ASU Scholarship Showcase

High proportions of autistic children suffer from gastrointestinal (GI) disorders, implying a link between autism and abnormalities in gut microbial functions. Increasing evidence from recent high-throughput sequencing analyses indicates that disturbances in composition and diversity of gut microbiome are associated with various disease conditions. However, microbiome-level studies on autism are limited and mostly focused on pathogenic bacteria. Therefore, here we aimed to define systemic changes in gut microbiome associated with autism and autism-related GI problems. We recruited 20 neurotypical and 20 autistic children accompanied by a survey of both autistic severity and GI symptoms. By pyrosequencing the V2/V3 regions in bacterial 16S rDNA from fecal DNA samples, we compared gut microbiomes of GI symptom-free neurotypical children with those of autistic children mostly presenting GI symptoms. Unexpectedly, the presence of autistic symptoms, rather than the severity of GI symptoms, was associated with less diverse gut microbiomes. Further, rigorous statistical tests with multiple testing corrections showed significantly lower abundances of the genera Prevotella, Coprococcus, and unclassified Veillonellaceae in autistic samples. These are intriguingly versatile carbohydrate-degrading and/or fermenting bacteria, suggesting a potential influence of unusual diet patterns observed in autistic children. However, multivariate analyses showed that autism-related changes in both overall diversity and individual genus abundances were correlated with the presence of autistic symptoms but not with their diet patterns. Taken together, autism and accompanying GI symptoms were characterized by distinct and less diverse gut microbial compositions with lower levels of Prevotella, Coprococcus, and unclassified Veillonellaceae.

Throughout the long history of virus-host co-evolution, viruses have developed delicate strategies to facilitate their invasion and replication of their genome, while silencing the host immune responses through various mechanisms. The systematic characterization of viral protein-host interactions would yield invaluable information in the understanding of viral invasion/evasion, diagnosis and therapeutic treatment of a viral infection, and mechanisms of host biology. With more than 2,000 viral genomes sequenced, only a small percent of them are well investigated. The access of these viral open reading frames (ORFs) in a flexible cloning format would greatly facilitate both in vitro and in vivo virus-host interaction studies. However, the overall progress of viral ORF cloning has been slow. To facilitate viral studies, we are releasing the initiation of our panviral proteome collection of 2,035 ORF clones from 830 viral genes in the Gateway® recombinational cloning system. Here, we demonstrate several uses of our viral collection including highly efficient production of viral proteins using human cell-free expression system in vitro, global identification of host targets for rubella virus using Nucleic Acid Programmable Protein Arrays (NAPPA) containing 10,000 unique human proteins, and detection of host serological responses using micro-fluidic multiplexed immunoassays. The studies presented here begin to elucidate host-viral protein interactions with our systemic utilization of viral ORFs, high-throughput cloning, and proteomic technologies. These valuable plasmid resources will be available to the research community to enable continued viral functional studies.

Background:
Ketogenic diets are high fat and low carbohydrate or very low carbohydrate diets, which render high production of ketones upon consumption known as nutritional ketosis (NK). Ketosis is also produced during fasting periods, which is known as fasting ketosis (FK). Recently, the combinations of NK and FK, as well as NK alone, have been used as resources for weight loss management and treatment of epilepsy.

Methods:
A crossover study design was applied to 11 healthy individuals, who maintained moderately sedentary lifestyle, and consumed three types of diet randomly assigned over a three-week period. All participants completed the diets in a randomized and counterbalanced fashion. Each weekly diet protocol included three phases: Phase 1 - A mixed diet with ratio of fat: (carbohydrate + protein) by mass of 0.18 or the equivalence of 29% energy from fat from Day 1 to Day 5. Phase 2- A mixed or a high-fat diet with ratio of fat: (carbohydrate + protein) by mass of approximately 0.18, 1.63, or 3.80 on Day 6 or the equivalence of 29%, 79%, or 90% energy from fat, respectively. Phase 3 - A fasting diet with no calorie intake on Day 7. Caloric intake from diets on Day 1 to Day 6 was equal to each individual’s energy expenditure. On Day 7, ketone buildup from FK was measured.

Results:
A statistically significant effect of Phase 2 (Day 6) diet was found on FK of Day 7, as indicated by repeated analysis of variance (ANOVA), F(2,20) = 6.73, p < 0.0058. Using a Fisher LDS pair-wise comparison, higher significant levels of acetone buildup were found for diets with 79% fat content and 90% fat content vs. 29% fat content (with p = 0.00159**, and 0.04435**, respectively), with no significant difference between diets with 79% fat content and 90% fat content. In addition, independent of the diet, a significantly higher ketone buildup capability of subjects with higher resting energy expenditure (R[superscript 2] = 0.92), and lower body mass index (R[superscript 2] = 0.71) was observed during FK.

Many drugs are effective in the early stage of treatment, but patients develop drug resistance after a certain period of treatment, causing failure of the therapy. An important example is Herceptin, a popular monoclonal antibody drug for breast cancer by specifically targeting human epidermal growth factor receptor 2 (Her2). Here we demonstrate a quantitative binding kinetics analysis of drug-target interactions to investigate the molecular scale origin of drug resistance. Using a surface plasmon resonance imaging, we measured the in situ Herceptin-Her2 binding kinetics in single intact cancer cells for the first time, and observed significantly weakened Herceptin-Her2 interactions in Herceptin-resistant cells, compared to those in Herceptin-sensitive cells. We further showed that the steric hindrance of Mucin-4, a membrane protein, was responsible for the altered drug-receptor binding. This effect of a third molecule on drug-receptor interactions cannot be studied using traditional purified protein methods, demonstrating the importance of the present intact cell-based binding kinetics analysis.

Rationale: Cell-free protein microarrays display naturally-folded proteins based on just-in-time in situ synthesis, and have made important contributions to basic and translational research. However, the risk of spot-to-spot cross-talk from protein diffusion during expression has limited the feature density of these arrays.

Methods: In this work, we developed the Multiplexed Nucleic Acid Programmable Protein Array (M-NAPPA), which significantly increases the number of displayed proteins by multiplexing as many as five different gene plasmids within a printed spot.

Results: Even when proteins of different sizes were displayed within the same feature, they were readily detected using protein-specific antibodies. Protein-protein interactions and serological antibody assays using human viral proteome microarrays demonstrated that comparable hits were detected by M-NAPPA and non-multiplexed NAPPA arrays. An ultra-high density proteome microarray displaying > 16k proteins on a single microscope slide was produced by combining M-NAPPA with a photolithography-based silicon nano-well platform. Finally, four new tuberculosis-related antigens in guinea pigs vaccinated with Bacillus Calmette-Guerin (BCG) were identified with M-NAPPA and validated with ELISA.

Conclusion: All data demonstrate that multiplexing features on a protein microarray offer a cost-effective fabrication approach and have the potential to facilitate high throughput translational research.

Sera from patients with ovarian cancer contain autoantibodies (AAb) to tumor-derived proteins that are potential biomarkers for early detection. To detect AAb, we probed high-density programmable protein microarrays (NAPPA) expressing 5177 candidate tumor antigens with sera from patients with serous ovarian cancer (n = 34 cases/30 controls) and measured bound IgG. Of these, 741 antigens were selected and probed with an independent set of ovarian cancer sera (n = 60 cases/60 controls). Twelve potential autoantigens were identified with sensitivities ranging from 13 to 22% at >93% specificity. These were retested using a Luminex bead array using 60 cases and 60 controls, with sensitivities ranging from 0 to 31.7% at 95% specificity. Three AAb (p53, PTPRA, and PTGFR) had area under the curve (AUC) levels >60% (p < 0.01), with the partial AUC (SPAUC) over 5 times greater than for a nondiscriminating test (p < 0.01). Using a panel of the top three AAb (p53, PTPRA, and PTGFR), if at least two AAb were positive, then the sensitivity was 23.3% at 98.3% specificity. AAb to at least one of these top three antigens were also detected in 7/20 sera (35%) of patients with low CA 125 levels and 0/15 controls. AAb to p53, PTPRA, and PTGFR are potential biomarkers for the early detection of ovarian cancer.

To address the need to study frozen clinical specimens using next-generation RNA, DNA, chromatin immunoprecipitation (ChIP) sequencing and protein analyses, we developed a biobank work flow to prospectively collect biospecimens from patients with renal cell carcinoma (RCC). We describe our standard operating procedures and work flow to annotate pathologic results and clinical outcomes. We report quality control outcomes and nucleic acid yields of our RCC submissions (N=16) to The Cancer Genome Atlas (TCGA) project, as well as newer discovery platforms, by describing mass spectrometry analysis of albumin oxidation in plasma and 6 ChIP sequencing libraries generated from nephrectomy specimens after histone H3 lysine 36 trimethylation (H3K36me3) immunoprecipitation. From June 1, 2010, through January 1, 2013, we enrolled 328 patients with RCC. Our mean (SD) TCGA RNA integrity numbers (RINs) were 8.1 (0.8) for papillary RCC, with a 12.5% overall rate of sample disqualification for RIN <7. Banked plasma had significantly less albumin oxidation (by mass spectrometry analysis) than plasma kept at 25°C (P<.001). For ChIP sequencing, the FastQC score for average read quality was at least 30 for 91% to 95% of paired-end reads. In parallel, we analyzed frozen tissue by RNA sequencing; after genome alignment, only 0.2% to 0.4% of total reads failed the default quality check steps of Bowtie2, which was comparable to the disqualification ratio (0.1%) of the 786-O RCC cell line that was prepared under optimal RNA isolation conditions. The overall correlation coefficients for gene expression between Mayo Clinic vs TCGA tissues ranged from 0.75 to 0.82. These data support the generation of high-quality nucleic acids for genomic analyses from banked RCC. Importantly, the protocol does not interfere with routine clinical care. Collections over defined time points during disease treatment further enhance collaborative efforts to integrate genomic information with outcomes.

Exposure to fine particles can cause various diseases, and an easily accessible method to monitor the particles can help raise public awareness and reduce harmful exposures. Here we report a method to estimate PM air pollution based on analysis of a large number of outdoor images available for Beijing, Shanghai (China) and Phoenix (US). Six image features were extracted from the images, which were used, together with other relevant data, such as the position of the sun, date, time, geographic information and weather conditions, to predict PM_2.5 index. The results demonstrate that the image analysis method provides good prediction of PM_2.5 indexes, and different features have different significance levels in the prediction.

A novel portable wireless volatile organic compound (VOC) monitoring device with disposable sensors is presented. The device is miniaturized, light, easy-to-use, and cost-effective. Different field tests have been carried out to identify the operational, analytical, and functional performance of the device and its sensors. The device was compared to a commercial photo-ionization detector, gas chromatography-mass spectrometry, and carbon monoxide detector. In addition, environmental operational conditions, such as barometric change, temperature change and wind conditions were also tested to evaluate the device performance. The multiple comparisons and tests indicate that the proposed VOC device is adequate to characterize personal exposure in many real-world scenarios and is applicable for personal daily use.

Quantifying the interactions of bacteria with external ligands is fundamental to the understanding of pathogenesis, antibiotic resistance, immune evasion, and mechanism of antimicrobial action. Due to inherent cell-to-cell heterogeneity in a microbial population, each bacterium interacts differently with its environment. This large variability is washed out in bulk assays, and there is a need of techniques that can quantify interactions of bacteria with ligands at the single bacterium level. In this work, we present a label-free and real-time plasmonic imaging technique to measure the binding kinetics of ligand interactions with single bacteria, and perform statistical analysis of the heterogeneity. Using the technique, we have studied interactions of antibodies with single Escherichia coli O157:H7 cells and demonstrated a capability of determining the binding kinetic constants of single live bacteria with ligands, and quantify heterogeneity in a microbial population.