Abundance Analysis Research Articles

Exploring the Microbiome Landscape of Dental Plaque: A Cross-Sectional Analysis in Periodontal Health and Disease.

This study aimed to comprehensively analyze the microbiome of dental plaque in individuals with varying periodontal statuses, encompassing both periodontal health and disease. The primary objectives were to identify microbial markers associated with different clinical conditions, explore variations in microbial diversity, and investigate potential correlations between the oral microbiome and clinical parameters. A cross-sectional design was employed, involving 164 participants aged 18 to 65 years. Inclusion criteria comprised individuals with good oral and systemic health for the periodontal health group and those diagnosed with various stages of periodontal disease for the periodontal disease group. Dental plaque samples were meticulously collected from diverse tooth surfaces, and clinical examinations were conducted to assess periodontal health status. High-throughput sequencing of the 16S ribosomal RNA (rRNA) gene was utilized for microbiome analysis. Demographic characteristics revealed a balanced distribution between the periodontal health and disease groups. Clinical parameters, including probing depth, clinical attachment loss, and bleeding on probing, exhibited significant differences between the two groups (p < 0.001). Microbial diversity indices indicated a higher diversity in the periodontal health group compared to the disease group (p < 0.001). Analysis of relative abundance of bacterial phyla identified significant variations, with Firmicutes, Bacteroidetes, and Actinobacteria showing differential prevalence between health and disease (p < 0.05). Differentially abundant taxa analysis highlighted specific species associated with each clinical condition, including Prevotella intermedia and Porphyromonas gingivalis. Network analysis revealed complex microbial interactions within the oral microbiome. Functional predictions indicated variations in metabolic capabilities between health and disease, with potential implications for virulence and antibiotic resistance. This study provides a comprehensive analysis of the oral microbiome in periodontal health and disease, revealing significant associations between microbial composition and clinical parameters. The identification of microbial markers and functional insights enhances our understanding of the complex interplay within the oral ecosystem. These findings hold promise for advancing diagnostic and therapeutic approaches tailored to individual microbial profiles.

Differential abundance in upper respiratory microbiome between smoking and non-smoking COVID-19 patients

The COVID-19 pandemic caused by SARS-CoV-2 has resulted in significant respiratory morbidity and mortality. Smoking is a well-established risk factor for adverse respiratory outcomes, and it may exacerbate COVID-19 outcomes by altering the upper respiratory tract (URT) microbiome. However, the association between smoking, the URT microbiome, and COVID-19 severity remains controversial. In this study, we investigated the microbial community in the UTR of smoking and non-smoking COVID-19 patients using 16S ribosomal RNA metagenomic datasets. By characterizing the microbial profiles of the URT of 88 COVID-19 patients with and without a smoking history, we examined the differences in the URT microbiome between smokers and non-smokers and identified bacteria that could serve as smoking signatures. Our study found that Prevotella was the most abundant genus in the URT microbiome of both smoking and non-smoking COVID-19 patients, followed by five other dominant genera. Notably, Fusobacterium was significantly higher in smokers than non-smokers, suggesting that smoking may affect the URT microbial composition. However, the alpha and beta diversity indices showed no significant differences within and across communities, regardless of smoking status or levels of severity. Differential abundance analysis suggested that the impact of smoking on the URT microbiome may lead to an increase in the abundance of Streptobacillus in smoking patients. Overall, this study highlights the potential impact of smoking on the URT microbiome and its relevance to COVID-19 outcomes. Thus, it provides insights into the underlying mechanisms that may lead to worsened COVID-19 outcomes due to smoking-induced alterations in the URT microbiome.

Microbiome composition recovery after liver transplantation correlates with initial liver disease severity and antibiotics treatment

Liver transplantation (LT) is crucial for end-stage liver disease, but it is linked to infection risks. Pathobionts, microorganisms potentially harmful under specific conditions, can cause complications posttransplant. Monitoring such pathogens in fecal samples can be challenging and therefore remains underexplored post-LT. This study aimed to analyze the gut microbiome before and after LT, tracking pathobionts and correlating clinical data. The study involved 17 liver transplant recipients, 17 healthy relatives (spouses), and 13 donors. Gut samples collected pretranplantation and posttransplantation underwent bacterial and fungal profiling through DNA sequencing. Quantitative polymerase chain reaction was used to assess microbial load. Statistical analyses included alpha and beta diversity measures, differential abundance analysis, and correlation tests between microbiome and clinical parameters. Microbiome analysis revealed dynamic changes in diversity posttransplant. Notably, high-severity patients showed persistent and greater dysbiosis during the first months post-LT compared with low-severity patients, partly due to an antibiotic treatment pre-LT. The analysis identified a higher proportion of pathogens such as Escherichia coli/Shigella flexneri in high-severity cases posttransplant. Furthermore, butyrate producers including Roseburia intestinalis, Anaerostipes hadrus, and Eubacterium coprostanoligenes were positively correlated with levels of albumin. This study offers valuable insights into post-LT microbiome changes, shedding light on the need for tailored prophylactic treatment post-LT.

Effect of Cucumber Continuous Monocropping on Traditional Chinese Medicine Residue through Analysis of Physicochemical Characteristics and Microbial Diversity

The use of traditional Chinese medicine (TCM) residue as a crop culture substrate has unique advantages in alleviating the obstacles associated with continuous monocropping, such as increasing production, improving quality and alleviating pests and diseases. However, the effect of TCM residue application on substrates in continuous monocropping practices has not been determined. In this study, the cucumber variety “Jinyou No. 10” was used as the material, and fermented TCM residue, vermiculite and perlite were used as organic substrates (3:1:1). The cucumbers were cultivated on substrates for different durations of continuous monocropping, which were the first cropping cycle (A1), second cropping cycle (A2), third cropping cycle (A3) and fourth cropping cycle (A4). The control (A0) was the substrate sample without any crop planted in it. After the cucumbers were harvested, substrate samples (areas around the cucumber roots) were collected. The physiochemical properties of the cultivated substrates were determined, and the microbial community structures were analyzed through 16S rRNA and ITS sequencing. The physiochemical indices of the substrates with different durations of continuous monocropping (A1–A4) were significantly different than those of the control (A0) substrate. Moreover, the continuous cropping of cucumber had greater effects on fungal communities than on bacterial communities. Bacterial community structure analysis revealed a greater proportion of important bacterial taxa (Proteobacteria, Chloroflexi, and Nitrospirae) in the continuous monocropping substrates than in the A0 substrate. For the fungal community, Ascomycota accounted for the largest percentage of the fungal community in all the samples. The diversity of the microbial community was found to be influenced primarily by electrical conductivity, organic matter content, pH and total potassium content according to the correlation analysis of physicochemical properties and relative abundance of the microbial community. Our study would provide a basis for addressing persistent challenges in continuous cropping and for obtaining the utmost benefit from using TCM organic residue waste.

Vaginal microbiomes of breast cancer survivors treated with aromatase inhibitors with and without vulvovaginal symptoms

Genitourinary syndrome of menopause (GSM) is the leading cause of vaginal symptoms in breast cancer survivors treated with aromatase inhibitors. However, there are currently no effective treatment options available for women with a history of breast cancer. Recent research has established that changes in the vaginal microbiome may be linked to GSM. Most studies have assessed the microbiome without accounting for the estrogen status. It remains unknown whether the vaginal microbiome differ among patients with a low estrogenic state with and without vulvovaginal symptoms. To address such research questions, our study compares the vaginal microbiomes among breast cancer survivors treated with aromatase inhibitors with and without vulvovaginal symptoms. A total of 50 breast cancer survivors treated with aromatase inhibitors were recruited, among whom 25 had vulvovaginal symptoms and 25 had no vulvovaginal symptoms. Vaginal swabs were collected. DNA extraction, followed by sequencing of the V3–V4 regions of the 16S ribosomal RNA gene, were performed. Differential abundance analysis was conducted by linear discriminant analysis effect size. Taxonomy assignment, alpha diversity and beta diversity were examined. The relative abundance of genus Sneathia and genus Gardnerella was significantly increased in vulvovaginal symptoms group with no differences in bacterial diversity and richness.

M giants with IGRINS

Context. To be able to investigate the chemical history of the entire Milky Way, it is imperative to also study its dust-obscured regions in detail, as this is where most of the mass lies. The Galactic Center is an example of such a region. Due to the intervening dust along the line of sight, near-infrared spectroscopic investigations are necessary to study this region of interest. Aims. The aim of this work is to demonstrate that M giants observed at high spectral resolution in the H- and K-bands (1.5–2.4 μm) can yield useful abundance ratio trends versus metallicity for 21 elements. These elements can then also be studied for heavily dust-obscured regions of the Galaxy, such as the Galactic Center. The abundance ratio trends will be important for further investigation of the Galactic chemical evolution in these regions. Methods. We observed near-infrared spectra of 50 M giants in the solar neighborhood at high signal-to-noise and at a high spectral resolution with the IGRINS spectrometer on the Gemini South telescope. The full H- and K-bands were recorded simultaneously at R = 45 000. Using a manual spectral synthesis method, we determined the fundamental stellar parameters for these stars and derived the stellar abundances for 21 atomic elements, namely, F, Mg, Si, S, Ca, Na, Al, K, Sc, Ti, V, Cr, Mn, Co, Ni, Cu, Zn, Y, Ce, Nd, and Yb. We systematically studied useful spectral lines of all these elements in the H- and K-bands. Results. We demonstrate that elements can be analyzed from H- and K-band high-resolution spectra, and we show which spectral lines can be used for an abundance analysis, identifying them line by line. We discuss the 21 abundance ratio trends and compare them with those determined from APOGEE and from the optical Giants in the Local Disk (GILD) sample. From high-resolution H- and K-band spectra, the trends of the heavy elements Cu, Zn, Y, Ce, Nd, and Yb can be retrieved. This opens up the nucleosynthetic channels, including the s-process and the r-process in dust-obscured populations. The [Mn/Fe] versus [Fe/H] trend is shown to be more or less flat at low metallicities, implying that existing non-local thermodynamic equilibrium correction is relevant. Conclusions. With high-resolution near-infrared spectra, it is possible to determine reliable abundance ratio trends versus metallicity for 21 elements, including elements formed in several different nucleosynthetic channels. It is also possible to determine the important neutron-capture elements, both s- and r-dominated elements. This opens up the possibility to study the chemical evolution in detail of dust-obscured regions of the Milky Way, such as the Galactic Center. The M giants are useful bright probes for these regions and for future studies of extra-galactic stellar populations. A careful analysis of high-quality spectra is needed to retrieve all of these elements, which are often from weak and blended lines. A spectral resolution of R ≳ 40 000 is a further quality that helps in deriving precise abundances for this range of elements. In comparison to APOGEE, we can readily obtain the abundances for Cu, Ce, Nd, and Yb from the H-band, demonstrating an advantage of analyzing high-resolution spectra.

MbDecoda: a debiased approach to compositional data analysis for microbiome surveys.

Potentially pathogenic or probiotic microbes can be identified by comparing their abundance levels between healthy and diseased populations, or more broadly, by linking microbiome composition with clinical phenotypes or environmental factors. However, in microbiome studies, feature tables provide relative rather than absolute abundance of each feature in each sample, as the microbial loads of the samples and the ratios of sequencing depth to microbial load are both unknown and subject to considerable variation. Moreover, microbiome abundance data are count-valued, often over-dispersed and contain a substantial proportion of zeros. To carry out differential abundance analysis while addressing these challenges, we introduce mbDecoda, a model-based approach for debiased analysis of sparse compositions of microbiomes. mbDecoda employs a zero-inflated negative binomial model, linking mean abundance to the variable of interest through a log link function, and it accommodates the adjustment for confounding factors. To efficiently obtain maximum likelihood estimates of model parameters, an Expectation Maximization algorithm is developed. A minimum coverage interval approach is then proposed to rectify compositional bias, enabling accurate and reliable absolute abundance analysis. Through extensive simulation studies and analysis of real-world microbiome datasets, we demonstrate that mbDecoda compares favorably with state-of-the-art methods in terms of effectiveness, robustness and reproducibility.

Bacterial community characterization by deep learning aided image analysis in soil chips

Soil microbes play an important role in governing global processes such as carbon cycling, but it is challenging to study them embedded in their natural environment and at the single cell level due to the opaque nature of the soil. Nonetheless, progress has been achieved in recent years towards visualizing microbial activities and organo-mineral interaction at the pore scale, especially thanks to the development of microfluidic ‘soil chips’ creating transparent soil model habitats. Image-based analyses come with new challenges as manual counting of bacteria in thousands of digital images taken from the soil chips is excessively time-consuming, while simple thresholding cannot be applied due to the background of soil minerals and debris. Here, we adopt the well-developed deep learning algorithm Mask-RCNN to quantitatively analyze the bacterial communities in soil samples from different locations in the world. This work demonstrates analysis of bacterial abundance from three contrasting locations (Greenland, Sweden and Kenya) using deep learning in microfluidic soil chips in order to characterize population and community dynamics. We additionally quantified cell- and colony morphology including cell size, shape and the cell aggregation level via calculation of the distance to the nearest neighbor. This approach allows for the first time an automated visual investigation of soil bacterial communities, and a crude biodiversity measure based on phenotypic cell morphology, which could become a valuable complement to molecular studies.

Constituents of stable commensal microbiota imply diverse colonic epithelial cell reactivity in patients with ulcerative colitis

BackgroundDespite extensive research on microbiome alterations in ulcerative colitis (UC), the role of the constituent stable microbiota remains unclear.ResultsThis study, employing 16S rRNA-gene sequencing, uncovers a persistent microbial imbalance in both active and quiescent UC patients compared to healthy controls. Using co-occurrence and differential abundance analysis, the study highlights microbial constituents, featuring Phocaeicola, Collinsella, Roseburia, Holdemanella, and Bacteroides, that are not affected during the course of UC. Co-cultivation experiments, utilizing commensal Escherichia coli and Phocaeicola vulgatus, were conducted with intestinal epithelial organoids derived from active UC patients and controls. These experiments reveal a tendency for a differential response in tight junction formation and maintenance in colonic epithelial cells, without inducing pathogen recognition and stress responses, offering further insights into the roles of these microorganisms in UC pathogenesis. These experiments also uncover high variation in patients’ response to the same bacteria, which indicate the need for more comprehensive, stratified analyses with an expanded sample size.ConclusionThis study reveals that a substantial part of the gut microbiota remains stable throughout progression of UC. Functional experiments suggest that members of core microbiota – Escherichia coli and Phocaeicola vulgatus – potentially differentially regulate the expression of tight junction gene in the colonic epithelium of UC patients and healthy individuals.

Abstract 2804: Comparative analysis of microbial diversity and abundance in colorectal cancer patients before and after surgery

Abstract Background: Previous studies have indicated differing patterns of the microbiome in colorectal cancer (CRC) patients compared to healthy individuals. Yet, the changes in microbial features of CRC patients before and after surgery remain underexplored. This study aims to investigate the impact of surgical intervention on the gut microbial diversity and abundance at the patient level. Methods: This hospital-based study analyzed data from 36 CRC patients who underwent CRC surgery at the Department of Surgery, Seoul National University Hospital between January 2020 and May 2022. Fecal samples from these patients were collected twice, both before surgery and during the post-surgery follow-up period. DNA extraction from stool specimens was performed using the MAG-Bind Universal Pathogen kit. This was followed by amplification and sequencing of the 16S rRNA gene V3-V4 region, with subsequent bioinformatics analyses conducted using QIIME 2. To compare the alpha diversity indices of patients before and after surgery, we employed both the paired t-test and the Wilcoxon signed-rank test. Results: Out of the 36 patients, 21 were male and 15 were female, with a mean age of 63.8 years. The average duration between surgery and post-surgery fecal sample collection was 2.3 years. Post-surgery, the alpha diversity indices decreased compared to pre-surgery, but the differences were not significant, except for Faith's PD. At the phylum level, Firmicutes and Actinobacteriota, both recognized as beneficial bacteria, increased in abundance after surgery, while Bacteriodota, typically considered harmful, significantly decreased. At the genus level, variations in microbial abundance also showed an increase in Blautia, Bifidobacterium, and Collinsella, and a decrease in Enterococcus, Romboutsia, and Faecalibacterium. The rise in Bifidobacterium abundance following surgery suggests a gut environment altered by the surgery to favor beneficial bacteria. However, a specific species decline in Bifidobacterium adolescentis was observed, highlighting that CRC surgery may have distinct impacts on different bacterial species. Conclusions: Our study indicates a slight decline in gut microbiome diversity following surgery. At the phylum level, there was an observed increase in beneficial bacteria and a decrease in harmful bacteria post-surgery, suggesting potential health improvements in CRC patients. Further research, particularly with a larger sample size and encompassing a broader range of bacteria, is needed to better understand the patterns and implications of microbiome variation in relation to surgical outcomes. Such insights could be pivotal in optimizing patient care and recovery strategies post-surgery. Citation Format: Hyeree Park, Min Jung Kim, Ji Won Park, Seung-Yong Jeong, Aesun Shin, Sung-Yup Cho, Eun Sung Jung, Dong Ho Suh, Sunyoung Lee, Yu Jin Park. Comparative analysis of microbial diversity and abundance in colorectal cancer patients before and after surgery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2804.

Abstract 3652: Spatial transcriptomic study of the tumor microenvironment in HNSCC

Abstract Background: The complex and dynamic nature of the tumor-immune microenvironment (TME) presents challenges for identification of robust and predictive biomarkers in immuno-oncology (IO). Standard multiplex immunohistochemistry (mIHC) or multiplex immuno-fluorescent assays (mIF) allow for the phenotyping on the cellular level but are limited by the number of markers available and the detection systems. Gene expression or next generation sequencing (NGS) platforms provide high-plex bulk data sets about the TME, but the spatial context of the tumor and immune cell interactions is lost. The NanoString GeoMx® Digital Spatial Profiler (DSP) combines spatial context, utilizing fluorescent morphological markers, with molecular profiling capabilities. This combination allows for the quantitative analysis of high-plex analyte abundance which can be traced back to a region of interest within a sample, providing biological inference in the region of interest. Methods: Formalin-fixed paraffin-embedded (FFPE) specimens from HNSCC patients were cut in 5µm sections for all analyses. Whole section tissue analysis was previously performed utilizing the NanoString nCounter® PanCancer IO 360™ Panel. Tumor inflammation signatures (TIS), an 18-gene signature measuring the adaptive immune response within tumors, was generated for each specimen and categorized each as immune-hot or immune-cold based on predefined parameters. Four specimens were selected based on their TIS score and two sets of cut sections were processed by either Labcorp lab in Morrisville, NC or Nanostring lab in Seattle, WA for precision evaluation, using the GeoMx Human Whole Transcriptome Atlas (WTA) panel (18,000 genes) with the NanoString GeoMx digital spatial profiling technology. The TME regions of interest (ROI) were identified using a set of morphology markers consisting of panCK, CD45 and Syto13; 12 ROIs were selected from each sectioned tumor material. The same ROIs were selected at the two testing sites with matching tissue location and morphology characters. Results: GeoMx WTA panel provided an unbiased view of 18,000 protein-coding genes at specific TME regions. These counts were successfully mapped to the 12 ROIs on the tissue. A pseudo-TIS score was generated using the same 18 gene signature from the WTA panel for each ROI. The pseudo-TIS scores from ROIs from the four specimens showed consistency with the specimen-wise TIS score, yet variations were observed across different TME/ROI within each specimen. Whole transcriptome expression results showed high correlation between the two testing sites, providing confidence in the reproducibility of the technology. High resolution fluorescent staining images were able to be fed to downstream quantitative digital analysis to generate complementary spatial data. Citation Format: Caitlin Schroyer, Shanshan Zhou. Spatial transcriptomic study of the tumor microenvironment in HNSCC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3652.

Aerobic digestibility of waste aerobic granular sludge (AGS) assessed by respirometry, physical-chemical analyses, modeling and 16S rRNA gene sequencing

Research has evolved on aerobic granular sludge (AGS) process, but still there are very few studies on the treatment of excess AGS sludge, with almost none considering its aerobic digestion. Here therefore, the aerobic digestibility of typical AGS sludge was assessed. Granules were produced from acetate-based synthetic wastewater (WW) and were subjected to aerobic digestion for 64 d. The stabilization process was monitored over time through physical-chemical parameters, oxygen uptake rates (OUR) and 16S rRNA gene sequencing. The microbial analyses revealed that the cultivated granules were dominated by slow-growing bacteria, mainly ordinary heterotrophic organisms with potential for polyhydroxyalkanoates (PHA) aerobic storage (PHA-OHOs), polyphosphate and glycogen accumulating organisms (PAOs and GAOs), fermentative anaerobes and nitrifiers (AOB and NOB). Differential abundance analysis of the bacterial data (before versus after digestion) discriminated between the most vulnerable microbiome genera and those most resistant to aerobic digestion. Furthermore, modeling of the stabilization process determined that the endogenous decay rate constant (bH) for the heterotrophs present in the granules was notably low; bH = 0.05 d−1 (average), four times less than for common activated sludge (AS), which is rated at 0.2 d−1. For first time, the research reveals another important feature of AGS sludge, i.e. the slow-decaying character of its bacteria (along with their known slow-growing character). This results in slower stabilization, need of bigger digesters and reconsideration of the specific OUR limits in biosolids regulations (SOUR limit of 1.5 mg/gTSS.h), for waste AGS compared to conventional waste AS. The study suggests that aerobic digestion of waste AGS (fully-granulated) could differ from that of conventional AS. Future work is needed on aerobic digestibility of real AGS sludges from municipal and industrial WWs, compared to synthetic WWs.

Abstract 6698: Tumor microbiota associated with immune enrichment in breast tumor

Abstract Asian cancer patients have enriched immune profiles when compared to Caucasians. This is associated with improved overall survival and potentially better response to immunotherapy. Yet the reason behind enriched Asian immune profiles is unclear. This study investigated microbes found in Malaysian breast tumors in association with immune profiles. We used an existing dataset of RNA-seq data from fresh frozen breast tumor tissue (n=978). Sequencing reads were trimmed and filtered to remove human reads. Non-human reads were mapped to Kraken2 and relative abundance was estimated using Bracken. Microbiome analysis was conducted on R following Microbiome OMA guidebook. The top five genera found in the Malaysian Breast Cancer (MyBrCa) cohort were Pseudomonas, Siphoviridae, Bacillus, Escherichia and Klebsiella. Alpha diversity of basal subtype microbiota was significantly higher than Her2 subtype (Shannon index, p=0.027) whilst the other subtypes were more homogenous. MyBrCa patients with low immune scores were significantly different from high immune scores while controlling for age, stage, grade, ethnicity and PAM50 subtype (PERMANOVA, F-statistic = 1.656, p=0.05). Differential abundance analysis revealed Sulfidibacter and Priestia to be significantly different in abundance between high vs low IFNg groups (FDR-adjusted p-value &amp;lt; 0.05) using ALDEx2, ANCOM-BC2 and ZicoSeq and LinDA algorithms. Sulfidibacter was significantly different between high vs low immune group for Bindea combined immune score and ESTIMATE score groups (FDR-adjusted p-value &amp;lt;0.05). An independent breast cancer cohort (n=419) was used as validation where Sulfidibacter and Priestia were also significantly different in abundance for immune high vs low groups. Machine learning using Random Forest model predicted immune group with an AUC of 0.79 (CI = 0.74, 0.83; p-value &amp;lt; 0.01). The top five most important features contributing to the random forest model were Sulfidibacter, Prestia, Cutibacterium, Acinetobacter and Rhizobium. This study is the largest to date that characterize the tumor microbiota of an Asian cohort of breast cancers, where Sulfidibacter and Priestia, both relatively new and uncharacterized bacteria, were differentially abundant among patients with high vs low immune scores. Citation Format: Li-Fang Yeo, Bernard KB Lim, Tania Islam, Li-Ying Teoh, Suniza Jamaris, Mee-Hoong See, Lai-Meng Looi, Nur Aishah Mohd Taib, Cheng-Har Yip, Pathmanathan Rajadurai, Soo-Hwang Teo, Jia Wern Pan. Tumor microbiota associated with immune enrichment in breast tumor [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6698.

Influence of angiotensin II on the gut microbiome: modest effects in comparison to experimental factors.

Animal models are regularly used to test the role of the gut microbiome in hypertension. Small-scale pre-clinical studies have investigated changes to the gut microbiome in the angiotensin II hypertensive model. However, the gut microbiome is influenced by internal and external experimental factors, which are not regularly considered in the study design. Once these factors are accounted for, it is unclear if microbiome signatures are reproduceable. We aimed to determine the influence of angiotensin II treatment on the gut microbiome using a large and diverse cohort of mice and to quantify the magnitude by which other factors contribute to microbiome variations. We conducted a retrospective study to establish a diverse mouse cohort resembling large human studies. We sequenced the V4 region of the 16S rRNA gene from 538 samples across the gastrointestinal tract of 303 male and female C57BL/6J mice randomized into sham or angiotensin II treatment from different genotypes, diets, animal facilities, and age groups. Analysing over 17 million sequencing reads, we observed that angiotensin II treatment influenced α-diversity (P = 0.0137) and β-diversity (i.e. composition of the microbiome, P < 0.001). Bacterial abundance analysis revealed patterns consistent with a reduction in short-chain fatty acid producers, microbial metabolites that lower blood pressure. Furthermore, animal facility, genotype, diet, age, sex, intestinal sampling site, and sequencing batch had significant effects on both α- and β-diversity (all P < 0.001). Sampling site (6.8%) and diet (6%) had the largest impact on the microbiome, while angiotensin II and sex had the smallest effect (each 0.4%). Our large-scale data confirmed findings from small-scale studies that angiotensin II impacted the gut microbiome. However, this effect was modest relative to most of the other factors studied. Accounting for these factors in future pre-clinical hypertensive studies will increase the likelihood that microbiome findings are replicable and translatable.

Abstract 1184: Combination pembrolizumab and radiotherapy induces anti-tumor immune responses in immunologically-cold non-small cell lung cancer

Abstract Introduction: Primary resistance to immune checkpoint inhibition (ICI) is in part driven by a “cold” tumor microenvironment in the context of low PD-L1 expression, low tumor mutation burden or oncogenic mutations that drive tumor immune exclusion. Radiation has the potential to sensitize tumors to ICI through extra-tumoral T cell activation in response to tumor antigens released from irradiated cancer cells (abscopal effect). Here, we investigate tumor microenvironment and T cell repertoire reshaping leveraging serial biospecimens from a phase 2 randomized clinical trial of stereotactic body radiation therapy followed by pembrolizumab (SBRT arm) vs. pembrolizumab alone (control arm) in non-small cell lung cancer (NSCLC; NCT02492568). Methods: We employed whole exome sequencing, RNA sequencing and T cell receptor (TCR) sequencing to analyze 293 serial tumor and peripheral blood samples collected at baseline and after 8 weeks of treatment from 72 patients in the control (n=35) and SBRT (n=37) arms. Immunologically-cold tumors were defined as low TMB (&amp;lt;300 mutations per exome, n=43), PD-L1 null (0% expression on immunohistochemistry, n=41), or WNT-activated (presence of mutations affecting genes in the WNT pathway, n=10). TCR clonotypic differential abundance analyses and RNA sequencing deconvolution and gene set enrichment analyses were performed on baseline and on-therapy blood and tumor samples from the control and SBRT arms. Results: Patients with immunologically-cold tumors in the SBRT arm had significantly longer progression free survival compared to the pembrolizumab monotherapy arm (log rank p=0.029 for TMB-low, p=0.022 for PD-L1-null, p=0.037 for the WNT-activated subsets). Induction of interferon-gamma, interferon-alpha, antigen processing and presentation, and inflammatory response gene sets was significantly enriched on-therapy in the SBRT arm (FDR adjusted p&amp;lt;0.01 for TMB-low, PD-L1-null, and WNT-activated tumors). We observed greater enrichment in newly expanded TCR clones on-therapy intratumorally and in peripheral blood in the SBRT arm compared to the control arm (mean 16.17 vs 7.54 clonotypes, p=0.022 in tumor; mean 4.56 vs 1.00, p=0.039 in blood; Mann-Whitney U test). Similarly, we noted greater expansion of pre-existing TCR clones in the SBRT arm than within the control arm (mean 34.83 vs 18.69, p=0.021 in tumor; mean 21.61 vs 8.15, p=0.009 in blood; Mann-Whitney U test). Within the SBRT arm, there was no significant difference in T cell expansion between TMB-stratified (&amp;lt;300 mutations vs &amp;gt;=300 mutations) or PD-L1-stratified (0% vs &amp;gt;= 1% expression) groups. Conclusions: SBRT followed by pembrolizumab was associated with priming and augmenting anti-tumor immune responses in immunologically-cold NSCLC, which opens a therapeutic window of opportunity for radiotherapy to overcome primary resistance to immune checkpoint inhibition. Citation Format: Justin Huang, Willemijn S. Theelen, Zineb Belcaid, Mimi Najjar, Christopher Cherry, Archana Balan, James R. White, Noushin Niknafs, Jaime Wehr, M.M. van den Heuvel, Rachel Karchin, Paul Baas, Victor E. Velculescu, Valsamo Anagnostou. Combination pembrolizumab and radiotherapy induces anti-tumor immune responses in immunologically-cold non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1184.

A universal plasma metabolites-derived signature predicts cardiovascular disease risk in MAFLD

BackgroundMetabolic associated fatty liver disease (MAFLD) is a novel concept proposed in 2020, which is more practical for identifying patients with fatty liver disease with high risk of disease progression. Fatty liver is a driver for extrahepatic complications, particularly cardiovascular diseases (CVD). Although the risk of CVD in MAFLD could be predicted by carotid ultrasound test, a very early stage prediction method before the formation of pathological damage is still lacking. MethodsStool microbiomes and plasma metabolites were compared across 196 well-characterized participants encompassing normal controls, simple MAFLD patients, MAFLD patients with carotid artery pathological changes, and MAFLD patients with diagnosed coronary artery disease (CAD). 16S rDNA sequencing data and untargeted metabolomic profiles were interrogatively analyzed using differential abundance analysis and random forest (RF) machine learning algorithm to identify discriminatory gut microbiomes and metabolomic. ResultsCharacteristic microbial changes in MAFLD patients with CVD risk were represented by the increase of Clostridia and Firmicutes-to-Bacteroidetes ratios. Faecalibacterium was negatively correlated with mean-intima-media thickness (IMT), TC, and TG. Megamonas, Bacteroides, Parabacteroides, and Escherichia were positively correlated with the exacerbation of pathological indexes. MAFLD patients with CVD risk were characterized by the decrease of lithocholic acid taurine conjugate, and the increase of ethylvanillin propylene glycol acetal, both of which had close relationship with Ruminococcus and Gemmiger. Biotin l-sulfoxide had positive correlation with mean-IMT, TG, and weight. The general auxin pesticide beta-naphthoxyacetic acid and the food additive glucosyl steviol were both positively correlated with the increase of mean-IMT. The model combining the metabolite signatures with 9 clinical parameters accurately distinguished MAFLD with CVD risk in the proband and validation cohort. It was found that citral was the most important discriminative metabolite marker, which was validated by both in vitro and in vivo experiments. ConclusionsSimple MAFLD patients and MAFLD patients with CVD risk had divergent gut microbes and plasma metabolites. The predictive model based on metabolites and 9 clinical parameters could effectively discriminate MAFLD patients with CVD risk at a very early stage.

Abstract 751: Gut microbiome and breast cancer: Report from a case-control study in Vietnam

Abstract The importance of gut microbiome on human health is being increasingly recognized. The gut microbiota may play a critical role in breast cancer etiology, likely through the roles of the gut microbiota in estrogen and nutrient metabolism as well as in immune regulation. However, evidence on associations between breast cancer and gut microbiota is limited and inconsistent. Using resources from the Vietnamese Breast Cancer Case-Control Study (VBCS), we evaluated differences in gut microbiome profiles between women with breast cancer and healthy women. Pre-treatment tool samples of 162 incident breast cancer cases (age: 50.0±9.5) and stool samples of 370 age-matched controls (age: 49.7±9.0) were included in the study. The gut microbiome was measured using shotgun metagenomic sequencing. Differences in gut microbiome α-diversity and β-diversity between breast cancer patients and healthy controls were evaluated via linear regression models and PERMANOVA testing, respectively. Case-control differences in gut microbial taxa abundance were assessed through the differential abundance analysis with adjustment for potential confounders, including age, income levels, residence, menopausal status, reproductive factors, body mass index, comorbidity, dietary intake, and physical activity. An association with a false discovery rate (FDR) &amp;lt;0.1 was considered statistically significant. No significant difference between breast cancer patients and healthy controls was observed for α- and β-diversities. A total of 2,905 gut microbial taxa were evaluated. Among them, significant case-control differences were found in the abundance of one phylum, two classes, four orders, three families, four gena, and 67 species (all p&amp;lt;0.05 and FDR &amp;lt;0.1). Compared to healthy controls, breast cancer patients had a decreased abundance of species Pauljensenia keddieii, Bifidobacterium bifidum (phylum Actinobacteriota), Clostridium Q symbiosum, Faecalicatena fissicatena, Massilioclostridium methylpentosum, Parvimonas micra (phylum Firmicutes A), Leptotrichia wadei (phylum Fusobacteriota), Enterococcus D gallinarum, Lactobacillus H fermentum, Gemella haemolysans B, genus Streptococcus and its 24 species such as S. anginosus C, S. constellatus, S. equinus, S. infantis I, S. mitis, S. oralis, S. pseudopneumoniae O (phylum Firmicutes) (all p&amp;lt;0.05 and FDR &amp;lt;0.1). The species Clostridium Q symbiosum showed the strongest association, with a log2 fold-change (SE) of -2.02 (0.45), p=7.35 × 10−6; FDR=0.006. Additionally, breast cancer patients had an increased abundance of three species belonging to the phylum Firmicutes A including MGYG-HGUT-03165, MGYG-HGUT-04111, and MGYG-HGUT-00903, compared to healthy controls (p&amp;lt;0.05 and FDR &amp;lt;0.1). These results suggest that the gut microbiome of breast cancer differs from that of control women. Additional analyses are ongoing to reveal the biological underpinning of the observed associations. Citation Format: Sang Minh Nguyen, Huong T. Tran, Jirong Long, Martha J. Shrubsole, Hui Cai, Yaohua Yang, Thuan V. Tran, Wei Zheng, Xiao-Ou Shu. Gut microbiome and breast cancer: Report from a case-control study in Vietnam [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 751.

Abstract 1281: Antibiotics aerosolization shrinks intratumoral Tregs and impairs lung tumor growth by perturbing the taxonomic structure of tumor-associated microbiota

Abstract The lung has been traditionally regarded as a sterile environment. Emerging evidence has overturned this belief unveiling the existence of a specific lung microbiota that plays a pivotal role in modulating lung immune tolerance. Recent research also sheds light on how the tissue microbiota is significantly altered by the presence of a growing tumor. Here, we investigated the compositional changes in lung-colonizing bacterial populations associated with the growth of LLC1 lung carcinoma cells in C57BL/6 mice, by analyzing the lung 16S rRNA profiling. While overall microbial diversity and structure were similar in tumor-bearing and healthy lungs, abundance analysis of amplicon sequence variants (ASVs) revealed that 48 ASVs were differentially modulated at significant level (FDR ≤ 0.05). Notably, local treatment with aerosolized vancomycin/neomycin antibiotics starting 4 days after tumor injection caused a different representation of 47 ASVs (FDR ≤ 0.05), mostly reducing those ASVs specifically emerging in the lung by the tumor growth, belonging to Bacterioidales, Clostridiales, Lactobacillales and Enterobacteriales. A different microbial composition was also suggested by the results of Partial Least Squares Discriminant Analysis (PLS-DA) that clustered lung tumor samples as saline or antibiotic-aerosolized. Based on the reported ability of some bacteria belonging to these orders to induce immunosuppressive cells in the gut, we examined the impact of antibiotic aerosol on tumor-associated Tregs in the lung. The presence of the tumor promoted the appearance of Tregs expressing CD69 and PD-1 activation markers, but vancomycin/neomycin aerosol treatment strongly dropped Treg frequency in tumor-bearing lungs, compared to saline. In a therapeutic setting, aerosolization of antibiotics starting 4 days after LLC1 tumor intravenous injection significantly reduced the number of lung metastases (p=0.0116). This effect was associated with an increased cytotoxic activity of immune infiltrating effector cells suggesting that the modulation of the lung microbiota impairs tumor growth and enhances antitumor immune response. Some cultivable bacterial strains, that were in agreement with metataxonomic profile results, were isolated from tumor-bearing lungs, but not from healthy lungs. Ongoing experiments suggest an effect of their in vivo transfer in the lung by aerosol on tumor growth and the ability to induce Treg expansion in vitro. Overall, this study unveils that the growth of the murine LLC1 tumor shapes the lung microbial composition by favoring the expansion of microbes with potential immunosuppressive activity eventually affecting the tumor progression. Moreover, our findings unravel the therapeutic potential of localized antibiotics to revert the immunosuppressive microenvironment by targeting tumor-associated bacteria. Citation Format: Giancarla Bernardo, Valentino Le Noci, Elena Montanari, Emerenziana Ottaviano, Giorgio Gargari, Elisa Borghi, Simone Guglielmetti, Federico Ambrogi, Tiziana Triulzi, Elda Tagliabue, Serenella M. Pupa, Michele Sommariva, Lucia Sfondrini. Antibiotics aerosolization shrinks intratumoral Tregs and impairs lung tumor growth by perturbing the taxonomic structure of tumor-associated microbiota [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1281.

Identification of plant microRNAs using convolutional neural network.

MicroRNAs (miRNAs) are of significance in tuning and buffering gene expression. Despite abundant analysis tools that have been developed in the last two decades, plant miRNA identification from next-generation sequencing (NGS) data remains challenging. Here, we show that we can train a convolutional neural network to accurately identify plant miRNAs from NGS data. Based on our methods, we also present a user-friendly pure Java-based software package called Small RNA-related Intelligent and Convenient Analysis Tools (SRICATs). SRICATs encompasses all the necessary steps for plant miRNA analysis. Our results indicate that SRICATs outperforms currently popular software tools on the test data from five plant species. For non-commercial users, SRICATs is freely available at https://sourceforge.net/projects/sricats.

Phytoplankton Composition in Waters Around Hot Water Waste of Steam Electricity Power Plant Nii Tanasa

Understanding the complex dynamics of aquatic ecosystems is very important for sustainable resource management in the field of aquatic resource management studies. This study aims to determine the distribution, abundance and diversity of phytoplankton and its correlation with water temperature around the PLTU Nii Tanasa area. The sampling method used is purposive sampling by taking water samples using a plankton net at predetermined stations. Data analysis of abundance, diversity, dominance, and correlation using Microsoft excel software. The number of samples found was 12 types of phytoplankton and came from several different classes. Abundance index (N) = low-moderate fertility (low enough), diversity index (H) = moderate category, dominance (C) = low, indicating stable community structure conditions. Water quality is classified as waters with moderate levels of pollution. Correlation of abundance, diversity and dominance of phytoplankton with water temperature has a positive and negative linear relationship.