Dasari, Surendra

SummaryBackgroundIrritable bowel syndrome (IBS) patients often experience meal‐associated symptoms. However, the underlying mechanisms are unclear.AimTo determine small intestinal mechanisms of lipid‐induced symptoms and rectal hypersensitivity in IBSMethodsWe recruited 26 IBS patients (12 IBS‐C, 14 IBS‐D) and 15 healthy volunteers (HV). In vivo permeability was assessed using saccharide excretion assay. Rectal sensitivity was assessed using a barostat before and after small bowel lipid infusion; symptoms were assessed throughout. Next, an extended upper endoscopy with probe‐based confocal laser endomicroscopy (pCLE) was performed with changes induced by lipids. Duodenal and jejunal mucosal biopsies were obtained for transcriptomics.ResultsFollowing lipid infusion, a higher proportion of HV than IBS patients reported no pain, no nausea, no fullness and no urgency (P < 0.05 for all). In a model adjusted for sex and anxiety, IBS‐C and IBS‐D patients had lower thresholds for first rectal sensation (P = 0.0007) and pain (P = 0.004) than HV. In vivo small intestinal permeability and mean pCLE scores were similar between IBS patients and HV. Post‐lipid, pCLE scores were higher than pre‐lipid but were not different between groups. Baseline duodenal transient receptor potential vanilloid (TRPV) 1 and 3 expression was increased in IBS‐D, and TRPV3 in IBS‐C. Duodenal TRPV1 expression correlated with abdominal pain (r = 0.51, FDR = 0.01), and inversely with first rectal sensation (r = −0.48, FDR = 0.01) and pain (r = −0.41, FDR = 0.02) thresholds.ConclusionLipid infusion elicits a greater symptom response in IBS patients than HV, which is associated with small intestinal expression of TRPV channels. TRPV‐mediated small intestinal chemosensitivity may mediate post‐meal symptoms in IBS.

Abstract Background We evaluated the analytical sensitivity and specificity of 4 rapid antigen diagnostic tests (Ag RDTs) for severe acute respiratory syndrome coronavirus 2, using reverse transcription quantitative PCR (RT-qPCR) as the reference method and further characterizing samples using droplet digital quantitative PCR (ddPCR) and a mass spectrometric antigen test. Methods Three hundred fifty (150 negative and 200 RT-qPCR positive) residual PBS samples were tested for antigen using the BD Veritor lateral flow (LF), ACON LF, ACON fluorescence immunoassay (FIA), and LumiraDx FIA. ddPCR was performed on RT-qPCR–positive samples to quantitate the viral load in copies/mL applied to each Ag RDT. Mass spectrometric antigen testing was performed on PBS samples to obtain a set of RT-qPCR–positive, antigen-positive samples for further analysis. Results All Ag RDTs had nearly 100% specificity compared to RT-qPCR. Overall analytical sensitivity varied from 66.5% to 88.3%. All methods detected antigen in samples with viral load &gt;1 500 000 copies/mL RNA, and detected ≥75% of samples with viral load of 500 000 to 1 500 000 copies/mL. The BD Veritor LF detected only 25% of samples with viral load between 50 000 to 500 000 copies/mL, compared to 75% for the ACON LF device and &gt;80% for LumiraDx and ACON FIA. The ACON FIA detected significantly more samples with viral load &lt;50 000 copies/mL compared to the BD Veritor. Among samples with detectable antigen and viral load &lt;50 000 copies/mL, sensitivity of the Ag RDT varied between 13.0% (BD Veritor) and 78.3% (ACON FIA). Conclusions Ag RDTs differ significantly in analytical sensitivity, particularly at viral load &lt;500 000 copies/mL.

AbstractBackgroundDespite the unprecedented success of ibrutinib in lymphoma therapy, the development of ibrutinib resistance due to acquired BTK or PLCγ2 mutations has become a new clinical problem. However, not all resistance is mediated by these mutations and these mechanisms are poorly understood due to a lack of study tools that truly recapitulate this clinical scenario.MethodsWe established a novel patient‐derived ibrutinib‐resistant mantle cell lymphoma (MCL) line named MCIR1. Using immunological, molecular, and cytogenetic approaches, we comprehensively characterized MCIR1 and further demonstrated its utility in the study of resistance mechanisms and treatments to overcome this resistance.ResultsWe show that MCIR1 is a bona fide ibrutinib‐resistant MCL cell line with normal BTK‐/PLCγ2 but ibrutinib‐resistant ERK1/2 and AKT1 signaling. RNA‐Seq analysis revealed a robust non‐canonical NF‐kB signaling that drives the ibrutinib resistance. We also demonstrate the potential utility of a MCIR1‐based cell and mouse model for the discovery of new treatments to overcome BTK inhibitor resistance.ConclusionsWe have established the first patient‐derived ibrutinib‐resistant MCL cell line MCIR1 that lacks BTK or PLCγ2 mutations but exhibits a hyperactive non‐canonical NF‐kB pathway. We further demonstrate its utility in the discovery and validation of new drugs to overcome this resistance.

Aging is associated with altered mitochondrial physiology and exercise responsiveness in skeletal muscle. Activation of the unfolded protein response in the endoplasmic reticulum (UPR ER ) and mitochondria (UPR MT ) is linked with exercise‐induced adaptations in skeletal muscle, but the possibility that these processes impact exercise responsiveness and mitochondrial function in older humans has not been fully elucidated. The purpose of this study was to evaluate age‐related changes in UPR ER and UPR MT gene transcripts and protein abundance in response to a single bout of exercise. Muscle biopsies were obtained from 12 young (27±5 yr) and 12 older (75±5 yr) well‐matched, healthy men and women before exercise and 18h following a single bout of unilateral leg extension. Before exercise, a gene set enrichment analysis (GSEA) revealed that 27 genes associated with the UPR ER were upregulated in the older subjects compared to the young (enrichment false discovery rate [FDR] = 0.0842). After exercise, GSEA indicated upregulated UPR ER genes in both the young (FDR = 0.0405) and older subjects (FDR = 0.0619), however, pathway activation analysis revealed significantly greater activation of UPR ER and UPR MT gene transcripts post‐exercise in the younger subjects ( P < 0.05). In contrast, age did not affect key UPR‐regulatory protein abundance 18h post‐exercise. Interestingly, statistically significant correlations were observed in the young subjects between the mitochondrial transcriptional coactivator, peroxisome proliferator‐activated receptor gamma coactivator 1‐alpha (PGC‐1α) and key regulatory genes associated with the UPR ER (GRP78, R = 0.67; ATF6, R = 0.70) and the UPR MT (HSPE1, R = 0.61; HSPD1, R = 0.55; and HSPA9, R = 0.79), which were not evident in the older subjects. In conclusion, transcriptomic analysis revealed an age‐related decline in the adaptive UPR ER and UPR MT transcriptional response following a single bout of exercise that could potentially explain, in part, impaired exercise responsiveness and mitochondrial dysfunction with age. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

8009 Background: Measuring response among patients with multiple myeloma is essential for the care of patients. Deeper responses have been associated with better progression free survival (PFS) and overall survival (OS). Serum (SIFE) and urine immunofixation are the currently used markers for biochemical documentation of CR after which marrow is tested for plasma cell clearance. Next generation flow cytometry and sequencing are used to document the presence of minimal residual disease (MRD). Mass spectrometry of blood by MALDI (Mass-Fix) is a new simple, inexpensive, sensitive, and specific means of detecting monoclonal immunoglobulins. To better test the hypothesis that Mass-Fix is superior to existing methodologies to predict for survival outcomes—especially SIFE-- samples from the STAMINA trial (NCT01109004), a trial comparing 3 transplant approaches among patients who have already received induction, were employed. Methods: Five-hundred and seventy-five patients were included. Samples from enrollment post-induction (post-I) and 1-year post enrollment (1YR) were tested when available. Four response parameters were assessed univariately: Mass-Fix, SIFE, complete response, and MRD by next generation flow cytometry. Mass spectrometry spectra were evaluated in a blinded fashion. Complete response was according to the 2006 International Myeloma Working Group criteria. Multivariate Cox proportional hazard models using stepwise regression were developed to explore the independent effect of the different response parameters on PFS and OS and interactions with other risk factors. Results: Of the 4 response measures, only MRD and Mass-Fix predicted for PFS and OS at multiple testing points on multivariate analyses (Table). Of the 4 post-I measurements, only MRD predicted for PFS; however, Mass-Fix was the only post-I measurement to predict for OS. Of all the 1-year measures, both 1YR Mass-Fix and 1YR MRD positivity predicted for inferior PFS and OS. In models including MRD and Mass-Fix, SIFE and CR were not prognostic for PFS or OS. Conclusions: Mass-Fix is a powerful means to track monoclonal proteins. The full utility of Mass-Fix was not exploited given the absence of a diagnostic sample and the fact that only serum (and not urine) was tested. Despite these limitations, it performed well at pre-induction and at 1 year. Mass-Fix provides a convenient and non-invasive means of predicting for myeloma outcomes. Clinical trial information: NCT01109004. [Table: see text]

Abstract Patients with immunoglobulin M (IgM) light chain (AL) amyloidosis have a distinct clinical presentation compared with those with non-IgM amyloidosis. We hypothesized that differential immunoglobulin light-chain variable region (IGVL) gene usage may explain the differences in organ involvement, because IGVL usage correlates with organ tropism. IGVL usage was evaluated by mass spectrometry of amyloid deposits (IgM, n = 45; non-IgM, n = 391) and differed across the 2 groups. In the λ family, LV2-08 (13% vs 2%; P &lt; .001) and LV2-14 (36% vs 10%; P &lt; .001) usage was more common in IgM vs non-IgM amyloidosis, whereas LV1-44 (0% vs 10%; P = .02) and LV6-57 (2% vs 18%; P = .004) usage was less common. In the κ family, there was a trend toward higher KV4-01 (11% vs 4%; P = .06) usage in IgM amyloidosis. IGVL usage correlated with disease characteristics/organ tropism. LV2-14 (more common in IgM amyloidosis) has historically been associated with peripheral nerve involvement and lower light chain burden, which were more frequent in IgM amyloidosis. LV1-44 (less common in IgM), associated with cardiac involvement, was less frequent in IgM patients. LV6-57 (less common in IgM) is associated with t(11;14), which was less frequent in IgM patients. In conclusion, IGVL gene usage differs in patients with IgM vs non-IgM amyloidosis and may explain the distinct clinical presentation.

BACKGROUND Type 1 diabetes (T1D) is a risk factor for dementia and structural brain changes. It remains to be determined whether transient insulin deprivation that frequently occurs in insulin-treated individuals with T1D alters brain function. METHODS We therefore performed functional and structural magnetic resonance imaging, magnetic resonance spectroscopy, and neuropsychological testing at baseline and following 5.4 ± 0.6 hours of insulin deprivation in 14 individuals with T1D and compared results with those from 14 age-, sex-, and BMI-matched nondiabetic (ND) participants with no interventions. RESULTS Insulin deprivation in T1D increased blood glucose, and β-hydroxybutyrate, while reducing bicarbonate levels. Participants with T1D showed lower baseline brain N-acetyl aspartate and myo-inositol levels but higher cortical fractional anisotropy, suggesting unhealthy neurons and brain microstructure. Although cognitive functions did not differ between participants with T1D and ND participants at baseline, significant changes in fine motor speed as well as attention and short-term memory occurred following insulin deprivation in participants with T1D. Insulin deprivation also reduced brain adenosine triphosphate levels and altered the phosphocreatine/adenosine triphosphate ratio. Baseline differences in functional connectivity in brain regions between participants with T1D and ND participants were noted, and on insulin deprivation further alterations in functional connectivity between regions, especially cortical and hippocampus-caudate regions, were observed. These alterations in functional connectivity correlated to brain metabolites and to changes in cognition. CONCLUSION Transient insulin deprivation therefore caused alterations in executive aspects of cognitive function concurrent with functional connectivity between memory regions and the sensory cortex. These findings have important clinical implications, as many patients with T1D inadvertently have periods of transient insulin deprivation. TRIAL REGISTRATION ClinicalTrials.gov NCT03392441. FUNDING Clinical and Translational Science Award (UL1 TR002377) from the National Center for Advancing Translational Science; NIH grants (R21 AG60139 and R01 AG62859); the Mayo Foundation.

Introduction: The routine incorporation of proteasome inhibitors, immunomodulatory drugs (IMIDs), and monoclonal antibody drugs (mAbs) in managing multiple myeloma (MM) has improved patients' prognosis. Unfortunately, triple refractory patients continue to do poorly. Immunotherapeutic approaches (mAbs, CAR-T cells) have shown the most promise in these patients. However, the efficacy of these therapies depends on a functional tumor immune microenvironment (iTME), which is extensively reshaped by existing MM therapies and disease evolution. Understanding how the iTME and malignant clone co-evolve over the disease course is crucial to optimize the use of immune-based treatments and understand their mechanisms of action. Methods: Newly diagnosed MM patients (NDMM), MM patients relapsing prior to anti-CD38 antibody exposure (RMM), and triple refractory MM patients (TRMM) were identified from the Mayo Clinic biospecimen database. We performed CyTOF (with a 37 marker lymphoid panel on CD138- bone marrow [BM] mononuclear cell samples) to assess the cellular iTME, Luminex (using BM plasma) to assess the humoral iTME (cytokines and chemokines) and RNAseq analysis (using BM CD138+ cells) to assess the malignant clone transcriptome. Astrolabe was used to identify immune cell clusters. Gene set enrichment analysis (GSEA) was used to identify gene pathways with differential expression across groups. JMP was used to perform unsupervised hierarchical clustering and descriptive statistics. An FDR corrected p value of &lt;0.05 was considered statistically significant for all comparisons. Results: This study included 39 patients (13 NDMM, 11 RMM, and 15 TRMM). Samples were hierarchically clustered by cellular iTME, humoral iTME and malignant cell transcriptome, separately. The cellular iTME best recapitulated known disease biology and was used as the "anchoring" variable for subsequent analyses. Three distinct immune clusters were identified (figure 1): cluster 1, comprised mainly of NDMM and RMM patients; and clusters 2 and 3, comprised primarily of TRMM patients. In cluster 2, 4/5 patient samples were collected immediately after progression on daratumumab-IMID therapy, whereas all 7 TRMM patient samples in cluster 3 were collected at a median of 2.8 (range 0-17.2) months after last daratumumab exposure (table 1). Most naïve T cell subsets (including the TIGIT+ T5 and the C38/CD127+ subsets T1, T2, and T12) were highest in cluster 1 compared to clusters 2 and 3. Several unfavorable T cell subsets were elevated in Cluster 2: 1) CD161+ T6, T7 and T17 subsets, which are thought to be Th17 polarized cells that promote MM growth; 2) T7, T9 CCR5+ subsets, which are thought to represent tumor-specific exhausted (PD-1+) T cell subsets; 3) senescent subsets (T-10, T14, T16,T18, T19); and 4) TCR-gd subsets with features of senescence (KLR, CD57), exhaustion (PD-1, TIGIT) and Th17 polarization (CD161). There were no significant differences in cytokine and chemokine expression based on disease status (NDMM, RMM, or TRMM) or iTME (immune clusters 1, 2, or 3). GSEA was performed to compare clusters 2 and 3 (TRMM enriched) with cluster 1. The most up-regulated gene pathways in clusters 2 and 3 related to cell cycle targets of E2F transcription factors and MYC proliferation pathways. The most down-regulated gene pathways in clusters 2 and 3 were IFN-gamma, IFN-alpha and TNF-alpha response and signalling. Conclusion: Changes in the cellular iTME mirror the disease course better than changes in the malignant clone or humoral iTME. Patients with more refractory MM have a smaller pool of naïve T cells which express TIGIT or CD127 and could be "revived" with anti-TIGIT antibodies or IL-7 prior to manufacturing CART cells or administering BCMA-based therapies. Significant heterogeneity exists within the more refractory subgroups, with some patients having more dysfunctional T cell subsets which may be less responsive to immunotherapeutic approaches. The malignant transcriptome of samples in clusters 2 and 3 parallels the aggressive clinical course, with myc upregulation and downregulation of cytokine pathways that mediate an antitumor immune response. This study suggests that the MM iTME becomes more dysfunctional with therapy and may explain the differential sensitivity of patients to immunotherapeutic approaches. Disclosures Kumar: MedImmune: Research Funding; Janssen Oncology: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; BMS: Consultancy, Research Funding; Tenebio: Other, Research Funding; Genecentrix: Consultancy; Sanofi: Research Funding; Genentech/Roche: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Oncopeptides: Consultancy, Other: Independent Review Committee; IRC member; Adaptive Biotechnologies: Consultancy; Takeda: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Merck: Consultancy, Research Funding; AbbVie: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Dr. Reddy's Laboratories: Honoraria; Amgen: Consultancy, Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments, Research Funding; Carsgen: Other, Research Funding; Novartis: Research Funding; Cellectar: Other; Kite Pharma: Consultancy, Research Funding; Karyopharm: Consultancy; Celgene/BMS: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments.