Mucosal Profiling of Pediatric-Onset Colitis and IBD Reveals Common Pathogenics and Therapeutic Pathways.

Pediatric-onset colitis and inflammatory bowel disease (IBD) have significant effects on the growth of infants and children, but the etiopathogenesis underlying disease subtypes remains incompletely understood. Here, we report single-cell clustering, immune phenotyping, and risk gene analysis for children with undifferentiated colitis, Crohn's disease, and ulcerative colitis. We demonstrate disease-specific characteristics, as well as common pathogenesis marked by impaired cyclic AMP (cAMP)-response signaling. Specifically, infiltration of PDE4B- and TNF-expressing macrophages, decreased abundance of CD39-expressing intraepithelial T cells, and platelet aggregation and release of 5-hydroxytryptamine at the colonic mucosae were common in colitis and IBD patients. Targeting these pathways by using the phosphodiesterase inhibitor dipyridamole restored immune homeostasis and improved colitis symptoms in a pilot study. In summary, comprehensive analysis of the colonic mucosae has uncovered common pathogenesis and therapeutic targets for children with colitis and IBD.

RIPK1 Promotes Energy Sensing by the mTORC1 Pathway.

The mechanisms of cellular energy sensing and AMPK-mediated mTORC1 inhibition are not fully delineated. Here, we discover that RIPK1 promotes mTORC1 inhibition during energetic stress. RIPK1 is involved in mediating the interaction between AMPK and TSC2 and facilitate TSC2 phosphorylation at Ser1387. RIPK1 loss results in a high basal mTORC1 activity that drives defective lysosomes in cells and mice, leading to accumulation of RIPK3 and CASP8 and sensitization to cell death. RIPK1-deficient cells are unable to cope with energetic stress and are vulnerable to low glucose levels and metformin. Inhibition of mTORC1 rescues the lysosomal defects and vulnerability to energetic stress and prolongs the survival of RIPK1-deficient neonatal mice. Thus, RIPK1 plays an important role in the cellular response to low energy levels and mediates AMPK-mTORC1 signaling. These findings shed light on the regulation of mTORC1 during energetic stress and unveil a point of crosstalk between pro-survival and pro-death pathways.

More than just pattern recognition: Prediction of uncommon protein structure features by AI methods.

The CASP14 experiment demonstrated the extraordinary structure modeling capabilities of artificial intelligence (AI) methods. That result has ignited a fierce debate about what these methods are actually doing. One of the criticisms has been that the AI does not have any sense of the underlying physics but is merely performing pattern recognition. Here, we address that issue by analyzing the extent to which the methods identify rare structural motifs. The rationale underlying the approach is that a pattern recognition machine tends to choose the more frequently occurring motifs, whereas some sense of subtle energetic factors is required to choose infrequently occurring ones. To reduce the possibility of bias from related experimental structures and to minimize the effect of experimental errors, we examined only CASP14 target protein crystal structures determined to a resolution limit better than 2 Å, which lacked significant amino acid sequence homology to proteins of known structure. In those experimental structures and in the corresponding models, we track cis peptides, π-helices, 310-helices, and other small 3D motifs that occur in the PDB database at a frequency of lower than 1% of total amino acid residues. The best-performing AI method, AlphaFold2, captured these uncommon structural elements exquisitely well. All discrepancies appeared to be a consequence of crystal environment effects. We propose that the neural network learned a protein structure potential of mean force, enabling it to correctly identify situations where unusual structural features represent the lowest local free energy because of subtle influences from the atomic environment.

Mitochondrial Apoptotic Signaling Involvement in Remodeling During Myogenesis and Skeletal Muscle Atrophy.

Mitochondria play a major role in apoptotic signaling. In addition to its role in eliminating dysfunctional cells, mitochondrial apoptotic signaling is implicated as a key component of myogenic differentiation and skeletal muscle atrophy. For example, the activation of cysteine-aspartic proteases (caspases; CASP's) can aid in the initial remodeling stages of myogenic differentiation by cleaving protein kinases, transcription factors, and cytoskeletal proteins. Precise regulation of these signals is needed to prevent excessive cell disassemble and subsequent cell death. During skeletal muscle atrophy, the activation of CASP's and mitochondrial derived nucleases participate in myonuclear fragmentation, a potential loss of myonuclei, and cleavage of contractile structures within skeletal muscle. The B cell leukemia/lymphoma 2 (BCL2) family of proteins play a significant role in regulating myogenesis and skeletal muscle atrophy by governing the initiating steps of mitochondrial apoptotic signaling. This review discusses the role of mitochondrial apoptotic signaling in skeletal muscle remodeling during myogenic differentiation and skeletal muscle pathological states, including aging, disuse, and muscular dystrophy.

A systematic review of quality of life (QoL) studies using the CASP scale in older adults.

PURPOSE: A systematic review of the use of the CASP Quality of Life (QoL) scale in older adults was carried out. METHODS: Articles were searched using PsycINFO, Web of Science (WOS), Scopus and Medline databases. Observational or experimental studies using any version of the CASP to analyze QoL in adults aged 50 and over and studies focusing on the psychometric properties of the CASP instrument or identifying factors associated with QoL scores. The quality of the studies was assessed using COSMIN and STROBE. RESULTS: A total of 519,339 participants were considered in the 51 studies selected. The 19- and 12-item CASP versions showed high internal consistency and low-to-moderate convergent validity. Best construct validity was reported for the 12-item short version generating a three-factor model (control/autonomy, pleasure & self-realization) and only modest evidence is provided for their cross-cultural validity. Longitudinal and cross-sectional evidence showed (1) a significant decrease in CASP scores at very old ages; (2) an absence of relationship with gender, which, however, may play a moderating role between QoL and health; (3) significant associations between CASP scores and health, psychosocial and socio-economic outcomes. CONCLUSION: The quality of the results was hindered by the lack of relevant information in some studies as well as by the proliferation of versions of the instrument. Nevertheless, we conclude that the CASP scale can capture the complex and multidimensional nature of QoL in older adults by reporting satisfaction of needs that go beyond that go beyond those related to health.

Time, age, gender and cultural measurement invariance of the CASP-12, a measure of psychological quality of life in adulthood.

PURPOSE: Whereas Quality of Life in older populations has long been conceptualized in regards to health or illness, the Control, Autonomy, Self-Realization and Pleasure scale (CASP-12) focuses on the positive facets of aging. Although the CASP is a widely used scale, its measurement invariance has seldom been examined. The present study aims to ascertain the measurement invariance of the CASP-12 over a period of 10 years and between age, culture and gender. METHODS: Secondary data analyses were conducted on the longitudinal data collected in four waves between 2006 and 2016-2017 of the Survey of Health, Ageing and Retirement in Europe study (SHARE). The factorial validity of the CASP-12 was examined and its measurement invariance was tested with a sample of 3684 men and 4955 women aged 30-99 years, coming from 10 different European countries. RESULTS: Results showed a strong theoretical and empirical dimensionality of the CASP-12, a well as invariance of time (10 years), age and culture. It was also found that the scale is gender invariant at the strict level. These results were replicated with two more waves of SHARE, measured six years apart. CONCLUSION: This study replicates the CASP-12 dimensional structure, factorial structure and factor loadings. The scale has demonstrated to be a reliable and valid measurement of the positive aspects of quality of life to be used across time, age, gender and culture. The Autonomy dimension of the scale warrants further investigation.

Effects of the nerve agent VX on hiPSC-derived motor neurons.

Poisoning with the organophosphorus nerve agent VX can be life-threatening due to limitations of the standard therapy with atropine and oximes. To date, the underlying pathomechanism of VX affecting the neuromuscular junction has not been fully elucidated structurally. Results of recent studies investigating the effects of VX were obtained from cells of animal origin or immortalized cell lines limiting their translation to humans. To overcome this limitation, motor neurons (MN) of this study were differentiated from in-house feeder- and integration-free-derived human-induced pluripotent stem cells (hiPSC) by application of standardized and antibiotic-free differentiation media with the aim to mimic human embryogenesis as closely as possible. For testing VX sensitivity, MN were initially exposed once to 400 µM, 600 µM, 800 µM, or 1000 µM VX and cultured for 5 days followed by analysis of changes in viability and neurite outgrowth as well as at the gene and protein level using µLC-ESI MS/HR MS, XTT, IncuCyte, qRT-PCR, and Western Blot. For the first time, VX was shown to trigger neuronal cell death and decline in neurite outgrowth in hiPSC-derived MN in a time- and concentration-dependent manner involving the activation of the intrinsic as well as the extrinsic pathway of apoptosis. Consistent with this, MN morphology and neurite network were altered time and concentration-dependently. Thus, MN represent a valuable tool for further investigation of the pathomechanism after VX exposure. These findings might set the course for the development of a promising human neuromuscular test model and patient-specific therapies in the future.

Exosomes derived from mir-337-3p over-expressing tendon stem cells protect against apoptosis of tenocytes via targeting caspase3.

BACKGROUND: Tendons are important dense fibrous structures connecting muscle to bone, and tendon stem cells (TDSCs) affect their repair and regeneration. The role of TDSC-derived exosomes (TDSC-Exos) is still being unexplored; therefore, this study aimed to investigate the protective effect of TDSC-Exos on tenocytes. METHODS: The TDSCs and tenocytes were all derived from Sprague Dawley (SD) rats. The expression of positive and negative markers of TDSCs were detected by flow cytometry, and the multi-differentiation ability was also detected to identify TDSCs. Exos were derived from TDSCs using ultracentrifugation; furthermore, Exos enriched with microRNA(miR)-377-3p were generated from TDSCs stably overexpressing miR-377-3p after transfection, identified with transmission electron microscopy (TEM), western blot and PKH26 staining assay. Moreover, the cell functions of tenocytes were evaluated by MTT, EdU, transwell, and flow cytometry. Dual luciferase reporter and RNA pull-down assays were used to verify the binding sites of miR-337-3p and caspase3 (CASP3) predicted by Targetscan. RESULTS: Exos (miR-337-3p) were taken up by tenocytes, and promoted the proliferation, migration, and invasion and suppressed the apoptosis of tenocytes in a dose-dependent manner. Bioinformatics analysis showed that CASP3 was a target of miR-377-3p, which was further verified by luciferase and RNA pull-down assays. Moreover, over-expressed CASP3 reversed the effects of Exos (miR-337-3p) on cell functions of tenocytes. CONCLUSIONS: Our findings suggest that Exos derived from miR-337-3p over-expressing TDSCs could potentially protect against tenocyte apoptosis by regulating CASP3. This novel therapeutic approach holds promise for the treatment of tendon injury, offering a glimmer of hope for improved patient outcomes.

Screening of polysaccharides from the differently processed products of Angelica sinensis with the best liver protection effect on chicken and the intervention mechanism study based on tandem mass tag proteomics and multiple reaction monitoring approach.

The incidence of colibacillosis in poultry is on the rise, significantly affecting the chicken industry. Ceftiofur sodium (CS) is frequently employed to treat this disease, resulting in lipopolysaccharide (LPS) buildup. Processing plays a vital role in traditional Chinese veterinary medicine. The potential intervention in liver injury by polysaccharides from the differently processed products of Angelica sinensis (PDPPAS) induced by combined CS and LPS remains unclear. This study aims to investigate the protective effect of PDPPAS on chicken liver injury caused by CS combined with LPS buildup and further identify the polysaccharides with the highest hepatoprotective activity in chickens. Furthermore, the study elucidates polysaccharides' intervention mechanism using tandem mass tag (TMT) proteomics and multiple reaction monitoring (MRM) methods. A total of 190 1-day-old layer chickens were randomly assigned into 12 groups, of which 14 chickens were in the control group and 16 in other groups, for a 10-day trial. The screening results showed that charred A. sinensis polysaccharide (CASP) had the most effective and the best hepatoprotective effect at 48 h. TMT proteomics and MRM validation results demonstrated that the intervention mechanism of the CASP high-dose (CASPH) intervention group was closely related to the protein expressions of FCER2, TBXAS1, CD34, AGXT, GCAT, COX7A2L, and CYP2AC1. Conclusively, the intervention mechanism of CASPH had multitarget, multicenter regulatory features.

Atramacronoid A induces the PANoptosis-like cell death of human breast cancer cells through the CASP-3/PARP-GSDMD-MLKL pathways.

Breast cancer is the most common malignant tumor and a major cause of mortality among women worldwide. Atramacronoid A (AM-A) is a unique natural sesquiterpene lactone isolated from the rhizome of Atractylodes macrocephala Koidz (known as Baizhu in Chinese). Our study demonstrated that AM-A triggers a specific form of cell death resembling PANoptosis-like cell death. Further analysis indicated that AM-A-induced PANoptosis-like cell death is associated with the CASP-3/PARP-GSDMD-MLKL pathways, which are mediated by mitochondrial dysfunction. These results suggest the potential of AM-A as a lead compound and offer insights for the development of therapeutic agents for breast cancer from natural products.

CoDock-Ligand: combined template-based docking and CNN-based scoring in ligand binding prediction.

For ligand binding prediction, it is crucial for molecular docking programs to integrate template-based modeling with a precise scoring function. Here, we proposed the CoDock-Ligand docking method that combines template-based modeling and the GNINA scoring function, a Convolutional Neural Network-based scoring function, for the ligand binding prediction in CASP15. Among the 21 targets, we obtained successful predictions in top 5 submissions for 14 targets and partially successful predictions for 4 targets. In particular, for the most complicated target, H1114, which contains 56 metal cofactors and small molecules, our docking method successfully predicted the binding of most ligands. Analysis of the failed systems showed that the predicted receptor protein presented conformational changes in the backbone and side chains of the binding site residues, which may cause large structural deviations in the ligand binding prediction. In summary, our hybrid docking scheme was efficiently adapted to the ligand binding prediction challenges in CASP15.

Role of environmental specificity in CASP results.

BACKGROUND: Recently, significant progress has been made in the field of protein structure prediction by the application of artificial intelligence techniques, as shown by the results of the CASP13 and CASP14 (Critical Assessment of Structure Prediction) competition. However, the question of the mechanism behind the protein folding process itself remains unanswered. Correctly predicting the structure also does not solve the problem of, for example, amyloid proteins, where a polypeptide chain with an unaltered sequence adopts a different 3D structure. RESULTS: This work was an attempt at explaining the structural variation by considering the contribution of the environment to protein structuring. The application of the fuzzy oil drop (FOD) model to assess the validity of the selected models provided in the CASP13, CASP14 and CASP15 projects reveals the need for an environmental factor to determine the 3D structure of proteins. Consideration of the external force field in the form of polar water (Fuzzy Oil Drop) and a version modified by the presence of the hydrophobic compounds, FOD-M (FOD-Modified) reveals that the protein folding process is environmentally dependent. An analysis of selected models from the CASP competitions indicates the need for structure prediction as dependent on the consideration of the protein folding environment. CONCLUSIONS: The conditions governed by the environment direct the protein folding process occurring in a certain environment. Therefore, the variation of the external force field should be taken into account in the models used in protein structure prediction.

Assessment of protein-ligand complexes in CASP15.

CASP15 introduced a new category, ligand prediction, where participants were provided with a protein or nucleic acid sequence, SMILES line notation, and stoichiometry for ligands and tasked with generating computational models for the three-dimensional structure of the corresponding protein-ligand complex. These models were subsequently compared with experimental structures determined by x-ray crystallography or cryoEM. To assess these predictions, two novel scores were developed. The Binding-Site Superposed, Symmetry-Corrected Pose Root Mean Square Deviation (BiSyRMSD) evaluated the absolute deviations of the models from the experimental structures. At the same time, the Local Distance Difference Test for Protein-Ligand Interactions (lDDT-PLI) assessed the ability of models to reproduce the protein-ligand interactions in the experimental structures. The ligands evaluated in this challenge range from single-atom ions to large flexible organic molecules. More than 1800 submissions were evaluated for their ability to predict 23 different protein-ligand complexes. Overall, the best models could faithfully reproduce the geometries of more than half of the prediction targets. The ligands' size and flexibility were the primary factors influencing the predictions' quality. Small ions and organic molecules with limited flexibility were predicted with high fidelity, while reproducing the binding poses of larger, flexible ligands proved more challenging.

Critical assessment of methods of protein structure prediction (CASP)-Round XV.

Computing protein structure from amino acid sequence information has been a long-standing grand challenge. Critical assessment of structure prediction (CASP) conducts community experiments aimed at advancing solutions to this and related problems. Experiments are conducted every 2 years. The 2020 experiment (CASP14) saw major progress, with the second generation of deep learning methods delivering accuracy comparable with experiment for many single proteins. There is an expectation that these methods will have much wider application in computational structural biology. Here we summarize results from the most recent experiment, CASP15, in 2022, with an emphasis on new deep learning-driven progress. Other papers in this special issue of proteins provide more detailed analysis. For single protein structures, the AlphaFold2 deep learning method is still superior to other approaches, but there are two points of note. First, although AlphaFold2 was the core of all the most successful methods, there was a wide variety of implementation and combination with other methods. Second, using the standard AlphaFold2 protocol and default parameters only produces the highest quality result for about two thirds of the targets, and more extensive sampling is required for the others. The major advance in this CASP is the enormous increase in the accuracy of computed protein complexes, achieved by the use of deep learning methods, although overall these do not fully match the performance for single proteins. Here too, AlphaFold2 based method perform best, and again more extensive sampling than the defaults is often required. Also of note are the encouraging early results on the use of deep learning to compute ensembles of macromolecular structures. Critically for the usability of computed structures, for both single proteins and protein complexes, deep learning derived estimates of both local and global accuracy are of high quality, however the estimates in interface regions are slightly less reliable. CASP15 also included computation of RNA structures for the first time. Here, the classical approaches produced better agreement with experiment than the new deep learning ones, and accuracy is limited. Also, for the first time, CASP included the computation of protein-ligand complexes, an area of special interest for drug design. Here too, classical methods were still superior to deep learning ones. Many new approaches were discussed at the CASP conference, and it is clear methods will continue to advance.

Assessment of the assessment-All about complexes.

Predicting model quality is a fundamental component of any modeling procedure, and blind assessment of these methods constitutes a crucial aspect of the Critical Assessment of Protein Structure Prediction (CASP) experiment. Historically, the main focus was on assessing methods that predict global and per-residue accuracies in tertiary structure models. This focus shifted with the community's increased efforts in modeling complexes and assemblies. We asked the community to process the models from the CASP15 assembly category and provide estimates of the accuracy of the predicted quaternary structure, both globally and at the local interface level. Besides identifying remarkable accuracy of modeling groups in assessing their own predictions, we set up a benchmarking pipeline to highlight different aspects of quaternary structure models and introduced a simple consensus EMA method as baseline. While participating methods showed commendable performance, the baseline was difficult to surpass. It is important to point out that prediction performance varies for the individual CASP targets, highlighting potential areas of improvement and challenges ahead.

Breaking the conformational ensemble barrier: Ensemble structure modeling challenges in CASP15.

For the first time, the 2022 CASP (Critical Assessment of Structure Prediction) community experiment included a section on computing multiple conformations for protein and RNA structures. There was full or partial success in reproducing the ensembles for four of the nine targets, an encouraging result. For protein structures, enhanced sampling with variations of the AlphaFold2 deep learning method was by far the most effective approach. One substantial conformational change caused by a single mutation across a complex interface was accurately reproduced. In two other assembly modeling cases, methods succeeded in sampling conformations near to the experimental ones even though environmental factors were not included in the calculations. An experimentally derived flexibility ensemble allowed a single accurate RNA structure model to be identified. Difficulties included how to handle sparse or low-resolution experimental data and the current lack of effective methods for modeling RNA/protein complexes. However, these and other obstacles appear addressable.

CASP15 cryo-EM protein and RNA targets: Refinement and analysis using experimental maps.

CASP assessments primarily rely on comparing predicted coordinates with experimental reference structures. However, experimental structures by their nature are only models themselves-their construction involves a certain degree of subjectivity in interpreting density maps and translating them to atomic coordinates. Here, we directly utilized density maps to evaluate the predictions by employing a method for ranking the quality of protein chain predictions based on their fit into the experimental density. The fit-based ranking was found to correlate well with the CASP assessment scores. Overall, the evaluation against the density map indicated that the models are of high accuracy, and occasionally even better than the reference structure in some regions of the model. Local assessment of predicted side chains in a 1.52 Å resolution map showed that side-chains are sometimes poorly positioned. Additionally, the top 118 predictions associated with 9 protein target reference structures were selected for automated refinement, in addition to the top 40 predictions for 11 RNA targets. For both proteins and RNA, the refinement of CASP15 predictions resulted in structures that are close to the reference target structure. This refinement was successful despite large conformational changes often being required, showing that predictions from CASP-assessed methods could serve as a good starting point for building atomic models in cryo-EM maps for both proteins and RNA. Loop modeling continued to pose a challenge for predictors, and together with the lack of consensus amongst models in these regions suggests that modeling, in combination with model-fit to the density, holds the potential for identifying more flexible regions within the structure.

To split or not to split: CASP15 targets and their processing into tertiary structure evaluation units.

Processing of CASP15 targets into evaluation units (EUs) and assigning them to evolutionary-based prediction classes is presented in this study. The targets were first split into structural domains based on compactness and similarity to other proteins. Models were then evaluated against these domains and their combinations. The domains were joined into larger EUs if predictors' performance on the combined units was similar to that on individual domains. Alternatively, if most predictors performed better on the individual domains, then they were retained as EUs. As a result, 112 evaluation units were created from 77 tertiary structure prediction targets. The EUs were assigned to four prediction classes roughly corresponding to target difficulty categories in previous CASPs: TBM (template-based modeling, easy or hard), FM (free modeling), and the TBM/FM overlap category. More than a third of CASP15 EUs were attributed to the historically most challenging FM class, where homology or structural analogy to proteins of known fold cannot be detected.

New prediction categories in CASP15.

Prediction categories in the Critical Assessment of Structure Prediction (CASP) experiments change with the need to address specific problems in structure modeling. In CASP15, four new prediction categories were introduced: RNA structure, ligand-protein complexes, accuracy of oligomeric structures and their interfaces, and ensembles of alternative conformations. This paper lists technical specifications for these categories and describes their integration in the CASP data management system.

Impact of AlphaFold on structure prediction of protein complexes: The CASP15-CAPRI experiment.

We present the results for CAPRI Round 54, the 5th joint CASP-CAPRI protein assembly prediction challenge. The Round offered 37 targets, including 14 homodimers, 3 homo-trimers, 13 heterodimers including 3 antibody-antigen complexes, and 7 large assemblies. On average ~70 CASP and CAPRI predictor groups, including more than 20 automatics servers, submitted models for each target. A total of 21 941 models submitted by these groups and by 15 CAPRI scorer groups were evaluated using the CAPRI model quality measures and the DockQ score consolidating these measures. The prediction performance was quantified by a weighted score based on the number of models of acceptable quality or higher submitted by each group among their five best models. Results show substantial progress achieved across a significant fraction of the 60+ participating groups. High-quality models were produced for about 40% of the targets compared to 8% two years earlier. This remarkable improvement is due to the wide use of the AlphaFold2 and AlphaFold2-Multimer software and the confidence metrics they provide. Notably, expanded sampling of candidate solutions by manipulating these deep learning inference engines, enriching multiple sequence alignments, or integration of advanced modeling tools, enabled top performing groups to exceed the performance of a standard AlphaFold2-Multimer version used as a yard stick. This notwithstanding, performance remained poor for complexes with antibodies and nanobodies, where evolutionary relationships between the binding partners are lacking, and for complexes featuring conformational flexibility, clearly indicating that the prediction of protein complexes remains a challenging problem.