Antibody decay, T cell immunity and breakthrough infections following two SARS-CoV-2 vaccine doses in inflammatory bowel disease patients treated with infliximab and vedolizumab

This is the final version. Available on open access from Nature Research via the DOI in this recordData availability: The study protocol including the statistical analysis plan is available at https://www.clarityibd.org/. Individual participant de-identified data that underlie the results reported in this article will be available immediately after publication for a period of 5 years. Due to the sensitive nature of the data, this will be made available to investigators whose proposed use of the data has been approved by an independent review committee. Analyses will be restricted to the aims in the approved proposal. Proposals should be directed to [email protected]. To gain access data requestors will need to sign a data access agreement. Data from the Virus Watch study is currently being archived on the Office of National Statistics Secure Research Service and will be available shortly. Source data are provided with this paper in the Source Data file. Source data are provided with this paper.Code availability: Statistical analyses were undertaken in R 4.1.2 (R Foundation for Statistical Computing, Vienna, Austria. Code has been made available at: https://github.com/exeteribd/clarityibd-public.Anti tumour necrosis factor (anti-TNF) drugs increase the risk of serious respiratory infection and impair protective immunity following pneumococcal and influenza vaccination. Here we report SARS-CoV-2 vaccine-induced immune responses and breakthrough infections in patients with inflammatory bowel disease, who are treated either with the anti-TNF antibody, infliximab, or with vedolizumab targeting a gut-specific anti-integrin that does not impair systemic immunity. Geometric mean [SD] anti-S RBD antibody concentrations are lower and half-lives shorter in patients treated with infliximab than vedolizumab, following two doses of BNT162b2 (566.7 U/mL [6.2] vs 4555.3 U/mL [5.4], p <0.0001; 26.8 days [95% CI 26.2 - 27.5] vs 47.6 days [45.5 - 49.8], p <0.0001); similar results are also observed with ChAdOx1 nCoV-19 vaccination (184.7 U/mL [5.0] vs 784.0 U/mL [3.5], p <0.0001; 35.9 days [34.9 - 36.8] vs 58.0 days [55.0 - 61.3], p value < 0.0001). One fifth of patients fail to mount a T cell response in both treatment groups. Breakthrough SARS-CoV-2 infections are more frequent (5.8% (201/3441) vs 3.9% (66/1682), p = 0.0039) in patients treated with infliximab than vedolizumab, and the risk of breakthrough SARS-CoV-2 infection is predicted by peak anti-S RBD antibody concentration after two vaccine doses. Irrespective of the treatments, higher, more sustained antibody levels are observed in patients with a history of SARS-CoV-2 infection prior to vaccination. Our results thus suggest that adapted vaccination schedules may be required to induce immunity in at-risk, anti-TNF-treated patients

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge, it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost