Design Gestational diabetes mellitus (GDM) is one of the most common pregnancy complications and its prevalence is constantly rising worldwide. Diagnosis is commonly in the late second or early third trimester of pregnancy, though the development of GDM starts early; hence, first-trimester diagnosis is feasible. Objective Our objective was to identify microRNAs that best distinguish GDM samples from those of healthy pregnant women and to evaluate the predictive value of microRNAs for GDM detection in the first trimester. Methods We investigated the abundance of circulating microRNAs in the plasma of pregnant women in their first trimester. Two populations were included in the study to enable population-specific as well as cross-population inspection of expression profiles. Each microRNA was tested for differential expression in GDM vs control samples, and their efficiency for GDM detection was evaluated using machine-learning models. Results Two upregulated microRNAs (miR-223 and miR-23a) were identified in GDM vs the control set, and validated on a new cohort of women. Using both microRNAs in a logistic-regression model, we achieved an AUC value of 0.91. We further demonstrated the overall predictive value of microRNAs using several types of multivariable machine-learning models that included the entire set of expressed microRNAs. All models achieved high accuracy when applied on the dataset (mean AUC = 0.77). The significance of the classification results was established via permutation tests. Conclusions Our findings suggest that circulating microRNAs are potential biomarkers for GDM in the first trimester. This warrants further examination and lays the foundation for producing a novel early non-invasive diagnostic tool for GDM.
Motivation: The study of RNA virus populations is a challenging task. Each population of RNA virus is composed of a collection of different, yet related genomes often referred to as mutant spectra or quasispecies. Virologists using deep sequencing technologies face major obstacles when studying virus population dynamics, both experimentally and in natural settings due to the relatively high error rates of these technologies and the lack of high performance pipelines. In order to overcome these hurdles we developed a computational pipeline, termed ViVan (Viral Variance Analysis). ViVan is a complete pipeline facilitating the identification, characterization and comparison of sequence variance in deep sequenced virus populations.Results: Applying ViVan on deep sequenced data obtained from samples that were previously characterized by more classical approaches, we uncovered novel and potentially crucial aspects of virus populations. With our experimental work, we illustrate how ViVan can be used for studies ranging from the more practical, detection of resistant mutations and effects of antiviral treatments, to the more theoretical temporal characterization of the population in evolutionary studies.Availability and implementation: Freely available on the web at http://www.vivanbioinfo.orgContact: nshomron@post.tau.ac.ilSupplementary information: Supplementary data are available at Bioinformatics online.
Preeclampsia is one of the most dangerous pregnancy complications, and the leading cause of maternal and perinatal mortality and morbidity. Although the clinical symptoms appear late, its origin is early, and hence detection is feasible already at the first trimester. In the current study, we investigated the abundance of circulating small non-coding RNAs in the plasma of pregnant women in their first trimester, seeking transcripts that best separate the preeclampsia samples from those of healthy pregnant women. To this end, we performed small non-coding RNAs sequencing of 75 preeclampsia and control samples, and identified 25 transcripts that were differentially expressed between preeclampsia and the control groups. Furthermore, we utilized those transcripts and created a pipeline for a supervised classification of preeclampsia. Our pipeline generates a logistic regression model using a 5-fold cross validation on numerous random partitions into training and blind test sets. Using this classification procedure, we achieved an average AUC value of 0.86. These findings suggest the predictive value of circulating small non-coding RNA in the first trimester, warranting further examination, and lay the foundation for producing a novel early non-invasive diagnostic tool for preeclampsia, which could reduce the life-threatening risk for both the mother and fetus.
T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive and often incurable disease. To uncover therapeutic vulnerabilities, we first developed T-ALL patient-derived tumor-xenografts (PDX) and exposed PDX cells to a library of 433 clinical-stage compounds in vitro. We identified 39 broadly active compounds with anti-leukemia activity. Since endothelial cells (ECs) can alter drug responses in T-ALL, we developed an endothelial cells (ECs) / T-ALL co-culture system. We found that ECs provide pro-tumorigenic signals and mitigate drug responses to individual T-ALL PDX. ECs broadly rescued several compounds in most of the models, while other drugs were rescued only in individual PDXs suggesting unique crosstalk interactions and/or intrinsic tumor features. Mechanistically, co-cultured T-ALL and ECs underwent bi-directional transcriptomic changes at the single-cell level, highlighting distinct "education signatures". These changes were linked to a bi-directional regulation of multiple pathways in T-ALL and ECs. Remarkably, in-vitro EC-educated T-ALL cells mirrored ex-vivo splenic T-ALL at the single-cell resolution. Lastly, five effective drugs from the two drug screenings were tested in vivo and shown to effectively delay tumor growth/dissemination and prolonging the overall survival (OS). We anticipate that this T-ALL-EC platform can contribute to elucidating leukemia-microenvironment interactions and identify effective compounds and therapeutic vulnerabilities.
Breast implant-associated lymphoma (BIA-ALCL) has recently been recognized as an independent peripheral T-cell lymphoma (PTCL) entity. In this study, we generated the first BIA-ALCL patient-derived tumor xenograft (PDTX) model (IL89) and a matching continuous cell line (IL89_CL#3488) to discover potential vulnerabilities and druggable targets. We characterized IL89 and IL89_CL#3488, both phenotypically and genotypically, and demonstrated that they closely resemble the matching human primary lymphoma. The tumor content underwent significant enrichment along passages, as confirmed by the increased variant allele frequency (VAF) of mutations. Known aberrations (JAK1 and KMT2C) were identified, together with novel hits, including PDGFB, PDGFRA, and SETBP1. A deep sequencing approach allowed the detection of mutations below the Whole Exome Sequencing (WES) sensitivity threshold, including JAK1G1097D, in the primary sample. RNA sequencing confirmed the expression of a signature of differentially expressed genes in BIA-ALCL. Next, we tested IL89’s sensitivity to the JAK inhibitor ruxolitinib and observed a potent anti-tumor effect, both in vitro and in vivo. We also implemented a high-throughput drug screening approach to identify compounds associated with increased responses in the presence of ruxolitinib. In conclusion, these new IL89 BIA-ALCL models closely recapitulate the primary correspondent lymphoma and represent an informative platform for dissecting the molecular features of BIA-ALCL and performing pre-clinical drug discovery studies, fostering the development of new precision medicine approaches.
Ricin, derived from the castor bean plant, is a highly potent toxin, classified as a potential bioterror agent. Current methods for early detection of ricin poisoning are limited in selectivity. MicroRNAs (miRNAs), which are naturally occurring, negative gene expression regulators, are known for their tissue specific pattern of expression and their stability in tissues and blood. While various approaches for ricin detection have been investigated, miRNAs remain underexplored. We evaluated the effect of pulmonary exposure to ricin on miRNA expression profiles in mouse lungs and peripheral blood mononuclear cells (PBMCs). Significant changes in lung tissue miRNA expression levels were detected following ricin intoxication, specifically regarding miRNAs known to be involved in innate immunity pathways. Transcriptome analysis of the same lung tissues revealed activation of several immune regulation pathways and immune cell recruitment. Our work contributes to the understanding of the role of miRNAs and gene expression in ricin intoxication.
Introduction: Peripheral T-cell lymphomas (PTCLs) include heterogeneous entities of rare and aggressive neoplasms. The improved understanding of the biological/molecular mechanisms driving T-cell transformation and tumor maintenance has powerfully propelled new therapeutic programs. However, despite this progress, PTCLs remain an unmet medical need. Recurrent aberrations and the deregulated activation of distinct signaling pathways have been mapped and linked to selective subtypes. The JAK/STAT signaling pathway's deregulated activation plays a pathogenetic role in PTCL, including ALCL subtypes. STATs regulate the differentiation/phenotype, survival and cell-growth, metabolism, and drug resistance of T-cell lymphomas as well as host immunosuppressive microenvironments. Although many drugs' discovery programs were launched, a plethora of compounds has failed. Methods: We have discovered heterobifunctional molecules by an iterative medicinal chemistry SAR campaign that potently and selectively degrade STAT3 in a proteasome-dependent manner. Conventional PTCL cell lines and Patient Derived Tumor Xenograft (PDTX) and/or derived cell lines (PDTX-CL), carrying either WT- or mutated-STAT3, were exposed to increasing amounts (50nM⇒5µM) of STAT3-degraders. Proteins and mRNA transcripts (2⇒144hrs) were assessed by deep-proteomics and paired-end RNA sequencing, combined with WB/flow cytometry and qRT-PCR. Cell-titer-glo, cell titer blue, Annexin-V and S-cell cycle analyses were used as readouts. Chromatin accessibility, STAT3 DNA binding, 3D chromosomal architecture reorganization and 5-hmC profiling were assessed by ATACseq, CHIPseq and Hi-C and H3K27ac Hi-CHIP and mass-spectrometry. Drug testing/discovery combinations (96-well-plate) were performed using a semi-automated flow-cytometry. A battery of PTCL PDTX models were tested in pre-clinical trials. Results: Treatment of ALK+ ALCL (SU-DHL1) led to the rapid (~6hrs.) and profound down-regulation of STAT3 followed by the loss of canonical STAT3-regulated proteins (SOCS3, MYC, Granzyme B, GAS1, and IL2RA), without appreciable changes for other STAT family members (STAT1, STAT5b). In vitro, cytoplasmic, nuclear, and mitochondrial STAT3 downregulation was maintained up to 144 hrs. Loss of STAT3 in ALK+/- ALCL and BIA-ALCL cells was associated with major transcriptional changes (7116-10615 and 15114 DEGs in ALK- and ALK+ ALCL, respectively), underscoring public/shared as well as private time-dependent signatures. Main down-regulated pathways included JAK-STAT, MAPK, NF-kB, PI3K, TGFb, and TNFa. Comparison of STAT3 shRNA (ALK+ ALCL) and STAT3 degrader (ALK-/ALK+ ALCL) signatures demonstrated a substantial and concordant gene modulation (24hrs) among all models with the highest overlaps between ALK+ ALCL (Figure 3). To identify direct STAT3 gene targets, we analyzed CHIPseq peaks and predicted bindings sites, demonstrating that canonical genes, i.e., SOCS3, Granzyme B, GAS1, IL2RA, STAT3, and CD30, were significantly downregulated. Conversely, CD58, CD274, and MCH-I/II were upregulated at late time points. By mapping the STAT3 binding sites in ALK+ and ALK- ALCL, we have identified 1077 and 2763 STAT3 peaks within promoter/5'-/3'- and distant intergenic regions, corresponding to both coding and non-coding genes. Therapeutically, in vitro treatments led to cell cycle arrest and profound growth inhibition, and over time increased cell death of PTCL cells, including ALCL. Accordingly, growth inhibition of ALCL xenograft and PDTX tumors was also achieved (Figure 2). To identify drugs that could synergize withSTAT3-degrader activity, we tested a compound library (40) targeting pro-tumorigenic PTCL pathways as well as FDA-approved compounds. Ongoing studies are in progress. Conclusion: We have discovered selective STAT3 degraders which control PTCL growth. STAT3 degraders are powerful tools to define the STAT3 pathogenetic mechanisms and dissect genes/pathways to be targeted for T-cell lymphoma eradication. These data provide additional rationale for testing STAT3 degraders in the clinic for the treatment of aggressive malignancies including PTCL/ALCL. Figure 1 Figure 1. Disclosures Yang: Kymera Therapeutics: Current Employment, Current equity holder in publicly-traded company. Sharma: Kymera Therapeutics: Current Employment, Current equity holder in publicly-traded company. Dey: Kymera Therapeutics: Current Employment, Current equity holder in publicly-traded company. Karnik: Kymera Therapeutics: Current Employment, Current equity holder in publicly-traded company. Elemento: Owkin: Consultancy, Other: Current equity holder; Volastra Therapeutics: Consultancy, Other: Current equity holder, Research Funding; Johnson and Johnson: Research Funding; Eli Lilly: Research Funding; Janssen: Research Funding; Champions Oncology: Consultancy; Freenome: Consultancy, Other: Current equity holder in a privately-held company; One Three Biotech: Consultancy, Other: Current equity holder; AstraZeneca: Research Funding. Horwitz: Affimed: Research Funding; Aileron: Research Funding; ADC Therapeutics, Affimed, Aileron, Celgene, Daiichi Sankyo, Forty Seven, Inc., Kyowa Hakko Kirin, Millennium /Takeda, Seattle Genetics, Trillium Therapeutics, and Verastem/SecuraBio.: Consultancy, Research Funding; Acrotech Biopharma, Affimed, ADC Therapeutics, Astex, Merck, Portola Pharma, C4 Therapeutics, Celgene, Janssen, Kura Oncology, Kyowa Hakko Kirin, Myeloid Therapeutics, ONO Pharmaceuticals, Seattle Genetics, Shoreline Biosciences, Inc, Takeda, Trillium Th: Consultancy; Celgene: Research Funding; C4 Therapeutics: Consultancy; Crispr Therapeutics: Research Funding; Daiichi Sankyo: Research Funding; Forty Seven, Inc.: Research Funding; Kura Oncology: Consultancy; Kyowa Hakko Kirin: Consultancy, Research Funding; Millennium/Takeda: Research Funding; Myeloid Therapeutics: Consultancy; ONO Pharmaceuticals: Consultancy; Seattle Genetics: Consultancy, Research Funding; Secura Bio: Consultancy; Shoreline Biosciences, Inc.: Consultancy; Takeda: Consultancy; Trillium Therapeutics: Consultancy, Research Funding; Tubulis: Consultancy; Verastem/Securabio: Research Funding. DeSavi: Kymera Therapeutics: Current Employment, Current equity holder in publicly-traded company. Liu: Kymera Therapeutics: Current Employment, Current equity holder in publicly-traded company.
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