Smoke and Mirrors

The incessant beeping of an alarm clock begins at 6 a.m. each day. Molly* fills her backpack and heads to the campus office where she works until 10 a.m. when she switches over to classes. Come 2 p.m. she is free of lecture halls but has to either return to work or go to the library to do her homework for the night. Molly grabs dinner on her way home, walks into the house and tosses her backpack aside as she settles into the couch, flips on Netflix and announces, “I’m gonna get high.

Molecular basis for intestinal mucin recognition by galectin-3 and C-type lectins

Intestinal mucins trigger immune responses upon recognition by dendritic cells via protein–carbohydrate interactions. We used a combination of structural, biochemical, biophysical, and cell-based approaches to decipher the specificity of the interaction between mucin glycans and mammalian lectins expressed in the gut, including galectin (Gal)-3 and C-type lectin receptors. Gal-3 differentially recognized intestinal mucins with different O-glycosylation profiles, as determined by mass spectrometry (MS). Modification of mucin glycosylation, via chemical treatment leading to a loss of terminal glycans, promoted the interaction of Gal-3 to poly-N-acetyllactosamine. Specific interactions were observed between mucins and mouse dendritic cell-associated lectin (mDectin)-2 or specific intercellular adhesion molecule–grabbing nonintegrin-related-1 (SIGN-R1), but not mDectin-1, using a cell-reporter assay, as also confirmed by atomic force spectroscopy. We characterized the N-glycosylation profile of mouse colonic mucin (Muc)-2 by MS and showed that the interaction with mDectin-2 was mediated by high-mannose N-glycans. Furthermore, we observed Gal-3 binding to the 3 C-type lectins by force spectroscopy. We showed that mDectin-1, mDectin-2, and SIGN-R1 are decorated by N-glycan structures that can be recognized by the carbohydrate recognition domain of Gal-3. These findings provide a structural basis for the role of mucins in mediating immune responses and new insights into the structure and function of major mammalian lectins.—Leclaire, C., Lecointe, K., Gunning, P. A., Tribolo, S., Kavanaugh, D. W., Wittmann, A., Latousakis, D., MacKenzie, D. A., Kawasaki, N., Juge, N. Molecular basis for intestinal mucin recognition by galectin-3 and C-type lectins

Structural basis for the role of Serine-Rich Repeat Proteins from Lactobacillus reuteri in gut microbe-host interactions

Lactobacillus reuteri, a Gram-positive bacterial species inhabiting the gastrointestinal tract of vertebrates displays remarkable host adaptation. Previous mutational analyses of rodent strain L. reuteri 100-23C identified a gene encoding a predicted surface-exposed serine-rich repeat protein (SRRP100-23) that was vital for L. reuteri biofilm formation in mice. SRRPs have emerged as an important group of surface proteins on many pathogens but no structural information is available in commensal bacteria. Here we report the 2.00 Å and 1.92 Å crystal structures of the binding regions (BRs) of SRRP100-23 and SRRP53608 from L. reuteri ATCC 53608, revealing a unique “β-solenoid” fold in this important adhesin family. BRSRRP53608 boundto host epithelial cells and DNA at neutral pH and recognised polygalacturonic acid (PGA), rhamnogalacturonan I or chondroitin sulfate A at acidic pH. Mutagenesis confirmed the role of the BR putative binding site in the interaction of BRSRRP53608 with PGA. Long molecular dynamics simulations showed that SRRP53608 undergoes a pH-dependent conformational change. Together, these findings shed new mechanistic insights into the role of SRRPs in host-microbe interactions and open new avenues of research into the use of biofilm-forming probiotics against clinically important pathogens

Serine-Rich Repeat Protein adhesins from Lactobacillus reuteri display strain specific glycosylation profiles

Lactobacillus reuteri is a gut symbiont inhabiting the gastrointestinal tract of numerous vertebrates. The surface-exposed Serine-Rich Repeat Protein (SRRP) is a major adhesin in Gram-positive bacteria. Using lectin and sugar nucleotide profiling of wild-type or L. reuteri isogenic mutants, MALDI-ToF-MS, LC-MS and GC-MS analyses of SRRPs, we showed that L. reuteri strains 100-23C (from rodent) and ATCC 53608 (from pig) can perform protein O-glycosylation and modify SRRP100-23 and SRRP53608 with Hex-Glc-GlcNAc and di-GlcNAc moieties, respectively. Furthermore, in vivo glycoengineering in E. coli led to glycosylation of SRRP53608 variants with α-GlcNAc and GlcNAcβ(1→6)GlcNAcα moieties. The glycosyltransferases involved in the modification of these adhesins were identified within the SecA2/Y2 accessory secretion system and their sugar nucleotide preference determined by saturation transfer difference NMR spectroscopy and differential scanning fluorimetry. Together, these findings provide novel insights into the cellular O-protein glycosylation pathways of gut commensal bacteria and potential routes for glycoengineering applications

Factors associated with adverse COVID-19 outcomes in patients with psoriasis-insights from a global registry-based study.

BACKGROUND: The multimorbid burden and use of systemic immunosuppressants in people with psoriasis may confer greater risk of adverse outcomes of coronavirus disease 2019 (COVID-19), but the data are limited. OBJECTIVE: Our aim was to characterize the course of COVID-19 in patients with psoriasis and identify factors associated with hospitalization. METHODS: Clinicians reported patients with psoriasis with confirmed/suspected COVID-19 via an international registry, Psoriasis Patient Registry for Outcomes, Therapy and Epidemiology of COVID-19 Infection. Multiple logistic regression was used to assess the association between clinical and/or demographic characteristics and hospitalization. A separate patient-facing registry characterized risk-mitigating behaviors. RESULTS: Of 374 clinician-reported patients from 25 countries, 71% were receiving a biologic, 18% were receiving a nonbiologic, and 10% were not receiving any systemic treatment for psoriasis. In all, 348 patients (93%) were fully recovered from COVID-19, 77 (21%) were hospitalized, and 9 (2%) died. Increased hospitalization risk was associated with older age (multivariable-adjusted odds ratio [OR] = 1.59 per 10 years; 95% CI = 1.19-2.13), male sex (OR = 2.51; 95% CI = 1.23-5.12), nonwhite ethnicity (OR = 3.15; 95% CI = 1.24-8.03), and comorbid chronic lung disease (OR = 3.87; 95% CI = 1.52-9.83). Hospitalization was more frequent in patients using nonbiologic systemic therapy than in those using biologics (OR = 2.84; 95% CI = 1.31-6.18). No significant differences were found between classes of biologics. Independent patient-reported data (n = 1626 across 48 countries) suggested lower levels of social isolation in individuals receiving nonbiologic systemic therapy than in those receiving biologics (OR = 0.68; 95% CI = 0.50-0.94). CONCLUSION: In this international case series of patients with moderate-to-severe psoriasis, biologic use was associated with lower risk of COVID-19-related hospitalization than with use of nonbiologic systemic therapies; however, further investigation is warranted on account of potential selection bias and unmeasured confounding. Established risk factors (being older, being male, being of nonwhite ethnicity, and having comorbidities) were associated with higher hospitalization rates

Smoke and Mirrors

The incessant beeping of an alarm clock begins at 6 a.m. each day. Molly* fills her backpack and heads to the campus office where she works until 10 a.m. when she switches over to classes. Come 2 p.m. she is free of lecture halls but has to either return to work or go to the library to do her homework for the night. Molly grabs dinner on her way home, walks into the house and tosses her backpack aside as she settles into the couch, flips on Netflix and announces, “I’m gonna get high.”</p

Artificial Intelligence Applications for Social Science Research

Our team developed a database of 250 Artificial Intelligence (AI) applications useful for social science research. To be included in our database, the AI tool had to be useful for: 1) literature reviews, summaries, or writing, 2) data collection, analysis, or visualizations, or 3) research dissemination. In the database, we provide a name, description, and links to each of the AI tools that were current at the time of publication on September 29, 2023. Supporting links were provided when an AI tool was found using other databases. To help users evaluate the potential usefulness of each tool, we documented information about costs, log-in requirements, and whether plug-ins or browser extensions are available for each tool. Finally, as we are a team of scientists who are also interested in studying social media data to understand social problems, we also documented when the AI tools were useful for text-based data, such as social media. This database includes 132 AI tools that may have use for literature reviews or writing; 146 tools that may have use for data collection, analyses, or visualizations; and 108 that may be used for dissemination efforts. While 170 of the AI tools within this database can be used for general research purposes, 18 are specific to social media data analyses, and 62 can be applied to both. Our database thus offers some of the recently published tools for exploring the application of AI to social science research

Data from: Postpartum family planning integration with maternal, newborn, and child health services: a cross-sectional analysis of client flow patterns in India and Kenya

Objectives: Maternal, newborn, and child health (MNCH) services represent opportunities to integrate postpartum family planning (PPFP). Objectives were to determine levels of MNCH-family planning (FP) integration and associations between integration, client characteristics, and service delivery factors in facilities that received programmatic PPFP support. Design and setting: Cross-sectional client flow assessment conducted May–July 2014, over 5 days at 10 purposively selected public sector facilities in India (four hospitals) and Kenya (two hospitals, four health centers). Participants: 2,158 client visits tracked (1,294 India; 864 Kenya). Women aged 18 or older accessing services while pregnant and/or with a child under 2 years. Interventions: PPFP/postpartum intrauterine device—Bihar, India (2012–2013); Jharkhand, India (2010–2014); Embu, Kenya (2008–2012). Maternal, infant, and young child nutrition/FP integration—Bondo, Kenya (2011–2013). Primary outcome measures: Proportion of visits where clients received integrated MNCH-FP services, client characteristics as predictors of MNCH-FP integration, and MNCH-FP integration as predictor of length of time spent at facility. Results: Levels of MNCH-FP integration varied widely across facilities (5.3% to 63.0%), as did proportion of clients receiving MNCH-FP integrated services by service area. Clients traveling 30–59 minutes were half as likely to receive integrated services versus those traveling under 30 minutes (odds ratio [OR] 0.5, 95% confidence interval [CI] 0.4–0.7, p<.001). Clients receiving MNCH-FP services (versus MNCH services only) spent an average of 10.5 minutes longer at the facility (95% CI −0.1–21.9, not statistically significant). Conclusions: Findings suggest importance of focused programmatic support for integration by MNCH service area. FP integration was highest in areas receiving specific support. Integration does not seem to impose an undue burden on clients in terms of time spent at the facility. Clients living furthest from facilities are least likely to receive integrated services

Use of Atomic Force Microscopy to Study the Multi-Modular Interaction of Bacterial Adhesins to Mucins

The mucus layer covering the gastrointestinal (GI) epithelium is critical in selecting and maintaining homeostatic interactions with our gut bacteria. However, the molecular details of these interactions are not well understood. Here, we provide mechanistic insights into the adhesion properties of the canonical mucus-binding protein (MUB), a large multi-repeat cell–surface adhesin found in Lactobacillus inhabiting the GI tract. We used atomic force microscopy to unravel the mechanism driving MUB-mediated adhesion to mucins. Using single-molecule force spectroscopy we showed that MUB displayed remarkable adhesive properties favouring a nanospring-like adhesion model between MUB and mucin mediated by unfolding of the multiple repeats constituting the adhesin. We obtained direct evidence for MUB self-interaction; MUB–MUB followed a similar binding pattern, confirming that MUB modular structure mediated such mechanism. This was in marked contrast with the mucin adhesion behaviour presented by Galectin-3 (Gal-3), a mammalian lectin characterised by a single carbohydrate binding domain (CRD). The binding mechanisms reported here perfectly match the particular structural organization of MUB, which maximizes interactions with the mucin glycan receptors through its long and linear multi-repeat structure, potentiating the retention of bacteria within the outer mucus layer

Client Flow Base Dataset 10 Oct 2014 (SPSS) (De-Identified)

Base dataset for client flow data collected under the study "A Descriptive Evaluation of Postpartum Family Planning Integration Models in Kenya and India," conducted in 2014 by the Maternal and Child Survival Program. SPSS version