SYSTEM AND METHOD FOR PERFORMING TRANSACTIONS USING UNIFIED TRANSACTION IDENTIFIER (ID)

The present disclosure discloses a method and a system for performing transactions using unified transaction identifier (ID). In the present disclosure, the method includes generating a unified ID using credentials of a user during registration process. Herein, the unified ID is linked with one or more accounts of the user, one or more debit cards, credit cards, and the like, of the user. Post registering, the user such as, a sender having a unified ID can initiate a transaction with a recipient having a unified ID. Herein, while initiating the transaction from the sender to the recipient, the sender provides unified ID of the recipient. Upon entering the unified ID of the recipient, the unified ID system determines a risk rate and a payment limit associated with the initiated transaction. Then, the determined risk rate associated with the transaction is notified to the sender to complete or terminate the transaction

Role of Murine Intestinal Interleukin-1 Receptor 1-Expressing Lymphoid Tissue Inducer-Like Cells in Salmonella Infection

Interleukin (IL)-1 signaling plays a critical role in intestinal immunology. Here, we report that the major population of intestinal lamina propria lymphocytes expressing IL-1 receptor 1 (IL-1R1) is the lymphoid tissue inducer (LTi)-like cell, a type of innate lymphoid cell. These cells are significant producers of IL-22, and this IL-22 production depends on IL-1R1 signaling. LTi-like cells are required for defense against Salmonella enterica serovar Typhimurium. Moreover, colonic LTi-like cell numbers depend on the presence of the intestinal microbiota. LTi-like cells require IL-1R1 for production of protective cytokines and confer protection in infectious colitis, and their cell numbers in the colon depend upon having a microbiome

Sutterella and its metabolic pathways positively correlate with vaccine-elicited antibody responses in infant rhesus macaques

IntroductionIt is becoming clearer that the microbiota helps drive responses to vaccines; however, little is known about the underlying mechanism. In this study, we aimed to identify microbial features that are associated with vaccine immunogenicity in infant rhesus macaques.MethodsWe analyzed 16S rRNA gene sequencing data of 215 fecal samples collected at multiple timepoints from 64 nursery-reared infant macaques that received various HIV vaccine regimens. PERMANOVA tests were performed to determine factors affecting composition of the gut microbiota throughout the first eight months of life in these monkeys. We used DESeq2 to identify differentially abundant bacterial taxa, PICRUSt2 to impute metagenomic information, and mass spectrophotometry to determine levels of fecal short-chain fatty acids and bile acids.ResultsComposition of the early-life gut microbial communities in nursery-reared rhesus macaques from the same animal care facility was driven by age, birth year, and vaccination status. We identified a Sutterella and a Rodentibacter species that positively correlated with vaccine-elicited antibody responses, with the Sutterella species exhibiting more robust findings. Analysis of Sutterella-related metagenomic data revealed five metabolic pathways that significantly correlated with improved antibody responses following HIV vaccination. Given these pathways have been associated with short-chain fatty acids and bile acids, we quantified the fecal concentration of these metabolites and found several that correlated with higher levels of HIV immunogen-elicited plasma IgG.DiscussionOur findings highlight an intricate bidirectional relationship between the microbiota and vaccines, where multiple aspects of the vaccination regimen modulate the microbiota and specific microbial features facilitate vaccine responses. An improved understanding of this microbiota–vaccine interplay will help develop more effective vaccines, particularly those that are tailored for early life

Moving beyond microbiome-wide associations to causal microbe identification

Microbiome-wide association studies have established that numerous diseases are associated with changes in the microbiota1,2. These studies typically generate a long list of commensals implicated as biomarkers of disease, with no clear relevance to disease pathogenesis1–5. If the field is to move beyond correlations and begin to address causation, an effective system is needed for refining this catalog of differentially abundant microbes and allow for subsequent mechanistic studies1,4. Herein, we demonstrate that triangulation of microbe–phenotype relationships is an effective method for reducing the noise inherent in microbiota studies and enabling identification of causal microbes. We found that gnotobiotic mice harboring different microbial communities exhibited differential survival in a colitis model. Co-housing of these mice generated animals that had hybrid microbiotas and displayed intermediate susceptibility to colitis. Mapping of microbe–phenotype relationships in parental mouse strains and in mice with hybrid microbiotas identified the bacterial family Lachnospiraceae as a correlate for protection from disease. Using directed microbial culture techniques, we discovered Clostridium immunis, a previously unknown bacterial species from this family, that—when administered to colitis-prone mice—protected them against colitis-associated death. To demonstrate the generalizability of our approach, we used it to identify several commensal organisms that induce intestinal expression of an antimicrobial peptide. Thus, we have used microbe–phenotype triangulation to move beyond the standard correlative microbiome study and identify causal microbes for two completely distinct phenotypes. Identification of disease-modulating commensals by microbe–phenotype triangulation may be more broadly applicable to human microbiome studies

Trimeric Autotransporters Require Trimerization of the Passenger Domain for Stability and Adhesive Activity

In recent years, structural studies have identified a number of bacterial, viral, and eukaryotic adhesive proteins that have a trimeric architecture. The prototype examples in bacteria are the Haemophilus influenzae Hia adhesin and the Yersinia enterocolitica YadA adhesin. Both Hia and YadA are members of the trimeric-autotransporter subfamily and are characterized by an internal passenger domain that harbors adhesive activity and a short C-terminal translocator domain that inserts into the outer membrane and facilitates delivery of the passenger domain to the bacterial surface. In this study, we examined the relationship between trimerization of the Hia and YadA passenger domains and the capacity for adhesive activity. We found that subunit-subunit interactions and stable trimerization are essential for native folding and stability and ultimately for full-level adhesive activity. These results raise the possibility that disruption of the trimeric architecture of trimeric autotransporters, and possibly other trimeric adhesins, may be an effective strategy to eliminate adhesive activity

IL-1R1 is required for IL-23-stimulated IL-17 and IL-22 production by LTi-like cells <b><i>in vitro</i></b><b>.</b>

<p>(A and B) Box and whiskers plot depicting percent of WT (W) or IL-1R1^−/− (I) colonic CD4⁺ LTi-like cells that produce IL-22 (A) or IL-17 (B). (C) Box and whiskers plot depicting percent of colonic LTi-like cells isolated from Rag1^−/− (R) C57BL/6J mice that produce IL-22. (D) Box and whiskers plot depicting percent of WT (W) or IL-1R1^−/− (I) colonic CD4⁺ LTi-like cells that produce IFN-γ. Except in (C), cells were isolated from WT (<i>top panels</i>) or IL-1R1^−/− C57BL/6J mice (<i>bottom panels</i>). Cells were stimulated by rIL-23 (23; <i>right panels</i>) or medium (M; <i>left panels</i>). Box and whisker plots representative of at least three independent experiments. *, <i>p</i><0.05; NS, not significant. For box and whisker plots, line represents median, box represents 25^th to 75^th percentile range, and whiskers represent range.</p

Number of colonic IL-1R1⁺ CD4⁺ LTi-like cells depends on the gut flora.

<p>(A) Number of IL-1R1⁺ CD4⁺ LTi-like cells in the cLP of Swiss-Webster mice that were either SPF, GF, monocolonized with <i>B. fragilis</i> (BF), or monocolonized with segmented filamentous bacteria (SFB). <i>n</i> = 8−10. (B) Number of IL-1R1⁺ CD4⁺ LTi-like cells in the cLP of SPF Swiss-Webster mice treated for five weeks with either vancomycin (vanco), neomycin (neo), or metronidazole (metro). N = 8−10. (C) Number of IL-1R1⁺ CD4⁺ LTi-like cells in the cLP of Swiss-Webster mice that were either SPF or born GF and co-housed at weaning age with SPF mice (GF→SPF). <i>n</i> = 4−5. *, <i>p</i><0.05; ***, <i>p</i><0.001; NS, not significant. For box and whisker plots, line represents median, box represents 25^th to 75^th percentile range, and whiskers represent range.</p

Colonic LTi-like cells are significant innate producers of IL-22.

<p>(A) Representative FACS histogram depicting percentage of CD4⁺ LTi-like cells that produce IL-22 under control conditions (<i>left panel</i>) and in DSS colitis (<i>right panel</i>) in Rag1^−/− C57BL/6J mice. (B) Graph summarizing percentage of CD4⁺ LTi-like cells producing IL-22. (C) Representative scatter plot depicting the phenotype of IL-22-producing lymphocytes under control conditions (<i>left panel</i>) and in DSS colitis (<i>right panel</i>) in Rag1^−/− C57BL/6J mice. Gated on IL-22⁺ lymphocytes. (D) Graph depicting percentage of IL-22-producing lymphocytes that are CD4⁺ LTi-like cells. All graphs represent three independent experiments. *, <i>p</i><0.05; NS, not significant. For box and whisker plots, line represents median, box represents 25^th to 75^th percentile range, and whiskers represent range.</p