c Clinical urine specimens are usually considered to be sterile when they do not yield uropathogens using standard clinical cultivation procedures. Our aim was to test if the adult female bladder might contain bacteria that are not identified by these routine procedures. An additional aim was to identify and recommend the appropriate urine collection method for the study of bacterial communities in the female bladder. Consenting participants who were free of known urinary tract infection provided urine samples by voided, transurethral, and/or suprapubic collection methods. The presence of bacteria in these samples was assessed by bacterial culture, light microscopy, and 16S rRNA gene sequencing. Bacteria that are not or cannot be routinely cultivated (hereinafter called uncultivated bacteria) were common in voided urine, urine collected by transurethral catheter (TUC), and urine collected by suprapubic aspirate (SPA), regardless of whether the subjects had urinary symptoms. Voided urine samples contained mixtures of urinary and genital tract bacteria. Communities identified in parallel urine samples collected by TUC and SPA were similar. Uncultivated bacteria are clearly present in the bladders of some women. It remains unclear if these bacteria are viable and/or if their presence is relevant to idiopathic urinary tract conditions. C ulture-dependent methods are typically used to test if clinical urine specimens contain uropathogens, and the results play a pivotal role in the diagnosis and treatment of urinary tract infection symptoms in women. Clinical urine cultures are considered positive when the colony count of a recognized uropathogen, such as Escherichia coli, Pseudomonas, Klebsiella, or group B Streptococcus reaches a predefined threshold (14). Bacterial urinary tract infections (UTI) caused by these typical uropathogens elicit symptoms that typically improve or resolve in response to appropriate antibiotic therapy. For other common urinary disorders, including overactive bladder, urinary incontinence, and a spectrum of pain disorders, e.g., painful bladder syndrome and interstitial cystitis, the clinical urine culture is negative and antibiotics are not given for clinical treatment. Under these conditions, the etiology is unknown, and research into these conditions so far has not incorporated culture-independent assessments of bladder infection, such as bacterial 16S rRNA PCR and metagenomic sequencing approaches.Recently, a concerted international effort, known as the Human Microbiome Project (http://commonfund.nih.gov/hmp/), has begun to catalogue the core microbial composition of the healthy human body in order to determine if changes to the core microbial communities affect health. Sequence analysis of 16S rRNA, the workhorse of that effort, has been used to determine the microflora composition of healthy skin (12, 13), the gastrointestinal tract (7, 9, 36), the mouth (24, 25, 28), and the vagina (11,16,27,39) and to correlate certain diseases with changes in this composition (9,11,27). A common the...

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.

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.

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Bacterial community composition in blood-sucking arthropods can shift dramatically across time and space. We used 16S rRNA gene amplification and pyrosequencing to investigate the relative impact of vertebrate host-related, arthropod-related and environmental factors on bacterial community composition in fleas and ticks collected from rodents in southern Indiana (USA). Bacterial community composition was largely affected by arthropod identity, but not by the rodent host or environmental conditions. Specifically, the arthropod group (fleas vs ticks) determined the community composition of bacteria, where bacterial communities of ticks were less diverse and more dependent on arthropod traits—especially tick species and life stage—than bacterial communities of fleas. Our data suggest that both arthropod life histories and the presence of arthropod-specific endosymbionts may mask the effects of the vertebrate host and its environment.

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 (Prevotella, Pseudomonas, and Tannerella) and nine OTUs were significantly different between diabetic patients and their non-diabetic counterparts. In the subjects carrying periodontitis, the abundances of three phyla (Actinobacteria, Proteobacteria, and Bacteriodetes), two genera (Actinomyces and Aggregatibacter), 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.

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.

The spatial organization of different types of cells in tumor tissues reveals important information about the tumor microenvironment (TME). To facilitate the study of cellular spatial organization and interactions, we developed Histology-based Digital-Staining, a deep learning-based computation model, to segment the nuclei of tumor, stroma, lymphocyte, macrophage, karyorrhexis, and red blood cells from standard hematoxylin and eosin-stained pathology images in lung adenocarcinoma. Using this tool, we identified and classified cell nuclei and extracted 48 cell spatial organization-related features that characterize the TME. Using these features, we developed a prognostic model from the National Lung Screening Trial dataset, and independently validated the model in The Cancer Genome Atlas lung adenocarcinoma dataset, in which the predicted high-risk group showed significantly worse survival than the low-risk group (P ¼ 0.001), with a HR of 2.23 (1.37-3.65) after adjusting for clinical variables. Further-more, the image-derived TME features significantly correlated with the gene expression of biological pathways. For example, transcriptional activation of both the T-cell receptor and programmed cell death protein 1 pathways positively correlated with the density of detected lymphocytes in tumor tissues, while expression of the extracellular matrix organization pathway positively correlated with the density of stromal cells. In summary, we demonstrate that the spatial organization of different cell types is predictive of patient survival and associated with the gene expression of biological pathways.Significance: These findings present a deep learning-based analysis tool to study the TME in pathology images and demonstrate that the cell spatial organization is predictive of patient survival and is associated with gene expression.See related commentary by Rodriguez-Antolin, p. 1912