Optimizing differential expression analysis for proteomics data via high-performing rules and ensemble inference

Identification of differentially expressed proteins in a proteomics workflow typically encompasses five key steps: raw data quantification, expression matrix construction, matrix normalization, missing value imputation (MVI), and differential expression analysis. The plethora of options in each step makes it challenging to identify optimal workflows that maximize the identification of differentially expressed proteins. To identify optimal workflows and their common properties, we conduct an extensive study involving 34,576 combinatoric experiments on 24 gold standard spike-in datasets. Applying frequent pattern mining techniques to top-ranked workflows, we uncover high-performing rules that demonstrate optimality has conserved properties. Via machine learning, we confirm optimal workflows are indeed predictable, with average cross-validation F1 scores and Matthew's correlation coefficients surpassing 0.84. We introduce an ensemble inference to integrate results from individual top-performing workflows for expanding differential proteome coverage and resolve inconsistencies. Ensemble inference provides gains in pAUC (up to 4.61%) and G-mean (up to 11.14%) and facilitates effective aggregation of information across varied quantification approaches such as topN, directLFQ, MaxLFQ intensities, and spectral counts. However, further development and evaluation are needed to establish acceptable frameworks for conducting ensemble inference on multiple proteomics workflows.

Metabolic Bypass Rescues Aberrant S-nitrosylation-Induced TCA Cycle Inhibition and Synapse Loss in Alzheimer's Disease Human Neurons

In Alzheimer's disease (AD), dysfunctional mitochondrial metabolism is associated with synaptic loss, the major pathological correlate of cognitive decline. Mechanistic insight for this relationship, however, is still lacking. Here, comparing isogenic wild-type and AD mutant human induced pluripotent stem cell (hiPSC)-derived cerebrocortical neurons (hiN), evidence is found for compromised mitochondrial energy in AD using the Seahorse platform to analyze glycolysis and oxidative phosphorylation (OXPHOS). Isotope-labeled metabolic flux experiments revealed a major block in activity in the tricarboxylic acid (TCA) cycle at the α-ketoglutarate dehydrogenase (αKGDH)/succinyl coenzyme-A synthetase step, metabolizing α-ketoglutarate to succinate. Associated with this block, aberrant protein S-nitrosylation of αKGDH subunits inhibited their enzyme function. This aberrant S-nitrosylation is documented not only in AD-hiN but also in postmortem human AD brains versus controls, as assessed by two separate unbiased mass spectrometry platforms using both SNOTRAP identification of S-nitrosothiols and chemoselective-enrichment of S-nitrosoproteins. Treatment with dimethyl succinate, a cell-permeable derivative of a TCA substrate downstream to the block, resulted in partial rescue of mitochondrial bioenergetic function as well as reversal of synapse loss in AD-hiN. These findings have therapeutic implications that rescue of mitochondrial energy metabolism can ameliorate synaptic loss in hiPSC-based models of AD.

ARRDC5 deficiency impairs spermatogenesis by affecting SUN5 and NDC1

Sperm with normal morphology and motility are essential for successful fertilization, and the strong attachment of the sperm head-tail coupling apparatus to the nuclear envelope during spermatogenesis is required to ensure the integrity of sperm for capacitation and fertilization. Here, we report that Arrdc5 is associated with spermatogenesis. The Arrdc5 knockout mouse model showed male infertility characterized by a high bent-head rate and reduced motility in sperm, which led to capacitation defects and subsequent fertilization failure. Through mass spectrometry, we found that ARRDC5 affects spermatogenesis by affecting NDC1 and SUN5. We further found that ARRDC5 might affect the vesicle-trafficking protein SEC22A-mediated transport and localization of NDC1, SUN5 and other head-tail coupling apparatus-related proteins that are responsible for initiating the attachment of the sperm head and tail. We finally performed intracytoplasmic sperm injection as a way to explore therapeutic strategies. Our findings demonstrate the essential role and the underlying molecular mechanism of ARRDC5 in anchoring the sperm head to the tail during spermatogenesis.

Baldur: Bayesian Hierarchical Modeling for Label-Free Proteomics with Gamma Regressing Mean-Variance Trends

Label-free proteomics is a fast-growing methodology to infer abundances in mass spectrometry proteomics. Extensive research has focused on spectral quantification and peptide identification. However, research toward modeling and understanding quantitative proteomics data is scarce. Here we propose a Bayesian hierarchical decision model (Baldur) to test for differences in means between conditions for proteins, peptides, and post-translational modifications. We developed a Bayesian regression model to characterize local mean-variance trends in data, to estimate measurement uncertainty and hyperparameters for the decision model. A key contribution is the development of a new gamma regression model that describes the mean-variance dependency as a mixture of a common and a latent trend-allowing for localized trend estimates. We then evaluate the performance of Baldur, limma-trend, and t test on six benchmark datasets: five total proteomics and one post-translational modification dataset. We find that Baldur drastically improves the decision in noisier post-translational modification data over limma-trend and t test. In addition, we see significant improvements using Baldur over the other methods in the total proteomics datasets. Finally, we analyzed Baldur's performance when increasing the number of replicates and found that the method always increases precision with sample size, while showing robust control of the false positive rate. We conclude that our model vastly improves over popular data analysis methods (limma-trend and t test) in several spike-in datasets by achieving a high true positive detection rate, while greatly reducing the false-positive rate.

Benefits of FAIMS to Improve the Proteome Coverage of Deteriorated and/or Cross-Linked TMT 10-Plex FFPE Tissue and Plasma-Derived Exosomes Samples

The proteome characterization of complex, deteriorated, or cross-linked protein mixtures as paired clinical FFPE or exosome samples isolated from low plasma volumes (250 µL) might be a challenge. In this work, we aimed at investigating the benefits of FAIMS technology coupled to the Orbitrap Exploris 480 mass spectrometer for the TMT quantitative proteomics analyses of these complex samples in comparison to the analysis of protein extracts from cells, frozen tissue, and exosomes isolated from large volume plasma samples (3 mL). TMT experiments were performed using a two-hour gradient LC-MS/MS with or without FAIMS and two compensation voltages (CV = -45 and CV = -60). In the TMT experiments of cells, frozen tissue, or exosomes isolated from large plasma volumes (3 mL) with FAIMS, a limited increase in the number of identified and quantified proteins accompanied by a decrease in the number of peptides identified and quantified was observed. However, we demonstrated here a noticeable improvement (>100%) in the number of peptide and protein identifications and quantifications for the plasma exosomes isolated from low plasma volumes (250 µL) and FFPE tissue samples in TMT experiments with FAIMS in comparison to the LC-MS/MS analysis without FAIMS. Our results highlight the potential of mass spectrometry analyses with FAIMS to increase the depth into the proteome of complex samples derived from deteriorated, cross-linked samples and/or those where the material was scarce, such as FFPE and plasma-derived exosomes from low plasma volumes (250 µL), which might aid in the characterization of their proteome and proteoforms and in the identification of dysregulated proteins that could be used as biomarkers.

S-Nitrosylation-mediated dysfunction of TCA cycle enzymes in synucleinopathy studied in postmortem human brains and hiPSC-derived neurons

A causal relationship between mitochondrial metabolic dysfunction and neurodegeneration has been implicated in synucleinopathies, including Parkinson disease (PD) and Lewy body dementia (LBD), but underlying mechanisms are not fully understood. Here, using human induced pluripotent stem cell (hiPSC)-derived neurons with mutation in the gene encoding α-synuclein (αSyn), we report the presence of aberrantly S-nitrosylated proteins, including tricarboxylic acid (TCA) cycle enzymes, resulting in activity inhibition assessed by carbon-labeled metabolic flux experiments. This inhibition principally affects α-ketoglutarate dehydrogenase/succinyl coenzyme-A synthetase, metabolizing α-ketoglutarate to succinate. Notably, human LBD brain manifests a similar pattern of aberrantly S-nitrosylated TCA enzymes, indicating the pathophysiological relevance of these results. Inhibition of mitochondrial energy metabolism in neurons is known to compromise dendritic length and synaptic integrity, eventually leading to neuronal cell death. Our evidence indicates that aberrant S-nitrosylation of TCA cycle enzymes contributes to this bioenergetic failure.

Multi-omics profiling of CSF from spinal muscular atrophy type 3 patients after nusinersen treatment: a 2-year follow-up multicenter retrospective study

Spinal muscular atrophy (SMA) is a neurodegenerative disorder caused by mutations in the SMN1 gene resulting in reduced levels of the SMN protein. Nusinersen, the first antisense oligonucleotide (ASO) approved for SMA treatment, binds to the SMN2 gene, paralogue to SMN1, and mediates the translation of a functional SMN protein. Here, we used longitudinal high-resolution mass spectrometry (MS) to assess both global proteome and metabolome in cerebrospinal fluid (CSF) from ten SMA type 3 patients, with the aim of identifying novel readouts of pharmacodynamic/response to treatment and predictive markers of treatment response. Patients had a median age of 33.5 [29.5; 38.25] years, and 80% of them were ambulant at time of the enrolment, with a median HFMSE score of 37.5 [25.75; 50.75]. Untargeted CSF proteome and metabolome were measured using high-resolution MS (nLC-HRMS) on CSF samples obtained before treatment (T0) and after 2 years of follow-up (T22). A total of 26 proteins were found to be differentially expressed between T0 and T22 upon VSN normalization and LIMMA differential analysis, accounting for paired replica. Notably, key markers of the insulin-growth factor signaling pathway were upregulated after treatment together with selective modulation of key transcription regulators. Using CombiROC multimarker signature analysis, we suggest that detecting a reduction of SEMA6A and an increase of COL1A2 and GRIA4 might reflect therapeutic efficacy of nusinersen. Longitudinal metabolome profiling, analyzed with paired t-Test, showed a significant shift for some aminoacid utilization induced by treatment, whereas other metabolites were largely unchanged. Together, these data suggest perturbation upon nusinersen treatment still sustained after 22 months of follow-up and confirm the utility of CSF multi-omic profiling as pharmacodynamic biomarker for SMA type 3. Nonetheless, validation studies are needed to confirm this evidence in a larger sample size and to further dissect combined markers of response to treatment.

Deoxyhypusine synthase mutations alter the post-translational modification of eukaryotic initiation factor 5A resulting in impaired human and mouse neural homeostasis

DHPS deficiency is a rare genetic disease caused by biallelic hypomorphic variants in the Deoxyhypusine synthase (DHPS) gene. The DHPS enzyme functions in mRNA translation by catalyzing the post-translational modification, and therefore activation, of eukaryotic initiation factor 5A (eIF5A). The observed clinical outcomes associated with human mutations in DHPS include developmental delay, intellectual disability, and seizures. Therefore, to increase our understanding of this rare disease, it is critical to determine the mechanisms by which mutations in DHPS alter neurodevelopment. In this study, we have generated patient-derived lymphoblast cell lines and demonstrated that human DHPS variants alter DHPS protein abundance and impair enzyme function. Moreover, we observe a shift in the abundance of the post-translationally modified forms of eIF5A; specifically, an increase in the nuclear localized acetylated form (eIF5A^AcK47) and concomitant decrease in the cytoplasmic localized hypusinated form (eIF5A^HYP). Generation and characterization of a mouse model with a genetic deletion of Dhps in the brain at birth shows that loss of hypusine biosynthesis impacts neuronal function due to impaired eIF5A^HYP-dependent mRNA translation; this translation defect results in altered expression of proteins required for proper neuronal development and function. This study reveals new insight into the biological consequences and molecular impact of human DHPS deficiency and provides valuable information toward the goal of developing treatment strategies for this rare disease.

Proteomic analysis of the initial wake up of vibrio splendidus persister cells

Vibrio splendidus is a ubiquitous pathogen that causes various diseases in aquaculture with a wide range of hosts. In our previous studies, we showed that L-glutamic acid was the optimal carbon source that could revive V. splendidus persister cells. In our present study, single cell observation under microscopy showed that V. splendidus could revive using L-glutamic acid as carbon source. A proteomic analysis was carried out to further illustrate the initial wake up of persister cells with L-glutamic acid. To collect the initially revived cells, SDS-PAGE was used to determine the revived time. The total proteins from the persister cells and the revived cells were analyzed using LC‒MS/MS. A total of 106 proteins, including 42 downregulated proteins and 64 upregulated proteins, were identified. GO analysis of the differentially expressed proteins (DEPs) showed that biological processes, including protein complex assembly, protein oligomerization, and arginine metabolism; cellular components, including extracellular membrane, plasma membrane and ribosome; and molecular functions, including the activities of arginine binding and structural constituent of ribosome, were enriched. KEGG analysis showed that lipopolysaccharide biosynthesis, porphyrin and chlorophyll metabolism, and peptidoglycan biosynthesis were upregulated, while the ribosome was downregulated. This is the first time to study the initial wake up of persister cells based on proteomic analysis, and the results revealed the main pathways involved in the early resuscitation of V. splendidus persister cells.

MKRN2 knockout causes male infertility through decreasing STAT1, SIX4, and TNC expression

Makorin-2 (Mkrn2) is an evolutionarily conserved gene whose biological functions are not fully known. Although recent studies have shed insights on the potential causes of male infertility, its underlining mechanisms still remain to be elucidated. We developed a Mrkn2 knockout mice model to study this gene and found that deletion of Mkrn2 in mice led to male infertility. Interestingly, the expression level of signal transducer and activator of the transcription (STAT)1 was significantly decreased in MKRN2 knockout testis and MEF cells. Co-IP assay showed an interaction between MKRN2 and STAT1. Moreover, our results further indicated that MKRN2 regulated the expression level of SIX4 and tenascin C (TNC) via the EBF transcription factor 2 (EBF2) in mice. The results of our study will provide insights into a new mechanism of male infertility.

Mechanistic insight into female predominance in Alzheimer's disease based on aberrant protein S-nitrosylation of C3

Protein S-nitros(yl)ation (SNO) is a posttranslational modification involved in diverse processes in health and disease and can contribute to synaptic damage in Alzheimer's disease (AD). To identify SNO proteins in AD brains, we used triaryl phosphine (SNOTRAP) combined with mass spectrometry (MS). We detected 1449 SNO proteins with 2809 SNO sites, representing a wide range of S-nitrosylated proteins in 40 postmortem AD and non-AD human brains from patients of both sexes. Integrative protein ranking revealed the top 10 increased SNO proteins, including complement component 3 (C3), p62 (SQSTM1), and phospholipase D3. Increased levels of S-nitrosylated C3 were present in female over male AD brains. Mechanistically, we show that formation of SNO-C3 is dependent on falling β-estradiol levels, leading to increased synaptic phagocytosis and thus synapse loss and consequent cognitive decline. Collectively, we demonstrate robust alterations in the S-nitrosoproteome that contribute to AD pathogenesis in a sex-dependent manner.

Proteomic and miRNA Profiles of Exosomes Derived from Myometrial Tissue in Laboring Women

Myometrial contraction is essential for successful delivery. Recent studies have highlighted the vital roles of tissue-derived exosomes in disease diagnostic, prognostic, and therapeutic applications; however, the characteristics of uterine myometrium-derived exosomes are unclear. Here, we successfully isolated exosomes from myometrial tissues, human myometrial smooth muscle cells (HMSMCs), and human umbilical vein endothelial cells (HUVECs), then performed quantitative liquid chromatography-tandem mass spectrometry and miRNA sequencing to investigate the cargo of the exosomes. Fifty-two proteins and five miRNAs were differentially expressed (DE) in term non-labor and term labor myometrium-derived exosomes. Among them, seven proteins (SERPINE1, THBS1, MGAT1, VIM, FGB, FGG, and VWF) were differentially expressed both in the myometrial exosomes and tissues, three miRNAs (miR-363-3p, miR-203a-3p, and miR-205-5p) target 13 DE genes. The top three miRNA derived from HMSMCs (miR-125b-1-3p, miR-337-5p, and miR-503-5p) and HUVECs (miR-663a, miR-4463, and miR-3622a-5p) were identified. Two proteins, GJA1 and SLC39A14, exist in female blood exosomes and are highly expressed in HMSMCs exosomes, are also upregulated in the laboring myometrium, which verified increased in laboring blood samples, might be novel potential biomarkers for myometrial activation. The proteomic and miRNA profile of exosomes derived from laboring myometrium revealed some molecules in the exosomes that affect the intercellular communication and the function of the myometrium.

Phosphoproteomic mapping reveals distinct signaling actions and activation of muscle protein synthesis by Isthmin-1

The secreted protein isthmin-1 (Ism1) mitigates diabetes by increasing adipocyte and skeletal muscle glucose uptake by activating the PI3K-Akt pathway. However, while both Ism1 and insulin converge on these common targets, Ism1 has distinct cellular actions suggesting divergence in downstream intracellular signaling pathways. To understand the biological complexity of Ism1 signaling, we performed phosphoproteomic analysis after acute exposure, revealing overlapping and distinct pathways of Ism1 and insulin. We identify a 53% overlap between Ism1 and insulin signaling and Ism1-mediated phosphoproteome-wide alterations in ~450 proteins that are not shared with insulin. Interestingly, we find several unknown phosphorylation sites on proteins related to protein translation, mTOR pathway, and, unexpectedly, muscle function in the Ism1 signaling network. Physiologically, Ism1 ablation in mice results in altered proteostasis, including lower muscle protein levels under fed and fasted conditions, reduced amino acid incorporation into proteins, and reduced phosphorylation of the key protein synthesis effectors Akt and downstream mTORC1 targets. As metabolic disorders such as diabetes are associated with accelerated loss of skeletal muscle protein content, these studies define a non-canonical mechanism by which this antidiabetic circulating protein controls muscle biology.

A proteomic and RNA-seq transcriptomic dataset of capsaicin-aggravated mouse chronic colitis model

An inappropriate diet is a risk factor for inflammatory bowel disease (IBD). It is established that the consumption of spicy food containing capsaicin is strongly associated with the recurrence and worsening of IBD symptoms. Moreover, capsaicin can induce neutrophil accumulation in the lamina propria, contributing to disease deterioration. To uncover the potential signaling pathway involved in capsaicin-induced relapse and the effects of capsaicin on neutrophil activation, we performed proteomic analyses of intestinal tissues from chronic colitis mice following capsaicin administration and transcriptomic analyses of dHL-60 cells after capsaicin stimulation. Collectively, these multiomic analyses identified proteins and genes that may be involved in disease flares, thereby providing new insights for future research.

Differential proteomic analysis of plasma-derived exosomes as diagnostic biomarkers for chronic HBV-related liver disease

Hepatitis B virus (HBV) infection is still a major public health problem worldwide. We aimed to identify new, non-invasive biomarkers for the early diagnosis of chronic HBV-related diseases, reveal alterations in the progression of chronic hepatitis B (CHB), liver cirrhosis (LC), and hepatocellular carcinoma (HCC). Here, exosomes were isolated and characterized through size exclusion chromatography and nanoparticle tracking analysis. Profiles of differentially expressed proteins (DEPs) were analyzed through liquid chromatography-tandem mass spectrometry (LC–MS/MS), Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes analyses. Results showed that the DEPs, including CO9, LBP, SVEP1, and VWF levels in extracellular vesicles (EVs) were significantly higher in CHB than in healthy controls (HCs). VWF expression levels in EVs were significantly lower in CHB than in those with LC. KV311 expression levels in EVs were significantly higher, whereas LBP levels were significantly lower in patients with CHB than in those with HCC. All biomarkers seemed to exhibit a high diagnostic capacity for HBV-related liver disease. Patients with HBV-induced chronic liver disease exhibit characteristic protein profiles in their EVs. Thus, serum exosomes may be used as novel, liquid biopsy biomarkers to provide useful clinical information for the diagnosis of HBV-related liver diseases at different stages.

Proteomics analysis of chronic skin injuries caused by mustard gas

Sulfur mustard (SM) is an alkylating and forming chemical that was widely used by Iraqi forces during the Iran–Iraq wars. One of the target organs of SM is the skin. Understanding the mechanisms involved in the pathogenesis of SM may help better identify complications and find appropriate treatments. The current study collected ten SM-exposed patients with long-term skin complications and ten healthy individuals. Proteomics experiments were performed using the high-efficiency TMT10X method to evaluate the skin protein profile, and statistical bioinformatics methods were used to identify the differentially expressed proteins. One hundred twenty-nine proteins had different expressions between the two groups. Of these 129 proteins, 94 proteins had increased expression in veterans' skins, while the remaining 35 had decreased expression. The hub genes included RPS15, ACTN1, FLNA, HP, SDHC, and RPL29, and three modules were extracted from the PPI network analysis. Skin SM exposure can lead to oxidative stress, inflammation, apoptosis, and cell proliferation.

ImShot: An Open-Source Software for Probabilistic Identification of Proteins In Situ and Visualization of Proteomics Data

Imaging mass spectrometry (IMS) has developed into a powerful tool allowing label-free detection of numerous biomolecules in situ. In contrast to shotgun proteomics, proteins/peptides can be detected directly from biological tissues and correlated to its morphology leading to a gain of crucial clinical information. However, direct identification of the detected molecules is currently challenging for MALDI-IMS, thereby compelling researchers to use complementary techniques and resource intensive experimental setups. Despite these strategies, sufficient information could not be extracted because of lack of an optimum data combination strategy/software. Here, we introduce a new open-source software ImShot that aims at identifying peptides obtained in MALDI-IMS. This is achieved by combining information from IMS and shotgun proteomics (LC-MS) measurements of serial sections of the same tissue. The software takes advantage of a two-group comparison to determine the search space of IMS masses after deisotoping the corresponding spectra. Ambiguity in annotations of IMS peptides is eliminated by introduction of a novel scoring system that identifies the most likely parent protein of a detected peptide in the corresponding IMS dataset. Thanks to its modular structure, the software can also handle LC-MS data separately and display interactive enrichment plots and enriched Gene Ontology terms or cellular pathways. The software has been built as a desktop application with a conveniently designed graphic user interface to provide users with a seamless experience in data analysis. ImShot can run on all the three major desktop operating systems and is freely available under Massachusetts Institute of Technology license.

Proteomics and Phosphoproteomics of Circulating Extracellular Vesicles Provide New Insights into Diabetes Pathobiology

The purpose of this study was to define the proteomic and phosphoproteomic landscape of circulating extracellular vesicles (EVs) in people with normal glucose tolerance (NGT), prediabetes (PDM), and diabetes (T2DM). Archived serum samples from 30 human subjects (n = 10 per group, ORIGINS study, NCT02226640) were used. EVs were isolated using EVtrap®. Mass spectrometry-based methods were used to detect the global EV proteome and phosphoproteome. Differentially expressed features, correlation, enriched pathways, and enriched tissue-specific protein sets were identified using custom R scripts. Phosphosite-centric analyses were conducted using directPA and PhosR software packages. A total of 2372 unique EV proteins and 716 unique EV phosphoproteins were identified among all samples. Unsupervised clustering of the differentially expressed (fold change ≥ 2, p < 0.05, FDR < 0.05) proteins and, particularly, phosphoproteins showed excellent discrimination among the three groups. CDK1 and PKCδ appear to drive key upstream phosphorylation events that define the phosphoproteomic signatures of PDM and T2DM. Circulating EVs from people with diabetes carry increased levels of specific phosphorylated kinases (i.e., AKT1, GSK3B, LYN, MAP2K2, MYLK, and PRKCD) and could potentially distribute activated kinases systemically. Among characteristic changes in the PDM and T2DM EVs, “integrin switching” appeared to be a central feature. Proteins involved in oxidative phosphorylation (OXPHOS), known to be reduced in various tissues in diabetes, were significantly increased in EVs from PDM and T2DM, which suggests that an abnormally elevated EV-mediated secretion of OXPHOS components may underlie the development of diabetes. A highly enriched signature of liver-specific markers among the downregulated EV proteins and phosphoproteins in both PDM and T2DM groups was also detected. This suggests that an alteration in liver EV composition and/or secretion may occur early in prediabetes. This study identified EV proteomic and phosphoproteomic signatures in people with prediabetes and T2DM and provides novel insight into the pathobiology of diabetes.

Benchmarking of analysis strategies for data-independent acquisition proteomics using a large-scale dataset comprising inter-patient heterogeneity

Numerous software tools exist for data-independent acquisition (DIA) analysis of clinical samples, necessitating their comprehensive benchmarking. We present a benchmark dataset comprising real-world inter-patient heterogeneity, which we use for in-depth benchmarking of DIA data analysis workflows for clinical settings. Combining spectral libraries, DIA software, sparsity reduction, normalization, and statistical tests results in 1428 distinct data analysis workflows, which we evaluate based on their ability to correctly identify differentially abundant proteins. From our dataset, we derive bootstrap datasets of varying sample sizes and use the whole range of bootstrap datasets to robustly evaluate each workflow. We find that all DIA software suites benefit from using a gas-phase fractionated spectral library, irrespective of the library refinement used. Gas-phase fractionation-based libraries perform best against two out of three reference protein lists. Among all investigated statistical tests non-parametric permutation-based statistical tests consistently perform best.

Data independent acquisition (DIA) has been gaining momentum in clinical proteomics. Here, the authors create a benchmark dataset comprising inter-patient heterogeneity to compare popular DIA data analysis workflows for identifying differentially abundant proteins.

POSREG: proteomic signature discovered by simultaneously optimizing its reproducibility and generalizability

Mass spectrometry-based proteomic technique has become indispensable in current exploration of complex and dynamic biological processes. Instrument development has largely ensured the effective production of proteomic data, which necessitates commensurate advances in statistical framework to discover the optimal proteomic signature. Current framework mainly emphasizes the generalizability of the identified signature in predicting the independent data but neglects the reproducibility among signatures identified from independently repeated trials on different sub-dataset. These problems seriously restricted the wide application of the proteomic technique in molecular biology and other related directions. Thus, it is crucial to enable the generalizable and reproducible discovery of the proteomic signature with the subsequent indication of phenotype association. However, no such tool has been developed and available yet. Herein, an online tool, POSREG, was therefore constructed to identify the optimal signature for a set of proteomic data. It works by (i) identifying the proteomic signature of good reproducibility and aggregating them to ensemble feature ranking by ensemble learning, (ii) assessing the generalizability of ensemble feature ranking to acquire the optimal signature and (iii) indicating the phenotype association of discovered signature. POSREG is unique in its capacity of discovering the proteomic signature by simultaneously optimizing its reproducibility and generalizability. It is now accessible free of charge without any registration or login requirement at https://idrblab.org/posreg/.

Gene Co-Expression Analysis of Human RNASEH2A Reveals Functional Networks Associated with DNA Replication, DNA Damage Response, and Cell Cycle Regulation

Simple Summary

RNASEH2A is the catalytic subunit of the ribonuclease (RNase) H2 ternary complex that plays an important role in maintaining DNA stability in cells. Recent studies have shown that the RNASEH2A subunit alone is highly expressed in certain cancer cell types. Via a series of bioinformatics approaches, we found that RNASEH2A is highly expressed in human proliferative tissues and many cancers. Our analyses reveal a possible involvement of RNASEH2A in cell cycle regulation in addition to its well established role in DNA replication and DNA repair. Our findings underscore that RNASEH2A could serve as a biomarker for cancer diagnosis and a therapeutic target.

Abstract

Ribonuclease (RNase) H2 is a key enzyme for the removal of RNA found in DNA-RNA hybrids, playing a fundamental role in biological processes such as DNA replication, telomere maintenance, and DNA damage repair. RNase H2 is a trimer composed of three subunits, RNASEH2A being the catalytic subunit. RNASEH2A expression levels have been shown to be upregulated in transformed and cancer cells. In this study, we used a bioinformatics approach to identify RNASEH2A co-expressed genes in different human tissues to underscore biological processes associated with RNASEH2A expression. Our analysis shows functional networks for RNASEH2A involvement such as DNA replication and DNA damage response and a novel putative functional network of cell cycle regulation. Further bioinformatics investigation showed increased gene expression in different types of actively cycling cells and tissues, particularly in several cancers, supporting a biological role for RNASEH2A but not for the other two subunits of RNase H2 in cell proliferation. Mass spectrometry analysis of RNASEH2A-bound proteins identified players functioning in cell cycle regulation. Additional bioinformatic analysis showed that RNASEH2A correlates with cancer progression and cell cycle related genes in Cancer Cell Line Encyclopedia (CCLE) and The Cancer Genome Atlas (TCGA) Pan Cancer datasets and supported our mass spectrometry findings.

High-throughput proteomics of breast cancer interstitial fluid: identification of tumor subtype-specific serologically relevant biomarkers

Despite significant advancements in breast cancer (BC) research, clinicians lack robust serological protein markers for accurate diagnostics and tumor stratification. Tumor interstitial fluid (TIF) accumulates aberrantly externalized proteins within the local tumor space, which can potentially gain access to the circulatory system. As such, TIF may represent a valuable starting point for identifying relevant tumor-specific serological biomarkers. The aim of the study was to perform comprehensive proteomic profiling of TIF to identify proteins associated with BC tumor status and subtype. A liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis of 35 TIFs of three main subtypes: luminal (19), Her2 (4), and triple-negative (TNBC) (12) resulted in the identification of > 8800 proteins. Unsupervised hierarchical clustering segregated the TIF proteome into two major clusters, luminal and TNBC/Her2 subgroups. High-grade tumors enriched with tumor infiltrating lymphocytes (TILs) were also stratified from low-grade tumors. A consensus analysis approach, including differential abundance analysis, selection operator regression, and random forest returned a minimal set of 24 proteins associated with BC subtypes, receptor status, and TIL scoring. Among them, a panel of 10 proteins, AGR3, BCAM, CELSR1, MIEN1, NAT1, PIP4K2B, SEC23B, THTPA, TMEM51, and ULBP2, was found to stratify the tumor subtype-specific TIFs. In particular, upregulation of BCAM and CELSR1 differentiates luminal subtypes, while upregulation of MIEN1 differentiates Her2 subtypes. Immunohistochemistry analysis showed a direct correlation between protein abundance in TIFs and intratumor expression levels for all 10 proteins. Sensitivity and specificity were estimated for this protein panel by using an independent, comprehensive breast tumor proteome dataset. The results of this analysis strongly support our data, with eight of the proteins potentially representing biomarkers for stratification of BC subtypes. Five of the most representative proteomics databases currently available were also used to estimate the potential for these selected proteins to serve as putative serological markers.

Proper imputation of missing values in proteomics datasets for differential expression analysis

Label-free shotgun proteomics is an important tool in biomedical research, where tandem mass spectrometry with data-dependent acquisition (DDA) is frequently used for protein identification and quantification. However, the DDA datasets contain a significant number of missing values (MVs) that severely hinders proper analysis. Existing literature suggests that different imputation methods should be used for the two types of MVs: missing completely at random or missing not at random. However, the simulated or biased datasets utilized by most of such studies offer few clues about the composition and thus proper imputation of MVs in real-life proteomic datasets. Moreover, the impact of imputation methods on downstream differential expression analysis-a critical goal for many biomedical projects-is largely undetermined. In this study, we investigated public DDA datasets of various tissue/sample types to determine the composition of MVs in them. We then developed simulated datasets that imitate the MV profile of real-life datasets. Using such datasets, we compared the impact of various popular imputation methods on the analysis of differentially expressed proteins. Finally, we make recommendations on which imputation method(s) to use for proteomic data beyond just DDA datasets.

Improved Integrated Whole Proteomic and Phosphoproteomic Profiles of Severe Acute Pancreatitis

Severe acute pancreatitis (SAP) is caused by complicated biological factors, and revealing its complex pathogenesis by single-target analysis is difficult. Systematic studies have developed slowly because extraction of degradable pancreatic proteins exposed to multiple proteases is challenging. We present integrated whole proteomic and phosphoproteomic profiles of SAP rats based on a modified protein extraction strategy with less protein degradation. Data-dependent acquisition (DDA) and data-independent acquisition (DIA) strategies were applied to select an appropriate method. Total 275 differentially expressed proteins and 757 differentially expressed phosphorylated proteins were identified by DIA-based quantitative proteomics. Several signal transduction pathways, including the AMPK, MAPK, and PI3K-Akt pathways, were enriched in SAP. Up-regulation of phosphorylated proteins involved in the process of TNFA signaling and inflammatory response was also detected in SAP. Our results improve the understanding of SAP development and progression.

CAncer bioMarker Prediction Pipeline (CAMPP)-A standardized framework for the analysis of quantitative biological data

With the improvement of -omics and next-generation sequencing (NGS) methodologies, along with the lowered cost of generating these types of data, the analysis of high-throughput biological data has become standard both for forming and testing biomedical hypotheses. Our knowledge of how to normalize datasets to remove latent undesirable variances has grown extensively, making for standardized data that are easily compared between studies. Here we present the CAncer bioMarker Prediction Pipeline (CAMPP), an open-source R-based wrapper (https://github.com/ELELAB/CAncer-bioMarker-Prediction-Pipeline -CAMPP) intended to aid bioinformatic software-users with data analyses. CAMPP is called from a terminal command line and is supported by a user-friendly manual. The pipeline may be run on a local computer and requires little or no knowledge of programming. To avoid issues relating to R-package updates, a renv .lock file is provided to ensure R-package stability. Data-management includes missing value imputation, data normalization, and distributional checks. CAMPP performs (I) k-means clustering, (II) differential expression/abundance analysis, (III) elastic-net regression, (IV) correlation and co-expression network analyses, (V) survival analysis, and (VI) protein-protein/miRNA-gene interaction networks. The pipeline returns tabular files and graphical representations of the results. We hope that CAMPP will assist in streamlining bioinformatic analysis of quantitative biological data, whilst ensuring an appropriate bio-statistical framework.

Proteins inform survival-based differences in patients with glioblastoma

BACKGROUND

Improving the care of patients with glioblastoma (GB) requires accurate and reliable predictors of patient prognosis. Unfortunately, while protein markers are an effective readout of cellular function, proteomics has been underutilized in GB prognostic marker discovery.

METHODS

For this study, GB patients were prospectively recruited and proteomics discovery using liquid chromatography-mass spectrometry analysis (LC-MS/MS) was performed for 27 patients including 13 short-term survivors (STS) (≤10 months) and 14 long-term survivors (LTS) (≥18 months).

RESULTS

Proteomics discovery identified 11 941 peptides in 2495 unique proteins, with 469 proteins exhibiting significant dysregulation when comparing STS to LTS. We verified the differential abundance of 67 out of these 469 proteins in a small previously published independent dataset. Proteins involved in axon guidance were upregulated in STS compared to LTS, while those involved in p53 signaling were upregulated in LTS. We also assessed the correlation between LS MS/MS data with RNAseq data from the same discovery patients and found a low correlation between protein abundance and mRNA expression. Finally, using LC-MS/MS on a set of 18 samples from 6 patients, we quantified the intratumoral heterogeneity of more than 2256 proteins in the multisample dataset.

CONCLUSIONS

These proteomic datasets and noted protein variations present a beneficial resource for better predicting patient outcome and investigating potential therapeutic targets.

Analysis of Virus and Host Proteomes During Productive HSV-1 and VZV Infection in Human Epithelial Cells

Herpes simplex virus 1 (HSV-1) and varicella-zoster virus (VZV) are two closely related human alphaherpesviruses that persistently infect most adults worldwide and cause a variety of clinically important diseases. Herpesviruses are extremely well adapted to their hosts and interact broadly with cellular proteins to regulate virus replication and spread. However, it is incompletely understood how HSV-1 and VZV interact with the host proteome during productive infection. This study determined the temporal changes in virus and host protein expression during productive HSV-1 and VZV infection in the same cell type. Results demonstrated the temporally coordinated expression of HSV-1 and VZV proteins in infected cells. Analysis of the host proteomes showed that both viruses affected extracellular matrix composition, transcription, RNA processing and cell division. Moreover, the prominent role of epidermal growth factor receptor (EGFR) signaling during productive HSV-1 and VZV infection was identified. Stimulation and inhibition of EGFR leads to increased and decreased virus replication, respectively. Collectively, the comparative temporal analysis of viral and host proteomes in productively HSV-1 and VZV-infected cells provides a valuable resource for future studies aimed to identify target(s) for antiviral therapy development.

Phosphoproteomic analysis reveals plant DNA damage signalling pathways with a functional role for histone H2AX phosphorylation in plant growth under genotoxic stress

DNA damage responses are crucial for plant growth under genotoxic stress. Accumulating evidence indicates that DNA damage responses differ between plant cell types. Here, quantitative shotgun phosphoproteomics provided high-throughput analysis of the DNA damage response network in callus cells. MS analysis revealed a wide network of highly dynamic changes in the phosphoprotein profile of genotoxin-treated cells, largely mediated by the ATAXIA TELANGIECTASIA MUTATED (ATM) protein kinase, representing candidate factors that modulate plant growth, development and DNA repair. A C-terminal dual serine target motif unique to H2AX in the plant lineage showed 171-fold phosphorylation that was absent in atm mutant lines. The physiological significance of post-translational DNA damage signalling to plant growth and survival was demonstrated using reverse genetics and complementation studies of h2ax mutants, establishing the functional role of ATM-mediated histone modification in plant growth under genotoxic stress. Our findings demonstrate the complexity and functional significance of post-translational DNA damage signalling responses in plants and establish the requirement of H2AX phosphorylation for plant survival under genotoxic stress.

Oncoproteomics: Current status and future opportunities

Oncoproteomics is the systematic study of cancer samples using omics technologies to detect changes implicated in tumorigenesis. Recent progress in oncoproteomics is already opening new avenues for the identification of novel biomarkers for early clinical stage cancer detection, targeted molecular therapies, disease monitoring, and drug development. Such information will lead to new understandings of cancer biology and impact dramatically on the future care of cancer patients. In this review, we will summarize the advantages and limitations of the key technologies used in (onco)proteogenomics, (the Omics Pipeline), explain how they can assist us in understanding the biology behind the overarching "Hallmarks of Cancer", discuss how they can advance the development of precision/personalised medicine and the future directions in the field.

Non-linear hierarchy of the quorum sensing signalling pathway in bloodstream form African trypanosomes

Trypanosoma brucei, the agents of African trypanosomiasis, undergo density-dependent differentiation in the mammalian bloodstream to prepare for transmission by tsetse flies. This involves the generation of cell-cycle arrested, quiescent, stumpy forms from proliferative slender forms. The signalling pathway responsible for the quorum sensing response has been catalogued using a genome-wide selective screen, providing a compendium of signalling protein kinases phosphatases, RNA binding proteins and hypothetical proteins. However, the ordering of these components is unknown. To piece together these components to provide a description of how stumpy formation arises we have used an extragenic suppression approach. This exploited a combinatorial gene knockout and overexpression strategy to assess whether the loss of developmental competence in null mutants of pathway components could be compensated by ectopic expression of other components. We have created null mutants for three genes in the stumpy induction factor signalling pathway (RBP7, YAK, MEKK1) and evaluated complementation by expression of RBP7, NEK17, PP1-6, or inducible gene silencing of the proposed differentiation inhibitor TbTOR4. This indicated that the signalling pathway is non-linear. Phosphoproteomic analysis focused on one pathway component, a putative MEKK, identified molecules with altered expression and phosphorylation profiles in MEKK1 null mutants, including another component in the pathway, NEK17. Our data provide a first molecular dissection of multiple components in a signal transduction cascade in trypanosomes.

African trypanosome parasites respond to density sensing information in the bloodstream of their mammalian hosts to generate their transmission stage, the stumpy form. Components of this ‘quorum sensing’ signalling cascade are known but their interactions and ordering are not. Here we have dissected the dependency relationships between molecules in the pathway by combinatorial gene knockout and ectopic expression, as well as by detailed phosphoproteomic analysis of one component. Our results provide a first analysis of the signal pathway architecture, revealing that it is non-linear. Moreover, phosphoproteome analysis reveals pathway hierarchy through identifying that the phosphorylation of a NEK kinase component of the pathway is reduced when a predicted upstream kinase is absent. This provides a framework for the coherent dissection of a signal transduction cascade in these parasites that use quorum sensing to control disease spread.