Custom scoring based on ecological topology of gut microbiota associated with cancer immunotherapy outcome.

The gut microbiota influences the clinical responses of cancer patients to immunecheckpoint inhibitors (ICIs). However, there is no consensus definition of detrimental dysbiosis. Based on metagenomics (MG) sequencing of 245 non-small cell lung cancer (NSCLC) patient feces, we constructed species-level co-abundance networks that were clustered into species-interacting groups (SIGs) correlating with overall survival. Thirty-seven and forty-five MG species (MGSs) were associated with resistance (SIG1) and response (SIG2) to ICIs, respectively. When combined with the quantification of Akkermansia species, this procedure allowed a person-based calculation of a topological score (TOPOSCORE) that was validated in an additional 254 NSCLC patients and in 216 genitourinary cancer patients. Finally, this TOPOSCORE was translated into a 21-bacterial probe set-based qPCR scoring that was validated in a prospective cohort of NSCLC patients as well as in colorectal and melanoma patients. This approach could represent a dynamic diagnosis tool for intestinal dysbiosis to guide personalized microbiota-centered interventions.

Pharmacodynamic biomarkers of long-term interferon beta-1a therapy in REFLEX and REFLEXION.

This post-hoc analysis evaluated candidate biomarkers of long-term efficacy of subcutaneous interferon beta-1a (sc IFN ß-1a) in REFLEX/REFLEXION studies of clinically isolated syndrome. Samples from 507 REFLEX and 287 REFLEXION study participants were analyzed. All investigated biomarkers were significantly upregulated 1.5-4-fold in response to sc IFN ß-1a treatment versus baseline (p ≤ 0.008). The validity of MX1, 2'5'OAS, and IL-1RA as biomarkers of response to sc IFN ß-1a was confirmed in this large patient cohort, with biomarkers consistently upregulated in a dose-dependent manner. Neopterin, TRAIL, and IP-10 were confirmed as biomarkers associated with long-term sc IFN ß-1a treatment efficacy over 5 years.

Plasma Proteomic Profiles of Cerebrospinal Fluid-Defined Alzheimer's Disease Pathology in Older Adults.

BACKGROUND: Cerebrospinal fluid (CSF) biomarkers of the beta-amyloid and microtubule associated protein tau metabolism have proven the capacity to improve classification of subjects developing Alzheimer's disease (AD). The blood plasma proteome was characterized to further elaborate upon the mechanisms involved and identify proteins that may improve classification of older adults developing an AD dementia. OBJECTIVE: Identify and describe plasma protein expressions that best classify subjects with CSF-defined presence of AD pathology and cerebral amyloidosis. METHODS: We performed a cross-sectional analysis of samples collected from community-dwelling elderly with (n = 72) or without (n = 48) cognitive impairment. CSF Aß1-42, tau, and phosphorylated tau (P-tau181) were measured using ELISA, and mass spectrometry quantified the plasma proteomes. Presence of AD pathology was defined as CSF P-tau181/Aß1-42 > 0.0779, and presence of amyloidosis was defined as CSF Aß1-42 < 724 pg/mL. RESULTS: Two hundred and forty-eight plasma proteins were quantified. Plasma proteins did not improve classification of the AD CSF biomarker profile in the whole sample. When the analysis was separately performed in the cognitively impaired individuals, the diagnosis accuracy of AD CSF profile was 88.9% with 19 plasma proteins included. Within the full cohort, there were 16 plasma proteins that improved diagnostic accuracy of cerebral amyloidosis to 92.4%. CONCLUSION: Plasma proteins improved classification accuracy of AD pathology in cognitively-impaired older adults and appeared representative of amyloid pathology. If confirmed, those candidates could serve as valuable blood biomarkers of the preclinical stages of AD or risk of developing AD.

One-carbon metabolism, cognitive impairment and CSF measures of Alzheimer pathology: homocysteine and beyond.

BACKGROUND: Hyperhomocysteinemia is a risk factor for cognitive decline and dementia, including Alzheimer disease (AD). Homocysteine (Hcy) is a sulfur-containing amino acid and metabolite of the methionine pathway. The interrelated methionine, purine, and thymidylate cycles constitute the one-carbon metabolism that plays a critical role in the synthesis of DNA, neurotransmitters, phospholipids, and myelin. In this study, we tested the hypothesis that one-carbon metabolites beyond Hcy are relevant to cognitive function and cerebrospinal fluid (CSF) measures of AD pathology in older adults. METHODS: Cross-sectional analysis was performed on matched CSF and plasma collected from 120 older community-dwelling adults with (n = 72) or without (n = 48) cognitive impairment. Liquid chromatography-mass spectrometry was performed to quantify one-carbon metabolites and their cofactors. Least absolute shrinkage and selection operator (LASSO) regression was initially applied to clinical and biomarker measures that generate the highest diagnostic accuracy of a priori-defined cognitive impairment (Clinical Dementia Rating-based) and AD pathology (i.e., CSF tau phosphorylated at threonine 181 [p-tau181]/ß-Amyloid 1-42 peptide chain [Aß1-42] >0.0779) to establish a reference benchmark. Two other LASSO-determined models were generated that included the one-carbon metabolites in CSF and then plasma. Correlations of CSF and plasma one-carbon metabolites with CSF amyloid and tau were explored. LASSO-determined models were stratified by apolipoprotein E (APOE) ε4 carrier status. RESULTS: The diagnostic accuracy of cognitive impairment for the reference model was 80.8% and included age, years of education, Aß1-42, tau, and p-tau181. A model including CSF cystathionine, methionine, S-adenosyl-L-homocysteine (SAH), S-adenosylmethionine (SAM), serine, cysteine, and 5-methyltetrahydrofolate (5-MTHF) improved the diagnostic accuracy to 87.4%. A second model derived from plasma included cystathionine, glycine, methionine, SAH, SAM, serine, cysteine, and Hcy and reached a diagnostic accuracy of 87.5%. CSF SAH and 5-MTHF were associated with CSF tau and p-tau181. Plasma one-carbon metabolites were able to diagnose subjects with a positive CSF profile of AD pathology in APOE ε4 carriers. CONCLUSIONS: We observed significant improvements in the prediction of cognitive impairment by adding one-carbon metabolites. This is partially explained by associations with CSF tau and p-tau181, suggesting a role for one-carbon metabolism in the aggregation of tau and neuronal injury. These metabolites may be particularly critical in APOE ε4 carriers.

Protein interaction mapping: a Drosophila case study.

The Drosophila (fruit fly) model system has been instrumental in our current understanding of human biology, development, and diseases. Here, we used a high-throughput yeast two-hybrid (Y2H)-based technology to screen 102 bait proteins from Drosophila melanogaster, most of them orthologous to human cancer-related and/or signaling proteins, against high-complexity fly cDNA libraries. More than 2300 protein-protein interactions (PPI) were identified, of which 710 are of high confidence. The computation of a reliability score for each protein-protein interaction and the systematic identification of the interacting domain combined with a prediction of structural/functional motifs allow the elaboration of known complexes and the identification of new ones. The full data set can be visualized using a graphical Web interface, the PIMRider (http://pim.hybrigenics.com), and is also accessible in the PSI standard Molecular Interaction data format. Our fly Protein Interaction Map (PIM) is surprisingly different from the one recently proposed by Giot et al. with little overlap between the two data sets. Analysis of the differences in data sets and methods suggests alternative strategies to enhance the accuracy and comprehensiveness of the post-genomic generation of broad-scale protein interaction maps.

Functional proteomics mapping of a human signaling pathway.

Access to the human genome facilitates extensive functional proteomics studies. Here, we present an integrated approach combining large-scale protein interaction mapping, exploration of the interaction network, and cellular functional assays performed on newly identified proteins involved in a human signaling pathway. As a proof of principle, we studied the Smad signaling system, which is regulated by members of the transforming growth factor beta (TGFbeta) superfamily. We used two-hybrid screening to map Smad signaling protein-protein interactions and to establish a network of 755 interactions, involving 591 proteins, 179 of which were poorly or not annotated. The exploration of such complex interaction databases is improved by the use of PIMRider, a dedicated navigation tool accessible through the Web. The biological meaning of this network is illustrated by the presence of 18 known Smad-associated proteins. Functional assays performed in mammalian cells including siRNA knock-down experiments identified eight novel proteins involved in Smad signaling, thus validating this integrated functional proteomics approach.

The HUPO PSI's molecular interaction format--a community standard for the representation of protein interaction data.

A major goal of proteomics is the complete description of the protein interaction network underlying cell physiology. A large number of small scale and, more recently, large-scale experiments have contributed to expanding our understanding of the nature of the interaction network. However, the necessary data integration across experiments is currently hampered by the fragmentation of publicly available protein interaction data, which exists in different formats in databases, on authors' websites or sometimes only in print publications. Here, we propose a community standard data model for the representation and exchange of protein interaction data. This data model has been jointly developed by members of the Proteomics Standards Initiative (PSI), a work group of the Human Proteome Organization (HUPO), and is supported by major protein interaction data providers, in particular the Biomolecular Interaction Network Database (BIND), Cellzome (Heidelberg, Germany), the Database of Interacting Proteins (DIP), Dana Farber Cancer Institute (Boston, MA, USA), the Human Protein Reference Database (HPRD), Hybrigenics (Paris, France), the European Bioinformatics Institute's (EMBL-EBI, Hinxton, UK) IntAct, the Molecular Interactions (MINT, Rome, Italy) database, the Protein-Protein Interaction Database (PPID, Edinburgh, UK) and the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING, EMBL, Heidelberg, Germany).