Quantifiable peptide library bridges the gap for proteomics based biomarker discovery and validation on breast cancer.

Mass spectrometry (MS) based proteomics is widely used for biomarker discovery. However, often, most biomarker candidates from discovery are discarded during the validation processes. Such discrepancies between biomarker discovery and validation are caused by several factors, mainly due to the differences in analytical methodology and experimental conditions. Here, we generated a peptide library which allows discovery of biomarkers in the equal settings as the validation process, thereby making the transition from discovery to validation more robust and efficient. The peptide library initiated with a list of 3393 proteins detectable in the blood from public databases. For each protein, surrogate peptides favorable for detection in mass spectrometry was selected and synthesized. A total of 4683 synthesized peptides were spiked into neat serum and plasma samples to check their quantifiability in a 10 min liquid chromatography-MS/MS run time. This led to the PepQuant library, which is composed of 852 quantifiable peptides that cover 452 human blood proteins. Using the PepQuant library, we discovered 30 candidate biomarkers for breast cancer. Among the 30 candidates, nine biomarkers, FN1, VWF, PRG4, MMP9, CLU, PRDX6, PPBP, APOC1, and CHL1 were validated. By combining the quantification values of these markers, we generated a machine learning model predicting breast cancer, showing an average area under the curve of 0.9105 for the receiver operating characteristic curve.

Accurate Prediction of y Ions in Beam-Type Collision-Induced Dissociation Using Deep Learning.

Peptide fragmentation spectra contain critical information for the identification of peptides by mass spectrometry. In this study, we developed an algorithm that more accurately predicts the high-intensity peaks among the peptide spectra. The training data are composed of 180,833 peptides from the National Institute of Standards and Technology and Proteomics Identification database, which were fragmented by either quadrupole time-of-flight or triple-quadrupole collision-induced dissociation methods. Exploratory analysis of the peptide fragmentation pattern was focused on the highest intensity peaks that showed proline, peptide length, and a sliding window of four amino acid combination that can be exploited as key features. The amino acid sequence of each peptide and each of the key features were allocated to different layers of the model, where recurrent neural network, convolutional neural network, and fully connected neural network were used. The trained model, PrAI-frag, accurately predicts the fragmentation spectra compared to previous machine learning-based prediction algorithms. The model excels at high-intensity peak prediction, which is advantageous to selective/multiple reaction monitoring application. PrAI-frag is provided via a Web server which can be used for peptides of length 6-15.

Characterization of Endoplasmic Reticulum (ER) in Human Pluripotent Stem Cells Revealed Increased Susceptibility to Cell Death upon ER Stress.

Human pluripotent stem cells (hPSCs), such as embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs), have a well-orchestrated program for differentiation and self-renewal. However, the structural features of unique proteostatic-maintaining mechanisms in hPSCs and their features, distinct from those of differentiated cells, in response to cellular stress remain unclear. We evaluated and compared the morphological features and stress response of hPSCs and fibroblasts. Compared to fibroblasts, electron microscopy showed simpler/fewer structures with fewer networks in the endoplasmic reticulum (ER) of hPSCs, as well as lower expression of ER-related genes according to meta-analysis. As hPSCs contain low levels of binding immunoglobulin protein (BiP), an ER chaperone, thapsigargin treatment sharply increased the gene expression of the unfolded protein response. Thus, hPSCs with decreased chaperone function reacted sensitively to ER stress and entered apoptosis faster than fibroblasts. Such ER stress-induced apoptotic processes were abolished by tauroursodeoxycholic acid, an ER-stress reliever. Hence, our results revealed that as PSCs have an underdeveloped structure and express fewer BiP chaperone proteins than somatic cells, they are more susceptible to ER stress-induced apoptosis in response to stress.

Elucidation of the bacterial communities associated with the harmful microalgae Alexandrium tamarense and Cochlodinium polykrikoides using nanopore sequencing.

Interactions between microalgae and bacteria are often obligatory for harmful algal blooms (HABs). Here, we investigated the specific bacterial communities associated with Alexandrium tamarense and Cochlodinium polykrikoides, which cause ecological and economic damage during their blooms. To this end, the bacterial metagenome was selectively isolated from the two dinoflagellates and subsequently used for 16S rRNA analysis via the Nanopore MinION and Illumina sequencing platforms. Although the full-length 16S rRNA reads from the MinION platform showed high correlation in higher taxonomic ranks to the partial-length 16S rRNA reads from the Illumina platform, there was less correlation at the genus and species levels. MinION reads that are similar in the V3-V4 hypervariable regions with Illumina reads are classified to different taxonomies due to the extra information encoded in the full-length 16S rRNA reads. This indicates that bias arising from the short length Illumina reads can be supplemented by MinION reads. Furthermore, integrated analysis of the Illumina and MinION data showed that A. tamarense was predominantly enriched in the Roseobacter clade and C. polykrikoides was enriched in Gammaproteobacteria and Alphaproteobacteria. These results suggest that the association of different bacterial communities with A. tamarense and C. polykrikoides may be required for HABs.

Genome-wide transcriptome analysis revealed organelle specific responses to temperature variations in algae.

Temperature is a critical environmental factor that affects microalgal growth. However, microalgal coping mechanisms for temperature variations are unclear. Here, we determined changes in transcriptome, total carbohydrate, total fatty acid methyl ester, and fatty acid composition of Tetraselmis sp. KCTC12432BP, a strain with a broad temperature tolerance range, to elucidate the tolerance mechanisms in response to large temperature variations. Owing to unavailability of genome sequence information, de novo transcriptome assembly coupled with BLAST analysis was performed using strand specific RNA-seq data. This resulted in 26,245 protein-coding transcripts, of which 83.7% could be annotated to putative functions. We identified more than 681 genes differentially expressed, suggesting an organelle-specific response to temperature variation. Among these, the genes related to the photosynthetic electron transfer chain, which are localized in the plastid thylakoid membrane, were upregulated at low temperature. However, the transcripts related to the electron transport chain and biosynthesis of phosphatidylethanolamine localized in mitochondria were upregulated at high temperature. These results show that the low energy uptake by repressed photosynthesis under low and high temperature conditions is compensated by different mechanisms, including photosystem I and mitochondrial oxidative phosphorylation, respectively. This study illustrates that microalgae tolerate different temperature conditions through organelle specific mechanisms.

Rational Protein Engineering Guided by Deep Mutational Scanning.

Sequence-function relationship in a protein is commonly determined by the three-dimensional protein structure followed by various biochemical experiments. However, with the explosive increase in the number of genome sequences, facilitated by recent advances in sequencing technology, the gap between protein sequences available and three-dimensional structures is rapidly widening. A recently developed method termed deep mutational scanning explores the functional phenotype of thousands of mutants via massive sequencing. Coupled with a highly efficient screening system, this approach assesses the phenotypic changes made by the substitution of each amino acid sequence that constitutes a protein. Such an informational resource provides the functional role of each amino acid sequence, thereby providing sufficient rationale for selecting target residues for protein engineering. Here, we discuss the current applications of deep mutational scanning and consider experimental design.

Elucidation of the growth delimitation of Dunaliella tertiolecta under nitrogen stress by integrating transcriptome and peptidome analysis.

Dunaliella tertiolecta LB 999 is an oleaginous microalgae species that produces large quantities of lipid and starch during nitrogen starvation; however, nitrogen starvation also limits the cell growth. In order to understand the underlying mechanisms of this phenomenon, the transcriptome and peptidome of D. tertiolecta LB 999 grown under different nitrogen and light conditions were analyzed. Integration of the de novo assembly of transcriptome sequencing reads with peptidome analysis revealed 13,861 protein-coding transcripts, including 33 transcripts whose expression patterns were significantly altered along with the growth phenotypes. Interestingly, 21 of these genes, which were highly enriched in the plastid region, were associated with chlorophyll synthesis and tetrahydrofolate-mediated C1 metabolism. Furthermore, intracellular glutamate levels are predicted to be the main factor that acts as a switch for the regulation of cell growth and carbon accumulation. These data provide the genetic information of D. tertiolecta for its future applications.

Exploring the functional residues in a flavin-binding fluorescent protein using deep mutational scanning.

Flavin mononucleotide (FMN)-based fluorescent proteins are versatile reporters that can monitor various cellular processes in both aerobic and anaerobic conditions. However, the understanding of the role of individual amino acid residues on the protein function has been limited and has restricted the development of better functional variants. Here we examine the functional amino acid residues of Escherichia coli flavin mononucleotide binding fluorescent protein (EcFbFP) using the application of high-throughput sequencing of functional variants, termed deep mutational scanning. The variants were classified into 329 function-retained (FR) and 259 function-loss (FL) mutations, and further the mutational enrichment in each amino acid residues was weighed to find the functionally important residues of EcFbFP. We show that the crucial amino acid residues of EcFbFP lie among the FMN-binding pocket, turns and loops of the protein where conformation changes occur, and spatially clustered residues near the E56-K97 salt bridges. In addition, the mutational sensitivity of the critical residues was confirmed by site-directed mutagenesis. The deep mutational scanning of EcFbFP has demonstrated important implications for constructing better functioning protein variants.