Mono a Mano: ZBP1’s Love–Hate Relationship with the Kissing Virus

Z-DNA binding protein (ZBP1) very much represents the nuclear option. By initiating inflammatory cell death (ICD), ZBP1 activates host defenses to destroy infectious threats. ZBP1 is also able to induce noninflammatory regulated cell death via apoptosis (RCD). ZBP1 senses the presence of left-handed Z-DNA and Z-RNA (ZNA), including that formed by expression of endogenous retroelements. Viruses such as the Epstein–Barr “kissing virus” inhibit ICD, RCD and other cell death signaling pathways to produce persistent infection. EBV undergoes lytic replication in plasma cells, which maintain detectable levels of basal ZBP1 expression, leading us to suggest a new role for ZBP1 in maintaining EBV latency, one of benefit for both host and virus. We provide an overview of the pathways that are involved in establishing latent infection, including those regulated by MYC and NF-κB. We describe and provide a synthesis of the evidence supporting a role for ZNA in these pathways, highlighting the positive and negative selection of ZNA forming sequences in the EBV genome that underscores the coadaptation of host and virus. Instead of a fight to the death, a state of détente now exists where persistent infection by the virus is tolerated by the host, while disease outcomes such as death, autoimmunity and cancer are minimized. Based on these new insights, we propose actionable therapeutic approaches to unhost EBV.     

Neuroprotective Effect of Bcl-2 on Lipopolysaccharide-Induced Neuroinflammation in Cortical Neural Stem Cells

Neuroinflammation is involved in the pathogenesis of neurodegenerative diseases due to increased levels of pro-inflammatory cytokines in the central nervous system (CNS). Chronic neuroinflammation induced by neurotoxic molecules accelerates neuronal damage. B-cell lymphoma 2 (Bcl-2) is generally accepted to be an important anti-apoptotic factor. However, the role of Bcl-2 in neuroprotection against neuroinflammation remains to be determined. The purpose of this study was to investigate the neuroprotective effect of Bcl-2 on lipopolysaccharide (LPS)-induced neuroinflammation in cortical neural stem cells (NSCs). LPS decreased mRNA and protein levels of Tuj-1, a neuron marker, and also suppressed neurite outgrowth, indicating that LPS results in inhibition of neuronal differentiation of NSCs. Furthermore, LPS treatment inhibited Bcl-2 expression during neuronal differentiation; inhibition of neuronal differentiation by LPS was rescued by Bcl-2 overexpression. LPS-induced pro-inflammatory cytokines, including interleukin (IL)-6 and tumor necrosis factor alpha (TNF-α), were decreased by Bcl-2 overexpression. Conversely, Bcl-2 siRNA increased the LPS-induced levels of IL-6 and TNF-α, and decreased neuronal differentiation of NSCs, raising the possibility that Bcl-2 mediates neuronal differentiation by inhibiting the LPS-induced inflammatory response in NSC. These results suggest that Bcl-2 has a neuroprotective effect by inhibiting the LPS-induced inflammatory response in NSCs.     

Systems Drug Discovery for Diffuse Large B Cell Lymphoma Based on Pathogenic Molecular Mechanism via Big Data Mining and Deep Learning Method

Diffuse large B cell lymphoma (DLBCL) is an aggressive heterogeneous disease. The most common subtypes of DLBCL include germinal center b-cell (GCB) type and activated b-cell (ABC) type. To learn more about the pathogenesis of two DLBCL subtypes (i.e., DLBCL ABC and DLBCL GCB), we firstly construct a candidate genome-wide genetic and epigenetic network (GWGEN) by big database mining. With the help of two DLBCL subtypes’ genome-wide microarray data, we identify their real GWGENs via system identification and model order selection approaches. Afterword, the core GWGENs of two DLBCL subtypes could be extracted from real GWGENs by principal network projection (PNP) method. By comparing core signaling pathways and investigating pathogenic mechanisms, we are able to identify pathogenic biomarkers as drug targets for DLBCL ABC and DLBCL GCD, respectively. Furthermore, we do drug discovery considering drug-target interaction ability, drug regulation ability, and drug toxicity. Among them, a deep neural network (DNN)-based drug-target interaction (DTI) model is trained in advance to predict potential drug candidates holding higher probability to interact with identified biomarkers. Consequently, two drug combinations are proposed to alleviate DLBCL ABC and DLBCL GCB, respectively.     

A Bright Horizon: Immunotherapy for Pediatric T-Cell Malignancies

Immunotherapy has transformed the treatment of hematologic malignancies in the past two decades. The treatment of acute lymphoblastic leukemia (ALL), in particular, has been highly impacted by multiple novel immunotherapies. For pediatric patients with T-cell malignancies, translating immunotherapies has proved more challenging due to the complexities of fratricide, risk of product contamination with malignant cells, and concerns over T-cell aplasia. Despite these hurdles, many creative and promising strategies are on the horizon. We review challenges in the development of immunotherapy for T-cell malignancies, strategies to overcome these challenges, as well as therapies currently being investigated and starting to reach the clinic. Immunotherapy will hopefully successfully treat patients with relapsed and refractory T-cell malignancies and may someday be incorporated in up-front protocols in order to prevent relapses.     

Evaluation of Orbital Lymphoproliferative and Inflammatory Disorders by Gene Expression Analysis

Non-specific orbital inflammation (NSOI) and IgG4-related orbital disease (IgG4-ROD) are often challenging to differentiate. Furthermore, it is still uncertain how chronic inflammation, such as IgG4-ROD, can lead to mucosa-associated lymphoid tissue (MALT) lymphoma. Therefore, we aimed to evaluate the diagnostic value of gene expression analysis to differentiate orbital autoimmune diseases and elucidate genetic overlaps. First, we established a database of NSOI, relapsing NSOI, IgG4-ROD and MALT lymphoma patients of our orbital center (2000–2019). In a consensus process, three typical patients of the above mentioned three groups (mean age 56.4 ± 17 years) at similar locations were selected. Afterwards, RNA was isolated using the RNeasy FFPE kit (Qiagen) from archived paraffin-embedded tissues. The RNA of these 12 patients were then subjected to gene expression analysis (NanoString nCounter^®), including a total of 1364 target genes. The most significantly upregulated and downregulated genes were used for a machine learning algorithm to distinguish entities. This was possible with a high probability (p < 0.0001). Interestingly, gene expression patterns showed a characteristic overlap of lymphoma with IgG4-ROD and NSOI. In contrast, IgG4-ROD shared only altered expression of one gene regarding NSOI. To validate our potential biomarker genes, we isolated the RNA of a further 48 patients (24 NSOI, 11 IgG4-ROD, 13 lymphoma patients). Then, gene expression pattern analysis of the 35 identified target genes was performed using a custom-designed CodeSet to assess the prediction accuracy of the multi-parameter scoring algorithms. They showed high accuracy and good performance (AUC ROC: IgG4-ROD 0.81, MALT 0.82, NSOI 0.67). To conclude, genetic expression analysis has the potential for faster and more secure differentiation between NSOI and IgG4-ROD. MALT-lymphoma and IgG4-ROD showed more genetic similarities, which points towards progression to lymphoma.     

Stress-induced DNA damage: a case study in diffuse large B-cell lymphoma

DNA damage is one of the mechanisms of mutagenesis. Sequence integrity may be affected by the action of thermal changes, chemical agents, both endogenous and exogenous, and other environmental issues. Abnormally high mutation rates are referred to as genomic instability: a phenomenon closely related to the onset of cancer. Mutant genotypes may be able to confer some kind of selective advantage on subclonal cell populations, leading them to multiply until dominance in a localized tissue environment that later becomes the tumour. Cellular stress, especially that of oxidative and ionic nature, is a recognized trigger for DNA-damaging processes. A physico-chemical model has shown that high hysteresis rates in DNA denaturation curves may be indicative of dissipative processes inducing DNA damage, thus potentially leading to uncontrolled mutagenesis and genome instability. We here study selectively to what extent this phenomenon may occur by analysing the sequence length and composition effects on the thermodynamic behaviour and the presence of hysteresis in pressure-driven DNA denaturation; pronounced hysteresis in the denaturation/renaturation curves may indicate thermal susceptibility to DNA damage. In particular, we consider highly mutated regions of the genome characterized in diffuse large B-cell lymphoma on a recent whole exome next-generation sequencing effort.     

Nanoscale distribution of CD20 on B‐cell lymphoma tumour cells and its potential role in the clinical efficacy of rituximab

Rituximab is an exciting monoclonal antibody drug approved for treating B‐cell lymphomas and its target is the CD20 antigen which is expressed on the surface of B cells. In recent years, the variable efficacies of rituximab among different lymphoma patients have become an important clinical issue and urgently need to be solved for further development of antibodies with enhanced efficacies. In this work, atomic force microscopy (AFM) was used to investigate the nanoscale distribution of CD20 on the surface of tumour B cells from lymphoma patients to examine its potential role in the clinical therapeutic effects of rituximab. By performing ROR1 fluorescence labelling (ROR1 is a specific tumour cell surface marker) on the bone marrow cells prepared from B‐cell lymphoma patients, the tumour B cells were recognized, and then AFM tips carrying rituximabs via polyethylene glycol crosslinkers were moved to the tumour cells to probe the specific CD20‐rituximab interactions. By applying AFM single‐molecule force spectroscopy (SMFS) at the local areas (500×500 nm²) on the surface of tumour B cells, the nanoscale distributions of CD20 on the surface of tumour B cells were mapped, visually showing that CD20 distributed heterogeneously on the cell surface. Bone marrow cell samples from three clinical B‐cell lymphoma cases were collected to analyze the binding affinity and nanoscale distribution of CD20 on tumour cells. The experimental results showed that CD20 distribution on tumour cells were to some extent related to the clinical therapeutic outcomes while the CD20‐rituximab binding forces did not have distinct effects to the clinical outcomes. These results can provide novel insights in understanding the rituximab's clinical efficacies from the nanoscale distribution of CD20 on the tumour cells at single‐cell and single‐molecule levels.     

Classification and Diagnosis of Lymphoma’s Histopathological Images Using Transfer Learning

Current cancer diagnosis procedure requires expert knowledge and is time-consuming, which raises the need to build an accurate diagnosis support system for lymphoma identification and classification. Many studies have shown promising results using Machine Learning and, recently, Deep Learning to detect malignancy in cancer cells. However, the diversity and complexity of the morphological structure of lymphoma make it a challenging classification problem. In literature, many attempts were made to classify up to four simple types of lymphoma. This paper presents an approach using a reliable model capable of diagnosing seven different categories of rare and aggressive lymphoma. These Lymphoma types are Classical Hodgkin Lymphoma, Nodular Lymphoma Predominant, Burkitt Lymphoma, Follicular Lymphoma, Mantle Lymphoma, Large B-Cell Lymphoma, and T-Cell Lymphoma. Our proposed approach uses Residual Neural Networks, ResNet50, with a Transfer Learning for lymphoma’s detection and classification. The model used results are validated according to the performance evaluation metrics: Accuracy, precision, recall, F-score, and kappa score for the seven multi-classes. Our algorithms are tested, and the results are validated on 323 images of 224 × 224 pixels resolution. The results are promising and show that our used model can classify and predict the correct lymphoma subtype with an accuracy of 91.6%.     

Spatial analysis of air pollution and cancer incidence rates in Haifa Bay, Israel

The Israel National Cancer Registry reported in 2001 that cancer incidence rates in the Haifa area are roughly 20% above the national average. Since Haifa has been the major industrial center in Israel since 1930, concern has been raised that the elevated cancer rates may be associated with historically high air pollution levels. This work tests whether persistent spatial patterns of metrics of chronic exposure to air pollutants are associated with the observed patterns of cancer incidence rates. Risk metrics of chronic exposure to PM₁₀, emitted both by industry and traffic, and to SO₂, a marker of industrial emissions, was developed. Ward-based maps of standardized incidence rates of three prevalent cancers: Non-Hodgkin's lymphoma, lung cancer and bladder cancer were also produced. Global clustering tests were employed to filter out those cancers that show sufficiently random spatial distribution to have a nil probability of being related to the spatial non-random risk maps. A Bayesian method was employed to assess possible associations between the morbidity and risk patterns, accounting for the ward-based socioeconomic status ranking. Lung cancer in males and bladder cancer in both genders showed non-random spatial patterns. No significant associations between the SO₂-based risk maps and any of the cancers were found. Lung cancer in males was found to be associated with PM₁₀, with the relative risk associated with an increase of 1 μg/m³ of PM₁₀ being 12%. Special consideration of wards with expected rates < 1 improved the results by decreasing the variance of the spatially correlated residual log-relative risk.