The immune system prevents recurrence of transplanted but not autochthonous antigenic tumors after oncogene inactivation therapy

Targeted oncogene inactivation by small molecule inhibitors can be very effective but tumor recurrence is a frequent problem in the clinic. Therapy by inactivation of the cancer-driving oncogene in transplanted tumors was shown to be augmented in the presence of T cells. However, these experiments did not take into account the long-term, usually tolerogenic, interaction of de novo malignancies with the immune system. Here, we employed mice, in which SV40 large T (Tag) and firefly luciferase (Luc) as fusion protein (TagLuc) could be regulated with the Tet-on system and upon activation resulted in tumors after a long latency. TagLuc inactivation induced profound tumor regression, demonstrating sustained oncogene addiction. While tumor relapse after TagLuc inactivation was prevented in immunocompetent mice bearing transplanted tumors, autochthonous tumors relapsed or recurred after therapy discontinuation indicating that the immune system that coevolved with the malignancy over an extended period of time lost the potency to mount an efficient anti-tumor immune response. By contrast, adoptively transferred CD8(+) T cells targeting the cancer-driving oncogene eradicated recurrent autochthonous tumors, highlighting a suitable therapy option in a clinically relevant model

Mod/Resc Parsimony Inference

We address in this paper a new computational biology problem that aims at understanding a mechanism that could potentially be used to genetically manipulate natural insect populations infected by inherited, intra-cellular parasitic bacteria. In this problem, that we denote by \textsc{Mod/Resc Parsimony Inference}, we are given a boolean matrix and the goal is to find two other boolean matrices with a minimum number of columns such that an appropriately defined operation on these matrices gives back the input. We show that this is formally equivalent to the \textsc{Bipartite Biclique Edge Cover} problem and derive some complexity results for our problem using this equivalence. We provide a new, fixed-parameter tractability approach for solving both that slightly improves upon a previously published algorithm for the \textsc{Bipartite Biclique Edge Cover}. Finally, we present experimental results where we applied some of our techniques to a real-life data set.Comment: 11 pages, 3 figure

On the convergence of cluster expansions for polymer gases

We compare the different convergence criteria available for cluster expansions of polymer gases subjected to hard-core exclusions, with emphasis on polymers defined as finite subsets of a countable set (e.g. contour expansions and more generally high- and low-temperature expansions). In order of increasing strength, these criteria are: (i) Dobrushin criterion, obtained by a simple inductive argument; (ii) Gruber-Kunz criterion obtained through the use of Kirkwood-Salzburg equations, and (iii) a criterion obtained by two of us via a direct combinatorial handling of the terms of the expansion. We show that for subset polymers our sharper criterion can be proven both by a suitable adaptation of Dobrushin inductive argument and by an alternative --in fact, more elementary-- handling of the Kirkwood-Salzburg equations. In addition we show that for general abstract polymers this alternative treatment leads to the same convergence region as the inductive Dobrushin argument and, furthermore, to a systematic way to improve bounds on correlations

Creating diamond color centers for quantum optical applications

Nitrogen vacancy (NV) centers in diamond have distinct promise as solid-state qubits. This is because of their large dipole moment, convenient level structure and very long room-temperature coherence times. In general, a combination of ion irradiation and subsequent annealing is used to create the centers, however for the rigorous demands of quantum computing all processes need to be optimized, and decoherence due to the residual damage caused by the implantation process itself must be mitigated. To that end we have studied photoluminescence (PL) from NV$^-$, NV$^0$ and GR1 centers formed by ion implantation of 2MeV He ions over a wide range of fluences. The sample was annealed at $600^{\circ}$C to minimize residual vacancy diffusion, allowing for the concurrent analysis of PL from NV centers and irradiation induced vacancies (GR1). We find non-monotic PL intensities with increasing ion fluence, monotonic increasing PL in NV$^0$/NV$^-$ and GR1/(NV$^0$ + NV$^1$) ratios, and increasing inhomogeneous broadening of the zero-phonon lines with increasing ion fluence. All these results shed important light on the optimal formation conditions for NV qubits. We apply our findings to an off-resonant photonic quantum memory scheme using vibronic sidebands

CcrZ is a pneumococcal spatiotemporal cell cycle regulator that interacts with FtsZ and controls DNA replication by modulating the activity of DnaA.

Most bacteria replicate and segregate their DNA concomitantly while growing, before cell division takes place. How bacteria synchronize these different cell cycle events to ensure faithful chromosome inheritance by daughter cells is poorly understood. Here, we identify Cell Cycle Regulator protein interacting with FtsZ (CcrZ) as a conserved and essential protein in pneumococci and related Firmicutes such as Bacillus subtilis and Staphylococcus aureus. CcrZ couples cell division with DNA replication by controlling the activity of the master initiator of DNA replication, DnaA. The absence of CcrZ causes mis-timed and reduced initiation of DNA replication, which subsequently results in aberrant cell division. We show that CcrZ from Streptococcus pneumoniae interacts directly with the cytoskeleton protein FtsZ, which places CcrZ in the middle of the newborn cell where the DnaA-bound origin is positioned. This work uncovers a mechanism for control of the bacterial cell cycle in which CcrZ controls DnaA activity to ensure that the chromosome is replicated at the right time during the cell cycle

State-of-the-art analytical methods of viral infections in human lung organoids

Human-based organ models can provide strong predictive value to investigate the tropism, virulence, and replication kinetics of viral pathogens. Currently, such models have received widespread attention in the study of SARS-CoV-2 causing the COVID-19 pandemic. Applicable to a large set of organoid models and viruses, we provide a step-by-step work instruction for the infection of human alveolar-like organoids with SARS-CoV-2 in this protocol collection. We also prepared a detailed description on state-of-the-art methodologies to assess the infection impact and the analysis of relevant host factors in organoids. This protocol collection consists of five different sets of protocols. Set 1 describes the protein extraction from human alveolar-like organoids and the determination of protein expression of angiotensin-converting enzyme 2 (ACE2), transmembrane serine protease 2 (TMPRSS2) and FURIN as exemplary host factors of SARS-CoV-2. Set 2 provides detailed guidance on the extraction of RNA from human alveolar-like organoids and the subsequent qPCR to quantify the expression level of ACE2, TMPRSS2, and FURIN as host factors of SARS-CoV-2 on the mRNA level. Protocol set 3 contains an in-depth explanation on how to infect human alveolar-like organoids with SARS-CoV-2 and how to quantify the viral replication by plaque assay and viral E gene-based RT-qPCR. Set 4 provides a step-by-step protocol for the isolation of single cells from infected human alveolar-like organoids for further processing in single-cell RNA sequencing or flow cytometry. Set 5 presents a detailed protocol on how to perform the fixation of human alveolar-like organoids and guides through all steps of immunohistochemistry and in situ hybridization to visualize SARS-CoV-2 and its host factors. The infection and all subsequent analytical methods have been successfully validated by biological replications with human alveolar-like organoids based on material from different donors

Temporal omics analysis in Syrian hamsters unravel cellular effector responses to moderate COVID-19

In COVID-19, immune responses are key in determining disease severity. However, cellular mechanisms at the onset of inflammatory lung injury in SARS-CoV-2 infection, particularly involving endothelial cells, remain ill-defined. Using Syrian hamsters as a model for moderate COVID-19, we conduct a detailed longitudinal analysis of systemic and pulmonary cellular responses, and corroborate it with datasets from COVID-19 patients. Monocyte-derived macrophages in lungs exert the earliest and strongest transcriptional response to infection, including induction of pro-inflammatory genes, while epithelial cells show weak alterations. Without evidence for productive infection, endothelial cells react, depending on cell subtypes, by strong and early expression of anti-viral, pro-inflammatory, and T cell recruiting genes. Recruitment of cytotoxic T cells as well as emergence of IgM antibodies precede viral clearance at day 5 post infection. Investigating SARS-CoV-2 infected Syrian hamsters thus identifies cell type-specific effector functions, providing detailed insights into pathomechanisms of COVID-19 and informing therapeutic strategies

Geographical Variability Affects CCHFV Detection by RT-PCR: A Tool for In-Silico Evaluation of Molecular Assays

The Crimean-Congo hemorrhagic fever virus (CCHFV) is considered to be a major emerging infectious threat, according to the WHO R&D blueprint. A wide range of CCHFV molecular assays have been developed, employing varied primer/probe combinations. The high genetic variability of CCHFV often hampers the efficacy of available molecular tests and can affect their diagnostic potential. Recently, increasing numbers of complete CCHFV genomic sequences have become available, allowing a better appreciation of the genomic evolution of this virus. We summarized the current knowledge on molecular methods and developed a new bioinformatics tool to evaluate the existing assays for CCHFV detection, with a special focus on strains c

Key benefits of dexamethasone and antibody treatment in COVID-19 hamster models revealed by single cell transcriptomics

For COVID-19, effective and well-understood treatment options are still scarce. Since vaccine efficacy is challenged by novel variants, short-lasting immunity and vaccine hesitancy, understanding and optimizing therapeutic options remains essential. We aimed at better understanding the effects of two standard-of-care drugs, dexamethasone and anti-SARS-CoV-2 antibodies, on infection and host responses. By using two COVID-19 hamster models, pulmonary immune responses were analyzed to characterize effects of single or combinatorial treatments. Pulmonary viral burden was reduced by anti-SARS-CoV-2 antibody treatment, and similar or increased by dexamethasone alone. Dexamethasone exhibited strong anti-inflammatory effects and prevented fulminant disease in a severe disease model. Combination therapy showed additive benefits with both anti-viral and anti-inflammatory potency. Bulk and single-cell transcriptomic analyses confirmed dampened inflammatory cell recruitment into lungs upon dexamethasone treatment, and identified a specifically responsive subpopulation of neutrophils, thereby indicating a potential mechanism of action. Our analyses confirm the anti-inflammatory properties of dexamethasone and suggest possible mechanisms, validate anti-viral effects of anti-SARS-CoV-2 antibody treatment, and reveal synergistic effects of a combination therapy, thus informing more effective COVID-19 therapies