Peptide analysis of tooth enamel - A sex estimation tool for archaeological, anthropological, or forensic research.

Proteomics has become an attractive method to study human and animal material, biological profile, and origin as an alternative to DNA analysis. It is limited by DNA amplification in ancient samples and its contamination, high cost, and limited preservation of nuclear DNA. Currently, three approaches are available to estimate sex-osteology, genomics, or proteomics, but little is known about the relative reliability of these methods in applied settings. Proteomics provides a new, seemingly simple, and relatively non-expensive way of sex estimation without the risk of contamination. Proteins can be preserved in hard teeth tissue (enamel) for tens of thousands of years. It uses two sexually distinct forms of the protein amelogenin in tooth enamel detectable by liquid chromatography-mass spectrometry; the protein amelogenin Y isoform is present in enamel dental tissue only in males, while amelogenin isoform X can be found in both sexes. From the point of view of archaeological, anthropological, and forensic research and applications, the reduced destruction of the methods used is essential, as well as the minimum requirements for sample size.

NetNorM: Capturing cancer-relevant information in somatic exome mutation data with gene networks for cancer stratification and prognosis.

Genome-wide somatic mutation profiles of tumours can now be assessed efficiently and promise to move precision medicine forward. Statistical analysis of mutation profiles is however challenging due to the low frequency of most mutations, the varying mutation rates across tumours, and the presence of a majority of passenger events that hide the contribution of driver events. Here we propose a method, NetNorM, to represent whole-exome somatic mutation data in a form that enhances cancer-relevant information using a gene network as background knowledge. We evaluate its relevance for two tasks: survival prediction and unsupervised patient stratification. Using data from 8 cancer types from The Cancer Genome Atlas (TCGA), we show that it improves over the raw binary mutation data and network diffusion for these two tasks. In doing so, we also provide a thorough assessment of somatic mutations prognostic power which has been overlooked by previous studies because of the sparse and binary nature of mutations.

The chiral proteomic analysis applied to aging collagens by LC-MS: Amino acid racemization, post-translational modifications, and sequence degradations during the aging process.

Collagen is the most abundant protein in the animal and human bodies, and it is not exempt from this aging phenomenon. Some age-related changes may appear on collagen sequences, such as increased surface hydrophobicity, the appearance of post-translational modifications, and amino acids racemization. This study has shown that the protein hydrolysis under deuterium conditions is privileged to limit the natural racemization during the hydrolysis. Indeed, under the deuterium condition, the homochirality of recent collagens is preserved whose amino acids are found in their L-form. However, in aging collagen, a natural amino acid racemization was observed. These results confirmed that the % d-amino acids are progressive according to age. The collagen sequence is degraded over time, and a fifth of the sequence information is lost during aging. Post-translational modifications (PTMs) in aging collagens can be a hypothesis to explain the modification of the hydrophobicity of the protein with the decrease of hydrophilic groups and the increase of hydrophobic groups. Finally, the exact positions of d-amino acids and PTMs have been correlated and elucidated.

Benchmarking missing-values approaches for predictive models on health databases.

BACKGROUND: As databases grow larger, it becomes harder to fully control their collection, and they frequently come with missing values. These large databases are well suited to train machine learning models, e.g., for forecasting or to extract biomarkers in biomedical settings. Such predictive approaches can use discriminative-rather than generative-modeling and thus open the door to new missing-values strategies. Yet existing empirical evaluations of strategies to handle missing values have focused on inferential statistics. RESULTS: Here we conduct a systematic benchmark of missing-values strategies in predictive models with a focus on large health databases: 4 electronic health record datasets, 1 population brain imaging database, 1 health survey, and 2 intensive care surveys. Using gradient-boosted trees, we compare native support for missing values with simple and state-of-the-art imputation prior to learning. We investigate prediction accuracy and computational time. For prediction after imputation, we find that adding an indicator to express which values have been imputed is important, suggesting that the data are missing not at random. Elaborate missing-values imputation can improve prediction compared to simple strategies but requires longer computational time on large data. Learning trees that model missing values-with missing incorporated attribute-leads to robust, fast, and well-performing predictive modeling. CONCLUSIONS: Native support for missing values in supervised machine learning predicts better than state-of-the-art imputation with much less computational cost. When using imputation, it is important to add indicator columns expressing which values have been imputed.

Modulation of the electroosmotic mobility using polyelectrolyte multilayer coatings for protein analysis by capillary electrophoresis.

Successive multiple ionic-polymer layers (SMIL) coatings have been often used in capillary electrophoresis due to their simplicity to implement and regenerate. However, the performances of the separation are strongly dependent on the nature of the polyelectrolyte partners used to build the SMIL coating. In this work, we investigate new couples of polyelectrolytes that were not tested before: namely, polybrene (PB), quaternized diethylaminoethyl dextran (DEAEDq) and ε-poly(lysine) (ε-PLL), as polycations, in combination with poly(acrylic acid), dextran sulfate, poly(styrenesulfonate), poly(methacrylic acid) and poly(l-lysine citramide), as polyanions. Systematic study of intra- and inter-capillaries repeatabilities/reproducibilities was performed based on the determination of migration time, separation efficiency and electroosmotic mobility. Interestingly, the electroosmotic flow was found to vary with the nature of the polycation on a broad range of electroosmotic mobility decreasing in magnitude in the order of PB>ε-PLL > DEAEDq, whatever the polyanion associated. Application of the coatings to the separation of proteins is illustrated in a 0.5 M acetic acid BGE, including CE-MS separation of ribonuclease B-glycoforms of the same mass (positional or structural isomers).