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Lieberman, M.

Publications and source records attributed to Lieberman, M..

4 recordsLinked to original sources

p38 blockade reverses the immune suppressive tumor microenvironment in metastatic breast cancer

Metastatic breast cancer (MBC) is a life-threatening disease with limited therapeutic options. The immune suppressive tumor microenvironment (TME) limits the potency of the antitumor immune response and facilitates disease progression and metastasis. Our current study demonstrates that p38 is a druggable target in the TME that regulates the outcome of the immune-tumor interaction. The study revealed that systemic blockade of p38 reduces metastasis, and this anti-metastatic response is negated by depletion of CD8+ T cells. Single-cell transcriptomic analysis of the immune-TME showed that pharmacological p38 inhibition (p38i) or tumor-specific inactivation of p38 by CRISPR/Cas9 (p38KO) resulted in a less exhausted and more activated CD8+ T cell phenotype. Immunophenotyping analyses demonstrated that p38 blockade reduced the expression of multiple inhibitory receptors on CD8+ T cells (i.e., PD-1, LAG-3, CTLA-4), indicating a reversal of immune exhaustion and enhanced immune activation systemically and in the TME. In contrast, p38 blockade did not exhibit inhibitory effects on T cells in proliferation assays in vitro and did not affect the proportion of regulatory T cells in vivo. The major negative impact of p38 blockade in vivo was on the myeloid populations, such as myeloid-derived suppressor cells (MDSCs) and tumor-associated macrophages (TAMs). Further, tumor p38 activity was required for the expression of cytokines/chemokines and tumor-derived exosomes with high chemotactic capacity for myeloid cells. Altogether, this study highlights a previously unrecognized p38-driven pathway that promotes an immune suppressive TME and metastasis, and that therapeutic blockade of p38 has important implications for improving antitumor immunity and patient outcomes. STATEMENT OF SIGNIFICANCEThis study highlights a previously unrecognized p38-driven tumor pathway that promotes an immune suppressive microenvironment and metastasis, and that therapeutic blockade of p38 has important implications for improving antitumor immunity and patient outcomes.

cancer biology↗

A K-mer based Bulked Segregant Analysis approach to map seed traits in unphased heterozygous potato genomes

High-throughput sequencing-based methods for bulked segregant analysis (BSA) allow for the rapid identification of genetic markers associated with traits of interest. BSA studies have successfully identified qualitative (binary) and quantitative trait loci using QTL mapping. However, most traditional QTL mapping approaches require a reference genome. Here, we combine high throughput short read sequencing with bulk segregant analysis of k-mers (BSA-k-mer). This method can be applied to any organism and is particularly useful for species with genomes too different from the closest sequenced genome. It is also instrumental when dealing with highly heterozygous and polyploid genomes without phased haplotype assemblies and for which a single haplotype can control a trait. Finally, it is flexible in terms of population structure. Here, we present the application of the BSA-k-mer method for the rapid identification of candidate regions related to seed spot and seed size in diploid potato. While conventional QTL mapping of parental genotypes did not generate any signal, candidate loci were identified for each trait using the BSA-k-mer approach. The seed spot loci match with previously identified loci associated with pigmentation in potato. The loci associated with seed size are novel. Both sets of loci are potentially instrumental in future breeding towards true seeds in potato. Article SummaryIdentifying genes linked to agronomic traits in potatoes is challenging because potato genomes are complex and variable. We are investigating the genetic basis of seed size and color. Seeds were categorized as large or small, or spotted or not, based on simple visual observation. Next, DNA sequences from each individual were mined for association between random short sequences (k-mers) and those two traits. This more flexible method allowed us to identify regions of the potato genome associated with both traits.

plant biology↗

Biomarker potential of real-world voice signals to predict abnormal blood glucose levels

BackgroundVoice signal analysis is an emerging non-invasive technique to examine health conditions, and is implemented in various real-life applications and devices. The purpose of this study was to evaluate the association of voice signals with blood glucose levels in healthy individuals. The study aimed to investigate the longitudinal stabilities of voice signals and identify voice biomarkers to predict abnormal blood glucose levels. MethodsWe created voice profiles composed of 17,552,688 voice signals from 44 participants and their 1,454 voice recordings. From each voice recording, 12,082 voice-features were extracted. Longitudinal stabilities of voice-features were quantified using linear mixed-effect modelling. Voice-features that showed significant difference between different blood glucose levels, strong intra-stability and the ability to make distinct choice in decision trees were selected as voice biomarker. Voice biomarkers were fed into a multi-class random forest classifier to predict high, normal, and low blood glucose levels. FindingsIn total, 196 voice biomarkers were characterized. Results showed a predictive model with an overall accuracy of 78.66%, overall AUC of 0.83 (95% confidence interval is 0.80 - 0.85), and 0.41 of Matthews Correlation Coefficient (MCC) to discriminate three different blood glucose levels in an independent test set. InterpretationOur voice biomarkers could serve as a noninvasive and conventional surrogate of blood glucose monitoring in daily life as well as a screening tool to estimate potential risk of poor glycemic control. FundingThis research was internally funded and received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

systems biology↗

"Scentsor": A Whole-Cell Yeast Biosensor with an Olfactory Reporter for Low-Cost and Equipment-Free Detection of Pharmaceuticals

Portable and inexpensive analytical tools are required to monitor pharmaceutical quality in technology limited settings including low- and middle-income countries (LMICs). Whole cell yeast biosensors have the potential to help meet this need. However, most of the read-outs for yeast biosensors require expensive equipment or reagents. To overcome this challenge, we have designed a yeast biosensor that produces a unique scent as a readout. This inducible scent biosensor, or "scentsor," does not require the user to administer additional reagents for reporter development and utilizes only the users nose to be "read." In this manuscript, we describe a scentsor that is responsive to the hormone estradiol (E2). The best estimate threshold (BET) for E2 detection with a panel of human volunteers (n = 49) is 39 nM E2 (15 nM when "non-smellers" are excluded). This concentration of E2 is sensitive enough to detect levels of E2 that would be found in dosage forms. This manuscript provides evidence that scent has potential for use in portable yeast biosensors as a read out, particularly for use in technology-limited environments.

synthetic biology↗