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Immune checkpoint blockade (ICB) has emerged as a novel therapeutic tool for cancer therapy in the last decade. Unfortunately, a small number of patients benefit from approved immune checkpoint inhibitors (ICIs). Therefore, multiple studies are being conducted to find new ICIs and combination strategies to improve the current ICIs. In this review, we discuss some approved immune checkpoints, such as PD-L1, PD-1, and CTLA-4, and also highlight newer emerging ICIs. For instance, HLA-E, overexpressed by tumor cells, represents an immune-suppressive feature by binding CD94/NKG2A, on NK and T cells. NKG2A blockade recruits CD8+ T cells and activates NK cells to decrease the tumor burden. NKG2D acts as an NK cell activating receptor that can also be a potential ICI. The adenosine A2A and A2B receptors, CD47-SIRPα, TIM-3, LAG-3, TIGIT, and VISTA are targets that also contribute to cancer immunoresistance and have been considered for clinical trials. Their antitumor immunosuppressive functions can be used to develop blocking antibodies. PARPs, mARTs, and B7-H3 are also other potential targets for immunosuppression. Additionally, miRNA, mRNA, and CRISPR-Cas9-mediated immunotherapeutic approaches are being investigated with great interest. Pre-clinical and clinical studies project these targets as potential immunotherapeutic candidates in different cancer types for their robust antitumor modulation.

The recent development of single-cell genomic techniques allows us to profile gene expression at the single-cell level easily, although many of these methods have limited throughput. Rosenberg et al. describe a strategy called split-pool ligation-based transcriptome sequencing, or SPLiT-seq, which uses combinatorial barcoding to profile single-cell transcriptomes without requiring the physical isolation of each cell. The authors used their method to profile >100,000 single-cell transcriptomes from mouse brains and spinal cords at 2 and 11 days after birth. Comparisons with in situ hybridization data on RNA expression from Allen Institute atlases linked these transcriptomes with spatial mapping, from which developmental lineages could be identified. Science , this issue p. 176 Single-cell analyses with SPLiT-seq (split-pool ligation-based transcriptome sequencing) elucidate development of the mouse nervous system. To facilitate scalable profiling of single cells, we developed split-pool ligation-based transcriptome sequencing (SPLiT-seq), a single-cell RNA-seq (scRNA-seq) method that labels the cellular origin of RNA through combinatorial barcoding. SPLiT-seq is compatible with fixed cells or nuclei, allows efficient sample multiplexing, and requires no customized equipment. We used SPLiT-seq to analyze 156,049 single-nucleus transcriptomes from postnatal day 2 and 11 mouse brains and spinal cords. More than 100 cell types were identified, with gene expression patterns corresponding to cellular function, regional specificity, and stage of differentiation. Pseudotime analysis revealed transcriptional programs driving four developmental lineages, providing a snapshot of early postnatal development in the murine central nervous system. SPLiT-seq provides a path toward comprehensive single-cell transcriptomic analysis of other similarly complex multicellular systems.

In a bipartite Bell scenario involving two local measurements per party and two outcomes per measurement, the measurement incompatibility in one wing is both necessary and sufficient to reveal the nonlocality. However, such a one-to-one correspondence fails when one of the observers performs more than two measurements. In such a scenario, the measurement incompatibility is necessary but not sufficient to reveal the nonlocality. In this work, within the formalism of general probabilistic theory (GPT), we demonstrate that unlike the nonlocality, the incompatibility of N arbitrary measurements in one wing is both necessary and sufficient for revealing the generalised contextuality for the sub-system in the other wing. Further, we formulate an elegant form of inequality for any GPT that is necessary for N -wise compatibility of N arbitrary observables. Moreover, we argue that any theory that violates the proposed inequality possess a degree of incompatibility that can be quantified through the amount of violation. We claim that it is the generalised contextuality that provides a restriction to the allowed degree of measurement incompatibility of any viable theory of nature and thereby super-select the quantum theory. Finally, we discuss the geometrical implications of our results.

Sterol biosynthesis is crucial for the function of biological membranes and an important target for anti-protozoan/anti-fungal drugs. In the trypanosomatid parasite Leishmania major, the deletion of sterol C14-demethylase (C14DM) results in hypersensitivity to heat, increased plasma membrane fluidity, profound mitochondrial dysfunctions, and reduced virulence in mice. In this study, we show that C14DM-null mutants are defective in their tolerance to membrane-disrupting agents and osmotic stress and their ability to form autophagosomes. In addition, C14DM-null mutants exhibit a heightened sensitivity to anti-trypanosomatid drugs including antimony, ethidium bromide, and pentamidine. The combination of itraconazole (a C14DM antagonist) and pentamidine synergistically inhibits the growth of Leishmania parasites. These findings reveal new insight into the roles of sterol synthesis in protozoan pathogens and highlight the potential of using drug combinations to achieve better treatment outcomes.

There exists a long-standing research interest to understand the molecular and signaling interactions between tumor cells and the innate and adaptive immune cells such as dendritic cells, macrophages, NK cells, and B and T cells that occur in the tumor microenvironment (TME) [...]

Sterol 14-α-demethylase (C14DM) is a key enzyme in the biosynthesis of sterols and the primary target of azoles. In Leishmania major, genetic or chemical inactivation of C14DM leads to accumulation of 14-methylated sterol intermediates and profound plasma membrane abnormalities including increased fluidity and failure to maintain ordered membrane microdomains. These defects likely contribute to the hypersensitivity to heat and severely reduced virulence displayed by the C14DM-null mutants (c14dm‾). In addition to plasma membrane, sterols are present in intracellular organelles. In this study, we investigated the impact of C14DM ablation on mitochondria. Our results demonstrate that c14dm‾ mutants have significantly higher mitochondrial membrane potential than wild type parasites. Such high potential leads to the buildup of reactive oxygen species in the mitochondria, especially under nutrient-limiting conditions. Consistent with these mitochondrial alterations, c14dm‾ mutants show impairment in respiration and are heavily dependent on glucose uptake and glycolysis to generate energy. Consequently, these mutants are extremely sensitive to glucose deprivation and such vulnerability can be rescued through the supplementation of glucose or glycerol. In addition, the accumulation of oxidants may also contribute to the heat sensitivity exhibited by c14dm‾. Finally, genetic or chemical ablation of C14DM causes increased susceptibility to pentamidine, an antimicrobial agent with activity against trypanosomatids. In summary, our investigation reveals that alteration of sterol synthesis can negatively affect multiple cellular processes in Leishmania parasites and make them vulnerable to clinically relevant stress conditions.

The temporal molecular changes that lead to disease onset and progression in Alzheimer’s disease (AD) are still unknown. Here we develop a temporal model for these unobserved molecular changes with a manifold learning method applied to RNA-Seq data collected from human postmortem brain samples collected within the ROS/MAP and Mayo Clinic RNA-Seq studies. We define an ordering across samples based on their similarity in gene expression and use this ordering to estimate the molecular disease stage–or disease pseudotime-for each sample. Disease pseudotime is strongly concordant with the burden of tau (Braak score, P  = 1.0 × 10 −5 ), Aβ (CERAD score, P  = 1.8 × 10 −5 ), and cognitive diagnosis ( P  = 3.5 × 10 −7 ) of late-onset (LO) AD. Early stage disease pseudotime samples are enriched for controls and show changes in basic cellular functions. Late stage disease pseudotime samples are enriched for late stage AD cases and show changes in neuroinflammation and amyloid pathologic processes. We also identify a set of late stage pseudotime samples that are controls and show changes in genes enriched for protein trafficking, splicing, regulation of apoptosis, and prevention of amyloid cleavage pathways. In summary, we present a method for ordering patients along a trajectory of LOAD disease progression from brain transcriptomic data. The limited understanding of the temporal molecular changes in late-onset Alzheimer’s disease hinder the development of therapeutic treatment. The authors use manifold learning to develop a molecular model for disease progression from RNASeq data from human postmortem brain samples.

[...]analysis of the TME and identifying factors affecting TME heterogeneity provides a promising source to develop immunotherapy biomarkers and design strategies to overcome acquired resistance to therapeutic modalities in cancer patients (8,9). The fact, that immune cell heterogeneity resulting from differences in composition, localization, density, and functional state of the immune cells, significantly modulates the immunotherapy response in cancer was highlighted in the research article byMolina et al.where they studied the immune cell composition by immunohistochemistry methods on radical prostatectomy specimens obtained from two cohorts of patients. The effect of the spatial distribution and functional heterogeneity of different subsets of leukocytes in the human head and neck squamous cell carcinoma TME was reported in the research article byNetzer et al.who demonstrated that CD68hi CD163lo and CD68hi CD163hi are mainly localized close to the tumor sites, whereas CD68loCD163hi are prominently accumulated in the tumor stroma.

Background Glioblastoma (GBM) is a primary brain tumor with a 5-year survival rate of ≤5%. We have shown earlier that GBM-antibody-linked curcumin (CC) and also phytosomal curcumin (CCP) rescue 50–60% of GBM-bearing mice while repolarizing the tumor-associated microglia/macrophages (TAM) from the tumor-promoting M2-type to the tumoricidal M1-type. However, systemic application of CCP yields only sub-IC50 concentrations of CC in the plasma, which is unlikely to kill GBM cells directly. This study investigates the role of CC-evoked intra-GBM recruitment of activated natural killer (NK) cells in the elimination of GBM and GBM stem cells. Methods We have used an immune-competent syngeneic C57BL6 mouse model with the mouse-GBM GL261 cells orthotopically implanted in the brain. Using immunohistochemistry and flow cytometry, we have quantitatively analyzed the role of the intra-GBM-recruited NK cells by (i) injecting (i.p.) the NK1.1 antibody (NK1.1Ab) to temporarily eliminate the NK cells and (ii) blocking NK recruitment by injecting an IL12 antibody (IL12Ab). The treatment cohorts used randomly-chosen GL261-implanted mice and data sets were compared using two-tailed t-test or ANOVA. Results CCP treatment caused the GBM tumor to acquire M1-type macrophages (50–60% of the TAM) and activated NK cells. The treatment also elicited (a) suppression of the M2-linked tumor-promoting proteins STAT3, ARG1, and IL10, (b) induction of the M1-linked anti-tumor proteins STAT1 and inducible nitric oxide synthase in the TAM, (c) elimination of CD133(+) GBM stem cells, and (d) activation of caspase3 in the GBM cells. Eliminating intra-GBM NK cell recruitment caused a partial reversal of each of these effects. Concomitantly, we observed a CCP-evoked dramatic induction of the chemokine monocyte chemotactic protein-1 (MCP-1) in the TAM. Conclusions The recruited NK cells mediate a major part of the CCP-evoked elimination of GBM and GBM stem cells and stabilization of the TAM in the M1-like state. MCP-1 is known to activate peripheral M1-type macrophages to secrete IL12, an activator of NK cells. Based on such observations, we postulate that by binding to peripheral M1-type macrophages and IL12-activated NK cells, the brain-released chemokine MCP-1 causes recruitment of peripheral immune cells into the GBM, thereby causing destruction of the GBM cells and GBM stem cells.

Differentially private (DP) synthetic datasets are a solution for sharing data while preserving the privacy of individual data providers. Understanding the effects of utilizing DP synthetic data in end-to-end machine learning pipelines impacts areas such as health care and humanitarian action, where data is scarce and regulated by restrictive privacy laws. In this work, we investigate the extent to which synthetic data can replace real, tabular data in machine learning pipelines and identify the most effective synthetic data generation techniques for training and evaluating machine learning models. We systematically investigate the impacts of differentially private synthetic data on downstream classification tasks from the point of view of utility as well as fairness. Our analysis is comprehensive and includes representatives of the two main types of synthetic data generation algorithms: marginal-based and GAN-based. To the best of our knowledge, our work is the first that: (i) proposes a training and evaluation framework that does not assume that real data is available for testing the utility and fairness of machine learning models trained on synthetic data; (ii) presents the most extensive analysis of synthetic dataset generation algorithms in terms of utility and fairness when used for training machine learning models; and (iii) encompasses several different definitions of fairness. Our findings demonstrate that marginal-based synthetic data generators surpass GAN-based ones regarding model training utility for tabular data. Indeed, we show that models trained using data generated by marginal-based algorithms can exhibit similar utility to models trained using real data. Our analysis also reveals that the marginal-based synthetic data generated using AIM and MWEM PGM algorithms can train models that simultaneously achieve utility and fairness characteristics close to those obtained by models trained with real data.