Can Mindfulness Address Maladaptive Eating Behaviors? Why Traditional Diet Plans Fail and How New Mechanistic Insights May Lead to Novel Interventions

Emotional and other maladaptive eating behaviors develop in response to a diversity of triggers, from psychological stress to the endless external cues in our modern food environment. While the standard approach to food- and weight-related concerns has been weight-loss through dietary restriction, these interventions have produced little long-term benefit, and may be counterproductive. A growing understanding of the behavioral and neurobiological mechanisms that underpin habit formation may explain why this approach has largely failed, and pave the way for a new generation of non-pharmacologic interventions. Here, we first review how modern food environments interact with human biology to promote reward-related eating through associative learning, i.e., operant conditioning. We also review how operant conditioning (positive and negative reinforcement) cultivates habit-based reward-related eating, and how current diet paradigms may not directly target such eating. Further, we describe how mindfulness training that targets reward-based learning may constitute an appropriate intervention to rewire the learning process around eating. We conclude with examples that illustrate how teaching patients to tap into and act on intrinsic (e.g., enjoying healthy eating, not overeating, and self-compassion) rather than extrinsic reward mechanisms (e.g., weighing oneself), is a promising new direction in improving individuals\u27 relationship with food

The Phosphatomes of the Multicellular Myxobacteria Myxococcus xanthus and Sorangium cellulosum in Comparison with Other Prokaryotic Genomes

BACKGROUND: Analysis of the complete genomes from the multicellular myxobacteria Myxococcus xanthus and Sorangium cellulosum identified the highest number of eukaryotic-like protein kinases (ELKs) compared to all other genomes analyzed. High numbers of protein phosphatases (PPs) could therefore be anticipated, as reversible protein phosphorylation is a major regulation mechanism of fundamental biological processes. METHODOLOGY: Here we report an intensive analysis of the phosphatomes of M. xanthus and S. cellulosum in which we constructed phylogenetic trees to position these sequences relative to PPs from other prokaryotic organisms. PRINCIPAL FINDINGS: PREDOMINANT OBSERVATIONS WERE: (i) M. xanthus and S. cellulosum possess predominantly Ser/Thr PPs; (ii) S. cellulosum encodes the highest number of PP2c-type phosphatases so far reported for a prokaryotic organism; (iii) in contrast to M. xanthus only S. cellulosum encodes high numbers of SpoIIE-like PPs; (iv) there is a significant lack of synteny among M. xanthus and S. cellulosum, and (v) the degree of co-organization between kinase and phosphatase genes is extremely low in these myxobacterial genomes. CONCLUSIONS: We conclude that there has been a greater expansion of ELKs than PPs in multicellular myxobacteria

An Integrated Disease/Pharmacokinetic/Pharmacodynamic Model Suggests Improved Interleukin-21 Regimens Validated Prospectively for Mouse Solid Cancers

Interleukin (IL)-21 is an attractive antitumor agent with potent immunomodulatory functions. Yet thus far, the cytokine has yielded only partial responses in solid cancer patients, and conditions for beneficial IL-21 immunotherapy remain elusive. The current work aims to identify clinically-relevant IL-21 regimens with enhanced efficacy, based on mathematical modeling of long-term antitumor responses. For this purpose, pharmacokinetic (PK) and pharmacodynamic (PD) data were acquired from a preclinical study applying systemic IL-21 therapy in murine solid cancers. We developed an integrated disease/PK/PD model for the IL-21 anticancer response, and calibrated it using selected “training” data. The accuracy of the model was verified retrospectively under diverse IL-21 treatment settings, by comparing its predictions to independent “validation” data in melanoma and renal cell carcinoma-challenged mice (R2>0.90). Simulations of the verified model surfaced important therapeutic insights: (1) Fractionating the standard daily regimen (50 µg/dose) into a twice daily schedule (25 µg/dose) is advantageous, yielding a significantly lower tumor mass (45% decrease); (2) A low-dose (12 µg/day) regimen exerts a response similar to that obtained under the 50 µg/day treatment, suggestive of an equally efficacious dose with potentially reduced toxicity. Subsequent experiments in melanoma-bearing mice corroborated both of these predictions with high precision (R2>0.89), thus validating the model also prospectively in vivo. Thus, the confirmed PK/PD model rationalizes IL-21 therapy, and pinpoints improved clinically-feasible treatment schedules. Our analysis demonstrates the value of employing mathematical modeling and in silico-guided design of solid tumor immunotherapy in the clinic

Prediction and Testing of Biological Networks Underlying Intestinal Cancer

Colorectal cancer progresses through an accumulation of somatic mutations, some of which reside in so-called “driver” genes that provide a growth advantage to the tumor. To identify points of intersection between driver gene pathways, we implemented a network analysis framework using protein interactions to predict likely connections – both precedented and novel – between key driver genes in cancer. We applied the framework to find significant connections between two genes, Apc and Cdkn1a (p21), known to be synergistic in tumorigenesis in mouse models. We then assessed the functional coherence of the resulting Apc-Cdkn1a network by engineering in vivo single node perturbations of the network: mouse models mutated individually at Apc (Apc1638N+/−) or Cdkn1a (Cdkn1a−/−), followed by measurements of protein and gene expression changes in intestinal epithelial tissue. We hypothesized that if the predicted network is biologically coherent (functional), then the predicted nodes should associate more specifically with dysregulated genes and proteins than stochastically selected genes and proteins. The predicted Apc-Cdkn1a network was significantly perturbed at the mRNA-level by both single gene knockouts, and the predictions were also strongly supported based on physical proximity and mRNA coexpression of proteomic targets. These results support the functional coherence of the proposed Apc-Cdkn1a network and also demonstrate how network-based predictions can be statistically tested using high-throughput biological data

Impact of primary kidney disease on the effects of empagliflozin in patients with chronic kidney disease: secondary analyses of the EMPA-KIDNEY trial

Background: The EMPA KIDNEY trial showed that empagliflozin reduced the risk of the primary composite outcome of kidney disease progression or cardiovascular death in patients with chronic kidney disease mainly through slowing progression. We aimed to assess how effects of empagliflozin might differ by primary kidney disease across its broad population. Methods: EMPA-KIDNEY, a randomised, controlled, phase 3 trial, was conducted at 241 centres in eight countries (Canada, China, Germany, Italy, Japan, Malaysia, the UK, and the USA). Patients were eligible if their estimated glomerular filtration rate (eGFR) was 20 to less than 45 mL/min per 1·73 m2, or 45 to less than 90 mL/min per 1·73 m2 with a urinary albumin-to-creatinine ratio (uACR) of 200 mg/g or higher at screening. They were randomly assigned (1:1) to 10 mg oral empagliflozin once daily or matching placebo. Effects on kidney disease progression (defined as a sustained ≥40% eGFR decline from randomisation, end-stage kidney disease, a sustained eGFR below 10 mL/min per 1·73 m2, or death from kidney failure) were assessed using prespecified Cox models, and eGFR slope analyses used shared parameter models. Subgroup comparisons were performed by including relevant interaction terms in models. EMPA-KIDNEY is registered with ClinicalTrials.gov, NCT03594110. Findings: Between May 15, 2019, and April 16, 2021, 6609 participants were randomly assigned and followed up for a median of 2·0 years (IQR 1·5–2·4). Prespecified subgroupings by primary kidney disease included 2057 (31·1%) participants with diabetic kidney disease, 1669 (25·3%) with glomerular disease, 1445 (21·9%) with hypertensive or renovascular disease, and 1438 (21·8%) with other or unknown causes. Kidney disease progression occurred in 384 (11·6%) of 3304 patients in the empagliflozin group and 504 (15·2%) of 3305 patients in the placebo group (hazard ratio 0·71 [95% CI 0·62–0·81]), with no evidence that the relative effect size varied significantly by primary kidney disease (pheterogeneity=0·62). The between-group difference in chronic eGFR slopes (ie, from 2 months to final follow-up) was 1·37 mL/min per 1·73 m2 per year (95% CI 1·16–1·59), representing a 50% (42–58) reduction in the rate of chronic eGFR decline. This relative effect of empagliflozin on chronic eGFR slope was similar in analyses by different primary kidney diseases, including in explorations by type of glomerular disease and diabetes (p values for heterogeneity all >0·1). Interpretation: In a broad range of patients with chronic kidney disease at risk of progression, including a wide range of non-diabetic causes of chronic kidney disease, empagliflozin reduced risk of kidney disease progression. Relative effect sizes were broadly similar irrespective of the cause of primary kidney disease, suggesting that SGLT2 inhibitors should be part of a standard of care to minimise risk of kidney failure in chronic kidney disease. Funding: Boehringer Ingelheim, Eli Lilly, and UK Medical Research Council

The impact of viral mutations on recognition by SARS-CoV-2 specific T cells.

We identify amino acid variants within dominant SARS-CoV-2 T cell epitopes by interrogating global sequence data. Several variants within nucleocapsid and ORF3a epitopes have arisen independently in multiple lineages and result in loss of recognition by epitope-specific T cells assessed by IFN-γ and cytotoxic killing assays. Complete loss of T cell responsiveness was seen due to Q213K in the A∗01:01-restricted CD8+ ORF3a epitope FTSDYYQLY207-215; due to P13L, P13S, and P13T in the B∗27:05-restricted CD8+ nucleocapsid epitope QRNAPRITF9-17; and due to T362I and P365S in the A∗03:01/A∗11:01-restricted CD8+ nucleocapsid epitope KTFPPTEPK361-369. CD8+ T cell lines unable to recognize variant epitopes have diverse T cell receptor repertoires. These data demonstrate the potential for T cell evasion and highlight the need for ongoing surveillance for variants capable of escaping T cell as well as humoral immunity.This work is supported by the UK Medical Research Council (MRC); Chinese Academy of Medical Sciences(CAMS) Innovation Fund for Medical Sciences (CIFMS), China; National Institute for Health Research (NIHR)Oxford Biomedical Research Centre, and UK Researchand Innovation (UKRI)/NIHR through the UK Coro-navirus Immunology Consortium (UK-CIC). Sequencing of SARS-CoV-2 samples and collation of data wasundertaken by the COG-UK CONSORTIUM. COG-UK is supported by funding from the Medical ResearchCouncil (MRC) part of UK Research & Innovation (UKRI),the National Institute of Health Research (NIHR),and Genome Research Limited, operating as the Wellcome Sanger Institute. T.I.d.S. is supported by a Well-come Trust Intermediate Clinical Fellowship (110058/Z/15/Z). L.T. is supported by the Wellcome Trust(grant number 205228/Z/16/Z) and by theUniversity of Liverpool Centre for Excellence in Infectious DiseaseResearch (CEIDR). S.D. is funded by an NIHR GlobalResearch Professorship (NIHR300791). L.T. and S.C.M.are also supported by the U.S. Food and Drug Administration Medical Countermeasures Initiative contract75F40120C00085 and the National Institute for Health Research Health Protection Research Unit (HPRU) inEmerging and Zoonotic Infections (NIHR200907) at University of Liverpool inpartnership with Public HealthEngland (PHE), in collaboration with Liverpool School of Tropical Medicine and the University of Oxford.L.T. is based at the University of Liverpool. M.D.P. is funded by the NIHR Sheffield Biomedical ResearchCentre (BRC – IS-BRC-1215-20017). ISARIC4C is supported by the MRC (grant no MC_PC_19059). J.C.K.is a Wellcome Investigator (WT204969/Z/16/Z) and supported by NIHR Oxford Biomedical Research Centreand CIFMS. The views expressed are those of the authors and not necessarily those of the NIHR or MRC