A Quantitative Proteome Map of the Human Body

The effect of thermal stress on the X-organ/sinus gland proteome of the estuarine blue crab Callinectes sapidus during the intermolt and premolt stages

Survival of brachyuran crabs is temperature-dependent and thermal stress promotes changes during molting. We aimed to decipher the impact of thermal stresses on the X-organ/sinus gland (XO/SG) complex, a temperature-sensitive neuroendocrine tissue involved in the molting regulation of Callinectes sapidus during the intermolt and premolt phases. We employed a proteogenomic approach using specimens subjected to control (24 °C), cold (19 °C), and heat (29 °C) temperatures. A total of 1463 protein groups with at least two unique peptides were identified and quantified. C. sapidus in the premolt stage exposed to the cold condition exhibited a proteome closely resembling that of the intermolt stage, as evidenced by measurements of circulating ecdysteroid levels. Compared to the intermolt at control temperature, the premolt stage exhibited increased energy metabolism, structural changes in the cuticle mediated by chitin metabolism and glycoproteins, biosynthesis of methyl farnesoate (MF), and elevated tissue levels of molt-inhibiting hormone (MIH) and crustacean hyperglycemic hormone (CHH), indicating lower secretion rates. Heat temperature (29 °C) seems to induce mitochondrial metabolism in the intermolt XO/SG, while cold temperature elicited a delayed molt cycle in the premolt phase, marked by reduced tissue levels of CHH, indicating increased secretion and Y-organ (YO) inhibition, and decreased MF production (reduced YO stimulation). SIGNIFICANCE STATEMENT: Temperature plays a pivotal role in regulating the metabolism, growth, molting, reproduction, and survival of crabs, such as the blue crab (Callinectes sapidus). Despite the blue crab's significance on both economic and ecological realms, there has been a notable lack of molecular information related to this species and therefore a gap in our knowledge of the blue crab's molecular makeup and genetic diversity. This research established a comprehensive proteome landscape to elucidate the molecular and functional changes in the XO/SG complex involved in the molting process of C. sapidus, and how thermal stresses significantly influence biotransformation processes. Utilizing a proteogenomics approach with multi-round homologous database analysis, we have generated a highly accurate protein repertoire with at least two unique peptide of XO/SG tissue proteome. This resource will be invaluable for future molecular analyses of this species. Our findings demonstrate that thermal stresses induced specific modifications in the XO/SG tissue, depending on the molt cycle phase. Temperature-mediated responses influences the biological processes, enhancing the functional morphogenesis and comprehensive metabolic adaptations on molting cycle supported by a relationship between the XO/SG tissue proteome and circulating ecdysteroid levels.

Isobaric Labeling Update in MaxQuant

We present an update of the MaxQuant software for isobaric labeling data and evaluate its performance on benchmark data sets. Impurity correction factors can be applied to labels mixing C- and N-type reporter ions such as TMT Pro. Application to a single-cell multispecies mixture benchmark shows the high accuracy of the impurity-corrected results. TMT data recorded with FAIMS separation can be analyzed directly in MaxQuant without splitting the raw data into separate files per FAIMS voltage. Weighted median normalization is applied to several data sets, including large-scale human body atlas data. In the benchmark data sets, the weighted median normalization either removes or strongly reduces the batch effects between different TMT plexes and results in clustering by biology. In data sets including reference channels, we find that weighted median normalization performs as well or better when the reference channels are ignored and only the sample channel intensities are used, suggesting that the measurement of reference channels is unnecessary when using weighted median normalization in MaxQuant. We demonstrate that MaxQuant including the weighted median normalization performs well on multinotch MS3 data, as well as on phosphorylation data. MaxQuant is freely available for any purpose and can be downloaded from https://www.maxquant.org/.

Neuronal autophagy in the control of synapse function

Neurons are long-lived postmitotic cells that capitalize on autophagy to remove toxic or defective proteins and organelles to maintain neurotransmission and the integrity of their functional proteome. Mutations in autophagy genes cause congenital diseases, sharing prominent brain dysfunctions including epilepsy, intellectual disability, and neurodegeneration. Ablation of core autophagy genes in neurons or glia disrupts normal behavior, leading to motor deficits, memory impairment, altered sociability, and epilepsy, which are associated with defects in synapse maturation, plasticity, and neurotransmitter release. In spite of the importance of autophagy for brain physiology, the substrates of neuronal autophagy and the mechanisms by which defects in autophagy affect synaptic function in health and disease remain controversial. Here, we summarize the current state of knowledge on neuronal autophagy, address the existing controversies and inconsistencies in the field, and provide a roadmap for future research on the role of autophagy in the control of synaptic function.

The proteomic landscape of stool-derived extracellular vesicles in patients with pre-cancerous lesions and colorectal cancer

Colorectal cancer (CRC) is the 2nd most fatal cancer in the United States, but when detected early it is highly curable. Stool-derived extracellular vesicles (EVs) are a novel biomarker source that could augment the sensitivity for detection of CRC precursors. However, standardization of isolation methods for stool-derived EVs remains underexplored. We previously reported that size-exclusion chromatography (SEC) followed by ultrafiltration (UF-100kDa) was suitable for human stool supernatant EV isolation. In this study, we first assess alternative EV concentration methods (ultrafiltration [UF]; 10 kDa, 30 kDa, 50 kDa, 100 kDa and speed vacuum [SV]). Second, we investigate the host/bacterial EV proteomes by mass spectrometry. We report no difference in recovery, RNA and soluble protein contamination among concentration methods. Proteomic analysis reveals a diverse bacterial proteome, while human-derived proteins are more abundant. Specifically, pancreatic enzymes are among the most abundant proteins, further exploration revealed that zymogen granules are likely co-isolated in stool EV preparations. To enable discovery of EV-based molecular signatures of CRC precursors with high sensitivity, immunocapture strategies will likely be needed. Notably, we identified 10 surface proteins that may serve as candidates for the purification of colon-derived EVs. This work serves as framework for the future discovery and validation of EV-based biomarkers for CRC.

Protein degradation and growth dependent dilution substantially shape mammalian proteomes

Cellular protein concentrations are maintained through a balance of synthesis and clearance. Clearance occurs through both protein degradation and growth-dependent dilution. At slow growth, clearance is dominated by degradation, which leads to the accumulation of long lived proteins. At fast growth, however, it is dominated by dilution, preventing this accumulation. Thus, the concentration of long lived proteins will be reduced unless cells compensate by preferentially increasing synthesis rates. To determine the dominant regulatory mechanisms, we quantified the degree of compensation between activated and resting human B cells and across mouse tissues. The results indicate that growth-dependent dilution is insufficiently compensated for by changes in protein synthesis, and it accounts for over a third of the concentration changes between high and low growth conditions. Furthermore, we find that about 25 % of the differences in protein concentration across all tissues are controlled by protein clearance. When comparing only slowly growing tissues such as the brain and pancreas, clearance differences explain as much as 42 %. Within a tissue or cell type, clearance variation is sufficient to account for 50 % of the abundance variation for all measured proteins at slow growth, contrasted with 7 % at fast growth. Thus, our model unifies previous observations with our new results and highlights a context-dependent and larger than previously appreciated contribution of protein degradation in shaping protein variation both across the proteome and across cell states.

Combined Prokaryotic Transcriptomics and Proteomics Analysis of Clinical Trueperella pyogenes Isolates with Distinctive Cytotoxicity

Trueperella pyogenes is a widely distributed opportunistic pathogenic bacterium that can infect livestock, wildlife, community animals, and humans, resulting in suppurative infection of tissue and organ mucosa, including pneumonia, liver abscessation, mastitis, metritis, endocarditis, and osteoarthritis. TP1804 and TP1808 were isolated from the uterine lavage fluid of cows with endometritis. This study analyzed the prokaryotic transcriptomics and proteomics of two strains of T. pyogenes with similar growth curves but different cytotoxicity. Studying the metabolic mechanisms of these differentially expressed genes and proteins can greatly promote the discovery of new biomarkers and improve the accuracy of biomarker identification, which is of great value for molecular mechanisms, biomarkers, early diagnosis of diseases, molecular typing, and prognosis. Our results indicate that the control of the virulence by tRNAs to bacteria during ribosome biosynthesis is crucial.

Integrated View of Baseline Protein Expression in Human Tissues Using Public Data Independent Acquisition Data Sets

The PRIDE database is the largest public data repository of mass spectrometry-based proteomics data and currently stores more than 40,000 data sets covering a wide range of organisms, experimental techniques, and biological conditions. During the past few years, PRIDE has seen a significant increase in the amount of submitted data-independent acquisition (DIA) proteomics data sets. This provides an excellent opportunity for large-scale data reanalysis and reuse. We have reanalyzed 15 public label-free DIA data sets across various healthy human tissues to provide a state-of-the-art view of the human proteome in baseline conditions (without any perturbations). We computed baseline protein abundances and compared them across various tissues, samples, and data sets. Our second aim was to compare protein abundances obtained here from the results of previous analyses using human baseline data-dependent acquisition (DDA) data sets. We observed a good correlation across some tissues, especially in the liver and colon, but weak correlations were found in others, such as the lung and pancreas. The reanalyzed results including protein abundance values and curated metadata are made available to view and download from the resource Expression Atlas.

Genes and Pathways Comprising the Human and Mouse ORFeomes Display Distinct Codon Bias Signatures that Can Regulate Protein Levels

Arginine, glutamic acid and selenocysteine based codon bias has been shown to regulate the translation of specific mRNAs for proteins that participate in stress responses, cell cycle and transcriptional regulation. Defining codon-bias in gene networks has the potential to identify other pathways under translational control. Here we have used computational methods to analyze the ORFeome of all unique human (19,711) and mouse (22,138) open-reading frames (ORFs) to characterize codon-usage and codon-bias in genes and biological processes. We show that ORFeome-wide clustering of gene-specific codon frequency data can be used to identify ontology-enriched biological processes and gene networks, with developmental and immunological programs well represented for both humans and mice. We developed codon over-use ontology mapping and hierarchical clustering to identify multi-codon bias signatures in human and mouse genes linked to signaling, development, mitochondria and metabolism, among others. The most distinct multi-codon bias signatures were identified in human genes linked to skin development and RNA metabolism, and in mouse genes linked to olfactory transduction and ribosome, highlighting species-specific pathways potentially regulated by translation. Extreme codon bias was identified in genes that included transcription factors and histone variants. We show that re-engineering extreme usage of C- or U-ending codons for aspartic acid, asparagine, histidine and tyrosine in the transcription factors CEBPB and MIER1, respectively, significantly regulates protein levels. Our study highlights that multi-codon bias signatures can be linked to specific biological pathways and that extreme codon bias with regulatory potential exists in transcription factors for immune response and development.

Targeting RNA splicing modulation: new perspectives for anticancer strategy?

The excision of introns from pre-mRNA is a crucial process in the expression of the majority of genes. Alternative splicing allows a single gene to generate diverse mRNA and protein products. Aberrant RNA splicing is recognized as a molecular characteristic present in almost all types of tumors. Therefore, identifying cancer-specific subtypes from aberrant processing offers new opportunities for therapeutic development. Numerous splicing modulators, each utilizing different mechanisms, have been developed as promising anticancer therapies, some of which are in clinical trials. In this review, we summarize the splice-altered signatures of cancer cell transcriptomes and the contributions of splicing aberrations to tumorigenesis and progression. Especially, we discuss current and emerging RNA splicing-targeted strategies for cancer therapy, including pharmacological approaches and splice-switching antisense oligonucleotides (ASOs). Finally, we address the challenges and opportunities in translating these findings into clinical practice.

Enhanced Discovery of Alternative Proteins (AltProts) in Mouse Cardiac Development Using Data-Independent Acquisition (DIA) Proteomics

Alternative proteins (AltProts) are a class of proteins encoded by DNA sequences previously classified as noncoding. Despite their historically being overlooked, recent studies have highlighted their widespread presence and distinctive biological roles. So far, direct detection of AltProt has been relying on data-dependent acquisition (DDA) mass spectrometry (MS). However, data-independent acquisition (DIA) MS, a method that is rapidly gaining popularity for the analysis of canonical proteins, has seen limited application in AltProt research, largely due to the complexities involved in constructing DIA libraries. In this study, we present a novel DIA workflow that leverages a fragmentation spectra predictor for the efficient construction of DIA libraries, significantly enhancing the detection of AltProts. Our method achieved a 2-fold increase in the identification of AltProts and a 50% reduction in missing values compared to DDA. We conducted a comprehensive comparison of four AltProt databases, four DIA-library construction strategies, and three analytical software tools to establish an optimal workflow for AltProt analysis. Utilizing this workflow, we investigated the mouse heart development process and identified over 50 AltProts with differential expression between embryonic and adult heart tissues. Over 30 unannotated mouse AltProts were validated, including ASDURF, which played a crucial role in cardiac development. Our findings not only provide a practical workflow for MS-based AltProt analysis but also reveal novel AltProts with potential significance in biological functions.

The roles of mitochondria in global and local intracellular calcium signalling

Activation of Ca2+ channels in Ca2+ stores in organelles and the plasma membrane generates cytoplasmic calcium ([Ca2+]c) signals that control almost every aspect of cell function, including metabolism, vesicle fusion and contraction. Mitochondria have a high capacity for Ca2+ uptake and chelation, alongside efficient Ca2+ release mechanisms. Still, mitochondria do not store Ca2+ in a prolonged manner under physiological conditions and lack the capacity to generate global [Ca2+]c signals. However, mitochondria take up Ca2+ at high local [Ca2+]c signals that originate from neighbouring organelles, and also during sustained global elevations of [Ca2+]c. Accumulated Ca2+ in the mitochondria stimulates oxidative metabolism and upon return to the cytoplasm, can produce spatially confined rises in [Ca2+]c to exert control over processes that are sensitive to Ca2+. Thus, the mitochondrial handling of [Ca2+]c is of physiological relevance. Furthermore, dysregulation of mitochondrial Ca2+ handling can contribute to debilitating diseases. We discuss the mechanisms and relevance of mitochondria in local and global calcium signals.

Robust collection and processing for label-free single voxel proteomics

With advanced mass spectrometry (MS)-based proteomics, genome-scale proteome coverage can be achieved from bulk tissues. However, such bulk measurement lacks spatial resolution and obscures tissue heterogeneity, precluding proteome mapping of tissue microenvironment. Here we report an integrated wet collection of single microscale tissue voxels and Surfactant-assisted One-Pot voxel processing method termed wcSOP for robust label-free single voxel proteomics. wcSOP capitalizes on buffer droplet-assisted wet collection of single voxels dissected by LCM to the tube cap and SOP voxel processing in the same collection cap. This method enables reproducible, label-free quantification of approximately 900 and 4600 proteins for single voxels at 20 µm × 20 µm × 10 µm (~1 cell region) and 200 µm × 200 µm × 10 µm (~100 cell region) from fresh frozen human spleen tissue, respectively. It can reveal spatially resolved protein signatures and region-specific signaling pathways. Furthermore, wcSOP-MS is demonstrated to be broadly applicable for OCT-embedded and FFPE human archived tissues as well as for small-scale 2D proteome mapping of tissues at high spatial resolutions. wcSOP-MS may pave the way for routine robust single voxel proteomics and spatial proteomics.

Regulation of protein abundance in normal human tissues

We report a systematic quantification of 10,841 unique proteins from over 700 GTEx samples, representing five human tissues. Sex, age and genetic factors are associated with variation in protein abundance. In total, 1981 cis-protein quantitative trait loci (cis-pQTL) are identified, of which a majority of protein targets have not been assayed in the recent plasma-based proteogenomic studies. Integrating transcriptomic information from matching tissues delineates concordant as well as discordant expression patterns at RNA and protein levels. Juxtaposition of data from different tissues indicates both shared and tissue-specific genetic architecture that underlie protein abundance. Complementing genomic annotation, RNA-based eQTL studies, as well as the recent establishment of plasma-based proteogenomic characterization, tissue-pQTLs shed light on biology underlying genotype-phenotype association of complex traits and diseases.

OrgXenomics: an integrated proteomic knowledge base for patient-derived organoid and xenograft

Patient-derived models (PDMs, particularly organoids and xenografts) are irreplaceable tools for precision medicine, from target development to lead identification, then to preclinical evaluation, and finally to clinical decision-making. So far, PDM-based proteomics has emerged to be one of the cutting-edge directions and massive data have been accumulated. However, such PDM-based proteomic data have not been provided by any of the available databases, and proteomics profiles of all proteins in proteomic study are also completely absent from existing databases. Herein, an integrated database named 'OrgXenomics' was thus developed to provide the proteomic data for PDMs, which was unique in (a) explicitly describing the establishment detail for a wide array of models, (b) systematically providing the proteomic profiles (expression/function/interaction) for all proteins in studied proteomic analysis and (c) comprehensively giving the raw data for diverse organoid/xenograft-based proteomic studies of various diseases. Our OrgXenomics was expected to server as one good complement to existing proteomic databases, and had great implication for the practice of precision medicine, which could be accessed at: https://idrblab.org/orgxenomics/.

Identifying prioritization of therapeutic targets for ankylosing spondylitis: a multi-omics Mendelian randomization study

BACKGROUND

To investigate the associations of methylation, expression, and protein quantitative trait loci (mQTL, eQTL, and pQTL) with ankylosing spondylitis (AS) and find out genetically supported drug targets for AS.

METHODS

The summary-data-based Mendelian randomization (SMR) and Bayesian co-localization analysis were used to assess the potential causality between AS and relevant genes. The GWAS data obtained from the International Genetics of Ankylosing Spondylitis Consortium (IGAS) were set as the discovery stage, and the FinnGen and UK Biobank databases were used to replicate the analysis as an external validation. We further integrated the multi-omics results to screen overlapped genes at different levels. The protein-protein interaction (PPI) network and enrichment analyses were used to explore the biological effect of SMR-identified genes on AS. Drug prediction and molecular docking were used to validate the medicinal value of candidate drug targets.

RESULTS

Based on the results of multi-omics evidence screening, we identified potential associations of TNFRSF1A, B3GNT2, ERAP1, and FCGR2A with AS at different regulatory levels. At the protein level, AIF1, TNXB, APOM, and B3GNT2 were found to be negatively associated with AS risk, whereas higher levels of FCGR2A, FCGR2B, IL12B, TNFRSF1A, and ERAP1 were associated with an increased risk of AS. The bioinformatics analyses showed that the SMR-identified genes were mainly involved in immune response. Molecular docking results displayed stable binding between predicted candidate drugs and these aforementioned proteins.

CONCLUSION

Our study found four AS-associated genes with multi-omics evidence and nine promising drug targets for AS, which may contribute to the understanding of the genetic mechanisms of AS and provide innovative perspectives into targeted therapy for AS.

Integrated proteomic and transcriptomic landscape of human placenta in small for gestational age infants

Small for gestational age (SGA) infants affected by placental insufficiency are exposed to the risk of stillbirth and long-term complications. Based on RNA-seq and mass spectrometry, we identified dysregulated RNAs and proteins from the comparisons of SGA placental tissues and controls. We revealed two SGA-relevant co-expression modules (SRMs) that also significantly distinguished SGA from controls. Then we performed an integrated analysis of transcriptomic and proteomic profiles to trace their links to SGA as well as their significant correlations. For the core functional molecules we screened, we revealed their potential upstream regulators and validated them experimentally in an independent cohort. Overall, we pointed out insights into different molecular pathways for the pathological mechanisms of SGA and indicated potential target molecules that may be drivers of placental aberrations in the SGA infants.

Phosphorylation of N-glycans in the brain: The case for a non-canonical pathway?

Asparagine-linked glycosylation (N-glycosylation) is a common co- and post-translational modification that refers to the addition of complex carbohydrates, called N-linked glycans (N-glycans), to asparagine residues within defined sequons of polypeptide acceptors. Some N-glycans can be modified by the addition of phosphate moieties to their monosaccharide residues, thus forming phospho-N-glycans (PNGs). The most prominent such carbohydrate modification is mannose-6-phosphate (M6P) which plays a well-established role in trafficking of acid hydrolases to lysosomes. However, comparatively little is known about potential alternative types of glycan phosphorylation, particularly when it comes to the brain which is especially rich in phosphorylated oligosaccharides. Combining data from the literature and novel insights derived from our own analyses of published datasets, here we present what is currently known about PNGs in the brain and the glycoproteins they modify. We show that brain PNGs exhibit several distinctive features that don't completely align with our current understanding of the canonical M6P pathway. Furthermore, we demonstrate that there are numerous differences in the way that lysosomal and non-lysosomal neural glycoproteins are modified by PNGs. Based on these observations, we put forward the hypothesis that, in addition to the conventional M6P pathway, the brain employs an alternative oligosaccharide phosphorylation mechanism for the modification of a discrete set of glycoproteins. Here we examine the evidence underpinning this hypothesis and discuss the implications that it raises. Overall, our work suggests that phosphorylation of N-glycans in the brain may be more complex and more diverse than previously recognised.

A prognostic molecular signature of hepatic steatosis is spatially heterogeneous and dynamic in human liver

Hepatic steatosis is a central phenotype in multi-system metabolic dysfunction and is increasing in parallel with the obesity pandemic. We use a translational approach integrating clinical phenotyping and outcomes, circulating proteomics, and tissue transcriptomics to identify dynamic, functional biomarkers of hepatic steatosis. Using multi-modality imaging and broad proteomic profiling, we identify proteins implicated in the progression of hepatic steatosis that are largely encoded by genes enriched at the transcriptional level in the human liver. These transcripts are differentially expressed across areas of steatosis in spatial transcriptomics, and several are dynamic during stages of steatosis. Circulating multi-protein signatures of steatosis strongly associate with fatty liver disease and multi-system metabolic outcomes. Using a humanized "liver-on-a-chip" model, we induce hepatic steatosis, confirming cell-specific expression of prioritized targets. These results underscore the utility of this approach to identify a prognostic, functional, dynamic "liquid biopsy" of human liver, relevant to biomarker discovery and mechanistic research applications.

Should Artificial Intelligence Play a Durable Role in Biomedical Research and Practice?

During the last decade, artificial intelligence (AI) was applied to nearly all domains of human activity, including scientific research. It is thus warranted to ask whether AI thinking should be durably involved in biomedical research. This problem was addressed by examining three complementary questions (i) What are the major barriers currently met by biomedical investigators? It is suggested that during the last 2 decades there was a shift towards a growing need to elucidate complex systems, and that this was not sufficiently fulfilled by previously successful methods such as theoretical modeling or computer simulation (ii) What is the potential of AI to meet the aforementioned need? it is suggested that recent AI methods are well-suited to perform classification and prediction tasks on multivariate systems, and possibly help in data interpretation, provided their efficiency is properly validated. (iii) Recent representative results obtained with machine learning suggest that AI efficiency may be comparable to that displayed by human operators. It is concluded that AI should durably play an important role in biomedical practice. Also, as already suggested in other scientific domains such as physics, combining AI with conventional methods might generate further progress and new applications, involving heuristic and data interpretation.

Tuning the affinity of probes with transmembrane proteins by constructing peptide-conjugated cis/trans isomers based on molecular scaffolds

For protein analysis, the current peptide-based probes rely almost on the specific recognition of the protein while neglecting the potential influence of the environment near the protein. Herein, we propose that to achieve high recognition of transmembrane protein integrin αvβ3, the interactions from the membrane substrate could be helpful. Moreover, to guarantee the additive effect of different interactions, the cis and trans isomers of peptide-based probes are distinguished. In detail, we synthesized the peptide-conjugated cis/trans isomers (cis-RTP and trans-RTP) by modifying the Arg-Gly-Asp (RGD)-targeting peptide and palmitic acid-conjugated Arg-Arg-Arg-Arg (Pal-RRRR) peptide to the two ends of the molecular scaffold-tetraphenylethene derivative. Due to the difference in spatial structure, isothermal titration calorimetry and simulation experiments demonstrated that cis-RTP can bind more stably to integrin αvβ3 than trans-RTP. As a result, cis-RTP has shown more excellent properties in inhibiting cell migration and killing cells by regulating actin and extracellular signal-regulated kinase. Unlike the existing probe design for protein, this study provides a concept of microenvironment-helpful recognition and a promising strategy of cis/trans isomers to modulate the interaction between proteins and probes.

Emergence of power law distributions in protein-protein interaction networks through study bias

Degree distributions in protein-protein interaction (PPI) networks are believed to follow a power law (PL). However, technical and study biases affect the experimental procedures for detecting PPIs. For instance, cancer-associated proteins have received disproportional attention. Moreover, bait proteins in large-scale experiments tend to have many false-positive interaction partners. Studying the degree distributions of thousands of PPI networks of controlled provenance, we address the question if PL distributions in observed PPI networks could be explained by these biases alone. Our findings are supported by mathematical models and extensive simulations, and indicate that study bias and technical bias suffice to produce the observed PL distribution. It is, hence, problematic to derive hypotheses about the topology of the true biological interactome from the PL distributions in observed PPI networks. Our study casts doubt on the use of the PL property of biological networks as a modeling assumption or quality criterion in network biology.

Improving design and normalization of multiplex proteomics study

Advances in multiplex mass spectrometry-based technologies have enabled high-throughput, quantitative proteome profiling of large cohort. However, certain experimental design configurations can amplify sample variability and introduce systematic biases. To address these challenges, we incorporated two novel features in a recent proteogenomic investigation: (1) the inclusion of two reference samples within each mass spectrometry run to serve as internal standards, and (2) the analysis of each specimen as technical replicates across two distinct mass spectrometry runs. Building on these enhancements, we present ProMix, a flexible analytical framework designed to fully leverage these supplementary experimental components. Using both simulated and real-world datasets, we demonstrate the improved performance of ProMix and highlight the advantages conferred by these refined experimental design strategies.

Proton pump inhibitor effect on esophageal protein signature of eosinophilic esophagitis, prediction, and evaluation of treatment response

BACKGROUND

Recently, we have identified a dysregulated protein signature in the esophageal epithelium of eosinophilic esophagitis (EoE) patients including proteins associated with inflammation and epithelial barrier function; however, the effect of proton pump inhibitor (PPI) treatment on this signature is unknown. Herein, we used a proteomic approach to investigate: (1) whether PPI treatment alters the esophageal epithelium protein profile observed in EoE patients and (2) whether the protein signature at baseline predicts PPI response.

METHODS

We evaluated the protein signature of esophageal biopsies using a cohort of adult EoE (n = 25) patients and healthy controls (C) (n = 10). In EoE patients, esophageal biopsies were taken before (pre) and after (post) an 8-week PPI treatment, determining the histologic response. Eosinophil count PostPPI was used to classify the patients: ≥15 eosinophils/hpf as non-responders (non-responder) and < 15 eosinophils/hpf as responders (R). Protein signature was determined and differentially accumulated proteins were characterized to identify altered biological processes and signaling pathways.

RESULTS

Comparative analysis of differentially accumulated proteins between groups revealed common signatures between three groups of patients with inflammation (responder-PrePPI, non-responder-PrePPI, and non-responder-PostPPI) and without inflammation (controls and responder-PostPPI). PPI therapy almost reversed the EoE specific esophageal protein signature, which is enriched in pathways associated with inflammation and epithelial barrier function, in responder-PostPPI. Furthermore, we identified a set of candidate proteins to differentiate responder-PrePPI and non-responder-PrePPI EoE patients before treatment.

CONCLUSION

These findings provide evidence that PPI therapy reverses the alterations in esophageal inflammatory and epithelial proteins characterizing EoE, thereby providing new insights into the mechanism of PPI clinical response. Interestingly, our results also suggest that PPI response could be predicted at baseline in EoE.

Durable Muscle Extracellular Matrix Engineered with Adhesive Phenolic Moieties for Effective Skeletal Muscle Regeneration in Muscle Atrophy

Muscle atrophy detrimentally impacts health and exacerbates physical disability, leading to increased mortality. In particular, sarcopenia, aging-related degenerative muscle loss, necessitates urgent remedies. Current approaches for treating muscle atrophy include exercise and nutrition, while drug exploration remains in its early stages. Cell therapy, focusing on satellite cells, faces significant challenge due to poor engraftment, safety issue, and high cost. Cell-free approach using extracellular matrix (ECM) shows a regenerative potential, but a lack of mechanical and adhesive properties hinders prolonged efficacy of ECM therapy. Here, durable muscle ECM (MEM) hydrogels for muscle atrophy by fortifying MEM with adhesive phenolic moieties including catechol and pyrogallol are demonstrated. The resultant phenolic MEM hydrogels exhibit enhanced mechanical and adhesive properties and provide sustained muscle-like microenvironments to address muscle atrophy. No local and systemic toxicities are observed after phenolic MEM injection into tibialis anterior muscle. Notably, these engineered MEM hydrogels, devoid of cells or drugs, induce tissue rejuvenation by promoting muscle protein synthesis and facilitating functional muscle recovery in mouse models of disuse- and age-induced atrophy. This study introduces cell-free, ECM-based therapeutics with translational potential for muscle atrophy by reversing muscle loss and restoring function.

A proteome-wide association study identifies putative causal proteins for breast cancer risk

BACKGROUND

Genome-wide association studies (GWAS) have identified more than 200 breast cancer risk-associated genetic loci, yet the causal genes and biological mechanisms for most loci remain elusive. Proteins, as final gene products, are pivotal in cellular function. In this study, we conducted a proteome-wide association study (PWAS) to identify proteins in breast tissue related to breast cancer risk.

METHODS

We profiled the proteome in fresh frozen breast tissue samples from 120 cancer-free European-ancestry women from the Susan G. Komen Tissue Bank (KTB). Protein expression levels were log2-transformed then normalized via quantile and inverse-rank transformations. GWAS data were also generated for these 120 samples. These data were used to build statistical models to predict protein expression levels via cis-genetic variants using the elastic net method. The prediction models were then applied to the GWAS summary statistics data of 133,384 breast cancer cases and 113,789 controls to assess the associations of genetically predicted protein expression levels with breast cancer risk overall and its subtypes using the S-PrediXcan method.

RESULTS

A total of 6388 proteins were detected in the normal breast tissue samples from 120 women with a high detection false discovery rate (FDR) p value < 0.01. Among the 5820 proteins detected in more than 80% of participants, prediction models were successfully built for 2060 proteins with R > 0.1 and P < 0.05. Among these 2060 proteins, five proteins were significantly associated with overall breast cancer risk at an FDR p value < 0.1. Among these five proteins, the corresponding genes for proteins COPG1, DCTN3, and DDX6 were located at least 1 Megabase away from the GWAS-identified breast cancer risk variants. COPG1 was associated with an increased risk of breast cancer with a p value of 8.54 × 10-4. Both DCTN3 and DDX6 were associated with a decreased risk of breast cancer with p values of 1.01 × 10-3 and 3.25 × 10-4, respectively. The corresponding genes for the remaining two proteins, LSP1 and DNAJA3, were located in previously GWAS-identified breast cancer risk loci. After adjusting for GWAS-identified risk variants, the association for DNAJA3 was still significant (p value of 9.15 × 10-5 and adjusted p value of 1.94 × 10-4). However, the significance for LSP1 became weaker with a p value of 0.62. Stratification analyses by breast cancer subtypes identified three proteins, SMARCC1, LSP1, and NCKAP1L, associated with luminal A, luminal B, and ER-positive breast cancer. NCKAP1L was located at least 1Mb away from the GWAS-identified breast cancer risk variants. After adjusting for GWAS-identified breast cancer risk variants, the association for protein LSP1 was still significant (adjusted p value of 6.43 × 10-3 for luminal B subtype).

CONCLUSION

We conducted the first breast-tissue-based PWAS and identified seven proteins associated with breast cancer, including five proteins not previously implicated. These findings help improve our understanding of the underlying genetic mechanism of breast cancer development.

Advancing Therapeutic and Vaccine Proteins: Switching from Recombinant to Ribosomal Delivery-A Humanitarian Cause

Recombinant therapeutic and vaccine proteins have revolutionized healthcare, but there remain challenges, as many are awaiting development due to their slow development speed and high development cost. Cell-free in vivo ribosomes offer one choice, but they come with similar constraints. The validation of in vivo messenger RNA (mRNA) technology has been accomplished for COVID-19 vaccines. The bioreactors inside the body, the ribosomes, deliver these proteins at a small cost, since these are chemical products and do not require extensive analytical and regulatory exercises. In this study, we test and validate the final product. A smaller fraction of the recombinant protein cost is needed, removing both constraints. Although thousands of in vivo mRNA products are under development, their regulatory classification remains unresolved: do they qualify as chemical drugs, biological drug, or gene therapy items? These questions will soon be resolved. Additionally, how would the copies of approved in vivo mRNA protein products be brought in, and how would they be treated: as new drugs, generic drugs, or new biological drugs? Researchers are currently working to answer these questions. Regardless, these products' cost of goods (COGs) remains much smaller than that of ex vivo mRNA or recombinant products. This is necessary to meet the needs of the approximately 6.5 billion people around the world who do not have access to biological drugs; these products will indeed serve the dire needs of humanity. Given the minor cost of establishing the manufacturing of these products, it will also prove financially attractive to investors.

Agent-based modeling for the tumor microenvironment (TME)

Cancer is a disease that arises from the uncontrolled growth of abnormal (tumor) cells in an organ and their subsequent spread into other parts of the body. If tumor cells spread to surrounding tissues or other organs, then the disease is life-threatening due to limited treatment options. This work applies an agent-based model to investigate the effect of intra-tumoral communication on tumor progression, plasticity, and invasion, with results suggesting that cell-cell and cell-extracellular matrix (ECM) interactions affect tumor cell behavior. Additionally, the model suggests that low initial healthy cell densities and ECM protein densities promote tumor progression, cell motility, and invasion. Furthermore, high ECM breakdown probabilities of tumor cells promote tumor invasion. Understanding the intra-tumoral communication under cellular stress can potentially lead to the design of successful treatment strategies for cancer.

A proteogenomic surfaceome study identifies DLK1 as an immunotherapeutic target in neuroblastoma

Cancer immunotherapies produce remarkable results in B cell malignancies; however, optimal cell surface targets for many solid cancers remain elusive. Here, we present an integrative proteomic, transcriptomic, and epigenomic analysis of tumor and normal tissues to identify biologically relevant cell surface immunotherapeutic targets for neuroblastoma, an often-fatal childhood cancer. Proteogenomic analyses reveal sixty high-confidence candidate immunotherapeutic targets, and we prioritize delta-like canonical notch ligand 1 (DLK1) for further study. High expression of DLK1 directly correlates with a super-enhancer. Immunofluorescence, flow cytometry, and immunohistochemistry show robust cell surface expression of DLK1. Short hairpin RNA mediated silencing of DLK1 in neuroblastoma cells results in increased cellular differentiation. ADCT-701, a DLK1-targeting antibody-drug conjugate (ADC), shows potent and specific cytotoxicity in DLK1-expressing neuroblastoma xenograft models. Since high DLK1 expression is found in several adult and pediatric cancers, our study demonstrates the utility of a proteogenomic approach and credentials DLK1 as an immunotherapeutic target.

IS-PRM-Based Peptide Targeting Informed by Long-Read Sequencing for Alternative Proteome Detection

Alternative splicing is a major contributor of transcriptomic complexity, but the extent to which transcript isoforms are translated into stable, functional protein isoforms is unclear. Furthermore, detection of relatively scarce isoform-specific peptides is challenging, with many protein isoforms remaining uncharted due to technical limitations. Recently, a family of advanced targeted MS strategies, termed internal standard parallel reaction monitoring (IS-PRM), have demonstrated multiplexed, sensitive detection of predefined peptides of interest. Such approaches have not yet been used to confirm existence of novel peptides. Here, we present a targeted proteogenomic approach that leverages sample-matched long-read RNA sequencing (lrRNA-seq) data to predict potential protein isoforms with prior transcript evidence. Predicted tryptic isoform-specific peptides, which are specific to individual gene product isoforms, serve as "triggers" and "targets" in the IS-PRM method, Tomahto. Using the model human stem cell line WTC11, LR RNaseq data were generated and used to inform the generation of synthetic standards for 192 isoform-specific peptides (114 isoforms from 55 genes). These synthetic "trigger" peptides were labeled with super heavy tandem mass tags (TMT) and spiked into TMT-labeled WTC11 tryptic digest, predicted to contain corresponding endogenous "target" peptides. Compared to DDA mode, Tomahto increased detectability of isoforms by 3.6-fold, resulting in the identification of five previously unannotated isoforms. Our method detected protein isoform expression for 43 out of 55 genes corresponding to 54 resolved isoforms. This lrRNA-seq-informed Tomahto targeted approach is a new modality for generating protein-level evidence of alternative isoforms─a critical first step in designing functional studies and eventually clinical assays.

Multiomic analysis of familial adenomatous polyposis reveals molecular pathways associated with early tumorigenesis

Familial adenomatous polyposis (FAP) is a genetic disease causing hundreds of premalignant polyps in affected persons and is an ideal model to study transitions of early precancer states to colorectal cancer (CRC). We performed deep multiomic profiling of 93 samples, including normal mucosa, benign polyps and dysplastic polyps, from six persons with FAP. Transcriptomic, proteomic, metabolomic and lipidomic analyses revealed a dynamic choreography of thousands of molecular and cellular events that occur during precancerous transitions toward cancer formation. These involve processes such as cell proliferation, immune response, metabolic alterations (including amino acids and lipids), hormones and extracellular matrix proteins. Interestingly, activation of the arachidonic acid pathway was found to occur early in hyperplasia; this pathway is targeted by aspirin and other nonsteroidal anti-inflammatory drugs, a preventative treatment under investigation in persons with FAP. Overall, our results reveal key genomic, cellular and molecular events during the earliest steps in CRC formation and potential mechanisms of pharmaceutical prophylaxis.

Protein nutritional support: The prevention and regulation of colorectal cancer and its mechanism research

Colorectal cancer (CRC) is a common malignant tumor of the digestive tract in China; its incidence rates and mortality rates have been on the rise in recent years, ranking third in terms of incidence and second in mortality. Rational dietary intervention plays an important role in human health, and prevention and adjuvant treatment of CRC through dietary supplementation is the most ideal and safest way to treat the disease at present. More importantly, dietary protein is the basis of our diet and the key nutrient to maintain the normal function of the human body. Therefore, this narrative review delivered an overview of the common causes and therapeutic treatments for CRC. It emphasized the importance of dietary interventions, with a particular focus on elucidating the distinct regulatory impacts of plant proteins, animal proteins, and their mixed proteins.

Systematic mapping of mitochondrial calcium uniporter channel (MCUC)-mediated calcium signaling networks

The mitochondrial calcium uniporter channel (MCUC) mediates mitochondrial calcium entry, regulating energy metabolism and cell death. Although several MCUC components have been identified, the molecular basis of mitochondrial calcium signaling networks and their remodeling upon changes in uniporter activity have not been assessed. Here, we map the MCUC interactome under resting conditions and upon chronic loss or gain of mitochondrial calcium uptake. We identify 89 high-confidence interactors that link MCUC to several mitochondrial complexes and pathways, half of which are associated with human disease. As a proof-of-concept, we validate the mitochondrial intermembrane space protein EFHD1 as a binding partner of the MCUC subunits MCU, EMRE, and MCUB. We further show a MICU1-dependent inhibitory effect of EFHD1 on calcium uptake. Next, we systematically survey compensatory mechanisms and functional consequences of mitochondrial calcium dyshomeostasis by analyzing the MCU interactome upon EMRE, MCUB, MICU1, or MICU2 knockdown. While silencing EMRE reduces MCU interconnectivity, MCUB loss-of-function leads to a wider interaction network. Our study provides a comprehensive and high-confidence resource to gain insights into players and mechanisms regulating mitochondrial calcium signaling and their relevance in human diseases.

ExoSloNano: Multi-Modal Nanogold Tags for identification of Macromolecules in Live Cells & Cryo-Electron Tomograms

In situ cryo-Electron Microscopy (cryo-EM) enables the direct interrogation of structure-function relationships by resolving macromolecular structures in their native cellular environment. Tremendous progress in sample preparation, imaging and data processing over the past decade has contributed to the identification and determination of large biomolecular complexes. However, the majority of proteins are of a size that still eludes identification in cellular cryo-EM data, and most proteins exist in low copy numbers. Therefore, novel tools are needed for cryo-EM to identify the vast majority of macromolecules across multiple size scales (from microns to nanometers). Here, we introduce and validate novel nanogold probes that enable the detection of specific proteins using cryo-ET (cryo-Electron Tomography) and resin-embedded correlated light and electron microscopy (CLEM). We demonstrate that these nanogold probes can be introduced into live cells, in a manner that preserves intact molecular networks and cell viability. We use this system to identify both cytoplasmic and nuclear proteins by room temperature EM, and resolve associated structures by cryo-ET. We further employ gold particles of different sizes to enable future multiplexed labeling and structural analysis. By providing high efficiency protein labeling in live cells and molecular specificity within cryo-ET tomograms, we establish a broadly enabling tool that significantly expands the proteome available to electron microscopy.

Inferring post-transcriptional regulation within and across cell types in human testis

Single-cell tissue atlases commonly use RNA abundances as surrogates for protein abundances. Yet, protein abundance also depends on the regulation of protein synthesis and degradation rates. To estimate the contributions of such post transcriptional regulation, we quantified the proteomes of 5,883 single cells from human testis using 3 distinct mass spectrometry methods (SCoPE2, pSCoPE, and plexDIA). To distinguish between biological and technical factors contributing to differences between protein and RNA levels, we developed BayesPG, a Bayesian model of transcript and protein abundance that systematically accounts for technical variation and infers biological differences. We use BayesPG to jointly model RNA and protein data collected from 29,709 single cells across different methods and datasets. BayesPG estimated consensus mRNA and protein levels for 3,861 gene products and quantified the relative protein-to-mRNA ratio (rPTR) for each gene across six distinct cell types in samples from human testis. About 28% of the gene products exhibited significant differences at protein and RNA levels and contributed to about 1, 500 significant GO groups. We observe that specialized and context specific functions, such as those related to spermatogenesis are regulated after transcription. Among hundreds of detected post translationally modified peptides, many show significant abundance differences across cell types. Furthermore, some phosphorylated peptides covary with kinases in a cell-type dependent manner, suggesting cell-type specific regulation. Our results demonstrate the potential of inferring protein regulation in from single-cell proteogenomic data and provide a generalizable model, BayesPG, for performing such analyses.

Spermatogenic Stem Cells: Core Biology, Defining Features, and Utilities

The actions of spermatogenic stem cells (SSCs) provide the foundation for continual spermatogenesis and regeneration of the cognate lineage following cytotoxic insult or transplantation. Several decades of research with rodent models have yielded knowledge about the core biology, morphological features, and molecular profiles of mammalian SSCs. Translation of these discoveries to utilities for human fertility preservation, improving animal agriculture, and wildlife conservation are actively being pursued. Here, we provide overviews of these aspects covering both historical and current states of understanding.

Clinicopathologic Characteristics of Trop Family Proteins (Trop-2 and EpCAM) in Gastric Carcinoma

PURPOSE

Trop family proteins, including epithelial cell adhesion molecule (EpCAM) and Trop-2, have garnered attention as potential therapeutic and diagnostic targets for various malignancies. This study aimed to elucidate the clinicopathological significance of these proteins in gastric carcinoma (GC) and to reinforce their potential as biomarkers for patient stratification in targeted therapies.

MATERIALS AND METHODS

Immunohistochemical (IHC) analyses of EpCAM and Trop-2 were performed on GC and precancerous lesions, following rigorous orthogonal validation of the antibodies to ensure specificity and sensitivity.

RESULTS

Strong membranous staining (3+) for Trop-2 was observed in 49.3% of the GC cases, whereas EpCAM was strongly expressed in almost all cases (93.2%), indicating its widespread expression in GC. A high Trop-2 expression level, characterized by an elevated H-score, was significantly associated with intestinal type by Lauren classification, gastric mucin type, presence of lymph node metastasis, human epidermal growth factor receptor 2-positivity, and Epstein-Barr virus (EBV)-positivity. Patients with a high Trop-2 expression level exhibited poorer survival outcomes on univariate and multivariate analyses. High EpCAM expression levels were prevalent in differentiated histologic type, microsatellite instability-high, and EBV-negative cancer, and were correlated with high densities of CD3 and CD8 T cells and elevated combined positive score for programmed death-ligand 1.

CONCLUSIONS

These results highlight the differential expression of Trop-2 and EpCAM and their prognostic implications in GC. The use of meticulously validated antibodies ensured the reliability of our IHC data, thereby offering a robust foundation for future therapeutic strategies targeting Trop family members in GC.

Statistically and functionally fine-mapped blood eQTLs and pQTLs from 1,405 humans reveal distinct regulation patterns and disease relevance

Studying the genetic regulation of protein expression (through protein quantitative trait loci (pQTLs)) offers a deeper understanding of regulatory variants uncharacterized by mRNA expression regulation (expression QTLs (eQTLs)) studies. Here we report cis-eQTL and cis-pQTL statistical fine-mapping from 1,405 genotyped samples with blood mRNA and 2,932 plasma samples of protein expression, as part of the Japan COVID-19 Task Force (JCTF). Fine-mapped eQTLs (n = 3,464) were enriched for 932 variants validated with a massively parallel reporter assay. Fine-mapped pQTLs (n = 582) were enriched for missense variations on structured and extracellular domains, although the possibility of epitope-binding artifacts remains. Trans-eQTL and trans-pQTL analysis highlighted associations of class I HLA allele variation with KIR genes. We contrast the multi-tissue origin of plasma protein with blood mRNA, contributing to the limited colocalization level, distinct regulatory mechanisms and trait relevance of eQTLs and pQTLs. We report a negative correlation between ABO mRNA and protein expression because of linkage disequilibrium between distinct nearby eQTLs and pQTLs.

A sexually dimorphic hepatic cycle of periportal VLDL generation and subsequent pericentral VLDLR-mediated re-uptake

Recent single-cell transcriptomes revealed spatiotemporal programmes of liver function on the sublobular scale. However, how sexual dimorphism affected this space-time logic remained poorly understood. We addressed this by performing scRNA-seq in the mouse liver, which revealed that sex, space and time together markedly influence xenobiotic detoxification and lipoprotein metabolism. The very low density lipoprotein receptor (VLDLR) exhibits a pericentral expression pattern, with significantly higher mRNA and protein levels in female mice. Conversely, VLDL assembly is periportally biased, suggesting a sexually dimorphic hepatic cycle of periportal formation and pericentral uptake of VLDL. In humans, VLDLR expression is also pericentral, with higher mRNA and protein levels in premenopausal women compared to similarly aged men. Individuals with low hepatic VLDLR expression show a high prevalence of atherosis in the coronary artery already at an early age and an increased incidence of heart attack.

Brain-body mitochondrial distribution patterns lack coherence and point to tissue-specific and individualized regulatory mechanisms

Energy transformation capacity is generally assumed to be a coherent individual trait driven by genetic and environmental factors. This predicts that some individuals should have high and others low mitochondrial oxidative phosphorylation (OxPhos) capacity across organ systems. Here, we test this assumption using multi-tissue molecular and enzymatic activities in mice and humans. Across up to 22 mouse tissues, neither mitochondrial OxPhos capacity nor mtDNA density were correlated between tissues (median r = -0.01-0.16), indicating that animals with high mitochondrial capacity in one tissue can have low capacity in other tissues. Similarly, the multi-tissue correlation structure of RNAseq-based indices of mitochondrial gene expression across 45 tissues from 948 women and men (GTEx) showed small to moderate coherence between only some tissues (regions of the same brain), but not between brain-body tissue pairs in the same person (median r = 0.01). Mitochondrial DNA copy number (mtDNAcn) also lacked coherence across organs and tissues. Mechanistically, tissue-specific differences in mitochondrial gene expression were attributable in part to i) tissue-specific activation of canonical energy sensing pathways including the transcriptional coactivator PGC-1 and the integrated stress response (ISR), and ii) proliferative activity across tissues. Finally, we identify subgroups of individuals with high mitochondrial gene expression in some tissues (e.g., heart) but low expression in others (e.g., skeletal muscle) who display different clinical phenotypic patterns. Taken together, these data raise the possibility that tissue-specific energy sensing pathways may contribute to the idiosyncratic mitochondrial distribution patterns associated with the inter-organ heterogeneity and phenotypic diversity among individuals.

Improved deconvolution of natural products' protein targets using diagnostic ions from chemical proteomics linkers

Identification of interactions between proteins and natural products or similar active small molecules is crucial for understanding of their mechanism of action on a molecular level. To search elusive, often labile, and low-abundant conjugates between proteins and active compounds, chemical proteomics introduces a feasible strategy that allows to enrich and detect these conjugates. Recent advances in mass spectrometry techniques and search algorithms provide unprecedented depth of proteome coverage and the possibility to detect desired modified peptides with high sensitivity. The chemical 'linker' connecting an active compound-protein conjugate with a detection tag is the critical component of all chemical proteomic workflows. In this review, we discuss the properties and applications of different chemical proteomics linkers with special focus on their fragmentation releasing diagnostic ions and how these may improve the confidence in identified active compound-peptide conjugates. The application of advanced search options improves the identification rates and may help to identify otherwise difficult to find interactions between active compounds and proteins, which may result from unperturbed conditions, and thus are of high physiological relevance.

Induction of oxidative- and endoplasmic-reticulum-stress dependent apoptosis in pancreatic cancer cell lines by DDOST knockdown

The dolichyl-diphosphooligosaccharide-protein glycosyltransferase non-catalytic subunit (DDOST) is a key component of the oligosaccharyltransferase complex catalyzing N-linked glycosylation in the endoplasmic reticulum lumen. DDOST is associated with several cancers and congenital disorders of glycosylation. However, its role in pancreatic cancer remains elusive, despite its enriched pancreatic expression. Using quantitative mass spectrometry, we identify 30 differentially expressed proteins and phosphopeptides (DEPs) after DDOST knockdown in the pancreatic ductal adenocarcinoma (PDAC) cell line PA-TU-8988T. We evaluated DDOST / DEP protein-protein interaction networks using STRING database, correlation of mRNA levels in pancreatic cancer TCGA data, and biological processes annotated to DEPs in Gene Ontology database. The inferred DDOST regulated phenotypes were experimentally verified in two PDAC cell lines, PA-TU-8988T and BXPC-3. We found decreased proliferation and cell viability after DDOST knockdown, whereas ER-stress, ROS-formation and apoptosis were increased. In conclusion, our results support an oncogenic role of DDOST in PDAC by intercepting cell stress events and thereby reducing apoptosis. As such, DDOST might be a potential biomarker and therapeutic target for PDAC.

Host-pathogen interactions from a metabolic perspective: methods of investigation

Metabolism shapes immune homeostasis in health and disease. This review presents the range of methods that are currently available to investigate the dialog between metabolism and immunity at the systemic, tissue and cellular levels, particularly during infection.

SPIDER: constructing cell-type-specific protein-protein interaction networks

Motivation

Protein-protein interactions (PPIs) play essential roles in the buildup of cellular machinery and provide the skeleton for cellular signaling. However, these biochemical roles are context dependent and interactions may change across cell type, time, and space. In contrast, PPI detection assays are run in a single condition that may not even be an endogenous condition of the organism, resulting in static networks that do not reflect full cellular complexity. Thus, there is a need for computational methods to predict cell-type-specific interactions.

Results

Here we present SPIDER (Supervised Protein Interaction DEtectoR), a graph attention-based model for predicting cell-type-specific PPI networks. In contrast to previous attempts at this problem, which were unsupervised in nature, our model's training is guided by experimentally measured cell-type-specific networks, enhancing its performance. We evaluate our method using experimental data of cell-type-specific networks from both humans and mice, and show that it outperforms current approaches by a large margin. We further demonstrate the ability of our method to generalize the predictions to datasets of tissues lacking prior PPI experimental data. We leverage the networks predicted by the model to facilitate the identification of tissue-specific disease genes.

Availability and implementation

Our code and data are available at https://github.com/Kuper994/SPIDER.

MICU1 and MICU2 control mitochondrial calcium signaling in the mammalian heart

Activating Ca2+-sensitive enzymes of oxidative metabolism while preventing calcium overload that leads to mitochondrial and cellular injury requires dynamic control of mitochondrial Ca2+ uptake. This is ensured by the mitochondrial calcium uptake (MICU)1/2 proteins that gate the pore of the mitochondrial calcium uniporter (mtCU). MICU1 is relatively sparse in the heart, and recent studies claimed the mammalian heart lacks MICU1 gating of mtCU. However, genetic models have not been tested. We find that MICU1 is present in a complex with MCU in nonfailing human hearts. Furthermore, using murine genetic models and pharmacology, we show that MICU1 and MICU2 control cardiac mitochondrial Ca2+ influx, and that MICU1 deletion alters cardiomyocyte mitochondrial calcium signaling and energy metabolism. MICU1 loss causes substantial compensatory changes in the mtCU composition and abundance, increased turnover of essential MCU regulator (EMRE) early on and, later, of MCU, that limit mitochondrial Ca2+ uptake and allow cell survival. Thus, both the primary consequences of MICU1 loss and the ensuing robust compensation highlight MICU1's relevance in the beating heart.

Systematic identification of minor histocompatibility antigens predicts outcomes of allogeneic hematopoietic cell transplantation

T cell alloreactivity against minor histocompatibility antigens (mHAgs)-polymorphic peptides resulting from donor-recipient (D-R) disparity at sites of genetic polymorphisms-is at the core of the therapeutic effect of allogeneic hematopoietic cell transplantation (allo-HCT). Despite the crucial role of mHAgs in graft-versus-leukemia (GvL) and graft-versus-host disease (GvHD) reactions, it remains challenging to consistently link patient-specific mHAg repertoires to clinical outcomes. Here we devise an analytic framework to systematically identify mHAgs, including their detection on HLA class I ligandomes and functional verification of their immunogenicity. The method relies on the integration of polymorphism detection by whole-exome sequencing of germline DNA from D-R pairs with organ-specific transcriptional- and proteome-level expression. Application of this pipeline to 220 HLA-matched allo-HCT D-R pairs demonstrated that total and organ-specific mHAg load could independently predict the occurrence of acute GvHD and chronic pulmonary GvHD, respectively, and defined promising GvL targets, confirmed in a validation cohort of 58 D-R pairs, for the prevention or treatment of post-transplant disease recurrence.

Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry

Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this Review will serve as a handbook for researchers who are new to the field of bottom-up proteomics.

DNA damage-associated protein co-expression network in cardiomyocytes informs on tolerance to genetic variation and disease

Cardiovascular disease (CVD) is associated with both genetic variants and environmental factors. One unifying consequence of the molecular risk factors in CVD is DNA damage, which must be repaired by DNA damage response proteins. However, the impact of DNA damage on global cardiomyocyte protein abundance, and its relationship to CVD risk remains unclear. We therefore treated induced pluripotent stem cell-derived cardiomyocytes with the DNA-damaging agent Doxorubicin (DOX) and a vehicle control, and identified 4,178 proteins that contribute to a network comprising 12 co-expressed modules and 403 hub proteins with high intramodular connectivity. Five modules correlate with DOX and represent distinct biological processes including RNA processing, chromatin regulation and metabolism. DOX-correlated hub proteins are depleted for proteins that vary in expression across individuals due to genetic variation but are enriched for proteins encoded by loss-of-function intolerant genes. While proteins associated with genetic risk for CVD, such as arrhythmia are enriched in specific DOX-correlated modules, DOX-correlated hub proteins are not enriched for known CVD risk proteins. Instead, they are enriched among proteins that physically interact with CVD risk proteins. Our data demonstrate that DNA damage in cardiomyocytes induces diverse effects on biological processes through protein co-expression modules that are relevant for CVD, and that the level of protein connectivity in DNA damage-associated modules influences the tolerance to genetic variation.

Mass spectrometry-based proteomic landscape of rice reveals a post-transcriptional regulatory role of N6-methyladenosine

Rice is one of the most important staple food and model species in plant biology, yet its quantitative proteomes are largely uncharacterized. Here we quantify the relative protein levels of over 15,000 genes across major rice tissues using a tandem mass tag strategy followed by intensive fractionation and mass spectrometry. We identify tissue-specific and tissue-enriched proteins that are linked to the functional specificity of individual tissues. Proteogenomic comparison of rice and Arabidopsis reveals conserved proteome expression, which differs from mammals in that there is a strong separation of species rather than tissues. Notably, profiling of N6-methyladenosine (m6A) across the rice major tissues shows that m6A at untranslated regions is negatively correlated with protein abundance and contributes to the discordance between RNA and protein levels. We also demonstrate that our data are valuable for identifying novel genes required for regulating m6A methylation. Taken together, this study provides a paradigm for further research into rice proteogenome.

Self-consistent signal transduction analysis for modeling context-specific signaling cascades and perturbations

Biological signal transduction networks are central to information processing and regulation of gene expression across all domains of life. Dysregulation is known to cause a wide array of diseases, including cancers. Here I introduce self-consistent signal transduction analysis, which utilizes genome-scale -omics data (specifically transcriptomics and/or proteomics) in order to predict the flow of information through these networks in an individualized manner. I apply the method to the study of endocrine therapy in breast cancer patients, and show that drugs that inhibit estrogen receptor α elicit a wide array of antitumoral effects, and that their most clinically-impactful ones are through the modulation of proliferative signals that control the genes GREB1, HK1, AKT1, MAPK1, AKT2, and NQO1. This method offers researchers a valuable tool in understanding how and why dysregulation occurs, and how perturbations to the network (such as targeted therapies) effect the network itself, and ultimately patient outcomes.