In vivo expression of the B7 costimulatory molecule by subsets of antigen-presenting cells and the malignant cells of Hodgkin's disease

The B-lymphocyte/accessory-cell activation antigen B7 (BB1) has been shown in vitro to stimulate T-lymphocyte proliferation and cytokine production via CD28 present on the latter cells. In this study, benign lymphoid tissues, lymphomas, and extralymphoid inflammatory sites were examined immunohistochemically using anti-B7 and other relevant monoclonal antibodies. B7 was expressed by benign transformed germinal center B cells, as it was by B cells of follicular lymphomas. B7 was also expressed by a subpopulation (a mean of 31% to 65%) of macrophages and dendritic cells in a variety of lymphoid tissues. It was present in abundance on all macrophages constituting sarcoid granulomas in lymph nodes. In extralymphoid inflammation, 17% to 35% of macrophages expressed B7 only weakly. Cases of Hodgkin's disease showed expression of B7 by the majority of Reed-Sternberg cells or malignant mononuclear variants, a phenomenon that potentially contributes to the lymphocytic accumulation that is a feature of this condition. CD28+ T cells were seen in all areas where T cells were present. B7+ and CD28+ cells colocalized in, for example, lymphoid follicles, lymph node paracortex, sarcoid granulomas, and Hodgkin's disease tissue, indicating a potential for cellular interaction via these molecules at these sites.     

Natural M-segment reassortment in Potosi and Main Drain viruses: implications for the evolution of orthobunyaviruses

Summary Recently, we identified Batai virus as the M-segment reassortment partner of Ngari virus. Extension of genetic analyses to other orthobunyaviruses related to the Bunyamwera serogroup indicates additional natural genome reassortments. Whereas the relative phylogenetic positions of all three genome segment sequences were similar for Northway and Kairi viruses, the relative positions of Potosi and Main Drain virus M-segment sequences diverged from those of their S- and L-segments. Our findings indicate M-segment reassortment in Potosi and Main Drain viruses and demonstrate natural genome reassortment as a driving force in the evolution of viruses of the Bunyamwera serogroup.     

Asymptomatic circulation of HEV71 in Norway

Widespread circulation of human enterovirus 71 was discovered in a prospective study of fecal samples obtained from healthy Norwegian children. Molecular characterization of the virus determined that it belonged to genotype C1. Complete sequencing of this strain, HEV71 804/NO/03, revealed differences in the 5'UTR and polymerase with respect to more pathogenic genotypes that may explain its reduced neurovirulence.     

Th1 immune response promotes severe bone resorption caused by Porphyromonas gingivalis

Bacterial infections of the dental pulp result in soft tissue and alveolar bone destruction. It has been suggested that Th1 responses promote disease, whereas Th2 responses are protective. However, other studies have challenged this notion. To address this question, bone destruction was evaluated in mice immunized to develop strong and polarized Th1- or Th2-biased responses to the oral pathogen Porphyromonas gingivalis. Th1 bias was confirmed by the presence of high titers of serum IgG2a and the production of high levels of interferon (IFN)-gamma and no interleukin (IL)-4 by lymph node cells stimulated with P. gingivalis antigens. In contrast, Th2-biased animals had high titer IgG1 and no IgG2a, and their lymph node cells produced high levels of IL-4 but no IFN-gamma. Subsequent infection of the dental pulp with P. gingivalis caused extensive inflammation and alveolar bone destruction in Th1-biased mice, whereas Th2-biased mice and controls developed minimal lesions. Inflammatory granulomas in Th1-biased mice were heavily infiltrated with osteoclasts and had high local expression of IFN-gamma, IL-1alpha, and IL-1beta. Little or no IFN-gamma/IL-1alpha/IL-1beta and no obvious osteoclasts were detected in lesions of Th2-biased and control groups. These results directly demonstrate that specific Th1 responses promote severe infection-stimulated alveolar bone loss.     

Genetic screening for colorectal cancer and intervention

There is a complex interaction between environmental/dietary factors and genetics underlying the pathogenesis of colon carcinogenesis. Little data exist concerning the impact of diet on the phenotypic expression of genetically linked colon cancer. As a result, it has been difficult to develop rationally designed dietary intervention studies in first-degree relatives of patients with established familial adenomatous polyposis (FAP), hereditary nonpolyposis colorectal cancer (HNPCC) and other familial colon cancer syndromes. Only 2 double-blinded, placebo-controlled trials have been published concerning the use of preventive strategies in patients with genetically inherited colorectal cancer syndromes, both in patients with FAP. One study evaluated the effects of vitamin C plus vitamin E with or without a high-dose wheat bran fiber supplement on the recurrence of rectal adenomas. Over a 48-month intervention period, only the wheat bran fiber intervention significantly reduced polyp growth. A second study reported that intervention with the NSAID sulindac for 9 months in young patients with FAP resulted in a significant reduction in both polyp number and size in the rectosigmoid colon. All of the large-scale (i.e., >500 randomized participants) phase III nutrient or chemopreventive agent intervention studies thus far have targeted participants with a history of non-familial, sporadic colorectal adenomas. Current clinical adenoma trials do not measure whether the regimen being tested can prevent genotoxic events occurring in early stages of abnormal cell development that contribute to the eventual formation of adenomas nor whether the agent(s) can inhibit events occurring during the progression of adenomas to carcinomas. Therefore, future clinical trial designs may have to consider (i) lengthening the clinical trial period before adenomas develop, (ii) testing at early patient ages and/or (iii) measuring the growth of adenomas as they progress to carcinomas. © 1996 Wiley-Liss, Inc.     

A full-proteome,interaction-specific characterization of mutational hotspots across human cancers

Rapid accumulation of cancer genomic data has led to the identification of an increasing number of mutational hotspots with uncharacterized significance. Here we present a biologically informed computational framework that characterizes the functional relevance of all 1107 published mutational hotspots identified in approximately 25,000 tumor samples across 41 cancer types in the context of a human 3D interactome network, in which the interface of each interaction is mapped at residue resolution. Hotspots reside in network hub proteins and are enriched on protein interaction interfaces, suggesting that alteration of specific protein–protein interactions is critical for the oncogenicity of many hotspot mutations. Our framework enables, for the first time, systematic identification of specific protein interactions affected by hotspot mutations at the full proteome scale. Furthermore, by constructing a hotspot-affected network that connects all hotspot-affected interactions throughout the whole-human interactome, we uncover genome-wide relationships among hotspots and implicate novel cancer proteins that do not harbor hotspot mutations themselves. Moreover, applying our network-based framework to specific cancer types identifies clinically significant hotspots that can be used for prognosis and therapy targets. Overall, we show that our framework bridges the gap between the statistical significance of mutational hotspots and their biological and clinical significance in human cancers.

Through DNA sequencing of tumor mutations, precision oncology has enabled the identification of cancer drivers, therapy targets, and prognostic mutations that can guide individualized therapies for many cancer patients. For example, what was once defined as melanoma is now delineated as BRAF-positive or BRAF-negative melanoma, a meaningful distinction with respect to therapy with BRAF and MAPK pathway inhibitors. Similarly, whether a tumor has deficient DNA mismatch repair defines whether the patient is eligible for immune checkpoint inhibitor monoclonal antibody therapy. Precision medicine now has become part of mainstream oncology, and in 2019, >80% of oncology drugs in development are personalized medicines (Personalized Medicine Coalition 2019). However, an important current limitation to precision medicine is the overwhelming number of total somatic mutations that accumulate during tumorigenesis and progression. A significant challenge is distinguishing bona fide driver mutations that promote tumor growth from passenger mutations that are neutral and have no mechanistic impact. To date, international efforts in cancer genomics have provided whole-exome sequencing for tens of thousands of human cancers (Forbes et al. 2008; The International Cancer Genome et al. 2010; The Cancer Genome Atlas Research et al. 2013). Subsequent computational analyses have identified cancer driver genes in which mutations occur more frequently than expected (Futreal et al. 2004; Ding et al. 2008; Chapman et al. 2011; Morin et al. 2011; Stransky et al. 2011; Wang et al. 2011; Lawrence et al. 2013). Yet not all mutations on driver genes are driver mutations. This is usually interpreted as the driver–passenger paradigm, in which the few recurrent mutations are viewed as drivers, whereas most mutations, especially rare ones, are passengers that do not contribute to oncogenesis (Stratton et al. 2009; Porta-Pardo et al. 2017). In this regard, statistical models were developed to detect mutational hotspots (highly recurrently mutated residues across tumor samples) as candidate drivers. Such candidate list was quickly populated by more than 1000 hotspots (Chang et al. 2016, 2018), but only a small number of them have well-defined functional consequences. It was recently reported that some hotspot mutations are in fact passengers that arose from the preference of APOBEC3A, a cytidine deaminase, for DNA stem-loops (Buisson et al. 2019). Thus, given the increasing number of cancer hotspots with uncertain significance, there is an urgent need to characterize their functional relevance toward translating the wealth of genomic data into biological and clinical insights.Although it is now possible to systematically test certain mutations by experiments (Ipe et al. 2017), genome-wide prioritization of candidate driver mutations still involves bioinformatics tools that predict the impact of mutations on protein function at the individual protein level (Adzhubei et al. 2010; Pollard et al. 2010; Kircher et al. 2014). However, not all mutations can be simply interpreted as causing a gross loss of protein. Many cancer mutations exert their oncogenic effects through altering specific aspects of protein activity and give cancer cells a selective advantage. One promising route to decipher this complexity is the view that the cell is a network of interacting biomolecules in which proteins carry out diverse functions by interacting with other proteins. We have previously shown that one key feature in understanding the functional impact of mutations is whether they fall in the binding interfaces that mediate interactions with other proteins and, critically, which specific interactions they mediate (Wang et al. 2012; Chen et al. 2018). Although studies of known disease mutations have already reported a strong association with protein interaction interfaces (Wei et al. 2014; Sahni et al. 2015), application of this feature has been largely limited by low coverage of structural information on interacting proteins; cocrystal structures and homology models together cover merely ∼6% of all known human interactions (Meyer et al. 2018). Here we leverage our newly established, the first human full-proteome 3D interactome with residue-resolution interface predictions (Interactome INSIDER) (Meyer et al. 2018) to systematically identify protein–protein interactions that are affected by mutational hotspots, aiming to offer a biologically informed framework that characterizes the functional relevance of mutational hotspots and nominate new cancer proteins across human cancers, with interaction-specific resolution at the full proteome scale.