALL). Whether or not these oncoproteins interfere with other DNA-dependent processes is largely unexplored. Here, we show that LMO2 is recruited to DNA replication origins by interaction with three essential replication enzymes: DNA polymerase delta (POLD1), DNA primase (PRIM1), and minichromosome 6 (MCM6). Furthermore, tethering LMO2 to synthetic DNA sequences is sufficient to transform these sequences into origins of replication. We next addressed the importance of LMO2 in erythroid and thymocyte development, two lineages in which cell cycle and differentiation are tightly coordinated. Lowering LMO2 levels in erythroid progenitors delays G1-S progression and arrests erythropoietin-dependent cell growth while favoring terminal differentiation. Conversely, ectopic expression in thymocytes induces DNA replication and drives these cells into cell cycle, causing differentiation blockade. Our results define a novel role for LMO2 in directly promoting DNA synthesis and G1-S progression.

SCL, LMO1 and Notch1 reprogram thymocytes into self-renewing cells.

The molecular determinants that render specific populations of normal cells susceptible to oncogenic reprogramming into self-renewing cancer stem cells are poorly understood. Here, we exploit T-cell acute lymphoblastic leukemia (T-ALL) as a model to define the critical initiating events in this disease. First, thymocytes that are reprogrammed by the SCL and LMO1 oncogenic transcription factors into self-renewing pre-leukemic stem cells (pre-LSCs) remain non-malignant, as evidenced by their capacities to generate functional T cells. Second, we provide strong genetic evidence that SCL directly interacts with LMO1 to activate the transcription of a self-renewal program coordinated by LYL1. Moreover, LYL1 can substitute for SCL to reprogram thymocytes in concert with LMO1. In contrast, inhibition of E2A was not sufficient to substitute for SCL, indicating that thymocyte reprogramming requires transcription activation by SCL-LMO1. Third, only a specific subset of normal thymic cells, known as DN3 thymocytes, is susceptible to reprogramming. This is because physiological NOTCH1 signals are highest in DN3 cells compared to other thymocyte subsets. Consistent with this, overexpression of a ligand-independent hyperactive NOTCH1 allele in all immature thymocytes is sufficient to sensitize them to SCL-LMO1, thereby increasing the pool of self-renewing cells. Surprisingly, hyperactive NOTCH1 cannot reprogram thymocytes on its own, despite the fact that NOTCH1 is activated by gain of function mutations in more than 55% of T-ALL cases. Rather, elevating NOTCH1 triggers a parallel pathway involving Hes1 and Myc that dramatically enhances the activity of SCL-LMO1 We conclude that the acquisition of self-renewal and the genesis of pre-LSCs from thymocytes with a finite lifespan represent a critical first event in T-ALL. Finally, LYL1 and LMO1 or LMO2 are co-expressed in most human T-ALL samples, except the cortical T subtype. We therefore anticipate that the self-renewal network described here may be relevant to a majority of human T-ALL.

Linking the transcriptional profiles and the physiological states of Mycobacterium tuberculosis during an extended intracellular infection.

Intracellular pathogens such as Mycobacterium tuberculosis have evolved strategies for coping with the pressures encountered inside host cells. The ability to coordinate global gene expression in response to environmental and internal cues is one key to their success. Prolonged survival and replication within macrophages, a key virulence trait of M. tuberculosis, requires dynamic adaptation to diverse and changing conditions within its phagosomal niche. However, the physiological adaptations during the different phases of this infection process remain poorly understood. To address this knowledge gap, we have developed a multi-tiered approach to define the temporal patterns of gene expression in M. tuberculosis in a macrophage infection model that extends from infection, through intracellular adaptation, to the establishment of a productive infection. Using a clock plasmid to measure intracellular replication and death rates over a 14-day infection and electron microscopy to define bacterial integrity, we observed an initial period of rapid replication coupled with a high death rate. This was followed by period of slowed growth and enhanced intracellular survival, leading finally to an extended period of net growth. The transcriptional profiles of M. tuberculosis reflect these physiological transitions as the bacterium adapts to conditions within its host cell. Finally, analysis with a Transcriptional Regulatory Network model revealed linked genetic networks whereby M. tuberculosis coordinates global gene expression during intracellular survival. The integration of molecular and cellular biology together with transcriptional profiling and systems analysis offers unique insights into the host-driven responses of intracellular pathogens such as M. tuberculosis.

Identifying cellular markers of focal cortical dysplasia type II with cell-type deconvolution and single-cell signatures.

Focal cortical dysplasia (FCD) is a brain malformation that causes medically refractory epilepsy. FCD is classified into three categories based on structural and cellular abnormalities, with FCD type II being the most common and characterized by disrupted organization of the cortex and abnormal neuronal development. In this study, we employed cell-type deconvolution and single-cell signatures to analyze bulk RNA-seq from multiple transcriptomic studies, aiming to characterize the cellular composition of brain lesions in patients with FCD IIa and IIb subtypes. Our deconvolution analyses revealed specific cellular changes in FCD IIb, including neuronal loss and an increase in reactive astrocytes (astrogliosis) when compared to FCD IIa. Astrogliosis in FCD IIb was further supported by a gene signature analysis and histologically confirmed by glial fibrillary acidic protein (GFAP) immunostaining. Overall, our findings demonstrate that FCD II subtypes exhibit differential neuronal and glial compositions, with astrogliosis emerging as a hallmark of FCD IIb. These observations, validated in independent patient cohorts and confirmed using immunohistochemistry, offer novel insights into the involvement of glial cells in FCD type II pathophysiology and may contribute to the development of targeted therapies for this condition.

Monoallelic Heb/Tcf12 Deletion Reduces the Requirement for NOTCH1 Hyperactivation in T-Cell Acute Lymphoblastic Leukemia.

Early T-cell development is precisely controlled by E proteins, that indistinguishably include HEB/TCF12 and E2A/TCF3 transcription factors, together with NOTCH1 and pre-T cell receptor (TCR) signalling. Importantly, perturbations of early T-cell regulatory networks are implicated in leukemogenesis. NOTCH1 gain of function mutations invariably lead to T-cell acute lymphoblastic leukemia (T-ALL), whereas inhibition of E proteins accelerates leukemogenesis. Thus, NOTCH1, pre-TCR, E2A and HEB functions are intertwined, but how these pathways contribute individually or synergistically to leukemogenesis remain to be documented. To directly address these questions, we leveraged Cd3e-deficient mice in which pre-TCR signaling and progression through ß-selection is abrogated to dissect and decouple the roles of pre-TCR, NOTCH1, E2A and HEB in SCL/TAL1-induced T-ALL, via the use of Notch1 gain of function transgenic (Notch1ICtg) and Tcf12+/- or Tcf3+/- heterozygote mice. As a result, we now provide evidence that both HEB and E2A restrain cell proliferation at the ß-selection checkpoint while the clonal expansion of SCL-LMO1-induced pre-leukemic stem cells in T-ALL is uniquely dependent on Tcf12 gene dosage. At the molecular level, HEB protein levels are decreased via proteasomal degradation at the leukemic stage, pointing to a reversible loss of function mechanism. Moreover, in SCL-LMO1-induced T-ALL, loss of one Tcf12 allele is sufficient to bypass pre-TCR signaling which is required for Notch1 gain of function mutations and for progression to T-ALL. In contrast, Tcf12 monoallelic deletion does not accelerate Notch1IC-induced T-ALL, indicating that Tcf12 and Notch1 operate in the same pathway. Finally, we identify a tumor suppressor gene set downstream of HEB, exhibiting significantly lower expression levels in pediatric T-ALL compared to B-ALL and brain cancer samples, the three most frequent pediatric cancers. In summary, our results indicate a tumor suppressor function of HEB/TCF12 in T-ALL to mitigate cell proliferation controlled by NOTCH1 in pre-leukemic stem cells and prevent NOTCH1-driven progression to T-ALL.

A comprehensive long-read isoform analysis platform and sequencing resource for breast cancer.

Tumors display widespread transcriptome alterations, but the full repertoire of isoform-level alternative splicing in cancer is unknown. We developed a long-read (LR) RNA sequencing and analytical platform that identifies and annotates full-length isoforms and infers tumor-specific splicing events. Application of this platform to breast cancer samples identifies thousands of previously unannotated isoforms; ~30% affect protein coding exons and are predicted to alter protein localization and function. We performed extensive cross-validation with -omics datasets to support transcription and translation of novel isoforms. We identified 3059 breast tumor­specific splicing events, including 35 that are significantly associated with patient survival. Of these, 21 are absent from GENCODE and 10 are enriched in specific breast cancer subtypes. Together, our results demonstrate the complexity, cancer subtype specificity, and clinical relevance of previously unidentified isoforms and splicing events in breast cancer that are only annotatable by LR-seq and provide a rich resource of immuno-oncology therapeutic targets.

Benchmarking the proteomic profile of animal models of mesial temporal epilepsy.

OBJECTIVES: We compared the proteomic signatures of the hippocampal lesion induced in three different animal models of mesial temporal lobe epilepsy with hippocampal sclerosis (MTLE+HS): the systemic pilocarpine model (PILO), the intracerebroventricular kainic acid model (KA), and the perforant pathway stimulation model (PPS). METHODS: We used shotgun proteomics to analyze the proteomes and find enriched biological pathways of the dorsal and ventral dentate gyrus (DG) isolated from the hippocampi of the three animal models. We also compared the proteomes obtained in the animal models to that from the DG of patients with pharmacoresistant MTLE+HS. RESULTS: We found that each animal model presents specific profiles of proteomic changes. The PILO model showed responses predominantly related to neuronal excitatory imbalance. The KA model revealed alterations mainly in synaptic activity. The PPS model displayed abnormalities in metabolism and oxidative stress. We also identified common biological pathways enriched in all three models, such as inflammation and immune response, which were also observed in tissue from patients. However, none of the models could recapitulate the profile of molecular changes observed in tissue from patients. SIGNIFICANCE: Our results indicate that each model has its own set of biological responses leading to epilepsy. Thus, it seems that only using a combination of the three models may one replicate more closely the mechanisms underlying MTLE+HS as seen in patients.

Predicting transcriptional regulatory interactions with artificial neural networks applied to E. coli multidrug resistance efflux pumps.

BACKGROUND: Little is known about bacterial transcriptional regulatory networks (TRNs). In Escherichia coli, which is the organism with the largest wet-lab validated TRN, its set of interactions involves only approximately 50% of the repertoire of transcription factors currently known, and ~25% of its genes. Of those, only a small proportion describes the regulation of processes that are clinically relevant, such as drug resistance mechanisms. RESULTS: We designed feed-forward (FF) and bi-fan (BF) motif predictors for E. coli using multi-layer perceptron artificial neural networks (ANNs). The motif predictors were trained using a large dataset of gene expression data; the collection of motifs was extracted from the E. coli TRN. Each network motif was mapped to a vector of correlations which were computed using the gene expression profile of the elements in the motif. Thus, by combining network structural information with transcriptome data, FF and BF predictors were able to classify with a high precision of 83% and 96%, respectively, and with a high recall of 86% and 97%, respectively. These results were found when motifs were represented using different types of correlations together, i.e., Pearson, Spearman, Kendall, and partial correlation. We then applied the best predictors to hypothesize new regulations for 16 operons involved with multidrug resistance (MDR) efflux pumps, which are considered as a major bacterial mechanism to fight antimicrobial agents. As a result, the motif predictors assigned new transcription factors for these MDR proteins, turning them into high-quality candidates to be experimentally tested. CONCLUSION: The motif predictors presented herein can be used to identify novel regulatory interactions by using microarray data. The presentation of an example motif to predictors will make them categorize whether or not the example motif is a BF, or whether or not it is an FF. This approach is useful to find new "pieces" of the TRN, when inspecting the regulation of a small set of operons. Furthermore, it shows that correlations of expression data can be used to discriminate between elements that are arranged in structural motifs and those in random sets of transcripts.

High-throughput screening in niche-based assay identifies compounds to target preleukemic stem cells.

Current chemotherapies for T cell acute lymphoblastic leukemia (T-ALL) efficiently reduce tumor mass. Nonetheless, disease relapse attributed to survival of preleukemic stem cells (pre-LSCs) is associated with poor prognosis. Herein, we provide direct evidence that pre-LSCs are much less chemosensitive to existing chemotherapy drugs than leukemic blasts because of a distinctive lower proliferative state. Improving therapies for T-ALL requires the development of strategies to target pre-LSCs that are absolutely dependent on their microenvironment. Therefore, we designed a robust protocol for high-throughput screening of compounds that target primary pre-LSCs maintained in a niche-like environment, on stromal cells that were engineered for optimal NOTCH1 activation. The multiparametric readout takes into account the intrinsic complexity of primary cells in order to specifically monitor pre-LSCs, which were induced here by the SCL/TAL1 and LMO1 oncogenes. We screened a targeted library of compounds and determined that the estrogen derivative 2-methoxyestradiol (2-ME2) disrupted both cell-autonomous and non-cell-autonomous pathways. Specifically, 2-ME2 abrogated pre-LSC viability and self-renewal activity in vivo by inhibiting translation of MYC, a downstream effector of NOTCH1, and preventing SCL/TAL1 activity. In contrast, normal hematopoietic stem/progenitor cells remained functional. These results illustrate how recapitulating tissue-like properties of primary cells in high-throughput screening is a promising avenue for innovation in cancer chemotherapy.

Network inference and network response identification: moving genome-scale data to the next level of biological discovery.

The escalating amount of genome-scale data demands a pragmatic stance from the research community. How can we utilize this deluge of information to better understand biology, cure diseases, or engage cells in bioremediation or biomaterial production for various purposes? A research pipeline moving new sequence, expression and binding data towards practical end goals seems to be necessary. While most individual researchers are not motivated by such well-articulated pragmatic end goals, the scientific community has already self-organized itself to successfully convert genomic data into fundamentally new biological knowledge and practical applications. Here we review two important steps in this workflow: network inference and network response identification, applied to transcriptional regulatory networks. Among network inference methods, we concentrate on relevance networks due to their conceptual simplicity. We classify and discuss network response identification approaches as either data-centric or network-centric. Finally, we conclude with an outlook on what is still missing from these approaches and what may be ahead on the road to biological discovery.

DASMiner: discovering and integrating data from DAS sources.

BACKGROUND: DAS is a widely adopted protocol for providing syntactic interoperability among biological databases. The popularity of DAS is due to a simplified and elegant mechanism for data exchange that consists of sources exposing their RESTful interfaces for data access. As a growing number of DAS services are available for molecular biology resources, there is an incentive to explore this protocol in order to advance data discovery and integration among these resources. RESULTS: We developed DASMiner, a Matlab toolkit for querying DAS data sources that enables creation of integrated biological models using the information available in DAS-compliant repositories. DASMiner is composed by a browser application and an API that work together to facilitate gathering of data from different DAS sources, which can be used for creating enriched datasets from multiple sources. The browser is used to formulate queries and navigate data contained in DAS sources. Users can execute queries against these sources in an intuitive fashion, without the need of knowing the specific DAS syntax for the particular source. Using the source's metadata provided by the DAS Registry, the browser's layout adapts to expose only the set of commands and coordinate systems supported by the specific source. For this reason, the browser can interrogate any DAS source, independently of the type of data being served. The API component of DASMiner may be used for programmatic access of DAS sources by programs in Matlab. Once the desired data is found during navigation, the query is exported in the format of an API call to be used within any Matlab application. We illustrate the use of DASMiner by creating integrative models of histone modification maps and protein-protein interaction networks. These enriched datasets were built by retrieving and integrating distributed genomic and proteomic DAS sources using the API. CONCLUSION: The support of the DAS protocol allows that hundreds of molecular biology databases to be treated as a federated, online collection of resources. DASMiner enables full exploration of these resources, and can be used to deploy applications and create integrated views of biological systems using the information deposited in DAS repositories.