Chia, Nicholas

Background: Primary sclerosing cholangitis (PSC) is a major risk factor for cholangiocarcinoma (CCA). We investigated biliary and fecal microbiota to determine whether specific microbes in the bile or stool are associated with PSC or CCA. Methods: Bile was obtained from 32 patients with PSC, 23 with CCA with PSC, 26 with CCA without PSC, and 17 controls. Over 90% of bile samples were from patients with perihilar CCA. Stool was obtained from 31 patients with PSC (11 were matched to bile), 16 with CCA with PSC (10 matched to bile), and 11 with CCA without PSC (6 matched to bile). Microbiota composition was assessed using 16SrRNA-marker-based sequencing and was compared between groups. Results: Bile has a unique microbiota distinguished from negative DNA controls and stool. Increased species richness and abundance of Fusobacteria correlated with duration of PSC and characterized the biliary microbiota in CCA. Stool microbiota composition showed no significant differences between groups. Conclusions: We identified a unique microbial signature in the bile of patients with increased duration of PSC or with CCA, suggesting a role for microbiota-driven inflammation in the pathogenesis and or progression to perihilar CCA. Further studies are needed to test this hypothesis.

Surgical site infections (SSI) are a significant burden to patients and health care systems. They increase mortality rates, length of hospital stays, and associated health care costs.

AbstractAlthough Streptococcus agalactiae periprosthetic joint infection (PJI) is not as prevalent as staphylococcal PJI, invasive S. agalactiae infection is not uncommon. Here, RNA‐seq was used to perform transcriptomic analysis of S. agalactiae PJI using fluid derived from sonication of explanted arthroplasties of subjects with S. agalactiae PJI, with results compared to those of S. agalactiae strain NEM316 grown in vitro. A total of 227 genes with outlier expression were found (164 upregulated and 63 downregulated) between PJI sonicate fluid and in vitro conditions. Functional enrichment analysis showed genes involved in mobilome and inorganic ion transport and metabolism to be most enriched. Genes involved in nickel, copper, and zinc transport, were upregulated. Among known virulence factors, cyl operon genes, encoding β‐hemolysin/cytolysin, were consistently highly expressed in PJI versus in vitro. The data presented provide insight into S. agalactiae PJI pathogenesis and may be a resource for identification of novel PJI therapeutics or vaccines against invasive S. agalactiae infections.

Although Streptococcus agalactiae periprosthetic joint infection (PJI) is not as prevalent as staphylococcal PJI, invasive S. agalactiae infection has recently increased in incidence. Here, RNA-Seq was used to perform transcriptomic analysis of S. agalactiae PJI using fluid derived from sonication of explanted arthroplasties of subjects with S. agalactiae PJI, with results compared to those of S. agalactiae strain NEM316 grown in vitro. 227 genes with outlier expression were found (164 up-regulated and 63 down-regulated) between PJI sonicate fluid and in vitro conditions. Functional enrichment analysis showed genes involved in mobilome and inorganic ion transport and metabolism to be most enriched. Genes involved in nickel, copper, and zinc transport, were upregulated. Among known virulence factors, cyl operon genes, encoding beta-hemolysin/cytolysin, were consistently highly expressed in PJI versus in vitro. The data presented provide insight into S. agalactiae PJI pathogenesis and may be a useful resource for the identification of novel PJI therapeutics or vaccines against invasive S. agalactiae infections.

Abstract Background: SARS-Cov-2 infections are associated with increased mortality and morbidity, largely due to inflammatory cascades and cytokine release syndrome (CRS). Clonal hematopoiesis of indeterminate potential (CHIP) is defined by the presence and subsequent expansion of somatic, leukemia-associated driver mutations in apparently healthy individuals with normal blood counts. CHIP has been associated with increased inflammation, with cytokines such as IL1-b, IL6 and TNF-a being elevated at baseline in affected individuals. We hypothesized that the presence of CHIP in patients with COVID-19 would result in excessive inflammation-related mortality and morbidity. Methods: We used the Mayo Clinic COVID-19 database to identify patients with COVID-19 on whom peripheral blood mononuclear cells (PBMC) were available for research use (IRB approved). We carried out target-capture next generation sequencing for 220 CH related genes, by previously described methods (1000 x coverage, variant allele fraction/VAF detection limit >0.5%; Kusne Y et al AJH 2021). CHIP was defined by the presence of a CH mutation with a VAF>1% in an individual with normal baseline blood counts. Demographics, blood counts, and inflammatory markers (CRP and cytokine levels- ELISA assay) at COVID-19 diagnosis and during follow-up (as clinically indicated) were collected. COVID-19 disease severity was classified based on the presence and severity of CRS, graded using the Penn Grading Scale (Porter et. al., 2018), and the WHO ordinal scale (WHO Blueprint, 2020). We used Fisher's exact test and the Wilcoxon rank sum test to compare categorical and continuous variables. Survival analysis was performed using the Kaplan-Meier method. We accounted for differences in age and sex using multivariable-adjusted proportional hazards regression models. Results: Seventy-two CHIP mutations were detected in 56 (25%) of the 227 patients with COVID-19 that had PBMC available; median age 69 years (range; 42-99 years), 61% male. Fifteen (26%) patients had 2 CHIP mutations, while 1 patient had 3 CHIP mutations. Common mutations encountered included DNMT3A (32%) , TET2 (19%) , SF3B1 (8%) , ASXL1 (6%) , MPL (5%) , and TP53 (5%; Figure 1A). COVID-19 patients with CHIP were older in age (median 69 vs 57 years; p<0.0001) and had higher baseline MCP-1 (p=0.04) levels. However, there were no differences in sex, comorbidities, blood counts, IL1-b, IL6 and TNF-a levels between the two groups. The median follow-up for the entire cohort was 9 months. The relative change from baseline in blood counts and inflammatory markers (CRP and cytokines) during follow-up was similar in CHIP and non-CHIP patients, with the exception that COVID-19-onset neutropenia was more common in CHIP patients (8% vs 1%; p=0.017) compared to those without CHIP. At last follow up neutropenia had resolved in all patients. Both groups had comparable number of patients with CRS (61% CHIP vs 53% non-CHIP patients, p=0.354, Figure 1B), however, CHIP patients had more severe CRS (median Penn Grade 3 versus 2 in non-CHIP, p=0.018, Figure 1C). Based on the WHO ordinal scale, CHIP patients were more likely to experience hospitalization with severe disease and death (61% versus 45% in non-CHIP, p = 0.049). Moreover, COVID-19 CHIP patients experienced worse overall survival in comparison to patients without CHIP (median 13.1 months vs not reached, p<0.001, Figure 1D). This association remained consistent after adjusting for age and sex at the time of COVID-19 diagnosis (HR 3.15, 95% 1.32 - 7.55, p = 0.010). At last follow-up, 22 deaths were documented: 13 (23%) in patients with CHIP and 9 (5%) in the non-CHIP group (p=0.02), with the primary cause for mortality being hypoxic respiratory failure (62% in CHIP vs 44% non-CHIP, p=0.04). Conclusions: In this study, we observe an age-independent impact of CHIP on COVID-19 associated inflammatory morbidity (CRS) and mortality (hypoxemic respiratory failure). We are currently carrying out detailed single cell (ssDNA, RNA and ATAC-seq) and proteomic studies (O-link PEA assays) to better elucidate this pathophysiology. Figure 1 Figure 1. Disclosures Patnaik: Kura Oncology: Research Funding; StemLine: Research Funding.

Abstract Background: Chronic myelomonocytic leukemia (CMML) is an aging disease that arises on the background of clonal hematopoiesis, commonly involving multiple TET2 mutations or TET2/SRSF2 co-mutations. In a prior study of 1084 CMML patients (Leukemia 2020; 34:1407-1421), we demonstrated a high prevalence of TET2 mutations (56%) in CMML, with approximately 30% of patients having >1 TET2 mutation. The cell-type and allele specific distribution of these mutations remains of great interest and are thus far poorly defined. To visualize the genotypic and immunophenotypic correlates, we performed single-cell DNA analysis and simultaneous protein profiling on 2 CMML patients with multiple TET2 mutations. Methods: After Mayo Clinic IRB approval, two CMML patients with dual TET2 mutations and available peripheral blood mononuclear cells were included in the study. Patient samples were labeled with TotalSeq TM -D Heme Oncology Cocktail (BioLegend, San Diego, CA) consisting of 45 unique oligo-conjugated cell surface markers. The samples were then washed and loaded into a Tapestri microfluidics cartridge, where single cells (3,500 cells/µL) were encapsulated, lysed and barcoded using the MissionBio Myeloid custom amplicon panel consisting of 312 amplicons in 45 genes relevant to myeloid disorders (MissionBio, San Francisco, CA). The individual barcoded DNA and protein for each cell was purified, recovered, and labeled with Illumina i5/i7 indices, quanitifed by Agilent Bioanalyzer and pooled for sequencing. The libraries were sequenced via Illumina NovaSeq by the Mayo Genomics Core, and data was recovered and analyzed using MissionBio Tapestri Insights v3.0.2. Results: Two dual TET2 mutated CMML patients were included in the study, both male, ages 79 and 83, and both with CMML-0. Case 1 had TET2 p.G1172Efs*53 (variant allele fraction/VAF-6%) and p.H1732Tfs*13 (VAF-13%) mutations, while case 2 had TET2 p.Y620Ffs* (VAF-85%), p.G898* (VAF-82%), and SRSF2 p.95H (VAF-45%) mutations, respectively. In both patients, TET2 mutations were found to be preferentially distributed in monocytes (CD11b+, CD14+, CD64+, CD13+, CD33+, HLA-DR+), with very few classically defined granulocyte (CD16+,CD13+.CD33+, HLA-DR neg), B (CD19+,CD22+)/T (CD2+,CD4+,CD5+,CD7+) lymphocytes, NK cells (CD16+/CD56+/HLA-DR neg) and plasma cells (CD38+/CD138+), demonstrating mutant TET2. Among monocytes, TET2 mutations were enriched in classical/M01 (CD11b+, CD14+,CD16-) and intermediate/M02 monocytes and were infrequent in the MO3 fraction, commensurate with monocyte repartitioning seen in CMML. Interestingly, a large number of single cells with TET2 mutations also demonstrated an aberrant surface phenotype, with universal expression of CD14+ and CD11b+, along with a mixture of other markers (CD163+, CD11c, CD4+, CD2+, and/or CD56+). With regards to allelic distribution, while several permutations and combination were seen involving heterozygosity and homozygosity for the TET2 mutations identified on bulk sequencing, data was corroborative for both TET2 mutations being located on trans alleles. Conclusions: In this study, we demonstrate that TET2 mutations in CMML are highly enriched in classical circulating monocytes and infrequently occur in classical granulocytes, B/T and NK cells; with multiple TET2 mutations most commonly co-existing on trans alleles. Analyses on additional samples along with copy number variation assessments are ongoing. Figure 1: Lollipop plot of TET2 exons with dual TET2 mutations labeled above and oxygenase domain labeled in green; below are number of cells identified with each genotype for Case 1 (A) and Case 2 (B). Mutations are labeled proximal (yellow) and distal (red) and alleles are in grey. The directionality of the arrows to different genotypes is based on decreasing cell number and genotype. Uniform manifold approximation and projection (UMAP) plot of Case 1 (C) and Case 2 (D) with cells clustered by immunophenotype and labeled by genotype (non TET-2 mutated purple, heterozygous TET2 in green, homozygous TET2 in yellow). Relative protein expression of T-cell phenotype (CD14-, CD3+, CD4+/-,CD8+/-, CD5+, CD7+, CD2+), or classical monocytes (CD14+/CD16-) for Case 1 (E & F) and Case 2 (G & H) overlayed on UMAP. Royal blue represents non TET2-mutated cells, and mutated cells are labeled in pastel blue, green and light orange by mutation type. Figure 1 Figure 1. Disclosures Durruthy-Durruthy: Mission Bio Inc.: Current Employment. Arribas-Layton: Mission Bio, Inc.: Current Employment. Patnaik: StemLine: Research Funding; Kura Oncology: Research Funding.

Abstract We present preliminary results and outline future work for the causal analysis of genetic factors influencing the occurrence and severity of colorectal cancer. The findings are based on publicly available datasets. We show that using relatively simple methods we are able to detect meaningful dependencies, reflecting the current biomedical knowledge about the causes of cancer and related conditions (such as infection and sepsis). We also argue that a deeper analysis, taking into account gene regulation, would require a significantly larger dataset.

Abstract Whole genome sequencing is emerging as a promising tool for the untargeted detection of a broad range of microbial species for diagnosis and analysis. However, it is logistically challenging to perform the multistep process from sample preparation to DNA amplification to sequencing and analysis within a short turnaround time. To address this challenge, we developed a digital microfluidic device for rapid whole genome amplification of low-abundance bacterial DNA and compared results with conventional in-tube DNA amplification. In this work, we chose Corynebacterium glutamicum DNA as a bacterial target for method development and optimization, as it is not a common contaminant. Sequencing was performed in a hand-held Oxford Nanopore Technologies MinION sequencer. Our results show that using an in-tube amplification approach, at least 1 pg starting DNA is needed to reach the amount required for successful sequencing within 2 h. While using a digital microfluidic device, it is possible to amplify as low as 10 fg of C. glutamicum DNA (equivalent to the amount of DNA within a single bacterial cell) within 2 h and to identify the target bacterium within 30 min of MinION sequencing100× lower than the detection limit of an in-tube amplification approach. We demonstrate the detection of C. glutamicum DNA in a mock community DNA sample and characterize the limit of bacterial detection in the presence of human cells. This approach can be used to identify microbes with minute amounts of genetic material in samples depleted of human cells within 3 h.

The impact of diet on the microbial composition in the gastrointestinal tract has been well documented. However, quantitation of the role of diet in shaping microbial composition has been difficult due to the presence of factors such as host genetics and environment. We used two genetically identical pigs housed together as a system for isolating diet‐induced changes in the microbiome. We used a withdrawal design across four 14‐d periods with two diets having different solubility of dietary fiber to study the fecal microbial community by 454‐pyrosequencing of the 16S rRNA gene. Shifts in the fecal microbial community composition were assessed with respect to dietary correlations. Similarity analysis revealed that the gut microbiomes from the two different diets clustered separately. Taxa‐based approaches detected significantly different bacteria composition in two different diets. Diversity analysis showed that bacterial diversity was associated with the level of dietary fiber solubility. To conclude, the impact of diet on gut microbiome composition was isolated from host genetics and environment. Diet‐induced changes in gut microbiome composition were observed and found to be reversible within a short time. Our results show that genetically identical pigs can be used as a model system for assessing the influence of diet and other variables on the composition of the gut microbial community. Grant Funding Source : University of Illinois Agricultural Experiment Station, USDA AG 2008–34480‐19328, USDA AG 2008–34480‐19328, USDA/ARS AG 58–5438‐7–317l, USDA/ARS AG 58–5438‐2–307