Essential Signatures Research Articles

Single Nuclei Multiomics Analysis of Transcriptional and Chromatin Accessibility of Tumor Cells Uncovers Molecular Signatures and Regulatory Elements of Malignant Clonal Evolution in Multiple Myeloma

Category Multiple Myeloma and Plasma Cell Dyscrasias: Basic and Translational Introduction Multiple myeloma (MM) is a hematological cancer resulting from abnormal proliferation of plasma cells (PC) in bone marrow. The progression of the disease from Monoclonal Gammopathy of Undetermined Significance (MGUS), Smoldering Multiple Myeloma (SMM), to MM involves multiple drivers and dynamic interactions of premalignant/malignant cells. In the advancement of single-cell technologies, the same-cell readouts of nucleus RNA sequencing (snRNA-seq) and Assay for Transposase-Accessible Chromatin sequencing (snATAC-seq) provide a less biased measurement of gene expressions and chromatin accessible regions. Profiling and analyzing both omics data at different stages of MM may reveal essential molecular signatures and regulatory elements for microenvironmental modification and evolution. These insights could benefit MM patients as novel targets for early therapeutic intervention at different stages of progression. Methods Bone marrow aspirates were collected from 33 individuals: Healthy donors (n=3), patients with MGUS (n=2), SMM (n=5), MM (n=21), and Plasma Cell Leukemia (PCL; n=2). Viable mononuclear cells underwent CD138+ sorting and nuclei isolation prior to library preparation. Libraries were prepared with the Chromium Multiome kit (10X Genomics) and sequenced on NovaSeq S4 flow cells at UAMS. The clonal V(D)J arrangements of each sample and expression level from HD samples were used to distinguish malignant (MM) from normal plasma cells (PC). The MM data from snATAC-seq and snRNA-seq were integrated using Signac with the weighted nearest neighbor methods from Seurat. All inter- and intra-tumor clusters were evaluated for markers of high-risk disease, differential gene expression, differentially accessible regions, and transcription factor activities. Results We performed snRNA-seq and snATAC-seq sequencing to generate multi-stage, molecular classified-specific chromatin accessibility and transcriptional profiles of plasma cell neoplasms comprising 439,134 cells across 33 samples. The malignant plasma cells for each sample were extracted by the unique clonotypic signature of the immunoglobulin gene rearrangement. Of these, we detected the highest clonality in MGUS samples, suggesting clonal competition at the premalignant stage. Based on our primary microarray data, we detected the overexpression of molecular signatures for most cells in each sample, confirming the intratumor cell heterogeneity. Furthermore, we used the ratio of 70-gene signature of aggressive disease and proliferation signatures to investigate the clonal composition of MM cells corresponding to the risk of relapse. Surprisingly, the proportion of high-risk cells increased respectively from low to high-risk samples. The expansion of high-risk clonality might exhibit competition among malignant cell subpopulations regarding microenvironmental adaptation. The multiomics integration generated less distinct clusters between MGUS and SMM, indicating the gradual complex alterations of subclonal gene expression and chromatin accessibility in malignant transformation. Interestingly, most accessible regions were found in intronic/distal intergenic regions implying the long-range gene regulation from cis-regulatory elements. Based on the positive/negative correlation of binding motifs within differentially accessible regions, we identified unique enriched transcription factors for each disease stage, suggesting the mechanism patterns in the evolutionary process of malignant cells. Conclusion In the largest multiomics study to date of CD138+ cells, we demonstrate the malignant clonal evolution using gene expression and chromatin accessibility profiling from patients with early-stage disease to highly aggressive form. Throughout the time of disease progression, we found an increase in the proportion of malignant cells with high-risk signatures. Also, we uncover unique signatures of chromatin accessibility for each molecular classification that can further refine our understanding of functional heterogeneity in multiple myeloma. Collectively, our joint single nuclei profiling reveals additional insights into the malignant clonal evolution and investigates novel regulatory elements involved in disease progression.

Integrative Multi-Omics Analysis of Tumor-Immune Microenvironment in Multiple Myeloma: Novel Insights and Therapeutic Implications

Despite notable therapeutic advances that have improved the survival of multiple myeloma (MM) patients, development of drug resistance remains a major problem. Transcriptomic analysis provides an opportunity to dissect the complexity of tumors, including the surrounding microenvironment, which has a significant impact on MM tumor progression and patients' response to treatment, as demonstrated by the effectiveness of immunomodulatory therapies. To improve the tailoring of targeted and immune based therapeutic strategies, it is crucial to decipher the tumor-immune microenvironment profile in MM patients. We used a multi-omics data integration approach, including RNAseq-based gene expression for MM tumor cells (MMCs) (n=196) and for the corresponding purified tumor microenvironment (TME) (n=124), single nucleotide variant data from whole exome sequencing of MMCs (n=100), deconvolution of TME immune subtypes (n=124), and relevant clinical metadata. This allowed us to thoroughly characterize both the tumor and its TME. Applying Multiomics Factor Analysis (MOFA) identified 13 principal factors capturing the diversity of the disease, encompassing both MM malignant and microenvironment components. Factors 1 and 2 specifically represented the heterogeneity in the TME, incorporating gene expression profiles and deconvolution-based immune cell subtypes. On the other hand, Factors 6, 9, 11, and 12 were specific to variations in the gene expression profile of the malignant component. Importantly, the analysis of the variables linked to these main factors uncovers not only genes with established functions in myeloma but also novel candidates with potential importance. Based on the top-weight in the gene expression and deconvolution data, Factor 1 showed a positive association with MMP8 expression by TME cells and a negative correlation with the presence of CD8 positive T cells in the TME. Factor 2, on the other hand, exhibited positive alignments with the expression of BHLHA15, TNFRSF17 (BCMA), KLF15, and IGF1 by TME cells. Regarding Factor 6 and Factor 11, which capture tumor fraction heterogeneity, Factor 6 showed alignment with the expression of cancer testis antigens ( MAGEA6, MAGEA3), CXCL12, and KLF4. Meanwhile, Factor 11 was associated with the expression of genes such as SULF2, as well as CRP levels, percentage of CD138+ cells, and the percentage of cells in cell cycle phase S. Next, we investigated the predictive potential of the latent factors inferred by MOFA in models of clinical outcomes. Interestingly, 6 out of the 13 factors identified by MOFA showed significant associations with patients' overall survival (cox regression, p <= 0.05). These factors include Factor 1 and 2, which are related to the composition of the TME, and Factor 6 and 9, which are associated with myeloma progression status, osteolysis, t(11;14), t(4;14), and MM molecular classification. Finally, performing unsupervised clustering using the latent MOFA factors, we identified two main groups with distinct TME subtypes. These groups can be further divided into nine subgroups, each exhibiting unique TME characteristics. Notably, the TME subtypes showed correlations with essential gene expression signatures related to immune response regulation, T cell activation, and natural killer mediated immunity. Additionally, the significant presence of deconvolution-based immune cell types, including T cell CD8+, T cell regulatory, T cell CD4+ (non-regulatory), NK cells and Monocytes was observed. Moreover, the TME subtypes were associated with patients' overall survival and response to daratumumab. Patients presenting TME enriched in immune cells were associated with a significant better outcome ( p = 0.0026). These findings highlight the importance of TME analysis in predicting treatment response and MM patients' outcome in the context of immune-based therapies. Our integrative multi-omics analysis revealed comprehensive MM heterogeneity and distinct immune subtypes that could be of therapeutic interest for personalized therapeutic approaches.

Measurements of fusion reaction history in inertially confined burning plasmas

Direct evidence of inertially confined fusion ignition appears in the abrupt temperature increase and consequent rapid increase in the thermonuclear burn rate as seen in the reaction history. The Gamma Reaction History (GRH) and Gas Cherenkov Detector (GCD) diagnostics are γ-based Cherenkov detectors that provide high quality measurements of deuterium–tritium fusion γ ray production and are, thus, capable of monitoring the thermonuclear burn rate. Temporal shifts in both peak burn time and burn width have been observed during recent high-yield shots (yields greater than 1017 neutrons) and are essential diagnostic signatures of the ignition process. While the current GRH and GCD detectors are fast enough to sense the changes of reaction history due to alpha heating, they do not have enough dynamic range to capture the onset of alpha heating. The next generation of instrumentation, GRH-15m, is proposed to increase the yield-rate coverage to measure the onset of alpha-heating.

Splicing signature database development to delineate cancer pathways using literature mining and transcriptome machine learning

Alternative splicing (AS) events modulate certain pathways and phenotypic plasticity in cancer. Although previous studies have computationally analyzed splicing events, it is still a challenge to uncover biological functions induced by reliable AS events from tremendous candidates. To provide essential splicing event signatures to assess pathway regulation, we developed a database by collecting two datasets: (i) reported literature and (ii) cancer transcriptome profile. The former includes knowledge-based splicing signatures collected from 63,229 PubMed abstracts using natural language processing, extracted for 202 pathways. The latter is the machine learning-based splicing signatures identified from pan-cancer transcriptome for 16 cancer types and 42 pathways. We established six different learning models to classify pathway activities from splicing profiles as a learning dataset. Top-ranked AS events by learning model feature importance became the signature for each pathway. To validate our learning results, we performed evaluations by (i) performance metrics, (ii) differential AS sets acquired from external datasets, and (iii) our knowledge-based signatures. The area under the receiver operating characteristic values of the learning models did not exhibit any drastic difference. However, random-forest distinctly presented the best performance to compare with the AS sets identified from external datasets and our knowledge-based signatures. Therefore, we used the signatures obtained from the random-forest model. Our database provided the clinical characteristics of the AS signatures, including survival test, molecular subtype, and tumor microenvironment. The regulation by splicing factors was additionally investigated. Our database for developed signatures supported retrieval and visualization system.

Direct comparison of non-osteoarthritic and osteoarthritic synovial fluid-induced intracellular chondrocyte signaling and phenotype changes

Since the joint microenvironment and tissue homeostasis are highly dependent on synovial fluid, we aimed to compare the essential chondrocyte signaling signatures of non-osteoarthritic vs end-stage osteoarthritic knee synovial fluid. Moreover, we determined the phenotypic consequence of the distinct signaling patterns on articular chondrocytes. Protein profiling of synovial fluid was performed using antibody arrays. Chondrocyte signaling and phenotypic changes induced by non-osteoarthritic and osteoarthritic synovial fluid were analyzed using a phospho-kinase array, luciferase-based transcription factor activity assays, and RT-qPCR. The origin of osteoarthritic synovial fluid signaling was evaluated by comparing the signaling responses of conditioned media from cartilage, synovium, infrapatellar fat pad and meniscus. Osteoarthritic synovial fluid induced pathway-phenotype relationships were evaluated using pharmacological inhibitors. Compared to non-osteoarthritic synovial fluid, osteoarthritic synovial fluid was enriched in cytokines, chemokines and growth factors that provoked differential MAPK, AKT, NFκB and cell cycle signaling in chondrocytes. Functional pathway analysis confirmed increased activity of these signaling events upon osteoarthritic synovial fluid stimulation. Tissue secretomes of osteoarthritic cartilage, synovium, infrapatellar fat pad and meniscus activated several inflammatory signaling routes. Furthermore, the distinct pathway signatures of osteoarthritic synovial fluid led to accelerated chondrocyte dedifferentiation via MAPK/ERK signaling, increased chondrocyte fibrosis through MAPK/JNK and PI3K/AKT activation, an elevated inflammatory response mediated by cPKC/NFκB, production of extracellular matrix-degrading enzymes by MAPK/p38 and PI3K/AKT routes, and enabling of chondrocyte proliferation. This study provides the first mechanistic comparison between non-osteoarthritic and osteoarthritic synovial fluid, highlighting MAPKs, cPKC/NFκB and PI3K/AKT as crucial OA-associated intracellular signaling routes.

FitDevo: accurate inference of single-cell developmental potential using sample-specific gene weight.

The quantification of developmental potential is critical for determining developmental stages and identifying essential molecular signatures in single-cell studies. Here, we present FitDevo, a novel method for inferring developmental potential using scRNA-seq data. The main idea of FitDevo is first to generate sample-specific gene weight (SSGW) and then infer developmental potential by calculating the correlation between SSGW and gene expression. SSGW is generated using a generalized linear model that combines sample-specific information and gene weight learned from a training dataset covering scRNA-seq data of 17 previously published datasets. We have rigorously validated FitDevo’s effectiveness using a testing dataset with scRNA-seq data from 28 existing datasets and have also demonstrated its superiority over current methods. Furthermore, FitDevo’s broad application scope has been illustrated using three practical scenarios: deconvolution analysis of epidermis, spatial transcriptomic data analysis of hearts and intestines, and developmental potential analysis of breast cancer. The source code and related data are available at https://github.com/jumphone/fitdevo.

A TRUSTED targeted mass spectrometry assay for pan-herpesvirus protein detection.

SUMMARYThe presence and abundance of viral proteins within host cells are part of the essential signatures of the cellular stages of viral infections. However, methods that can comprehensively detect and quantify these proteins are still limited, particularly for viruses with large protein coding capacity. Here, we design and experimentally validate a mass spectrometry-based Targeted herpesviRUS proTEin Detection (TRUSTED) assay for monitoring human viruses representing the three Herpesviridae subfamilies—herpes simplex virus type 1, human cytomegalovirus (HCMV), and Kaposi sarcoma-associated herpesvirus. We demonstrate assay applicability for (1) capturing the temporal cascades of viral replication, (2) detecting proteins throughout a range of virus concentrations and in in vivo models of infection, (3) assessing the effects of clinical therapeutic agents and sirtuin-modulating compounds, (4) studies using different laboratory and clinical viral strains, and (5) discovering a role for carbamoyl phosphate synthetase 1 in supporting HCMV replication.

Modeling the signatures of interaction in Type II supernovae: UV emission, high-velocity features, broad-boxy profiles

Because mass loss is a fundamental phenomenon in massive stars, an interaction with circumstellar material (CSM) should be universal in core-collapse supernovae (SNe). Leaving aside the extreme CSM density, extent, or mass typically encountered in Type IIn SNe, we investigate the diverse long-term radiative signatures of an interaction between a Type II SN ejecta and CSM corresponding to mass-loss rates up to 10−3 M⊙ yr−1. Because these CSM are relatively tenuous and optically thin to electron scattering beyond a few stellar radii, radiation hydrodynamics is not essential and one may treat the interaction directly as an additional power source in the non-local thermodynamic equilibrium radiative transfer problem. The CSM accumulated since shock breakout forms a dense shell in the outer ejecta and leads to high-velocity absorption features in spectral lines, even for a negligible shock power. In addition to Balmer lines, such features may appear in Na I D and He I lines, among others. A stronger interaction strengthens the continuum flux (preferentially in the UV), quenches the absorption of P-Cygni profiles, boosts the Mg IIλλ 2795, 2802 doublet, and fosters the production of a broad-boxy Hα emission component. The rise in ionization in the outer ejecta may quench some lines (e.g., the Ca II near-infrared triplet). The interaction power emerges preferentially in the UV, in particular at later times, shifting the optical color to the blue, but increasing the optical luminosity modestly. Strong thermalization and clumping seem to be required to make an interaction superluminous in the optical. The UV range contains essential signatures that provide critical constraints to infer the mass-loss history and inner workings of core-collapse SN progenitors at death.

Featuring few essential Raman spectroscopic signatures between heterogeneous cells.

Here we demonstrate it is instructive to quantify cell Raman spectroscopy by sparse regularization. To be able to extract the specific spectral differences in a heterogeneous cell system with great spectroscopic similarities derived from many common biomolecular components, the maximum information entropy probability was proposed and exemplified by identifying normal lymphocytes from leukemia cells. The essential spectroscopic features were observed to locate at three Raman peaks whose spectral signatures were commensurate. The applicability of the extracted features was acknowledged by that the predicted identification accuracy of up to 93% was still achieved when only two peaks were loaded into decision tree model, which may provide the possibility of a clinically rapid hematological malignancy detection.

Integrative ontology and pathway-based approach identifies distinct molecular signatures in transcriptomes of esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC) remains a serious concern globally due to many factors that including late diagnosis, lack of an ideal biomarker for diagnosis and prognosis, and high rate of mortality. In this study, we aimed to identify the essential dysregulated genes and molecular signatures associated with the progression and development of ESCC. The dataset with 15 ESCCs and the 15 adjacent normal tissue samples from the surrounding histopathologically tumor-free mucosa was selected. We applied bioinformatics pipelines including various topological parameters from MCODE, CytoNCA, and cytoHubba to prioritize the most significantly associated DEGs with ESCC. We performed functional enrichment annotation for the identified DEGs using DAVID and MetaCore™ GeneGo platforms. Furthermore, we validated the essential core genes in TCGA and GTEx datasets between the normal mucosa and ESCC for their expression levels. These DEGs were primarily enriched in positive regulation of transferase activity, negative regulation of organelle organization, cell cycle mitosis/S-phase transition, spindle organization/assembly, development, and regulation of angiogenesis. Subsequently, the DEGs were associated with the pathways such as oocyte meiosis, cell cycle, and DNA replication. Our study identified the eight-core genes (AURKA, AURKB, MCM2, CDC20, TPX2, PLK1, FOXM1, and MCM7) that are highly expressed among the ESCC, and TCGA dataset. The multigene comparison and principal component analysis resulted in elevated signals for the AURKA, MCM2, CDC20, TPX2, PLK1, and FOXM1. Overall, our study reported GO profiles and molecular signatures that might help researchers to grasp the pathological mechanisms underlying ESCC development and eventually provide novel therapeutic and diagnostic strategies.

Possibility of mixed helical p-wave pairings in Sr2RuO4

The exact nature of unconventional superconductivity in Sr$_2$RuO$_4$ remains a mystery. At the phenomenological level, no superconducting order parameter proposed thus far seems able to coherently account for all essential experimental signatures. Among the latter is the prominent polar Kerr effect, which implies a nonzero ac anomalous Hall conductivity. Assuming the Kerr effect is intrinsic, it can be accounted for by a bulk chiral Cooper pairing with nonzero orbital angular momentum, such as $p+ip$ or $d+id$, which, however, has difficulties in being reconciled with other experimental results. Given the situation, in this paper, we propose alternative possibilities with complex mixtures of distinct helical p-wave order parameters, namely, $A_{1u}+iA_{2u}$ and $B_{1u}+iB_{2u}$ in the group theory nomenclature. These states essentially consist of two copies of chiral p-wave pairings with opposite chirality and different pairing amplitudes, and therefore support intrinsic Hall and Kerr effects. We further show that these states exhibit salient features that may explain several other key observations in this material, including the absence of spontaneous edge current, a substantial Knight shift drop, and possibly signatures in uniaxial strain and ultrasound measurements.

Ground Target Recognition Using Carrier-Free UWB Radar Sensor With a Semi-Supervised Stacked Convolutional Denoising Autoencoder

This paper proposes a scheme for ground-target recognition based on the carrier-free ultra-wideband (UWB) radar sensor for the first time. Carrier-free UWB system emits an extremely short pulse, which can provide potential advantages over other UWB systems, such as high range resolution, short blind range, enhanced anti-multipath jamming ability. These characteristics above guarantee that UWB echoes contain more comprehensive and detailed information with respect to the target of interest. Feature extraction is fundamental and crucial for target recognition. In this paper a deep network named semi-supervised stacked convolutional denoising autoencoder (SCDAE) is developed to extract discriminative features. As an extension of stacked denoising autoencoders (SDAE), SCDAE replaces fully-connected layers with one-dimensional convolutional layers as middle structures. In order to capture essential signatures exactly and improve classification accuracy, we build up a semi-supervised learning mechanism via binding a label regularization term with SCDAE. Moreover, given that echoes observed at different angles belonging to the same target are different, a new multi-level label coding method is proposed and embedded in SCDAE. Experimental results demonstrate that the proposed algorithm can effectively learn essential representation and improve classification accuracy in the presence of low signal-to-noise ratios (SNRs), making it very suitable for use in a classification scheme.

Identifying the Immunological Gene Signatures of Immune Cell Subtypes

The immune system is a complicated defensive system that comprises multiple functional cells and molecules acting against endogenous and exogenous pathogenic factors. Identifying immune cell subtypes and recognizing their unique immunological functions are difficult because of the complicated cellular components and immunological functions of the immune system. With the development of transcriptomics and high-throughput sequencing, the gene expression profiling of immune cells can provide a new strategy to explore the immune cell subtyping. On the basis of the new profiling data of mouse immune cell gene expression from the Immunological Genome Project (ImmGen), a novel computational pipeline was applied to identify different immune cell subtypes, including αβ T cells, B cells, γδ T cells, and innate lymphocytes. First, the profiling data was analyzed by a powerful feature selection method, Monte-Carlo Feature Selection, resulting in a feature list and some informative features. For the list, the two-stage incremental feature selection method, incorporating random forest as the classification algorithm, was applied to extract essential gene signatures and build an efficient classifier. On the other hand, a rule learning scheme was applied on the informative features to construct quantitative expression rules. A group of gene signatures was found as qualitatively related to the biological processes of four immune cell subtypes. The quantitative expression rules can efficiently cluster immune cells. This work provides a novel computational tool for immune cell quantitative subtyping and biomarker recognition.

Design, synthesis and SAR of antitubercular benzylpiperazine ureas.

N-furfuryl piperazine ureas disclosed by scientists at GSK Tres Cantos were chosen as antimycobacterial hits from a phenotypic whole-cell screen. Bioisosteric replacement of the furan ring in the GSK Tres Cantos molecules with a phenyl ring led to molecule (I) with an MIC of 1μM against Mtb H37Rv, low cellular toxicity (HepG2 IC50 ~ 80μM), good DMPK properties and specificity for Mtb. With the aim of delineating the SAR associated with (I), fifty-five analogs were synthesized and screened against Mtb. The SAR suggests that the piperazine ring, benzyl urea and piperonyl moieties are essential signatures of this series. Active compounds in this series are metabolically stable, have low cellular toxicity and are valuable leads for optimization. Molecular docking suggests these molecules occupy the Q0 site of QcrB like Q203. Bioisosteric replacement of N-furfuryl piperazine-1-carboxamides yielded molecule (I) a novel lead with satisfactory PD, metabolism, and toxicity profiles.

Increased temperature-related adeno-associated virus vectors transduction of ovarian cancer cells - essential signatures of AAV receptor and heat shock proteins.

Recombinant adeno-associated viruses (rAAVs) are becoming more commonly used in clinical trials involving gene therapy. Additionally AAV-based drugs have already been registered. Gene therapy aims to increase transduction efficiency, increase in vivo selectivity and reduce side effects. One approach to achieve this is the use of physical factors, such as temperature or more specifically, hyperthermia, which is already utilized in oncology. The aim of the present study was to investigate the effect of hyperthermic conditions (40°C and 43°C) on the rAAV transduction efficiency of ovarian cancer cells (Caov-3 and NIH:OVCAR-3) and non-cancerous cells (AAV-293). The present study was designed to identify functional associations between the level of gene transfer and the expression of representative genes for rAAV transmission (AAVR (AAV receptor), heparan sulfate proteoglycan (HSPG) 1 and HSPG2) and heat shock proteins (HSPs). The expressions of selected genes were measured via reverse transcription-quantitative PCR and cell adhesion/invasion chamber tests were also performed. The results revealed that ovarian cancer cell lines were more efficiently transduced with rAAV vectors at an elevated temperature. Additionally, the expression patterns of AAVR, HSPG1 and HSPG2 genes were different between the tested lines. The expression of certain receptors in ascites-derived NIH:OVCAR-3 ovarian cancer cells was higher compared with tumor-derived Caov-3 cells at 37, 40 and 43°C, which indicates a higher transduction efficiency in the formerly mentioned cells. Ascites-derived ovarian cancer cells were characterized by high expressions of HSP40, HSP90 and HSP70 families. Lower levels of HSP expression were demonstrated in less-effectively transduced Caov-3 cells. Furthermore, expressions of the examined genes changed with increasing temperature. The results indicated that temperature-dependent transduction is associated with the expression of the rAAV receptor and HSP genes. The results of the current study may aid the design of effective protocols for ovarian cancer gene therapy.

Two Phase Transitions in the Two-Dimensional Nematic Three-Vector Model with No Quasi-Long-Range Order: MonteCarlo Simulation of the Density of States.

The presence of stable topological defects in a two-dimensional (d=2) liquid crystal model allowing molecular reorientations in three dimensions (n=3) was largely believed to induce a defect-mediated Berzenskii-Kosterlitz-Thouless-type transition to a low temperature phase with quasi-long-range order. However, earlier MonteCarlo (MC) simulations could not establish certain essential signatures of the transition, suggesting further investigations. We study this model by computing its equilibrium properties through MC simulations, based on the determination of the density of states of the system. Our results show that, on cooling, the high temperature disordered phase deviates from its initial progression towards the topological transition, crossing over to a new fixed point, condensing into a nematic phase with exponential correlations of its director fluctuations. The thermally induced topological kinetic processes continue, however, limited to the length scales set by the nematic director fluctuations, and lead to a second topological transition at a lower temperature. It is argued that in the (d=2, n=3) system with an attractive biquadratic Hamiltonian, the presence of additional molecular degrees of freedom and local Z_{2} symmetry associated with lattice sites together promote the onset of an additional relevant scaling field at matching length scales in the high temperature region, leading to a crossover.

Essential signatures and canonical bases of irreducible representations of simple Lie algebras

Essential signatures and canonical bases of irreducible representations of simple Lie algebras

Deconvoluting essential gene signatures for cancer growth from genomic expression in compound-treated cells.

Essential gene signatures for cancer growth have been typically identified via RNAi or CRISPR-Cas9. Here, we propose an alternative method that reveals the essential gene signatures by analysing genomic expression profiles in compound-treated cells. With a large amount of the existing compound-induced data, essential gene signatures at genomic scale are efficiently characterized without technical challenges in the previous techniques. An essential gene is characterized as a gene presenting positive correlation between its down-regulation and cell growth inhibition induced by diverse compounds, which were collected from LINCS and CGP. Among 12741 genes, 1092, 1228827962, 1664580 and 829 essential genes are characterized for each of A375, A549, BT20, LNCAP, MCF7, MDAMB231 and PC3 cell lines (P-value ≤ 1.0E-05). Comparisons to the previously identified essential genes yield significant overlaps in A375 and A549 (P-value ≤ 5.0E-05) and the 103 common essential genes are enriched in crucial processes for cancer growth. In most comparisons in A375, MCF7, BT20 and A549, the characterized essential genes yield more essential characteristics than those of the previous techniques, i.e. high gene expression, high degrees of protein-protein interactions, many homologs and few paralogs. Remarkably, the essential genes commonly characterized by both the previous and proposed techniques show more significant essential characteristics than those solely relied on the previous techniques. We expect that this work provides new aspects in essential gene signatures. The Python implementations are available at https://github.com/jmjung83/deconvolution_of_essential_gene_signitures. Supplementary data are available at Bioinformatics online.

Development and Functional Characterization of Murine Tolerogenic Dendritic Cells.

The immune system operates by maintaining a tight balance between coordinating responses against foreign antigens and maintaining an unresponsive state against self-antigens as well as antigens derived from commensal organisms. The disruption of this immune homeostasis can lead to chronic inflammation and to the development of autoimmunity. Dendritic cells (DCs) are the professional antigen-presenting cells of the innate immune system involved in activating naïve T cells to initiate immune responses against foreign antigens. However, DCs can also be differentiated into TolDCs that act to maintain and promote T cell tolerance and to suppress effector cells contributing to the development of either autoimmune or chronic inflammation conditions. The recent advancement in our understanding of TolDCs suggests that DC tolerance can be achieved by modulating their differentiation conditions. This phenomenon has led to tremendous growth in developing TolDC therapies for numerous immune disorders caused due to break in immune tolerance. Successful studies in preclinical autoimmunity murine models have further validated the immunotherapeutic utility of TolDCs in the treatment of autoimmune disorders. Today, TolDCs have become a promising immunotherapeutic tool in the clinic for reinstating immune tolerance in various immune disorders by targeting pathogenic autoimmune responses while leaving protective immunity intact. Although an array of strategies has been proposed by multiple labs to induce TolDCs, there is no consistency in characterizing the cellular and functional phenotype of these cells. This protocol provides a step-by-step guide for the development of bone marrow-derived DCs in large numbers, a unique method used to differentiate them into TolDCs with a synthetic triterpenoid 2-cyano-3,12-dioxooleana-1,9-dien-28-oic acid-difluoro-propyl-amide (CDDO-DFPA), and the techniques used to confirm their phenotype, including analyses of essential molecular signatures of TolDCs. Finally, we show a method to assess TolDC function by testing their immunosuppressive response in vitro and in vivo in a preclinical model of multiple sclerosis.

A Monte Carlo Study of Lambda Hyperon Polarization at BM@N

Heavy strange objects (hyperons) can provide essential signatures of the excited and compressed baryonic matter. At NICA, it is planned to study hyperons both in the collider mode (MPD detector) and the fixed-target one (BM@N setup). Measurements of strange hyperon polarization can give additional information on the strong interaction mechanisms. In heavy-ion collisions, such measurements are even more valuable since the polarization is expected to be sensitive to characteristics of the QCD medium (vorticity, hydrodynamic helicity) and to QCD anomalous transport. In this analysis, the possibility to measure at BM@N the polarization of the lightest strange hyperon Λ is studied in Monte Carlo event samples of Au + Au collisions produced with the DCM-QGSM generator. It is shown that the detector will allow to measure polarization with a precision required to check the model predictions.