A "data sharing trust" model for rapid, collaborative science

Complex datasets provide opportunities for discoveries beyond their initial scope. Effective and rapid data sharing andmanagement practices are crucial to realize this potential; however, they are harder to implement than post-publication access. Here, we introduce the concept of a "data sharing trust'' tomaximize the value of large datasets

A meta-analysis approach to gene regulatory network inference identifies key regulators of cardiovascular diseases

Cardiovascular diseases (CVDs) represent a major concern for global health, whose mechanistic understanding is complicated by a complex interplay between genetic predisposition and environmental factors. Specifically, heart failure (HF), encompassing dilated cardiomyopathy (DC), ischemic cardiomyopathy (ICM), and hypertrophic cardiomyopathy (HCM), is a topic of substantial interest in basic and clinical research. Here, we used a Partial Correlation Coefficient-based algorithm (PCC) within the context of a meta-analysis framework to construct a Gene Regulatory Network (GRN) that identifies key regulators whose activity is perturbed in Heart Failure. By integrating data from multiple independent studies, our approach unveiled crucial regulatory associations between transcription factors (TFs) and structural genes, emphasizing their pivotal roles in regulating metabolic pathways, such as fatty acid metabolism, oxidative stress response, epithelial-to-mesenchymal transition, and coagulation. In addition to known associations, our analysis also identified novel regulators, including the identification of TFs FPM315 and OVOL2, which are implicated in dilated cardiomyopathies, and TEAD1 and TEAD2 in both dilated and ischemic cardiomyopathies. Moreover, we uncovered alterations in adipogenesis and oxidative phosphorylation pathways in hypertrophic cardiomyopathy and discovered a role for IL2 STAT5 signaling in heart failure. Our findings underscore the importance of TF activity in the initiation and progression of cardiac disease, highlighting their potential as pharmacological targets

Phospho3D: a database of three-dimensional structures of protein phosphorylation sites

Phosphorylation is the most common protein post-translational modification. Phosphorylated residues (serine, threonine and tyrosine) play critical roles in the regulation of many cellular processes. Since the amount of data produced by screening assays is growing continuously, the development of computational tools for collecting and analysing experimental data has become a pivotal task for unravelling the complex network of interactions regulating eukaryotic cell life. Here we present Phospho3D, , a database of 3D structures of phosphorylation sites, which stores information retrieved from the phospho.ELM database and is enriched with structural information and annotations at the residue level. The database also collects the results of a large-scale structural comparison procedure providing clues for the identification of new putative phosphorylation sites

3dLOGO:a web server for the identification, analysis and use of conserved protein substructures

3dLOGO is a web server for the identification and analysis of conserved protein 3D substructures. Given a set of residues in a PDB (Protein Data Bank) chain, the server detects the matching substructure(s) in a set of user-provided protein structures, generates a multiple structure alignment centered on the input substructures and highlights other residues whose structural conservation becomes evident after the defined superposition. Conserved residues are proposed to the user for highlighting functional areas, deriving refined structural motifs or building sequence patterns. Residue structural conservation can be visualized through an expressly designed Java application, 3dProLogo, which is a 3D implementation of a sequence logo. The 3dLOGO server, with related documentation, is available at http://3dlogo.uniroma2.it

Phosphate binding sites identification in protein structures

Nearly half of known protein structures interact with phosphate-containing ligands, such as nucleotides and other cofactors. Many methods have been developed for the identification of metal ions-binding sites and some for bigger ligands such as carbohydrates, but none is yet available for the prediction of phosphate-binding sites. Here we describe Pfinder, a method that predicts binding sites for phosphate groups, both in the form of ions or as parts of other non-peptide ligands, in proteins of known structure. Pfinder uses the Query3D local structural comparison algorithm to scan a protein structure for the presence of a number of structural motifs identified for their ability to bind the phosphate chemical group. Pfinder has been tested on a data set of 52 proteins for which both the apo and holo forms were available. We obtained at least one correct prediction in 63% of the holo structures and in 62% of the apo. The ability of Pfinder to recognize a phosphate-binding site in unbound protein structures makes it an ideal tool for functional annotation and for complementing docking and drug design methods. The Pfinder program is available at http://pdbfun.uniroma2.it/pfinder

Comprehensive Immune Monitoring of Clinical Trials to Advance Human Immunotherapy.

The success of immunotherapy has led to a myriad of clinical trials accompanied by efforts to gain mechanistic insight and identify predictive signatures for personalization. However, many immune monitoring technologies face investigator bias, missing unanticipated cellular responses in limited clinical material. We present here a mass cytometry (CyTOF) workflow for standardized, systems-level biomarker discovery in immunotherapy trials. To broadly enumerate immune cell identity and activity, we established and extensively assessed a reference panel of 33 antibodies to cover major cell subsets, simultaneously quantifying activation and immune checkpoint molecules in a single assay. This assay enumerates ≥98% of peripheral immune cells with ≥4 positively identifying antigens. Robustness and reproducibility are demonstrated on multiple samples types, across two research centers and by orthogonal measurements. Using automated analysis, we identify stratifying immune signatures in bone marrow transplantation-associated graft-versus-host disease. Together, this validated workflow ensures comprehensive immunophenotypic analysis and data comparability and will accelerate biomarker discovery

Modular architecture of nucleotide-binding pockets

Recently, modularity has emerged as a general attribute of complex biological systems. This is probably because modular systems lend themselves readily to optimization via random mutation followed by natural selection. Although they are not traditionally considered to evolve by this process, biological ligands are also modular, being composed of recurring chemical fragments, and moreover they exhibit similarities reminiscent of mutations (e.g. the few atoms differentiating adenine and guanine). Many ligands are also promiscuous in the sense that they bind to many different protein folds. Here, we investigated whether ligand chemical modularity is reflected in an underlying modularity of binding sites across unrelated proteins. We chose nucleotides as paradigmatic ligands, because they can be described as composed of well-defined fragments (nucleobase, ribose and phosphates) and are quite abundant both in nature and in protein structure databases. We found that nucleotide-binding sites do indeed show a modular organization and are composed of fragment-specific protein structural motifs, which parallel the modular structure of their ligands. Through an analysis of the distribution of these motifs in different proteins and in different folds, we discuss the evolutionary implications of these findings and argue that the structural features we observed can arise both as a result of divergence from a common ancestor or convergent evolution

Systemic immunity is required for effective cancer immunotherapy

Immune responses involve coordination across cell types and tissues. However, studies in cancer immunotherapy have focused heavily on local immune responses in the tumor microenvironment. To investigate immune activity more broadly, we performed an organism-wide study in genetically engineered cancer models using mass cytometry. We analyzed immune responses in several tissues after immunotherapy by developing intuitive models for visualizing single-cell data with statistical inference. Immune activation was evident in the tumor and systemically shortly after effective therapy was administered. However, during tumor rejection, only peripheral immune cells sustained their proliferation. This systemic response was coordinated across tissues and required for tumor eradication in several immunotherapy models. An emergent population of peripheral CD4 T cells conferred protection against new tumors and was significantly expanded in patients responding to immunotherapy. These studies demonstrate the critical impact of systemic immune responses that drive tumor rejection

Comprehensive Immune Monitoring of Clinical Trials to Advance Human Immunotherapy

The success of immunotherapy has led to a myriad of clinical trials accompanied by efforts to gain mechanistic insight and identify predictive signatures for personalization. However, many immune monitoring technologies face investigator bias, missing unanticipated cellular responses in limited clinical material. We present here a mass cytometry (CyTOF) workflow for standardized, systems-level biomarker discovery in immunotherapy trials. To broadly enumerate immune cell identity and activity, we established and extensively assessed a reference panel of 33 antibodies to cover major cell subsets, simultaneously quantifying activation and immune checkpoint molecules in a single assay. This assay enumerates >= 98% of peripheral immune cells with >= 4 positively identifying antigens. Robustness and reproducibility are demonstrated on multiple samples types, across two research centers and by orthogonal measurements. Using automated analysis, we identify stratifying immune signatures in bone marrow transplantation-associated graft-versus-host disease. Together, this validated workflow ensures comprehensive immunophenotypic analysis and data comparability and will accelerate biomarker discovery

MetaCyto: A Tool for Automated Meta-analysis of Mass and Flow Cytometry Data

While meta-analysis has demonstrated increased statistical power and more robust estimations in studies, the application of this commonly accepted methodology to cytometry data has been challenging. Different cytometry studies often involve diverse sets of markers. Moreover, the detected values of the same marker are inconsistent between studies due to different experimental designs and cytometer configurations. As a result, the cell subsets identified by existing auto-gating methods cannot be directly compared across studies. We developed MetaCyto for automated meta-analysis of both flow and mass cytometry (CyTOF) data. By combining clustering methods with a silhouette scanning method, MetaCyto is able to identify commonly labeled cell subsets across studies, thus enabling meta-analysis. Applying MetaCyto across a set of ten heterogeneous cytometry studies totaling 2,926 samples enabled us to identify multiple cell populations exhibiting differences in abundance between demographic groups