Fecal galectin-1 as a potential marker for colorectal cancer and disease severity

© 2019 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved. Background/Aim. Colorectal cancer (CRC) represents one of the most common cancers worldwide. CRC is frequently diagnosed at advanced stages with poor prognosis, indicating the need for new diagnostic and prognostic markers. The aim of this study was to determine systemic and fecal values of galectin-1 (gal-1) and ratios between gal-1 and proinflammatory cytokines: tumor necrosis factor-alpha (TNF-α), interleukin-1 beta (IL-1β) and interferon gamma (IFN-γ), in the patients with CRC and the relationship with clinicopathological aspects of the disease. Methods. The blood samples and feces liquid fraction of 58 patients with CRC were analyzed. The serum and fecal levels of TNF-α, IL-1β and IFN-γ and gal-1 were measured using sensitive enzyme-linked immunosorbent assay (ELISA) kits. Results. The fecal level of gal-1 was increased in the CRC patients with higher nuclear grade and poor tumor tissue differentiation. The gal-1/TNF-α ratio in the serum and feces had a higher trend in the patients with the advanced tumor-nodemetastasis (TNM) stage as well as the detectable lymphatic and blood vessel invasion. The gal-1/TNF-α and gal-1/IFN-γ ratios were increased in the serum of patients with presence of lung/liver metastasis or peritoneal carcinomatosis, while the enhanced gal-1/IL-1 ratio was detected only in the serum of patients with lung metastasis. A positive correlation between the gal-1 value in feces and histological differentiation of tumor and biomarkers alpha-fetoprotein (AFP) and cancer antigen19-9 (CA 19-9), respectively, was also observed. The fecal values of gal-1 higher than 13,708.29 pg/g presented a highly sensitive and specific marker for histological differentiation of tumor tissue. Conclusion. We believe that the predomination of gal-1 over pro-inflammatory cytokines TNF-α, IL-1β and IFNγ in the patients with advanced and progressive CRC may implicate on an immunomodulatory role of gal-1 in the limiting ongoing proinflammatory processes. The fecal values of gal-1 can be used as a valuable marker for the severity of CRC

Solution structure of \u3ci\u3eArchaeglobus fulgidis\u3c/i\u3e peptidyl-tRNA hydrolase (Pth2) provides evidence for an extensive conserved family of Pth2 enzymes in archea, bacteria, and eukaryotes

The solution structure of protein AF2095 from the thermophilic archaea Archaeglobus fulgidis, a 123- residue (13.6-kDa) protein, has been determined by NMR methods. The structure of AF2095 is comprised of four α-helices and a mixed β-sheet consisting of four parallel and anti-parallel β-strands, where the α-helices sandwich the β-sheet. Sequence and structural comparison of AF2095 with proteins from Homo sapiens, Methanocaldococcus jannaschii, and Sulfolobus solfataricus reveals that AF2095 is a peptidyltRNA hydrolase (Pth2). This structural comparison also identifies putative catalytic residues and a tRNA interaction region for AF2095. The structure of AF2095 is also similar to the structure of protein TA0108 from archaea Thermoplasma acidophilum, which is deposited in the Protein Data Bank but not functionally annotated. The NMR structure of AF2095 has been further leveraged to obtain good-quality structural models for 55 other proteins. Although earlier studies have proposed that the Pth2 protein family is restricted to archeal and eukaryotic organisms, the similarity of the AF2095 structure to human Pth2, the conservation of key active-site residues, and the good quality of the resulting homology models demonstrate a large family of homologous Pth2 proteins that are conserved in eukaryotic, archaeal, and bacterial organisms, providing novel insights in the evolution of the Pth and Pth2 enzyme families

MODBASE, a database of annotated comparative protein structure models, and associated resources

MODBASE (http://salilab.org/modbase) is a relational database of annotated comparative protein structure models for all available protein sequences matched to at least one known protein structure. The models are calculated by MODPIPE, an automated modeling pipeline that relies on the MODELLER package for fold assignment, sequence–structure alignment, model building and model assessment (http:/salilab.org/modeller). MODBASE uses the MySQL relational database management system for flexible querying and CHIMERA for viewing the sequences and structures (http://www.cgl.ucsf.edu/chimera/). MODBASE is updated regularly to reflect the growth in protein sequence and structure databases, as well as improvements in the software for calculating the models. For ease of access, MODBASE is organized into different data sets. The largest data set contains 1 262 629 models for domains in 659 495 out of 1 182 126 unique protein sequences in the complete Swiss-Prot/TrEMBL database (August 25, 2003); only models based on alignments with significant similarity scores and models assessed to have the correct fold despite insignificant alignments are included. Another model data set supports target selection and structure-based annotation by the New York Structural Genomics Research Consortium; e.g. the 53 new structures produced by the consortium allowed us to characterize structurally 24 113 sequences. MODBASE also contains binding site predictions for small ligands and a set of predicted interactions between pairs of modeled sequences from the same genome. Our other resources associated with MODBASE include a comprehensive database of multiple protein structure alignments (DBALI, http://salilab.org/dbali) as well as web servers for automated comparative modeling with MODPIPE (MODWEB, http://salilab.org/modweb), modeling of loops in protein structures (MODLOOP, http://salilab.org/modloop) and predicting functional consequences of single nucleotide polymorphisms (SNPWEB, http://salilab.org/snpweb)

Development of a Coronavirus Disease 2019 (COVID-19) Application Ontology for the Accrual to Clinical Trials (ACT) network

Clinical data networks that leverage large volumes of data in electronic health records (EHRs) are significant resources for research on coronavirus disease 2019 (COVID-19). Data harmonization is a key challenge in seamless use of multisite EHRs for COVID-19 research. We developed a COVID-19 application ontology in the national Accrual to Clinical Trials (ACT) network that enables harmonization of data elements that are critical to COVID-19 research. The ontology contains over 50 000 concepts in the domains of diagnosis, procedures, medications, and laboratory tests. In particular, it has computational phenotypes to characterize the course of illness and outcomes, derived terms, and harmonized value sets for severe acute respiratory syndrome coronavirus 2 laboratory tests. The ontology was deployed and validated on the ACT COVID-19 network that consists of 9 academic health centers with data on 14.5M patients. This ontology, which is freely available to the entire research community on GitHub at https://github.com/shyamvis/ACT-COVID-Ontology, will be useful for harmonizing EHRs for COVID-19 research beyond the ACT network

Development of a COVID-19 Application Ontology for the ACT Network

Clinical data networks that leverage large volumes of data in electronic health records (EHRs) are significant resources for research on coronavirus disease 2019 (COVID-19). Data harmonization is a key challenge in seamless use of multisite EHRs for COVID-19 research. We developed a COVID-19 application ontology in the national Accrual to Clinical Trials (ACT) network that enables harmonization of data elements that that are critical to COVID-19 research. The ontology contains over 50,000 concepts in the domains of diagnosis, procedures, medications, and laboratory tests. In particular, it has computational phenotypes to characterize the course of illness and outcomes, derived terms, and harmonized value sets for SARS-CoV-2 laboratory tests. The ontology was deployed and validated on the ACT COVID-19 network that consists of nine academic health centers with data on 14.5M patients. This ontology, which is freely available to the entire research community on GitHub at https://github.com/shyamvis/ACT-COVID-Ontology, will be useful for harmonizing EHRs for COVID-19 research beyond the ACT network

Solution structure of Archaeglobus fulgidis peptidyl-tRNA hydrolase (Pth2) provides evidence for an extensive conserved family of Pth2 enzymes in archea, bacteria, and eukaryotes

The solution structure of protein AF2095 from the thermophilic archaea Archaeglobus fulgidis, a 123-residue (13.6-kDa) protein, has been determined by NMR methods. The structure of AF2095 is comprised of four α-helices and a mixed β-sheet consisting of four parallel and anti-parallel β-strands, where the α-helices sandwich the β-sheet. Sequence and structural comparison of AF2095 with proteins from Homo sapiens, Methanocaldococcus jannaschii, and Sulfolobus solfataricus reveals that AF2095 is a peptidyl-tRNA hydrolase (Pth2). This structural comparison also identifies putative catalytic residues and a tRNA interaction region for AF2095. The structure of AF2095 is also similar to the structure of protein TA0108 from archaea Thermoplasma acidophilum, which is deposited in the Protein Data Bank but not functionally annotated. The NMR structure of AF2095 has been further leveraged to obtain good-quality structural models for 55 other proteins. Although earlier studies have proposed that the Pth2 protein family is restricted to archeal and eukaryotic organisms, the similarity of the AF2095 structure to human Pth2, the conservation of key active-site residues, and the good quality of the resulting homology models demonstrate a large family of homologous Pth2 proteins that are conserved in eukaryotic, archaeal, and bacterial organisms, providing novel insights in the evolution of the Pth and Pth2 enzyme families