Bacterial Communities of the Coronal Sulcus and Distal Urethra of Adolescent Males

Lactobacillus-dominated vaginal microbiotas are associated with reproductive health and STI resistance in women, whereas altered microbiotas are associated with bacterial vaginosis (BV), STI risk and poor reproductive outcomes. Putative vaginal taxa have been observed in male first-catch urine, urethral swab and coronal sulcus (CS) specimens but the significance of these observations is unclear. We used 16 S rRNA sequencing to characterize the microbiota of the CS and urine collected from 18 adolescent men over three consecutive months. CS microbiotas of most participants were more stable than their urine microbiotas and the composition of CS microbiotas were strongly influenced by circumcision. BV-associated taxa, including Atopobium, Megasphaera, Mobiluncus, Prevotella and Gemella, were detected in CS specimens from sexually experienced and inexperienced participants. In contrast, urine primarily contained taxa that were not abundant in CS specimens. Lactobacilllus and Streptococcus were major urine taxa but their abundance was inversely correlated. In contrast, Sneathia, Mycoplasma and Ureaplasma were only found in urine from sexually active participants. Thus, the CS and urine support stable and distinct bacterial communities. Finally, our results suggest that the penis and the urethra can be colonized by a variety of BV-associated taxa and that some of these colonizations result from partnered sexual activity

Bacterial Community Profiling of Milk Samples as a Means to Understand Culture-Negative Bovine Clinical Mastitis

Inflammation and infection of bovine mammary glands, commonly known as mastitis, imposes significant losses each year in the dairy industry worldwide. While several different bacterial species have been identified as causative agents of mastitis, many clinical mastitis cases remain culture negative, even after enrichment for bacterial growth. To understand the basis for this increasingly common phenomenon, the composition of bacterial communities from milk samples was analyzed using culture independent pyrosequencing of amplicons of 16S ribosomal RNA genes (16S rDNA). Comparisons were made of the microbial community composition of culture negative milk samples from mastitic quarters with that of non-mastitic quarters from the same animals. Genomic DNA from culture-negative clinical and healthy quarter sample pairs was isolated, and amplicon libraries were prepared using indexed primers specific to the V1–V2 region of bacterial 16S rRNA genes and sequenced using the Roche 454 GS FLX with titanium chemistry. Evaluation of the taxonomic composition of these samples revealed significant differences in the microbiota in milk from mastitic and healthy quarters. Statistical analysis identified seven bacterial genera that may be mainly responsible for the observed microbial community differences between mastitic and healthy quarters. Collectively, these results provide evidence that cases of culture negative mastitis can be associated with bacterial species that may be present below culture detection thresholds used here. The application of culture-independent bacterial community profiling represents a powerful approach to understand long-standing questions in animal health and disease

Developing a Phylogeny Based Machine Learning Algorithm for Metagenomics

Metagenomics is the study of the totality of the complete genetic elements discovered from a defined environment. Different from traditional microbiology study, which only analyzes a small percent of microbes that could survive in laboratory, metagenomics allows researchers to get entire genetic information from all the samples in the communities. So metagenomics enables understanding of the target environments and the hidden relationships between bacteria and diseases. In order to efficiently analyze the metagenomics data, cutting-edge technologies for analyzing the relationships among microbes and communities are required. To overcome the challenges brought by rapid growth in metagenomics datasets, advances in novel methodologies for interpreting metagenomics data are clearly needed. The first two chapters of this dissertation summarize and compare the widely-used methods in metagenomics and integrate these methods into pipelines. Properly analyzing metagenomics data requires a variety of bioinformatcis and statistical approaches to deal with different situations. The raw reads from sequencing centers need to be processed and denoised by several steps and then be further interpreted by ecological and statistical analysis. So understanding these algorithms and combining different approaches could potentially reduce the influence of noises and biases at different steps. And an efficient and accurate pipeline is important to robustly decipher the differences and functionality of bacteria in communities. Traditional statistical analysis and machine learning algorithms have their limitations on analyzing metagenomics data. Thus, rest three chapters describe a new phylogeny based machine learning and feature selection algorithm to overcome these problems. The new method outperforms traditional algorithms and can provide more robust candidate microbes for further analysis. With the frowing sample size, deep neural network could potentially describe more complicated characteristic of data and thus improve model accuracy. So a deep learning framework is designed on top of the shallow learning algorithm stated above in order to further improve the prediction and selection accuracy. The present dissertation work provides a powerful tool that utilizes machine learning techniques to identify signature bacteria and key information from huge amount of metagenomics data

Image-based quantification of histological features as a function of spatial location using the Tissue Positioning SystemResearch in context

Summary: Background: Tissues such as the liver lobule, kidney nephron, and intestinal gland exhibit intricate patterns of zonated gene expression corresponding to distinct cell types and functions. To quantitatively understand zonation, it is important to measure cellular or genetic features as a function of position along a zonal axis. While it is possible to manually count, characterize, and locate features in relation to the zonal axis, it is labor-intensive and difficult to do manually while maintaining precision and accuracy. Methods: We addressed this challenge by developing a deep-learning-based quantification method called the “Tissue Positioning System” (TPS), which can automatically analyze zonation in the liver lobule as a model system. Findings: By using algorithms that identified vessels, classified vessels, and segmented zones based on the relative position along the portal vein to central vein axis, TPS was able to spatially quantify gene expression in mice with zone specific reporters. Interpretation: TPS could discern expression differences between zonal reporter strains, ages, and disease states. TPS could also reveal the zonal distribution of cells previously thought to be positioned randomly. The design principles of TPS could be generalized to other tissues to explore the biology of zonation. Funding: CPRIT (RP190208, RP220614, RP230330) and NIH (P30CA142543, R01AA028791, R01CA251928, R01DK1253961, R01GM140012, 1R01GM141519, 1R01DE030656, 1U01CA249245). The Pollack Foundation, Simmons Comprehensive Cancer Center Cancer & Obesity Translational Pilot Award, and the Emerging Leader Award from the Mark Foundation For Cancer Research (#21-003-ELA)

Bacterial Community Profiling of Milk Samples as a Means to Understand Culture-Negative Bovine Clinical Mastitis

Inflammation and infection of bovine mammary glands, commonly known as mastitis, imposes significant losses each year in the dairy industry worldwide. While several different bacterial species have been identified as causative agents of mastitis, many clinical mastitis cases remain culture negative, even after enrichment for bacterial growth. To understand the basis for this increasingly common phenomenon, the composition of bacterial communities from milk samples was analyzed using culture independent pyrosequencing of amplicons of 16S ribosomal RNA genes (16S rDNA). Comparisons were made of the microbial community composition of culture negative milk samples from mastitic quarters with that of non-mastitic quarters from the same animals. Genomic DNA from culture-negative clinical and healthy quarter sample pairs was isolated, and amplicon libraries were prepared using indexed primers specific to the V1–V2 region of bacterial 16S rRNA genes and sequenced using the Roche 454 GS FLX with titanium chemistry. Evaluation of the taxonomic composition of these samples revealed significant differences in the microbiota in milk from mastitic and healthy quarters. Statistical analysis identified seven bacterial genera that may be mainly responsible for the observed microbial community differences between mastitic and healthy quarters. Collectively, these results provide evidence that cases of culture negative mastitis can be associated with bacterial species that may be present below culture detection thresholds used here. The application of culture-independent bacterial community profiling represents a powerful approach to understand long-standing questions in animal health and disease.This article is from PLOS ONE 8 (2013); e61959, doi: 10.1371/journal.pone.0061959. Posted with permission.</p

Investigation of the Effect of Type 2 Diabetes Mellitus on Subgingival Plaque Microbiota by High-Throughput 16S rDNA Pyrosequencing

<div><p>Diabetes mellitus is a major risk factor for chronic periodontitis. We investigated the effects of type 2 diabetes on the subgingival plaque bacterial composition by applying culture-independent 16S rDNA sequencing to periodontal bacteria isolated from four groups of volunteers: non-diabetic subjects without periodontitis, non-diabetic subjects with periodontitis, type 2 diabetic patients without periodontitis, and type 2 diabetic patients with periodontitis. A total of 71,373 high-quality sequences were produced from the V1-V3 region of 16S rDNA genes by 454 pyrosequencing. Those 16S rDNA sequences were classified into 16 phyla, 27 classes, 48 orders, 85 families, 126 genera, and 1141 species-level OTUs. Comparing periodontally healthy samples with periodontitis samples identified 20 health-associated and 15 periodontitis-associated OTUs. In the subjects with healthy periodontium, the abundances of three genera (<i>Prevotella</i>, <i>Pseudomonas</i>, and <i>Tannerella</i>) and nine OTUs were significantly different between diabetic patients and their non-diabetic counterparts. In the subjects carrying periodontitis, the abundances of three phyla (<i>Actinobacteria</i>, <i>Proteobacteria</i>, and <i>Bacteriodetes</i>), two genera (<i>Actinomyces</i> and <i>Aggregatibacter</i>), and six OTUs were also significantly different between diabetics and non-diabetics. Our results show that type 2 diabetes mellitus could alter the bacterial composition in the subgingival plaque.</p></div

Artificial Intelligence in Lung Cancer Pathology Image Analysis

Objective: Accurate diagnosis and prognosis are essential in lung cancer treatment selection and planning. With the rapid advance of medical imaging technology, whole slide imaging (WSI) in pathology is becoming a routine clinical procedure. An interplay of needs and challenges exists for computer-aided diagnosis based on accurate and efficient analysis of pathology images. Recently, artificial intelligence, especially deep learning, has shown great potential in pathology image analysis tasks such as tumor region identification, prognosis prediction, tumor microenvironment characterization, and metastasis detection. Materials and Methods: In this review, we aim to provide an overview of current and potential applications for AI methods in pathology image analysis, with an emphasis on lung cancer. Results: We outlined the current challenges and opportunities in lung cancer pathology image analysis, discussed the recent deep learning developments that could potentially impact digital pathology in lung cancer, and summarized the existing applications of deep learning algorithms in lung cancer diagnosis and prognosis. Discussion and Conclusion: With the advance of technology, digital pathology could have great potential impacts in lung cancer patient care. We point out some promising future directions for lung cancer pathology image analysis, including multi-task learning, transfer learning, and model interpretation

Subgingival plaque bacterial community composition comparison.

<p>The figure shows the results of non-metric multidimensional scaling (NMDS) applied to the unweighted UniFrac distances between different subsets of samples. The X- and Y-axes represent the first and second NMDS dimensions, respectively. The label next to each data point indicates the sample name. (A) Among diabetes-negative samples, a PERMANOVA test indicates significant (p<0.01) differences in the UniFrac distances according to the presence or absence of periodontitis. (B) A similar comparison among diabetes-positive samples is also significant (p<0.01). (C) Among periodontitis-negative samples, there is no clear separation based on diabetes status (p = 0.06). (D) In periodontitis-positive samples, however, significant differences do exist based on diabetes status (p<0.01).</p