Characterization and quantitation of the circulating forms of serum transferrin receptor using domain-specific antibodies

To characterize the nature of the immunoreactive transferrin receptor in human serum, antisera were developed to peptide sequences of the extracellular domain of human transferrin receptor between amino acids 107 and 120 and the intracellular domain between amino acids 40 and 54. Antisera against the extracellular domain exhibited reactivity against both purified intact receptor and immunopurified circulating receptor, whereas antisera against the intracellular domain reacted only with intact receptor. Using competitive binding enzyme-linked immunosorbent assays, transferrin receptor in ultracentrifuged sera from normal subjects and patients with sickle cell anemia could be detected with antisera against the extracellular but not the intracellular domain. When the pellet obtained by ultracentrifugation of these sera was assayed after solubilization in 1% teric (polyoxyethylene-9-lauryl ether), only 0.6% of total serum receptor was detected in normal subjects and 3.8% in subjects with sickle cell disease. Roughly equal amounts of this pelleted immunoactivity were detected with antibodies against the extracellular and intracellular domains. These results indicate that less than 1% of transferrin receptor in normal human sera is intact receptor consistent with an exosomal origin and that virtually all circulating transferrin receptor is in the form of a truncated extracellular domain.     

A single amino acid substitution strongly modulates the activity and substrate specificity of the mouse mdr1 and mdr3 drug efflux pumps.

Specific protein domains and amino acids responsible for the apparent capacity of P-glycoprotein (mdr) to recognize and transport a large group of structurally unrelated drugs have not been identified. We have introduced a single Ser----Phe substitution within the predicted TM11 domain of mdr1 (position 941) and mdr3 (position 939) and analyzed the effect of these substitutions on the drug-resistance profiles of these two proteins. Mutations at this residue drastically altered the overall degree of drug resistance conveyed by mdr1 and mdr3. The modulating effect of this mutation on mdr1 and mdr3 varied for the drugs tested: it was very strong for colchicine and adriamycin and moderate for vinblastine. For mdr1, the Ser941----Phe941 substitution produced a unique mutant protein that retained the capacity to confer vinblastine resistance but lost the ability to confer adriamycin and colchicine resistance. These results strongly suggest that the predicted TM11 domain of proteins encoded by mdr and mdr-like genes plays an important role in the recognition and transport of their specific substrates.     

Understanding HPV Vaccine Uptake Among Cambodian American Girls

Cervical cancer incidence rates vary substantially among racial/ethnic groups in the United States (US) with women of Southeast Asian descent having the highest rates. Up to 70 % of cervical cancers could be prevented by widespread use of the human papillomavirus (HPV) vaccine. However, there is a lack of information about HPV vaccine uptake among Southeast Asian girls in the US. We conducted a telephone survey of Cambodian women with daughters who were age-eligible for HPV vaccination. Survey items addressed HPV vaccination barriers, facilitators and uptake. Our study group included 86 Cambodian mothers who lived in the Seattle metropolitan area. The proportions of survey participants who reported their daughter had initiated and completed the HPV vaccine series were only 29 and 14 %, respectively. Higher levels of vaccine uptake were significantly associated with mothers having heard about the HPV vaccine from a health professional and having received a recent Pap test. Commonly cited barriers to HPV vaccination included lack of knowledge about the HPV vaccine, not having received a physician recommendation for HPV vaccination and thinking the HPV vaccine is unnecessary in the absence of health problems. Linguistically and culturally appropriate HPV educational programs should be developed and implemented in Cambodian American communities. These programs should aim to enhance understanding of disease prevention measures, increase knowledge about the HPV vaccine and empower women to ask their daughter’s doctors for HPV vaccination.     

Comprehensive morphometry of subcortical grey matter structures in early‐stage Parkinson's disease

Previous imaging studies that investigated morphometric group differences of subcortical regions outside the substantia nigra between non‐demented Parkinson's patients and controls either did not find any significant differences, or reported contradictory results. Here, we performed a comprehensive morphometric analysis of 20 cognitively normal, early‐stage PD patients and 19 matched control subjects. In addition to relatively standard analyses of whole‐brain grey matter volume and overall regional volumes, we examined subtle localized surface shape differences in striatal and limbic grey matter structures and tested their utility as a diagnostic marker. Voxel‐based morphometry and volumetric comparisons did not reveal significant group differences. Shape analysis, on the other hand, demonstrated significant between‐group shape differences for the right pallidum. Careful diffusion tractography analysis showed that the affected parts of the pallidum are connected subcortically with the subthalamic nucleus, the pedunculopontine nucleus, and the thalamus and cortically with the frontal lobe. Additionally, microstructural measurements along these pathways, but not along other pallidal connections, were significantly different between the two groups. Vertex‐wise linear discriminant analysis, however, revealed limited accuracy of pallidal shape for the discrimination between patients and controls. We conclude that localized disease‐related changes in the right pallidum in early Parkinson's disease, undetectable using standard voxel‐based morphometry or volumetry, are evident using sensitive shape analysis. However, the subtle nature of these changes makes it unlikely that shape analysis alone will be useful for early diagnosis. Hum Brain Mapp 35:1681–1690, 2014. © 2013 Wiley Periodicals, Inc.     

Landscape of surfaceome and endocytome in human glioma is divergent and depends on cellular spatial organization

Therapeutic strategies directed at the tumor surfaceome (TS), including checkpoint inhibitor blocking antibodies, antibody drug conjugates (ADCs), and chimeric antigen receptor T (CAR-T) cells, provide a new armament to fight cancer. However, a remaining bottleneck is the lack of strategies to comprehensively interrogate patient tumors for potential TS targets. Here, we have developed a platform (tumor surfaceome mapping [TS-MAP]) integrated with a newly curated TS classifier (SURFME) that allows profiling of primary 3D cultures and intact patient glioma tumors with preserved tissue architecture. Moreover, TS-MAP specifically identifies proteins capable of endocytosis as tractable targets for ADCs and other modalities requiring toxic payload internalization. In high-grade gliomas that remain among the most aggressive forms of cancer, we show that cellular spatial organization (2D vs. 3D) fundamentally transforms the surfaceome and endocytome (e.g., integrins, proteoglycans, semaphorins, and cancer stem cell markers) with general implications for target screening approaches, as exemplified by an ADC targeting EGFR. The TS-MAP platform was further applied to profile the surfaceome and endocytome landscape in a cohort of freshly resected gliomas. We found a highly diverse TS repertoire between patient tumors, not directly associated with grade and histology, which highlights the need for individualized approaches. Our data provide additional layers of understanding fundamental to the future development of immunotherapy strategies, as well as procedures for proteomics-based target identification and selection. The TS-MAP platform should be widely applicable in efforts aiming at a better understanding of how to harness the TS for personalized immunotherapy.

Cell-surface proteins have a key role in drug development, and approximately two-thirds of approved human drugs target a cell-surface protein (1). Recently, tumor cell–surface proteins integrated with the plasma membrane (tumor surfaceome [TS]) have attracted considerable attention as targets for immunotherapies in cancer. Immune checkpoint-blocking antibodies (e.g., ipilimumab and nivolumab), antibody drug conjugates (ADCs, e.g., trastuzumab emtansin), radioimmunotherapy (RIT, e.g., ⁹⁰Y ibritumomab tiuxetan), and chimeric antigen receptor T (CAR-T) cells are all directed at the TS and currently revolutionize cancer treatment (2–6). With the impressive development of creative methods for antibody and T cell engineering, a remaining challenge is the lack of strategies to comprehensively map potential TS target antigens for the design of more rational, individualized treatments (7). Although advancements in DNA and RNA sequencing provide high throughput data for prediction algorithms, e.g., personalized peptide vaccine trials (8, 9), the predicted proteome derived from these platforms is not necessarily expressed and available for targeting. Moreover, proteomics-based strategies involve analysis of the bulk from disintegrated tumor tissue, resulting in loss of spatial information and limited coverage of the less abundant and hydrophobic TS proteins (10, 11). Of particular relevance, ADC, RIT, and other intracellular drug delivery strategies rely on TS targets that functionally engage in endocytic internalization (12). Clearly, despite its great targeting potential in cancer immunotherapy, the TS remains an elusive treasure for further discovery.Procedures for unbiased mapping of the TS and target identification should include specific labeling of the TS in freshly resected patient tumors with preserved tissue architecture. Enrichment of TS proteins and reduction of noise from intracellular proteins as well as abundant extracellular matrix collagens and glycoproteins would greatly improve downstream mass spectrometry analysis. Moreover, the approach should allow functional and dynamic profiling of TS internalization in an intact tissue environment. With the aim to address these challenges and to provide insight into the complexity of the TS, we have developed a versatile technology for TS mapping (TS-MAP). As proof of concept, we focused on primary brain tumors that remain among the most aggressive forms of cancer and for which attempts to conquer the most common variant, glioblastoma (GBM) (World Health Organization [WHO] grade IV) have failed so far (13). TS-MAP is compatible with spheroids from primary human stem cell–like GBM cultures, as well as mouse and patient brain tumors, and separately profiles surface resident and internalized TS proteins. Moreover, a TS classifier (SURFME) was curated for filtering and categorization of bona fide membrane proteins exposed to the extracellular space. We find significant differences in the TS between the 2D and 3D spheroid format, which underlines the importance of cellular spatial organization. In strong support of the need of individualized approaches, our findings suggest substantial intertumoral heterogeneity in the relative abundance of TS proteins in a cohort of freshly resected patient gliomas.     

Genetic control of anti-idiotypic vaccination against coronavirus infection

The idiotypic network can be experimentally altered to induce protective immune responses against microbial pathogens. Both internal image and noninternal image anti-idiotypic (anti-Id) antibodies have been shown to trigger antigen (Ag)-specific immune responses. Therefore, mechanisms of anti-Id vaccination appear to go beyond structural mimicry of Ag, but remain undefined. Using the neurotropic murine coronavirus animal model, we have previously shown that a polyclonal noninternal image anti-Id (Ab2) could vaccinate BALB/c mice. To characterize its mode of action, we have examined the immune modulating capability of this Ab2 in vivo in strains of mice with different H-2 haplotypes. Even though only internal image anti-Id are expected to induce non-genetically restricted immunity, this noninternal image Ab2 induced protective immunity in four of eight genetically different strains of mice susceptible to coronavirus infection. These were BALB/c (H-2^d), DBA/1 (H-2^d), DBA/1 (H-2^q), and SWR (H-2^q) mice. Protection was generally correlated with the induction of specific antiviral Ab (Ab3) that showed biological properties, such as virus neutralization in vitro, similar to the initial Ab1. To evaluate the genetic implication of the H-2 haplotypes in protection, congenic mice were also tested. Vaccination profiles suggest that cooperation between background gene(s) of the BALB/c mouse with H-2^d and H-2^q loci is necessary for an optimal protective immune response, although the main genetic element(s) regulating the antiviral response to Ab2 inoculation appeared to be located outside the major histocompatibility complex. These results are consistent with the ability of Ab2 to induce protective antiviral antibodies in genetically different animals by biological mimicry.     

Rare bioparticle detection via deep metric learning

Recent deep neural networks have shown superb performance in analyzing bioimages for disease diagnosis and bioparticle classification. Conventional deep neural networks use simple classifiers such as SoftMax to obtain highly accurate results. However, they have limitations in many practical applications that require both low false alarm rate and high recovery rate, e.g., rare bioparticle detection, in which the representative image data is hard to collect, the training data is imbalanced, and the input images in inference time could be different from the training images. Deep metric learning offers a better generatability by using distance information to model the similarity of the images and learning function maps from image pixels to a latent space, playing a vital role in rare object detection. In this paper, we propose a robust model based on a deep metric neural network for rare bioparticle (Cryptosporidium or Giardia) detection in drinking water. Experimental results showed that the deep metric neural network achieved a high accuracy of 99.86% in classification, 98.89% in precision rate, 99.16% in recall rate and zero false alarm rate. The reported model empowers imaging flow cytometry with capabilities of biomedical diagnosis, environmental monitoring, and other biosensing applications.

Conventional deep neural networks use simple classifiers to obtain highly accurate results. However, they have limitations in practical applications. This study demonstrates a robust deep metric neural network model for rare bioparticle detection.