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Survival rates of patients with metastatic castration-resistant prostate cancer (mCRPC) are low due to lack of response or acquired resistance to available therapies, such as abiraterone (Abi). A better understanding of the underlying molecular mechanisms is needed to identify effective targets to overcome resistance. Given the complexity of the transcriptional dynamics in cells, differential gene expression analysis of bulk transcriptomics data cannot provide sufficient detailed insights into resistance mechanisms. Incorporating network structures could overcome this limitation to provide a global and functional perspective of Abi resistance in mCRPC. Here, we developed TraRe, a computational method using sparse Bayesian models to examine phenotypically driven transcriptional mechanistic differences at three distinct levels: transcriptional networks, specific regulons, and individual transcription factors (TF). TraRe was applied to transcriptomic data from 46 patients with mCRPC with Abi-response clinical data and uncovered abrogated immune response transcriptional modules that showed strong differential regulation in Abi-responsive compared with Abi-resistant patients. These modules were replicated in an independent mCRPC study. Furthermore, key rewiring predictions and their associated TFs were experimentally validated in two prostate cancer cell lines with different Abi-resistance features. Among them, ELK3, MXD1, and MYB played a differential role in cell survival in Abi-sensitive and Abi-resistant cells. Moreover, ELK3 regulated cell migration capacity, which could have a direct impact on mCRPC. Collectively, these findings shed light on the underlying transcriptional mechanisms driving Abi response, demonstrating that TraRe is a promising tool for generating novel hypotheses based on identified transcriptional network disruptions. Significance: The computational method TraRe built on Bayesian machine learning models for investigating transcriptional network structures shows that disruption of ELK3, MXD1, and MYB signaling cascades impacts abiraterone resistance in prostate cancer.

Inflammation is a common feature in neurodegenerative diseases that contributes to neuronal loss. Previously, we demonstrated that the basal inflammatory tone differed between brain regions and, consequently, the reaction generated to a pro-inflammatory stimulus was different. In this study, we assessed the innate immune reaction in the midbrain and in the striatum using an experimental model of Parkinson's disease. An adeno-associated virus serotype 9 expressing the α-synuclein and mCherry genes or the mCherry gene was administered into the substantia nigra. Myeloid cells (CD11b

For the last two decades, the amount of genomic data produced by scientific and medical applications has been growing at a rapid pace. To enable software solutions that analyze, process and transmit these data in an efficient and interoperable way, ISO and IEC released the first version of the compression standard MPEG-G in 2019. However, non-proprietary implementations of the standard are not openly available so far, limiting fair scientific assessment of the standard and therefore hindering its broad adoption. In this paper, we present Genie, the first open-source encoder that compresses genomic data according to the MPEG-G standard. We demonstrate that Genie reaches state-of-the-art compression ratios while offering interoperability with any other standard-compliant decoder independent from its manufacturer. Finally, the ISO/IEC ecosystem ensures the long-term sustainability and decodability of the compressed data through the ISO/IEC-supported reference decoder.

Excel file with regulon edges and ChIP-seq enrichment scores for TraRe, GRNboost2 and ARACNE-AP

Survival rates of patients with metastatic castration-resistant prostate cancer (mCRPC) are low due to lack of response or acquired resistance to available therapies, such as abiraterone (Abi). A better understanding of the underlying molecular mechanisms is needed to identify effective targets to overcome resistance. Given the complexity of the transcriptional dynamics in cells, differential gene expression analysis of bulk transcriptomics data cannot provide sufficient detailed insights into resistance mechanisms. Incorporating network structures could overcome this limitation to provide a global and functional perspective of Abi resistance in mCRPC. Here, we developed TraRe, a computational method using sparse Bayesian models to examine phenotypically driven transcriptional mechanistic differences at three distinct levels: transcriptional networks, specific regulons, and individual transcription factors (TF). TraRe was applied to transcriptomic data from 46 patients with mCRPC with Abi-response clinical data and uncovered abrogated immune response transcriptional modules that showed strong differential regulation in Abi-responsive compared with Abi-resistant patients. These modules were replicated in an independent mCRPC study. Furthermore, key rewiring predictions and their associated TFs were experimentally validated in two prostate cancer cell lines with different Abi-resistance features. Among them, ELK3, MXD1, and MYB played a differential role in cell survival in Abi-sensitive and Abi-resistant cells. Moreover, ELK3 regulated cell migration capacity, which could have a direct impact on mCRPC. Collectively, these findings shed light on the underlying transcriptional mechanisms driving Abi response, demonstrating that TraRe is a promising tool for generating novel hypotheses based on identified transcriptional network disruptions.Significance:The computational method TraRe built on Bayesian machine learning models for investigating transcriptional network structures shows that disruption of ELK3, MXD1, and MYB signaling cascades impacts abiraterone resistance in prostate cancer.

Supplementary File 3: Excel file with Supplementary tables S6 - S9

Supplementary File 2: Excel file with CommunityAMARETTO Enrichment results

Supplementary Notes (S1-S5)Supplementary Tables (S1 - S5; S10 - S11)Supplementary Figures (S1 - S12)

Despite the abundance of multi-modal data, suitable statistical models that can improve our understanding of diseases with genetic underpinnings are challenging to develop. Here we present SparseGMM, a novel statistical approach for gene regulatory network discovery. SparseGMM uniquely uses latent variable modeling with sparsity constraints regulators to learn gaussian mixtures from multi-omic data. By combining co-expression patterns with a Bayesian framework, sparseGMM quantitatively measures confidence in regulators and uncertainty in target gene assignment by computing gene entropy. We apply SparseGMM to liver cancer and normal liver tissue data and evaluate the discovered gene modules in an independent scRNA-seq dataset. sparseGMM identifies PROCR as a regulator of angiogenesis, and PDCD1LG2 and HNF4A as regulators of immune response and blood coagulation in cancer, respectively. Additionally, we show that more genes have significantly higher entropy in cancer compared to normal liver; among high entropy genes are key multifunctional components shared by critical pathways, such as p53 and estrogen signaling.Software availabilityThe software is available athttps://hub.docker.com/r/shaimaabakr/sparse_gmmOne-sentence summaryA novel statistical approach for gene regulatory network discovery recovers modules and corresponding regulators of diverse normal liver functions, important liver cancer processes, as well as shared biology between liver cancer and normal tissue.

Early hematopoiesis is a continuous process in which hematopoietic stem and progenitor cells (HSPCs) gradually differentiate toward specific lineages. Aging and myeloid malignant transformation are characterized by changes in the composition and regulation of HSPCs. In this study, we used single-cell RNA sequencing (scRNA-seq) to characterize an enriched population of human HSPCs obtained from young and elderly healthy individuals.Based on their transcriptional profile, we identified changes in the proportions of progenitor compartments during aging, and differences in their functionality, as evidenced by gene set enrichment analysis. Trajectory inference revealed that altered gene expression dynamics accompanied cell differentiation, which could explain aging-associated changes in hematopoiesis. Next, we focused on key regulators of transcription by constructing gene regulatory networks (GRNs) and detected regulons that were specifically active in elderly individuals. Using previous findings in healthy cells as a reference, we analyzed scRNA-seq data obtained from patients with myelodysplastic syndrome (MDS) and detected specific alterations of the expression dynamics of genes involved in erythroid differentiation in all patients with MDS such as TRIB2. In addition, the comparison between transcriptional programs and GRNs regulating normal HSPCs and MDS HSPCs allowed identification of regulons that were specifically active in MDS cases such as SMAD1, HOXA6, POU2F2, and RUNX1 suggesting a role of these transcription factors (TFs) in the pathogenesis of the disease.In summary, we demonstrate that the combination of single-cell technologies with computational analysis tools enable the study of a variety of cellular mechanisms involved in complex biological systems such as early hematopoiesis and can be used to dissect perturbed differentiation trajectories associated with perturbations such as aging and malignant transformation. Furthermore, the identification of abnormal regulatory mechanisms associated with myeloid malignancies could be exploited for personalized therapeutic approaches in individual patients.

The development and progress of high-throughput sequencing technologies have transformed the sequencing of DNA from a scientific research challenge to practice. With the release of the latest generation of sequencing machines, the cost of sequencing a whole human genome has dropped to less than $\$ $ 600. Such achievements open the door to personalized medicine, where it is expected that genomic information of patients will be analyzed as a standard practice. However, the associated costs, related to storing, transmitting, and processing the large volumes of data, are already comparable to the costs of sequencing. To support the design of new and interoperable solutions for the representation, compression, and management of genomic sequencing data, the Moving Picture Experts Group (MPEG) jointly with working group 5 of ISO/TC276 “Biotechnology” has started to produce the ISO/IEC 23092 series, known as MPEG-G. MPEG-G does not only offer higher levels of compression compared with the state of the art but it also provides new functionalities, such as built-in support for random access in the compressed domain, support for data protection mechanisms, flexible storage, and streaming capabilities. MPEG-G only specifies the decoding syntax of compressed bitstreams, as well as a file format and a transport format. This allows for the development of new encoding solutions with higher degrees of optimization while maintaining compatibility with any existing MPEG-G decoder.

Identification of new markers associated with long-term efficacy in patients treated with CAR T cells is a current medical need, particularly in diseases such as multiple myeloma. In this study, we address the impact of CAR density on the functionality of BCMA CAR T cells. Functional and transcriptional studies demonstrate that CAR T cells with high expression of the CAR construct show an increased tonic signaling with up-regulation of exhaustion markers and increased in vitro cytotoxicity but a decrease in in vivo BM infiltration. Characterization of gene regulatory networks using scRNA-seq identified regulons associated to activation and exhaustion up-regulated in CAR High T cells, providing mechanistic insights behind differential functionality of these cells. Last, we demonstrate that patients treated with CAR T cell products enriched in CAR High T cells show a significantly worse clinical response in several hematological malignancies. In summary, our work demonstrates that CAR density plays an important role in CAR T activity with notable impact on clinical response.