On protein abundance distributions in complex mixtures

Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview

Artificial Neural Networks (ANNs) are transforming how we understand chemical mixtures, providing an expressive view of the chemical space and multiscale processes. Their hybridization with physical knowledge can bridge the gap between predictivity and understanding of the underlying processes. This overview explores recent progress in ANNs, particularly their potential in the 'recomposition' of chemical mixtures. Graph-based representations reveal patterns among mixture components, and deep learning models excel in capturing complexity and symmetries when compared to traditional Quantitative Structure-Property Relationship models. Key components, such as Hamiltonian networks and convolution operations, play a central role in representing multiscale mixtures. The integration of ANNs with Chemical Reaction Networks and Physics-Informed Neural Networks for inverse chemical kinetic problems is also examined. The combination of sensors with ANNs shows promise in optical and biomimetic applications. A common ground is identified in the context of statistical physics, where ANN-based methods iteratively adapt their models by blending their initial states with training data. The concept of mixture recomposition unveils a reciprocal inspiration between ANNs and reactive mixtures, highlighting learning behaviors influenced by the training environment.

Differential protein analysis of saline-alkali promoting the oil accumulation in Nitzschia palea

BACKGROUND

The increasingly severe salinization of the aquatic environment has led to serious damage to the habitats of aquatic organisms. Benthic diatoms are commonly employed as indicator species for assessing water quality and serve as a reflection of the overall health of the aquatic ecosystem. Nitzschia palea is a common diatom found in freshwater, with high oil content, rapid reproductive rate, and it is a commonly dominant species in various rivers.

RESULTS

The results showed that after 4 days (d) of saline-alkali stress, the cell density and chlorophyll a content of Nitzschia palea reached their maximum values. Therefore, we selected Nitzschia palea under 4 d stress for Tandem Mass Tag (TMT) quantitative proteomic analysis to explore the molecular adaptation mechanism of freshwater diatoms under saline-alkali stress. Totally, 854 proteins were enriched, of which 439 differentially expressed proteins were identified. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and subcellular fractionation analysis revealed that these proteins were mainly enriched in the photosynthesis pathway, citric acid cycle (TCA cycle), fatty acid synthesis, and glutathione cycle.

CONCLUSIONS

This study aims to reveal the physiological, biochemical and proteomic mechanisms of salt and alkali tolerance and molecular adaptation of Nitzschia palea under different saline-alkali concentrations. This study showed that Nitzschia palea is one candidate of the environmental friendly, renewable bioenergy microalgae. Meantime, Nitzschia palea reveals for the proteome of the freshwater and provides the basis, it became a model algal species for freshwater diatoms.

Can the post-ruminal urea release impact liver metabolism, and nutritional status of beef cows at late gestation?

We aimed to evaluate the effects of post-ruminal supply of urea (PRU) on nutritional status, and liver metabolism of pregnant beef cows during late gestation. Twenty-four Brahman dams, pregnant from a single sire, and weighing 545 kg ± 23 kg were confined into individual pens at 174 ± 23 d of gestation, and randomly assigned into one of two dietary treatments up to 270 d of gestation: Control (CON, n = 12), consisting of a basal diet supplemented with conventional urea, where the cows were fed with diets containing 13.5 g conventional urea per kg dry matter; and PRU (PRU, n = 12), consisting of a basal diet supplemented with a urea coated to extensively prevent ruminal degradation while being intestinally digestible, where the cows were fed with diets containing 14,8 g urea protected from ruminal degradation per kg dry matter. Post-ruminal supply of urea reduced the urine levels of 3-methylhistidine (P = 0.02). There were no differences between treatments for dry matter intake (DMI; P = 0.76), total digestible nutrient (TDN) intake (P = 0.30), and in the body composition variables, such as, subcutaneous fat thickness (SFT; P = 0.72), and rib eye area (REA; P = 0.85). In addition, there were no differences between treatments for serum levels of glucose (P = 0.87), and serum levels of glucogenic (P = 0.28), ketogenic (P = 0.72), glucogenic, and ketogenic (P = 0.45) amino acids, neither for urea in urine (P = 0.51) as well as urea serum (P = 0.30). One the other hand, enriched pathways were differentiated related to carbohydrate digestion, and absorption, glycolysis, pyruvate metabolism, oxidative phosphorylation, pentose phosphate pathway, and biosynthesis of amino acids of the exclusively expressed proteins in PRU cows. Shifting urea supply from the rumen to post-ruminal compartments decreases muscle catabolism in cows during late gestation. Our findings indicate that post-ruminal urea supplementation for beef cows at late gestation may improve the energy metabolism to support maternal demands. In addition, the post-ruminal urea release seems to be able to trigger pathways to counterbalance the oxidative stress associated to the increase liver metabolic rate.

Oncogenic Proteomics Approaches for Translational Research and HIV-Associated Malignancy Mechanisms

Recent advances in the field of proteomics have allowed extensive insights into the molecular regulations of the cell proteome. Specifically, this allows researchers to dissect a multitude of signaling arrays while targeting for the discovery of novel protein signatures. These approaches based on data mining are becoming increasingly powerful for identifying both potential disease mechanisms as well as indicators for disease progression and overall survival predictive and prognostic molecular markers for cancer. Furthermore, mass spectrometry (MS) integrations satisfy the ongoing demand for in-depth biomarker validation. For the purpose of this review, we will highlight the current developments based on MS sensitivity, to place quantitative proteomics into clinical settings and provide a perspective to integrate proteomics data for future applications in cancer precision medicine. We will also discuss malignancies associated with oncogenic viruses such as Acquire Immunodeficiency Syndrome (AIDS) and suggest novel mechanisms behind this phenomenon. Human Immunodeficiency Virus type-1 (HIV-1) proteins are known to be oncogenic per se, to induce oxidative and endoplasmic reticulum stresses, and to be released from the infected or expressing cells. HIV-1 proteins can act alone or in collaboration with other known oncoproteins, which cause the bulk of malignancies in people living with HIV-1 on ART.

Prediction and Consequences of Cofragmentation in Metaproteomics

Metaproteomics can provide critical information about biological systems, but peptides are found within a complex background of other peptides. This complex background can change across samples, in some cases drastically. Cofragmentation, the coelution of peptides with similar mass to charge ratios, is one factor that influences which peptides are identified in an LC-MS/MS experiment: it is dependent on the nature and complexity of this dynamic background. Metaproteomics applications are particularly susceptible to cofragmentation-induced bias; they have vast protein sequence diversity and the abundance of those proteins can span many orders of magnitude. We have developed a mechanistic model that determines the number of potentially cofragmenting peptides in a given sample (called cobia, https://github.com/bertrand-lab/cobia ). We then used previously published data sets to validate our model, showing that the resulting peptide-specific score reflects the cofragmentation "risk" of peptides. Using an Antarctic sea ice edge metatranscriptome case study, we found that more rare taxonomic and functional groups are associated with higher cofragmentation bias. We also demonstrate how cofragmentation scores can be used to guide the selection of protein- or peptide-based biomarkers. We illustrate potential consequences of cofragmentation for multiple metaproteomic approaches, and suggest practical paths forward to cope with cofragmentation-induced bias.

A power law distribution of metabolite abundance levels in mice regardless of the time and spatial scale of analysis

Biomolecule abundance levels change with the environment and enable a living system to adapt to the new conditions. Although, the living system maintains at least some characteristics, e.g. homeostasis. One of the characteristics maintained by a living system is a power law distribution of biomolecule abundance levels. Previous studies have pointed to a universal characteristic of biochemical reaction networks, with data obtained from lysates of multiple cells. As a result, the spatial scale of the data related to the power law distribution of biomolecule abundance levels is not clear. In this study, we researched the scaling law of metabolites in mouse tissue with a spatial scale of quantification that was changed stepwise between a whole-tissue section and a single-point analysis (25 μm). As a result, metabolites in mouse tissues were found to follow the power law distribution independently of the spatial scale of analysis. Additionally, we tested the temporal changes by comparing data from younger and older mice. Both followed similar power law distributions, indicating that metabolite composition is not diversified by aging to disrupt the power law distribution. The power law distribution of metabolite abundance is thus a robust characteristic of a living system regardless of time and space.

Multiple marker abundance profiling: combining selected reaction monitoring and data-dependent acquisition for rapid estimation of organelle abundance in subcellular samples

Measuring changes in protein or organelle abundance in the cell is an essential, but challenging aspect of cell biology. Frequently-used methods for determining organelle abundance typically rely on detection of a very few marker proteins, so are unsatisfactory. In silico estimates of protein abundances from publicly available protein spectra can provide useful standard abundance values but contain only data from tissue proteomes, and are not coupled to organelle localization data. A new protein abundance score, the normalized protein abundance scale (NPAS), expands on the number of scored proteins and the scoring accuracy of lower-abundance proteins in Arabidopsis. NPAS was combined with subcellular protein localization data, facilitating quantitative estimations of organelle abundance during routine experimental procedures. A suite of targeted proteomics markers for subcellular compartment markers was developed, enabling independent verification of in silico estimates for relative organelle abundance. Estimation of relative organelle abundance was found to be reproducible and consistent over a range of tissues and growth conditions. In silico abundance estimations and localization data have been combined into an online tool, multiple marker abundance profiling, available in the SUBA4 toolbox (http://suba.live).

A Bayesian algorithm for detecting differentially expressed proteins and its application in breast cancer research

Presence of considerable noise and missing data points make analysis of mass-spectrometry (MS) based proteomic data a challenging task. The missing values in MS data are caused by the inability of MS machines to reliably detect proteins whose abundances fall below the detection limit. We developed a Bayesian algorithm that exploits this knowledge and uses missing data points as a complementary source of information to the observed protein intensities in order to find differentially expressed proteins by analysing MS based proteomic data. We compared its accuracy with many other methods using several simulated datasets. It consistently outperformed other methods. We then used it to analyse proteomic screens of a breast cancer (BC) patient cohort. It revealed large differences between the proteomic landscapes of triple negative and Luminal A, which are the most and least aggressive types of BC. Unexpectedly, majority of these differences could be attributed to the direct transcriptional activity of only seven transcription factors some of which are known to be inactive in triple negative BC. We also identified two new proteins which significantly correlated with the survival of BC patients, and therefore may have potential diagnostic/prognostic values.