ASU Scholarship Showcase

Panama disease caused by Fusarium oxysporum f. sp. cubense infection on banana is devastating banana plantations worldwide. Biological control has been proposed to suppress Panama disease, though the stability and survival of bio-control microorganisms in field setting is largely unknown. In order to develop a bio-control strategy for this disease, 16S rRNA gene sequencing was used to assess the microbial community of a disease-suppressive soil. Bacillus was identified as the dominant bacterial group in the suppressive soil. For this reason, B. amyloliquefaciens NJN-6 isolated from the suppressive soil was selected as a potential bio-control agent. A bioorganic fertilizer (BIO), formulated by combining this isolate with compost, was applied in nursery pots to assess the bio-control of Panama disease. Results showed that BIO significantly decreased disease incidence by 68.5%, resulting in a doubled yield. Moreover, bacterial community structure was significantly correlated to disease incidence and yield and Bacillus colonization was negatively correlated with pathogen abundance and disease incidence, but positively correlated to yield. In total, the application of BIO altered the rhizo-bacterial community by establishing beneficial strains that dominated the microbial community and decreased pathogen colonization in the banana rhizosphere, which plays an important role in the management of Panama disease.

Recent studies suggest a role for the microbiota in autism spectrum disorders (ASD), potentially arising from their role in modulating the immune system and gastrointestinal (GI) function or from gut–brain interactions dependent or independent from the immune system. GI problems such as chronic constipation and/or diarrhea are common in children with ASD, and significantly worsen their behavior and their quality of life. Here we first summarize previously published data supporting that GI dysfunction is common in individuals with ASD and the role of the microbiota in ASD. Second, by comparing with other publically available microbiome datasets, we provide some evidence that the shifted microbiota can be a result of westernization and that this shift could also be framing an altered immune system. Third, we explore the possibility that gut–brain interactions could also be a direct result of microbially produced metabolites.

Critical flicker fusion thresholds (CFFTs) describe when quick amplitude modulations of a light source become undetectable as the frequency of the modulation increases and are thought to underlie a number of visual processing skills, including reading. Here, we compare the impact of two vision-training approaches, one involving contrast sensitivity training and the other directional dot-motion training, compared to an active control group trained on Sudoku. The three training paradigms were compared on their effectiveness for altering CFFT. Directional dot-motion and contrast sensitivity training resulted in significant improvement in CFFT, while the Sudoku group did not yield significant improvement. This finding indicates that dot-motion and contrast sensitivity training similarly transfer to effect changes in CFFT. The results, combined with prior research linking CFFT to high-order cognitive processes such as reading ability, and studies showing positive impact of both dot-motion and contrast sensitivity training in reading, provide a possible mechanistic link of how these different training approaches impact reading abilities.

Although autism spectrum disorder (ASD) is a serious lifelong condition, its underlying neural mechanism remains unclear. Recently, neuroimaging-based classifiers for ASD and typically developed (TD) individuals were developed to identify the abnormality of functional connections (FCs). Due to over-fitting and interferential effects of varying measurement conditions and demographic distributions, no classifiers have been strictly validated for independent cohorts. Here we overcome these difficulties by developing a novel machine-learning algorithm that identifies a small number of FCs that separates ASD versus TD. The classifier achieves high accuracy for a Japanese discovery cohort and demonstrates a remarkable degree of generalization for two independent validation cohorts in the USA and Japan. The developed ASD classifier does not distinguish individuals with major depressive disorder and attention-deficit hyperactivity disorder from their controls but moderately distinguishes patients with schizophrenia from their controls. The results leave open the viable possibility of exploring neuroimaging-based dimensions quantifying the multiple-disorder spectrum.

Visual perceptual learning (VPL) is defined as visual performance improvement after visual experiences. VPL is often highly specific for a visual feature presented during training. Such specificity is observed in behavioral tuning function changes with the highest improvement centered on the trained feature and was originally thought to be evidence for changes in the early visual system associated with VPL. However, results of neurophysiological studies have been highly controversial concerning whether the plasticity underlying VPL occurs within the visual cortex. The controversy may be partially due to the lack of observation of neural tuning function changes in multiple visual areas in association with VPL. Here using human subjects we systematically compared behavioral tuning function changes after global motion detection training with decoded tuning function changes for 8 visual areas using pattern classification analysis on functional magnetic resonance imaging (fMRI) signals. We found that the behavioral tuning function changes were extremely highly correlated to decoded tuning function changes only in V3A, which is known to be highly responsive to global motion with human subjects. We conclude that VPL of a global motion detection task involves plasticity in a specific visual cortical area.

Background: Autism spectrum disorders (ASD) are complex neurobiological disorders that impair social interactions and communication and lead to restricted, repetitive, and stereotyped patterns of behavior, interests, and activities. The causes of these disorders remain poorly understood, but gut microbiota, the 1013 bacteria in the human intestines, have been implicated because children with ASD often suffer gastrointestinal (GI) problems that correlate with ASD severity. Several previous studies have reported abnormal gut bacteria in children with ASD. The gut microbiome-ASD connection has been tested in a mouse model of ASD, where the microbiome was mechanistically linked to abnormal metabolites and behavior. Similarly, a study of children with ASD found that oral non-absorbable antibiotic treatment improved GI and ASD symptoms, albeit temporarily. Here, a small open-label clinical trial evaluated the impact of Microbiota Transfer Therapy (MTT) on gut microbiota composition and GI and ASD symptoms of 18 ASD-diagnosed children.

Results: MTT involved a 2-week antibiotic treatment, a bowel cleanse, and then an extended fecal microbiota transplant (FMT) using a high initial dose followed by daily and lower maintenance doses for 7–8 weeks. The Gastrointestinal Symptom Rating Scale revealed an approximately 80% reduction of GI symptoms at the end of treatment, including significant improvements in symptoms of constipation, diarrhea, indigestion, and abdominal pain. Improvements persisted 8 weeks after treatment. Similarly, clinical assessments showed that behavioral ASD symptoms improved significantly and remained improved 8 weeks after treatment ended. Bacterial and phage deep sequencing analyses revealed successful partial engraftment of donor microbiota and beneficial changes in the gut environment. Specifically, overall bacterial diversity and the abundance of Bifidobacterium, Prevotella, and Desulfovibrio among other taxa increased following MTT, and these changes persisted after treatment stopped (followed for 8 weeks).

Conclusions: This exploratory, extended-duration treatment protocol thus appears to be a promising approach to alter the gut microbiome and virome and improve GI and behavioral symptoms of ASD. Improvements in GI symptoms, ASD symptoms, and the microbiome all persisted for at least 8 weeks after treatment ended, suggesting a long-term impact.

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.

Background: Meiotic recombination has traditionally been explained based on the structural requirement to stabilize homologous chromosome pairs to ensure their proper meiotic segregation. Competing hypotheses seek to explain the emerging findings of significant heterogeneity in recombination rates within and between genomes, but intraspecific comparisons of genome-wide recombination patterns are rare. The honey bee (Apis mellifera) exhibits the highest rate of genomic recombination among multicellular animals with about five cross-over events per chromatid.

Results: Here, we present a comparative analysis of recombination rates across eight genetic linkage maps of the honey bee genome to investigate which genomic sequence features are correlated with recombination rate and with its variation across the eight data sets, ranging in average marker spacing ranging from 1 Mbp to 120 kbp. Overall, we found that GC content explained best the variation in local recombination rate along chromosomes at the analyzed 100 kbp scale. In contrast, variation among the different maps was correlated to the abundance of microsatellites and several specific tri- and tetra-nucleotides.

Conclusions: The combined evidence from eight medium-scale recombination maps of the honey bee genome suggests that recombination rate variation in this highly recombining genome might be due to the DNA configuration instead of distinct sequence motifs. However, more fine-scale analyses are needed. The empirical basis of eight differing genetic maps allowed for robust conclusions about the correlates of the local recombination rates and enabled the study of the relation between DNA features and variability in local recombination rates, which is particularly relevant in the honey bee genome with its exceptionally high recombination rate.

Background: The shift from solitary to social behavior is one of the major evolutionary transitions. Primitively eusocial bumblebees are uniquely placed to illuminate the evolution of highly eusocial insect societies. Bumblebees are also invaluable natural and agricultural pollinators, and there is widespread concern over recent population declines in some species. High-quality genomic data will inform key aspects of bumblebee biology, including susceptibility to implicated population viability threats.

Results: We report the high quality draft genome sequences of Bombus terrestris and Bombus impatiens, two ecologically dominant bumblebees and widely utilized study species. Comparing these new genomes to those of the highly eusocial honeybee Apis mellifera and other Hymenoptera, we identify deeply conserved similarities, as well as novelties key to the biology of these organisms. Some honeybee genome features thought to underpin advanced eusociality are also present in bumblebees, indicating an earlier evolution in the bee lineage. Xenobiotic detoxification and immune genes are similarly depauperate in bumblebees and honeybees, and multiple categories of genes linked to social organization, including development and behavior, show high conservation. Key differences identified include a bias in bumblebee chemoreception towards gustation from olfaction, and striking differences in microRNAs, potentially responsible for gene regulation underlying social and other traits.

Conclusions: These two bumblebee genomes provide a foundation for post-genomic research on these key pollinators and insect societies. Overall, gene repertoires suggest that the route to advanced eusociality in bees was mediated by many small changes in many genes and processes, and not by notable expansion or depauperation.

We present a microarray nonlinear calibration (MiNC) method for quantifying antibody binding to the surface of protein microarrays that significantly increases the linear dynamic range and reduces assay variation compared with traditional approaches. A serological analysis of guinea pig Mycobacterium tuberculosis models showed that a larger number of putative antigen targets were identified with MiNC, which is consistent with the improved assay performance of protein microarrays. MiNC has the potential to be employed in biomedical research using multiplex antibody assays that need quantitation, including the discovery of antibody biomarkers, clinical diagnostics with multi-antibody signatures, and construction of immune mathematical models.