Search

Heatmap of protein functions

The human microbiome has been strongly correlated with disease pathology and outcomes, yet remains relatively underexplored in patients with malignant endometrial disease. In this study, vaginal microbiome samples were prospectively collected at the time of hysterectomy from 61 racially and ethnically diverse patients from three disease conditions: (i) benign gynecologic disease (controls, n = 11), (ii) low-grade endometrial carcinoma (n = 30), and (iii) high-grade endometrial carcinoma (n = 20). Extracted DNA underwent shotgun metagenomics sequencing, and microbial α and β diversities were calculated. Hierarchical clustering was used to describe community state types (CST), which were then compared by microbial diversity and grade. Differential abundance was calculated, and machine learning utilized to assess the predictive value of bacterial abundance to distinguish grade and histology. Both α- and β-diversity were associated with patient tumor grade. Four vaginal CST were identified that associated with grade of disease. Different histologies also demonstrated variation in CST within tumor grades. Using supervised clustering algorithms, critical microbiome markers at the species level were used to build models that predicted benign versus carcinoma, high-grade carcinoma versus benign, and high-grade versus low-grade carcinoma with high accuracy. These results confirm that the vaginal microbiome segregates not just benign disease from endometrial cancer, but is predictive of histology and grade. Further characterization of these findings in large, prospective studies is needed to elucidate their potential clinical applications.Significance:The vaginal microbiome reliably segregates not just benign gynecologic condition from endometrial cancer, but also predicts cancer grade and histology. Patterns of microbial abundance and gene expression should be increasingly considered as a factor in the evolution of precision medicine approaches, especially as they relate to cancer screening, disease pathogenesis, and patient-centered outcomes.

Heatmap of abundance values

Additional statistical methods

Biomarker discovery by histology

Individual Specimen Reads

Analysis of Confounders

Functional pathway analysis

Microbial alpha diversities

Microbial abundance by histotype

The human microbiome has been strongly correlated with disease pathology and outcomes, yet remains relatively underexplored in patients with malignant endometrial disease. In this study, vaginal microbiome samples were prospectively collected at the time of hysterectomy from 61 racially and ethnically diverse patients from three disease conditions: (i) benign gynecologic disease (controls, n = 11), (ii) low-grade endometrial carcinoma (n = 30), and (iii) high-grade endometrial carcinoma (n = 20). Extracted DNA underwent shotgun metagenomics sequencing, and microbial α and β diversities were calculated. Hierarchical clustering was used to describe community state types (CST), which were then compared by microbial diversity and grade. Differential abundance was calculated, and machine learning utilized to assess the predictive value of bacterial abundance to distinguish grade and histology. Both α- and β-diversity were associated with patient tumor grade. Four vaginal CST were identified that associated with grade of disease. Different histologies also demonstrated variation in CST within tumor grades. Using supervised clustering algorithms, critical microbiome markers at the species level were used to build models that predicted benign versus carcinoma, high-grade carcinoma versus benign, and high-grade versus low-grade carcinoma with high accuracy. These results confirm that the vaginal microbiome segregates not just benign disease from endometrial cancer, but is predictive of histology and grade. Further characterization of these findings in large, prospective studies is needed to elucidate their potential clinical applications. Significance: The vaginal microbiome reliably segregates not just benign gynecologic condition from endometrial cancer, but also predicts cancer grade and histology. Patterns of microbial abundance and gene expression should be increasingly considered as a factor in the evolution of precision medicine approaches, especially as they relate to cancer screening, disease pathogenesis, and patient-centered outcomes.

Authorsâ original file for figure 4

of Comparative transcriptomics among floral organs of the basal eudicot Eschscholzia californica as reference for floral evolutionary developmental studies

This is the open peer reviewers comments and recommendations regarding the submitted GigaScience article and/or dataset.

Supplementary Text. Detailed Methods. (DOCX 324Â kb)

In this study, two designed primers were evaluated to identify soil Streptomyces and to detect streptomycin production by strb1 targeted PCR. Potential Streptomyces-specific signatures were identified in their 16S rDNA sequences in regions located around nucleotide positions 576 and 995. Primer pair RI7/RI8 derived from these regions was investigated for its specificity in detecting and identifying Streptomyces isolates by PCR assays using DNA from pure cultures. The constructed primer pair showed high specificity in detecting and identifying Streptomyces type strains as well as soil isolates. Streptomycin-producers were detected by PCR assays through the selective amplification of streptomycin biosynthetic gene (strb1). Results suggest that PCR assay facilitates the differential identification of Streptomyces-specific antibiotic producers and a resident population of Streptomyces in Jordan with the capacity of streptomycin-production is present.

A total of 60 different Streptomyces isolates were recovered from 12 soil samples collected from the Badia region north-eastern of Jordan. These were then characterized by conventional methods and assessed for their antagonistic activity against three pathogenic and antibiotic-resistant bacteria. Results indicated that grey and white colour series were the most abundant with 30% of all isolates active against, at least, one of the test organisms;Staphylococcusaureus , Escherichia coli and Klebsiella pneumoniae. Overall screening results revealed the characterization of four Streptomyces isolates (I7, AC32, Bb36 and Z11) which inhibited the growth of all tested pathogens. The unusual antibiotic profile of these isolates underlined their potential as a source of novel antibiotics.

Detection and identification of members of the genus Streptomyces are of great value because they provide a rich source of antibiotics. Toward the goal of identifying additional novel antibiotics, a total of 292 different Streptomyces isolates were recovered from 54 soil samples collected from 28 different locations in Jordan. These were then characterized by conventional methods and assessed for their activity against two antibiotic-resistant Gram-negative isolates of Escherichia coli and Klebsiella pneumoniae. Results revealed that grey, white and yellow series isolates were the most abundant, with 15% of the Streptomyces isolates active against at least one of the test pathogens. Most of the active isolates exhibited activity against E. coli (96%), while less activity was exhibited against K. pneumoniae (18%). Overall screening revealed the characterization of six Streptomyces isolates (I7, AC32, G17, Z11, Bb36 and AQ16) which inhibited the growth of both pathogens. All were obtained from a region characterized by low-nutrient soils and harsh conditions. The unusual antibiotic profile of these isolates stressed their potential as a source of novel antibiotics.

In this study, we revealed rapid detection of streptomycin-producing Streptomyces spp. by extraction of total soil DNA from 14 soil samples using a modified lysis method followed by PCR amplification ofa genus-specific sequence in the Streptomyces' 16S rDNA gene. DNA band of the expected size (438 bp) was seen with all the samples. Additionally, specific amplification of the streptomycin-coding gene (strb1) directly from soil revealed the presence of a single DNA band of 940 bp. These results indicate that PCR-amplification of Streptomyces specific genes could be used for direct detection of streptomycin-producing Streptomyces species from soil.

IL-10 plays an essential role in maintaining homeostasis in the gut in response to the microbiome. However, it is unknown whether IL-10 also facilitates immune homeostasis at distal sites. To address this, we asked whether splenic immune populations were altered in IL-10-deficient mice with differential susceptibility to microbiome-dependent spontaneous enterocolitis. Susceptible mice exhibited a significant increase in splenic macrophages, neutrophils and marginal zone (MZ) B cells that was inhibited by IL-10 signaling in macrophages, but not B cells. The increase in myeloid cells was due to increased proliferation that correlated with increased MZ B cell differentiation. Cohousing and antibiotic treatment suggested that the alteration in splenic immune homeostasis was microbiome-dependent. 16S rRNA sequencing reveled that susceptible mice harbored a more diverse microbiome with a significant increase in the abundance of the bacterial genus Helicobacter. The introduction of H. hepaticus to the gut of nonsusceptible mice was sufficient to drive myeloid cell expansion and MZ B cell development. Given that MZ B cells are part of the first line of defense against blood-borne-pathogens, their increase following a breach in the gut epithelial barrier would be protective. Thus IL-10 is an essential gatekeeper that maintains immune homeostasis at distal sites that can become functionally imbalanced upon the introduction of specific bacteria to the intestinal track.

Three sets of known Salmonella enterica-specific primers were used collectively, for the first time, to evaluate the use of multiplex polymerase chain reaction (m-PCR) as a diagnostic tool to detect Salmonella enterica in naturally contaminated meat and poultry products. For this purpose a total of 300 samples representing the most frequently used fresh and frozen meat (beef and lamb) and poultry (chicken) products (whole, cut, ground, and processed) were collected from eight locations within Irbid city (Jordan). After an enrichment step, DNA was extracted directly from each food sample and amplified using six Salmonella enterica specific primers. Samples were also analyzed using conventional microbiological methods for the confirmation of Salmonella enterica presence. Out of 300 samples, 93 samples were positive by m-PCR. On the other hand, 67 samples were positive by conventional microbiological methods and only 26 samples were positive by m-PCR alone. The primer pairs, used here, proved to be highly specific for Salmonella enterica. Using m-PCR, Salmonella enterica detection could be achieved easily and the results had been confirmed effortlessly within a short period of time (24-36 hours) compared to 3-8 days for the conventional microbiological methods. Our findings emphasize the value of using a combination of specific primer pairs instead of a single primer pair in the detection process to minimize any chance for any inherent experimental or natural error.

Random amplified polymorphic DNA (RAPD) analysis has been used to determine the relatedness of 73 antibiotic-producing soil Streptomyces isolates that were recovered from different soil habitats in Jordan based on their RAPD-PCR fingerprints. Genetic polymorphisms between these isolates showed three common bands of 2777, 800 and 250 bp shared by approximately (95%) of them. Some specific bands were also observed. Further analysis of RAPD patterns with the UPGMA resulted in clustering the tested isolates into two main super clusters. Super cluster I was more homogenous than super cluster II and contained all the reference strains. However, super cluster II consists of unrelated isolates within five small groups. As RAPD fingerprints of the tested isolates linked to their phenotypes, differentiation between isolates with different cultural properties was observed.