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Bacillus coagulans, a Gram-positive thermophilic bacterium, is recognized for its probiotic properties and recent development as a microbial cell factory. Despite its importance for biotechnological applications, the current understanding of B. coagulans ' robustness is limited, especially for undomesticated strains. To fill this knowledge gap, we characterized the metabolic capability and performed functional genomics and systems analysis of a novel, robust strain, B. coagulans B-768. Genome sequencing revealed that B-768 has the largest B. coagulans genome known to date (3.94 Mbp), about 0.63 Mbp larger than the average genome of sequenced B. coagulans strains, with expanded carbohydrate metabolism and mobilome. Functional genomics identified a well-equipped genetic portfolio for utilizing a wide range of C5 (xylose, arabinose), C6 (glucose, mannose, galactose), and C12 (cellobiose) sugars present in biomass hydrolysates, which was validated experimentally. For growth on individual xylose and glucose, the dominant sugars in biomass hydrolysates, B-768 exhibited distinct phenotypes and proteome profiles. Faster growth and glucose uptake rates resulted in lactate overflow metabolism, which makes B. coagulans a lactate overproducer; however, slower growth and xylose uptake diminished overflow metabolism due to the high energy demand for sugar assimilation. Carbohydrate Transport and Metabolism (COG-G), Translation (COG-J), and Energy Conversion and Production (COG-C) made up 60%-65% of the measured proteomes but were allocated differently when growing on xylose and glucose. The trade-off in proteome reallocation, with high investment in COG-C over COG-G, explains the xylose growth phenotype with significant upregulation of xylose metabolism, pyruvate metabolism, and tricarboxylic acid (TCA) cycle. Strain B-768 tolerates and effectively utilizes inhibitory biomass hydrolysates containing mixed sugars and exhibits hierarchical sugar utilization with glucose as the preferential substrate.IMPORTANCEThe robustness of B. coagulans makes it a valuable microorganism for biotechnology applications; yet, this phenotype is not well understood at the cellular level. Through phenotypic characterization and systems analysis, this study elucidates the functional genomics and robustness of a novel, undomesticated strain, B. coagulans B-768, capable of utilizing inhibitory switchgrass biomass hydrolysates. The genome of B-768, enriched with carbohydrate metabolism genes, demonstrates high regulatory capacity. The coordination of proteome reallocation in Carbohydrate Transport and Metabolism (COG-G), Translation (COG-J), and Energy Conversion and Production (COG-C) is critical for effective cell growth, sugar utilization, and lactate production via overflow metabolism. Overall, B-768 is a novel, robust, and promising B. coagulans strain that can be harnessed as a microbial biomanufacturing platform to produce chemicals and fuels from biomass hydrolysates., Competing Interests: The authors declare no conflict of interest.

Alcoholic liver cirrhosis (ALC) is a result of excessive and chronic alcohol consumption. Because alchol can cause DNA damage, extrachromosomal circular DNA (eccDNA) was investigated in ALC liver due to it can be a result of DNA damage. Considering eccDNA has ability to lead to genomic instability as an enhancer of gene transcription, we utilized Circle-Seq to identify differences in eccDNA profiles and gene expression patterns in liver samples obtained from ALC patients (n = 3) and healthy controls (n = 3) to investigate the role of eccDNA in the development of ALC. The abundance of eccDNA in ALC (mean = 13,349) were higher than the healthy control (mean = 11,557) without significant difference (pvalue = 0.6530). We observed 1,032 eccDNA containing genes showed higher expression in ALC patients compared to healthy controls (p < 0.05, log2FC > 1). Notably, we discovered seven genes that exhibited a significant positive correlation between eccDNA abundance and gene expression levels. These genes include A disintegrin and metalloproteinase with thrombospondin motifs 2 (ADAMTS2), Voltage-dependent L-type calcium channel subunit alpha-1C (CACNA1C), Protein TANC1 (TANC1), Integrin alpha-2 (ITGA2), EH domain-containing protein 4 (EHD4), Phosphofurin acidic cluster sorting protein 1 (PACS1), and Neuron navigator 2 (NAV2). Through mass spectrometry proteomics, ITGA2 were found to have significantly higher abbudance in ALC. Integrins are a family of proteins plays key roles in the fibrosis development of liver. Thus, our study opens a new perspective for liver fibrosis development., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Chronic obstructive pulmonary disease (COPD) is India's second largest cause of death and is largely caused by smoking. Asymptomatic smokers develop COPD due to genetic, environmental, and molecular variables, making early screening crucial. Data-independent acquisition mass spectrometry (DIA-MS) based-proteomics offers an unbiased method to analyze proteomic profiles. This study is the first to use DIA-based proteomics to analyze individual serum samples from three distinct male cohorts: healthy individuals ( n = 10), asymptomatic smokers ( n = 10), and COPD patients ( n = 10). This comprehensive approach identified 667 proteins with a 1% false discovery rate. Differentially expressed proteins included 40 in the normal versus asymptomatic comparison, 88 in the COPD versus normal comparison, and 40 in the COPD versus asymptomatic comparison. Among them, protein-associated genes such as PRDX6 , ELANE , PRKCSH , PRTN3 , and MNDA could help differentiate COPD from asymptomatic smokers, while ELANE , H3-3A , IGHE , SLC4A1 , and SERPINA11 could differentiate COPD from healthy subjects. Pathway enrichment and protein-protein interaction analyses revealed significant alterations in hemostasis, immune system functions, fibrin clot formation, and post-translational protein modifications. Key proteins were validated using a parallel reaction monitoring assay. DIA data are available via ProteomeXchange with identifier PXD055242. Our findings reveal key protein classifiers in COPD patients, asymptomatic smokers, and healthy individuals, helping clinicians understand disease pathobiology and improve disease management and quality of life.

Glioblastoma multiforme (GBM) is the most prevalent and aggressive brain tumor found in adult humans with a poor prognosis and average survival of 14-15 months. In order to have a comprehensive understanding of proteome and identify novel therapeutic targets, this study focused mainly on the differentially abundant proteins (DAPs) of Ras V12 -induced GBM. Ras V12 is a constitutively active Ras mutant form essential for tumor progression by continuously activating signaling pathways leading to uncontrolled tumor growth. This study used a transgenic Drosophila model with Ras V12 overexpression using the repo-GAL4 driver line, specifically in glial cells, to study GBM. The high-resolution mass spectrometry (HRMS)-based proteomic analysis of the GBM larval central nervous system identified three novel DAPs specific to mitochondria. These DAPs, probable maleylacetoacetate isomerase 2 (Q9VHD2), bifunctional methylene tetrahydrofolate dehydrogenase (Q04448), and glutamine synthetase1 (P20477), identified through HRMS were further validated by qRT-PCR. The protein-protein interaction analysis revealed interactions between Ras V12 and DAPs, with functional links to mitochondrial dynamics regulators such as Drp1, Marf, Parkin, and HtrA2. Notably, altered expressions of Q9VHD2, P20477, and Q04448 were observed during GBM progression, which offers new insights into the involvement of mitochondrial dynamic regulators in Ras V12 -induced GBM pathophysiology.

Protein copy numbers constrain systems-level properties of regulatory networks, but proportional proteomic data remain scarce compared to RNA-seq. We related mRNA to protein statistically using best-available data from quantitative proteomics and transcriptomics for 4366 genes in 369 cell lines. The approach starts with a protein's median copy number and hierarchically appends mRNA-protein and mRNA-mRNA dependencies to define an optimal gene-specific model linking mRNAs to protein. For dozens of cell lines and primary samples, these protein inferences from mRNA outmatch stringent null models, a count-based protein-abundance repository, empirical mRNA-to-protein ratios, and a proteogenomic DREAM challenge winner. The optimal mRNA-to-protein relationships capture biological processes along with hundreds of known protein-protein complexes, suggesting mechanistic relationships. We use the method to identify a viral-receptor abundance threshold for coxsackievirus B3 susceptibility from 1489 systems-biology infection models parameterized by protein inference. When applied to 796 RNA-seq profiles of breast cancer, inferred copy-number estimates collectively re-classify 26-29% of luminal tumors. By adopting a gene-centered perspective of mRNA-protein covariation across different biological contexts, we achieve accuracies comparable to the technical reproducibility of contemporary proteomics., (© 2024. The Author(s).)

Purpose: Recurrent pregnancy loss (RPL) represents a common disorder with consequences on family and society. As more than half of the RPL cases do not have a clearly identified cause, uncovering the mechanisms behind the idiopathic RPL is urgently needed., Experimental Design: Using label-free data-independent LC-MS/MS acquisition coupled with ion mobility, we compared the proteome of chorionic villi from 13 RPL cases with 10 age and gestational week-matched elective pregnancies. Transcriptional levels of selected candidate biomarkers were determined in chorionic villi of 35 RPL cases and 25 controls using quantitative polymerase chain reaction (qPCR)., Results: Statistically significant difference in abundance (Benjamini-Hochberg [B-H] p ≤ 0.05) and fold change ≥1.5 showed 128 proteins. Bioinformatics analysis identified complement and coagulation cascades, platelet activation, tricarboxylic acid cycle (TCA) cycle, and ferroptosis as pathways with the highest significance. Correlation with transcriptome datasets revealed a weak statistically significant positive correlation with 45% of the co-differentially expressed proteins/genes displaying the same regulation trend. The transcription levels of neurofilament light polypeptide (NEFL), dihydrolipoyllysine-residue succinyltransferase component of 2-oxoglutarate dehydrogenase complex&#95;mitochondrial (DLST), nitric oxide synthase 3 (NOS3), and ceruloplasmin (CP) were significantly increased in the RPL, consistent with the proteomics findings., Conclusions and Clinical Relevance: Our data suggests alteration of several pathways as potential causes of idiopathic RPL from the fetal side and opens the way for investigations concerning clinical management., (© 2024 Wiley‐VCH GmbH.)

Psychiatric disorders are highly heritable yet polygenic, potentially involving hundreds of risk genes. Genome-wide association studies have identified hundreds of genomic susceptibility loci with susceptibility to psychiatric disorders; however, the contribution of these loci to the underlying psychopathology and etiology remains elusive. Here we generated deep human brain proteomics data by quantifying 11,608 proteins across 268 subjects using 11-plex tandem mass tag coupled with two-dimensional liquid chromatography-tandem mass spectrometry. Our analysis revealed 788 cis-acting protein quantitative trait loci associated with the expression of 883 proteins at a genome-wide false discovery rate <5%. In contrast to expression at the transcript level and complex diseases that are found to be mainly influenced by noncoding variants, we found protein expression level tends to be regulated by non-synonymous variants. We also provided evidence of 76 shared regulatory signals between gene expression and protein abundance. Mediation analysis revealed that for most (88%) of the colocalized genes, the expression levels of their corresponding proteins are regulated by cis-pQTLs via gene transcription. Using summary data-based Mendelian randomization analysis, we identified 4 proteins and 19 genes that are causally associated with schizophrenia. We further integrated multiple omics data with network analysis to prioritize candidate genes for schizophrenia risk loci. Collectively, our findings underscore the potential of proteome-wide linkage analysis in gaining mechanistic insights into the pathogenesis of psychiatric disorders., (© 2024. The Author(s).)

In this study, "Longjia (LJ)" and "Yangpao (YP)"exhibited higher contents of major nutrients compared to "Niangqing (NQ)" walnuts. The combination of transcriptome and proteome by RNA sequencing and isotope labeling for relative and absolute quantification techniques provides new insights into the molecular mechanisms underlying the nutritional quality of the three walnut species. A total of 4146 genes and 139 proteins showed differential expression levels in the three comparison groups. Combined transcriptome and proteome analyses revealed that these genes and proteins were mainly enriched in signaling pathways such as fatty acid biosynthesis, protein processing in endoplasmic reticulum, and amino acid metabolism, revealing their relationship with the nutritional quality of walnut kernels. This study identified key genes and proteins associated with nutrient metabolism and accumulation in walnut kernels, provided transcriptomic and proteomic information on the molecular mechanisms of nutrient differences in walnut kernels, and contributed to the elucidation of the mechanisms of nutrient differences and the selection and breeding of high-quality walnut seedlings.

An important role of a particular synonymous codon composition of a gene in its expression level is well known. There are a number of algorithms optimizing codon usage of recombinant genes to maximize their expression in host cells. Nevertheless, the underlying mechanism remains unsolved and is of significant relevance. In the realm of modern biotechnology, directing protein production to a specific level is crucial for metabolic engineering, genome rewriting and a growing number of other applications. In this study, we propose two new simple statistical and empirical methods for predicting the protein expression level from the nucleotide sequence of the corresponding gene: Codon Expression Index Score (CEIS) and Codon Productivity Score (CPS). Both of these methods are based on the influence of each individual codon in the gene on the overall expression level of the encoded protein and the frequencies of isoacceptors in the species. Our predictions achieve a correlation level of up to r = 0.7 with experimentally measured quantitative proteome data of Escherichia coli , which is superior to any previously proposed methods. Our work helps understand how codons determine protein abundances. Based on these methods, it is possible to design proteins optimized for expression in a particular organism.

Cancer genomes are rife with genetic variants; one key outcome of this variation is widespread gain-of-cysteine mutations. These acquired cysteines can be both driver mutations and sites targeted by precision therapies. However, despite their ubiquity, nearly all acquired cysteines remain unidentified via chemoproteomics; identification is a critical step to enable functional analysis, including assessment of potential druggability and susceptibility to oxidation. Here, we pair cysteine chemoproteomics-a technique that enables proteome-wide pinpointing of functional, redox sensitive, and potentially druggable residues-with genomics to reveal the hidden landscape of cysteine genetic variation. Our chemoproteogenomics platform integrates chemoproteomic, whole exome, and RNA-seq data, with a customized two-stage false discovery rate (FDR) error controlled proteomic search, which is further enhanced with a user-friendly FragPipe interface. Chemoproteogenomics analysis reveals that cysteine acquisition is a ubiquitous feature of both healthy and cancer genomes that is further elevated in the context of decreased DNA repair. Reference cysteines proximal to missense variants are also found to be pervasive, supporting heretofore untapped opportunities for variant-specific chemical probe development campaigns. As chemoproteogenomics is further distinguished by sample-matched combinatorial variant databases and is compatible with redox proteomics and small molecule screening, we expect widespread utility in guiding proteoform-specific biology and therapeutic discovery., (© 2024. The Author(s).)

Whipple disease caused by Tropheryma whipplei a gram-positive bacterium is a systemic disorder that impacts not only the gastrointestinal tract but also the vascular system, joints, central nervous system, and cardiovascular system. Due to the lack of an approved vaccine, this study aimed to utilize immunoinformatic approaches to design multiepitope -based vaccine by utilizing the proteomes of five representative T. whipplei strains. The genomes initially comprised a total of 4,844 proteins ranging from 956 to 1012 proteins per strain. We collected 829 nonredundant lists of core proteins, that were shared among all the strains. Following subtractive proteomics, one extracellular protein, WP&#95;033800108.1, a WhiB family transcriptional regulator, was selected for the chimeric-based multiepitope vaccine. Five immunodominant epitopes were retrieved from the WhiB family transcriptional regulator protein, indicating MHC-I and MHC-II with a global population coverage of 70.61%. The strong binding affinity, high solubility, nontoxicity, nonallergenic properties and high antigenicity scores make the selected epitopes more appropriate. Integration of the epitopes into a chimeric vaccine was carried out by applying appropriate adjuvant molecules and linkers, leading to the vaccine construct having enhanced immunogenicity and successfully eliciting both innate and adaptive immune responses. Moreover, the abilityof the vaccine to bind TLR4, a core innate immune receptor, was confirmed. Molecular dynamics simulations have also revealed the promising potential stability of the designed vaccine at 400 ns. In summary, we have designed a potential vaccine construct that has the ability not only to induce targeted immunogenicity for one strain but also for global T. whipplei strains. This study proposes a potential universal vaccine, reducing Whipple's disease risk and laying the groundwork for future research on multi-strain pathogens., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This study analyzed transcriptomic and proteomic data to identify molecular changes during heart failure (HF). Additionally,we embarked on an exploration of the prospect of therapeutic intervention through the manipulation of proteins implicated in ferroptosis. Three publicly available microarray datasets (GSE135055, GSE147236, GSE161472) profiling left ventricular samples from HF patients and healthy controls were obtained. Differentially expressed genes were identified in each dataset and cross-analyzed to determine shared gene signatures. Enrichment analysis of Gene Ontology (GO) terms, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and gene set enrichment analysis were performed. Differentially expressed proteins were obtained from published proteomic studies and integrated with the transcriptomic results. To validate findings, a HF mouse model was generated and ferroptosis-related proteins were evaluated. Additionally, the effect of suppression of ferroptosis on hypoxia-induced ischemia model in HL-1 cardiomyocytes was assessed by knocking down Acyl-CoA synthetase long-chain family member 4 (ACSL4) using small interfering RNA (siRNA).Cross-analysis of differentially expressed genes (DEGs) in the GSE135055, GSE147236 and GSE161472 datasets revealed 224 up-regulated and 187 down-regulated potential genes which showed high enrichment in immune, inflammatory and metabolic pathways. Notably, four proteins, among them ACSL4, displayed consistent alterations at both the transcriptional and protein levels. In the HF mouse model, ACSL4 exhibited an elevation, whereas negative regulators of ferroptosis witnessed a decrement. Subsequently, knockdown of ACSL4 in a hypoxia-induced ischemic HL-1 cardiomyocyte cell model upregulated the expression of ferroptosis inhibitory protein and decreased the levels of reactive oxygen species (ROS), malondialdehyde (MDA)., and free iron and increased cell viability. Comprehensive multi-omics analysis revealed that the expression of the molecular target ACSL4 was increased in HF. Targeting ACSL4 to inhibit ferroptosis may represent a novel therapeutic strategy for HF treatment., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Lysine and arginine methylation is an important regulator of enzyme activity and transcription in eukaryotes. However, little is known about this covalent modification in bacteria. In this work, we investigated the role of methylation in bacteria. By reanalyzing a large phyloproteomics data set from 48 bacterial strains representing six phyla, we found that almost a quarter of the bacterial proteome is methylated. Many of these methylated proteins are conserved across diverse bacterial lineages, including those involved in central carbon metabolism and translation. Among the proteins with the most conserved methylation sites is ribosomal protein L11 (bL11). bL11 methylation has been a mystery for five decades, as the deletion of its methyltransferase PrmA causes no cell growth defects. Comparative proteomics analysis combined with inorganic polyphosphate and guanosine tetra/pentaphosphate assays of the ΔprmA mutant in Escherichia coli revealed that bL11 methylation is important for stringent response signaling. In the stationary phase, we found that the ΔprmA mutant has impaired guanosine tetra/pentaphosphate production. This leads to a reduction in inorganic polyphosphate levels, accumulation of RNA and ribosomal proteins, and an abnormal polysome profile. Overall, our investigation demonstrates that the evolutionarily conserved bL11 methylation is important for stringent response signaling and ribosomal activity regulation and turnover., Importance: Protein methylation in bacteria was first identified over 60 years ago. Since then, its functional role has been identified for only a few proteins. To better understand the functional role of methylation in bacteria, we analyzed a large phyloproteomics data set encompassing 48 diverse bacteria. Our analysis revealed that ribosomal proteins are often methylated at conserved residues, suggesting that methylation of these sites may have a functional role in translation. Further analysis revealed that methylation of ribosomal protein L11 is important for stringent response signaling and ribosomal homeostasis., Competing Interests: The authors declare no conflict of interest.

Background: Chlamydomonas reinhardtii is gaining recognition as a promising expression system for the production of recombinant proteins. However, its performance as a cellular biofactory remains suboptimal, especially with respect to consistent expression of heterologous genes. Gene silencing mechanisms, position effect, and low nuclear transgene expression are major drawbacks for recombinant protein production in this model system. To unveil the molecular changes following transgene insertion, retention, and expression in this species, we genetically engineered C. reinhardtii wild type strain 137c (strain cc-125 mt+) to express the fluorescent protein mVenus and subsequently analysed its intracellular proteome., Results: The obtained transgenic cell lines showed differences in abundance in more than 400 proteins, with multiple pathways altered post-transformation. Proteins involved in chromatin remodelling, translation initiation and elongation, and protein quality control and transport were found in lower abundance. On the other hand, ribosomal proteins showed higher abundance, a signal of ribosomal stress response., Conclusions: These results provide new insights into the modifications of C. reinhardtii proteome after transformation, highlighting possible pathways involved in gene silencing. Moreover, this study identifies multiple protein targets for future genetic engineering approaches to improve the prospective use of C. reinhardtii as cell biofactory for industrial applications., (© 2024. The Author(s).)

Genome-wide association studies have successfully identified many genetic risk loci for dementia, but exact biological mechanisms through which genetic risk factors contribute to dementia remains unclear. Integrating CSF proteomic data with dementia risk loci could reveal intermediate molecular pathways connecting genetic variance to the development of dementia. We tested to what extent effects of known dementia risk loci can be observed in CSF levels of 665 proteins [proximity extension-based (PEA) immunoassays] in a deeply-phenotyped mixed memory clinic cohort [n = 502, mean age (standard deviation, SD) = 64.1 (8.7) years, 181 female (35.4%)], including patients with Alzheimer's disease (AD, n = 213), dementia with Lewy bodies (DLB, n = 50) and frontotemporal dementia (FTD, n = 93), and controls (n = 146). Validation was assessed in independent cohorts (n = 99 PEA platform, n = 198, mass reaction monitoring-targeted mass spectroscopy and multiplex assay). We performed additional analyses stratified according to diagnostic status (AD, DLB, FTD and controls separately), to explore whether associations between CSF proteins and genetic variants were specific to disease or not. We identified four AD risk loci as protein quantitative trait loci (pQTL): CR1-CR2 (rs3818361, P = 1.65 × 10-8), ZCWPW1-PILRB (rs1476679, P = 2.73 × 10-32), CTSH-CTSH (rs3784539, P = 2.88 × 10-24) and HESX1-RETN (rs186108507, P = 8.39 × 10-8), of which the first three pQTLs showed direct replication in the independent cohorts. We identified one AD-specific association between a rare genetic variant of TREM2 and CSF IL6 levels (rs75932628, P = 3.90 × 10-7). DLB risk locus GBA showed positive trans effects on seven inter-related CSF levels in DLB patients only. No pQTLs were identified for FTD loci, either for the total sample as for analyses performed within FTD only. Protein QTL variants were involved in the immune system, highlighting the importance of this system in the pathophysiology of dementia. We further identified pQTLs in stratified analyses for AD and DLB, hinting at disease-specific pQTLs in dementia. Dissecting the contribution of risk loci to neurobiological processes aids in understanding disease mechanisms underlying dementia., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Guarantors of Brain.)

Recent advances in AI-based methods have revolutionized the field of structural biology. Concomitantly, high-throughput sequencing and functional genomics have generated genetic variants at an unprecedented scale. However, efficient tools and resources are needed to link disparate data types-to 'map' variants onto protein structures, to better understand how the variation causes disease, and thereby design therapeutics. Here we present the Genomics 2 Proteins portal ( https://g2p.broadinstitute.org/ ): a human proteome-wide resource that maps 20,076,998 genetic variants onto 42,413 protein sequences and 77,923 structures, with a comprehensive set of structural and functional features. Additionally, the Genomics 2 Proteins portal allows users to interactively upload protein residue-wise annotations (for example, variants and scores) as well as the protein structure beyond databases to establish the connection between genomics to proteins. The portal serves as an easy-to-use discovery tool for researchers and scientists to hypothesize the structure-function relationship between natural or synthetic variations and their molecular phenotypes., (© 2024. The Author(s).)

The integration of proteomics data with constraint-based reconstruction and analysis (COBRA) models plays a pivotal role in understanding the relationship between genotype and phenotype and bridges the gap between genome-level phenomena and functional adaptations. Integrating a generic genome-scale model with information on proteins enables generation of a context-specific metabolic model which improves the accuracy of model prediction. This review explores methodologies for incorporating proteomics data into genome-scale models. Available methods are grouped into four distinct categories based on their approach to integrate proteomics data and their depth of modeling. Within each category section various methods are introduced in chronological order of publication demonstrating the progress of this field. Furthermore, challenges and potential solutions to further progress are outlined, including the limited availability of appropriate in vitro data, experimental enzyme turnover rates, and the trade-off between model accuracy, computational tractability, and data scarcity. In conclusion, methods employing simpler approaches demand fewer kinetic and omics data, consequently leading to a less complex mathematical problem and reduced computational expenses. On the other hand, approaches that delve deeper into cellular mechanisms and aim to create detailed mathematical models necessitate more extensive kinetic and omics data, resulting in a more complex and computationally demanding problem. However, in some cases, this increased cost can be justified by the potential for more precise predictions., (© 2024 The Author(s). Protein Science published by Wiley Periodicals LLC on behalf of The Protein Society.)

INS C94Y transgenic pigs represent a model for mutant insulin gene-induced diabetes of youth, with impaired insulin secretion and beta cell loss, leading to elevated fasting blood glucose levels. A key complication of diabetes mellitus is diabetic retinopathy (DR), characterized by hyperglycemia-induced abnormalities in the retina. Adjacent to the retina lies the vitreous, a gelatinous matrix vital for ocular function. It harbors proteins and signaling molecules, offering insights into vitreous biology and ocular health. Moreover, as a reservoir for secreted molecules, the vitreous illuminates molecular processes within intraocular structures, especially under pathological conditions. To uncover the proteomic profile of porcine vitreous and explore its relevance to DR, we employed discovery proteomics to compare vitreous samples from INS C94Y transgenic pigs and wild-type controls. Our analysis identified 1404 proteins, with 266 showing differential abundance in INS C94Y vitreous. Notably, the abundances of ITGB1, COX2, and GRIFIN were significantly elevated in INS C94Y vitreous. Gene Set Enrichment Analysis unveiled heightened MYC and mTORC1 signaling in INS C94Y vitreous, shedding light on its biological significance in diabetes-associated ocular pathophysiology. These findings deepen our understanding of vitreous involvement in DR and provide valuable insights into potential therapeutic targets. Raw data are accessible via ProteomeXchange (PXD038198)., (© 2024 The Author(s). PROTEOMICS published by Wiley‐VCH GmbH.)

Current evidence suggests that some form of cellular organization arose well before the time of the last universal common ancestor (LUCA). Standard phylogenetic analyses have shown that several protein families associated with membrane translocation, membrane transport, and membrane bioenergetics were very likely present in the proteome of the LUCA. Despite these cellular systems emerging prior to the LUCA, extant archaea, bacteria, and eukaryotes have significant differences in cellular infrastructure and the molecular functions that support it, leading some researchers to argue that true cellularity did not evolve until after the LUCA. Here, we use recently reconstructed minimal proteomes of the LUCA as well as the last archaeal common ancestor (LACA) and the last bacterial common ancestor (LBCA) to characterize the evolution of cellular systems along the first branches of the tree of life. We find that a broad set of functions associated with cellular organization were already present by the time of the LUCA. The functional repertoires of the LACA and LBCA related to cellular organization nearly doubled along each branch following the divergence of the LUCA. These evolutionary trends created the foundation for similarities and differences in cellular organization between the taxonomic domains that are still observed today., (© 2024. The Author(s).)

Developmental biology has greatly profited from genetic and reverse genetic approaches to indirectly studying protein function. More recently, nanobodies and other protein binders derived from different synthetic scaffolds have been used to directly dissect protein function. Protein binders have been fused to functional domains, such as to lead to protein degradation, relocalization, visualization, or posttranslational modification of the target protein upon binding. The use of such functionalized protein binders has allowed the study of the proteome during development in an unprecedented manner. In the coming years, the advent of the computational design of protein binders, together with further advances in scaffold engineering and synthetic biology, will fuel the development of novel protein binder-based technologies. Studying the proteome with increased precision will contribute to a better understanding of the immense molecular complexities hidden in each step along the way to generate form and function during development.

Aspergillus fumigatus causes aspergillosis and relies on asexual spores (conidia) for initiating host infection. There is scarce information about A. fumigatus proteins involved in fungal evasion and host immunity modulation. Here we analysed the conidial surface proteome of A. fumigatus, two closely related non-pathogenic species, Aspergillus fischeri and Aspergillus oerlinghausenensis, as well as pathogenic Aspergillus lentulus, to identify such proteins. After identifying 62 proteins exclusively detected on the A. fumigatus conidial surface, we assessed null mutants for 42 genes encoding these proteins. Deletion of 33 of these genes altered susceptibility to macrophage, epithelial cells and cytokine production. Notably, a gene that encodes a putative glycosylasparaginase, modulating levels of the host proinflammatory cytokine IL-1β, is important for infection in an immunocompetent murine model of fungal disease. These results suggest that A. fumigatus conidial surface proteins are important for evasion and modulation of the immune response at the onset of fungal infection., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

In most Proteome-Wide Association Studies (PWAS), variants near the protein-coding gene (±1 Mb), also known as cis single nucleotide polymorphisms (SNPs), are used to predict protein levels, which are then tested for association with phenotypes. However, proteins can be regulated through variants outside of the cis region. An intermediate GWAS step to identify protein quantitative trait loci (pQTL) allows for the inclusion of trans SNPs outside the cis region in protein-level prediction models. Here, we assess the prediction of 540 proteins in 1002 individuals from the Women's Health Initiative (WHI), split equally into a GWAS set, an elastic net training set, and a testing set. We compared the testing r 2 between measured and predicted protein levels using this proposed approach, to the testing r 2 using only cis SNPs. The two methods usually resulted in similar testing r 2 , but some proteins showed a significant increase in testing r 2 with our method. For example, for cartilage acidic protein 1, the testing r 2 increased from 0.101 to 0.351. We also demonstrate reproducible findings for predicted protein association with lipid and blood cell traits in WHI participants without proteomics data and in UK Biobank utilizing our PWAS weights., (© 2024 Wiley Periodicals LLC.)

Background: Preeclampsia is a leading cause of maternal and perinatal morbidity and mortality. However, the current understanding of its underlying biological pathways remains limited., Methods: In this study, we performed a cross-platform proteome- and transcriptome-wide genetic analysis aimed at evaluating the causal relevance of >2000 circulating proteins with preeclampsia, supported by data on the expression of over 15 000 genes across 36 tissues leveraging large-scale preeclampsia genetic association data from women of European ancestry., Results: We demonstrate genetic associations of 18 circulating proteins with preeclampsia (SULT1A1 [sulfotransferase 1A1], SH2B3 [SH2B adapter protein 3], SERPINE2 [serpin family E member 2], RGS18 [regulator of G-protein signaling 18], PZP [pregnancy zone protein], NOTUM [notum, palmitoleoyl-protein carboxylesterase], METAP1 [methionyl aminopeptidase 1], MANEA [mannosidase endo-alpha], jun-D [JunD proto-oncogene], GDF15 [growth differentiation factor 15], FGL1 [fibrinogen like 1], FGF5 [fibroblast growth factor 5], FES [FES proto-oncogene], APOBR [apolipoprotein B receptor], ANP [natriuretic peptide A], ALDH-E2 [aldehyde dehydrogenase 2 family member], ADAMTS13 [ADAM metallopeptidase with thrombospondin type 1 motif 13], and 3MG [N-methylpurine DNA glycosylase]), among which 11 were either directly or indirectly supported by gene expression data, 9 were supported by Bayesian colocalization analyses, and 5 (SERPINE2, PZP, FGF5, FES, and ANP) were supported by all lines of evidence examined. Protein interaction mapping identified potential shared biological pathways through natriuretic peptide signaling, blood pressure regulation, immune tolerance, and thrombin activity regulation., Conclusions: This investigation identified multiple targetable proteins linked to cardiovascular, inflammatory, and coagulation pathways, with SERPINE2, PZP, FGF5, FES, and ANP identified as pivotal proteins with likely causal roles in the development of preeclampsia. The identification of these potential targets may guide the development of targeted therapies for preeclampsia., Competing Interests: Dr Natarajan reports research grants from Allelica, Amgen, Apple, Boston Scientific, Genentech/Roche, and Novartis; personal fees from Allelica, Apple, AstraZeneca, Blackstone Life Sciences, Creative Education Concepts, CRISPR Therapeutics, Eli Lilly & Co, Foresite Labs, Genentech/Roche, GV, HeartFlow, Magnet Biomedicine, Merck, and Novartis; scientific advisory board membership of Esperion Therapeutics, Preciseli, and TenSixteen Bio; is a scientific co-founder of TenSixteen Bio; has equity in MyOme, Preciseli, and TenSixteen Bio, and spousal employment at Vertex Pharmaceuticals, all unrelated to the present work. Dr Butterworth reports institutional grants from AstraZeneca, Bayer, Biogen, BioMarin, Bioverativ, Novartis, Regeneron, and Sanofi. Dr Honigberg reports consulting fees from Comanche Biopharma, advisory board service for Miga Health, and grant support from Genentech, all unrelated to the present work. The other authors report no conflicts.

Emerging and re-emerging viruses pose unpredictable and significant challenges to global health. Emerging zoonotic infectious diseases, which are transmitted between humans and non-human animals, have been estimated to be responsible for nearly two-thirds of emerging infectious disease events and emergence events attributed to these pathogens have been increasing in frequency with the potential for high global health and economic burdens. In this review we will focus on the application of highthroughput OMICS approaches to emerging zoonotic virus investigtations. We highlight the key contributions of transcriptome and proteome investigations to emerging zoonotic virus preparedness and response activities with a focus on SARS-CoV-2, avian influenza virus subtype H5N1, and Orthoebolavirus investigations., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. JK is funded by a Tier 2 Canada Research Chair in the Molecular Pathogenesis of Emerging and Re-Emerging Viruses provided by the Canadian Institutes of Health Research (grant no. 950-231498) and a Canadian Institutes of Health Research project grant (grant no. PJT-175098)., (Copyright © 2024. Published by Elsevier B.V.)

Backgroud/Objectives: Ferula spp. is an essential crop in Central Asia with pronounced economic benefits governed by its flowering process. However, the mechanisms of the flowering phenotype remain unclear. Methods: In this study, using F. sinkiangensis as a model plant, we integrated transcriptome, proteome, and metabolome analyses to compare the multilayer differences in leaves and roots of plants with flowering and unflowering phenotypes. Results: We found that several variations in the transcriptome, proteome, and metabolome were closely associated with flowering. The Photosynthesis and Phenylpropanoid biosynthesis pathways in plants with the flowering phenotype were more active. Additionally, three flowering genes, named FL2-FL4 , were upregulated in the leaves of flowering plants. Notably, six transcription factors were potentially responsible for regulating the expression of FL2-FL4 in the leaves to mediate flowering process of F. sinkiangensis . Moreover, genes relevant to Photosynthesis and Phenylpropanoid biosynthesis were also involved in regulating the expression of FL2-FL4 in flowering plants. Conclusions: The active regulation network together with Photosynthesis and Phenylpropanoid biosynthesis were essential for inducing the expression of flowering-related genes in leaves to promote the flowering process of F. sinkiangensis .

Background: As intensive aquaculture practices have progressed, the prevalence of bacterial diseases in the Chinese soft-shell turtle ( Pelodiscus sinensis ) has escalated, particularly infections caused by Aeromonas hydrophila , such as ulcerative dermatitis and abscess disease. Despite this, little is known about their immune defenses against this pathogen., Methods: Our study pioneers an integrated analysis of transcriptomics and proteomics to investigate the immune responses of Chinese soft-shelled turtles to A. hydrophila infection., Results: The investigation revealed significant differences in immune-related pathways between groups susceptible and resistant to A. hydrophila infection after 4 days. A total of 4667 and 3417 differentially expressed genes (DEGs), 763 and 568 differentially expressed proteins (DEPs), and 13 and 5 correlated differentially expressed genes and proteins (cor-DEGs-DEPs) were identified in susceptible and resistant Chinese soft-shelled turtles, respectively. In the resistant group, upregulation of immune-related genes, such as CD3ε and CD45, enhanced T-cell activation and the immune response. The proteomic analysis indicated that immune proteins, such as NF-κB1, were significantly upregulated in the resistant group. The correlation analysis between transcriptomics and proteomics demonstrated that the CD40 gene and protein, differentially expressed in the resistant group compared to the control group, were commonly upregulated within the Toll-like receptor signaling pathway., Conclusions: The transcriptomic and proteomic data obtained from this study provide a scientific foundation for understanding the immune mechanisms that enable the Chinese soft-shelled turtle to resist A. hydrophila infection.

We describe a next-generation Drosophila protein interaction map-"DPIM2"-established from affinity purification-mass spectrometry of 5,805 baits, covering the largest fraction of the Drosophila proteome. The network contains 32,668 interactions among 3,644 proteins, organized into 632 clusters representing putative functional modules. Our analysis expands the pool of known protein interactions in Drosophila, provides annotation for poorly studied genes, and postulates previously undescribed protein interaction relationships. The predictive power and functional relevance of this network are probed through the lens of the Notch signaling pathway, and we find that newly identified members of complexes that include known Notch modifiers can also modulate Notch signaling. DPIM2 allows direct comparisons with a recently published human protein interaction network, defining the existence of functional interactions conserved across species. Thus, DPIM2 defines a valuable resource for predicting protein co-complex memberships and functional associations as well as generates functional hypotheses regarding specific protein interactions., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Microorganisms are shown to actively partition their intracellular resources, such as proteins, for growth optimization. Recent experiments have begun to reveal molecular components unpinning the partition; however, quantitatively, it remains unclear how individual parts orchestrate to yield precise resource allocation that is both robust and dynamic. Here, we developed a coarse-grained mathematical framework that centers on guanosine pentaphosphate (ppGpp)-mediated regulation and used it to systematically uncover the design principles of proteome allocation in Escherichia coli . Our results showed that the cellular ability of resource partition lies in an ultrasensitive, negative feedback-controlling topology with the ultrasensitivity arising from zero-order amino acid kinetics and the negative feedback from ppGpp-controlled ribosome synthesis. In addition, together with the time-scale separation between slow ribosome kinetics and fast turnovers of ppGpp and amino acids, the network topology confers the organism an optimization mechanism that mimics sliding mode control, a nonlinear optimization strategy that is widely used in man-made systems. We further showed that such a controlling mechanism is robust against parameter variations and molecular fluctuations and is also efficient for biomass production over time. This work elucidates the fundamental controlling mechanism of E. coli proteome allocation, thereby providing insights into quantitative microbial physiology as well as the design of synthetic gene networks.

Unlike other cancers with widespread screening (breast, colorectal, cervical, prostate, and skin), lung nodule biopsies for positive screenings have higher morbidity with clinical complications. Development of non-invasive diagnostic biomarkers could thereby significantly enhance lung cancer management for at-risk patients. Here, we leverage Mendelian Randomization (MR) to investigate the plasma proteome and metabolome for potential biomarkers relevant to lung cancer. Utilizing bidirectional MR and co-localization analyses, we identify novel associations, highlighting inverse relationships between plasma proteins SFTPB and KDELC2 in lung adenocarcinoma (LUAD) and positive associations of TCL1A with lung squamous cell carcinoma (LUSC) and CNTN1 with small cell lung cancer (SCLC). Additionally, our work reveals significant negative correlations between metabolites such as theobromine and paraxanthine, along with paraxanthine-related ratios, in both LUAD and LUSC. Conversely, positive correlations are found in caffeine/paraxanthine and arachidonate (20:4n6)/paraxanthine ratios with these cancer types. Through single-cell sequencing data of normal lung tissue, we further explore the role of lung tissue-specific protein SFTPB in carcinogenesis. These findings offer new insights into lung cancer etiology, potentially guiding the development of diagnostic biomarkers and therapeutic approaches., (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Hepatocellular carcinoma (HCC) mortality rates continue to increase faster than those of other cancer types due to high heterogeneity, which limits diagnosis and treatment. Pathological and molecular subtyping have identified that HCC tumors with poor outcomes are characterized by intratumoral collagenous accumulation. However, the translational and post-translational regulation of tumor collagen, which is critical to the outcome, remains largely unknown. Here, we investigate the spatial extracellular proteome to understand the differences associated with HCC tumors defined by Hoshida transcriptomic subtypes of poor outcome (Subtype 1; S1; n = 12) and better outcome (Subtype 3; S3; n = 24) that show differential stroma-regulated pathways. Collagen-targeted mass spectrometry imaging (MSI) with the same-tissue reference libraries, built from untargeted and targeted LC-MS/MS was used to spatially define the extracellular microenvironment from clinically-characterized, formalin-fixed, paraffin-embedded tissue sections. Collagen α-1(I) chain domains for discoidin-domain receptor and integrin binding showed distinctive spatial distribution within the tumor microenvironment. Hydroxylated proline (HYP)-containing peptides from the triple helical regions of fibrillar collagens distinguished S1 from S3 tumors. Exploratory machine learning on multiple peptides extracted from the tumor regions could distinguish S1 and S3 tumors (with an area under the receiver operating curve of ≥0.98; 95% confidence intervals between 0.976 and 1.00; and accuracies above 94%). An overall finding was that the extracellular microenvironment has a high potential to predict clinically relevant outcomes in HCC.

This study presents the Maize PeptideAtlas resource (www.peptideatlas.org/builds/maize) to help solve questions about the maize proteome. Publicly available raw tandem mass spectrometry (MS/MS) data for maize collected from ProteomeXchange were reanalyzed through a uniform processing and metadata annotation pipeline. These data are from a wide range of genetic backgrounds and many sample types and experimental conditions. The protein search space included different maize genome annotations for the B73 inbred line from MaizeGDB, UniProtKB, NCBI RefSeq, and for the W22 inbred line. 445 million MS/MS spectra were searched, of which 120 million were matched to 0.37 million distinct peptides. Peptides were matched to 66.2% of proteins in the most recent B73 nuclear genome annotation. Furthermore, most conserved plastid- and mitochondrial-encoded proteins (NCBI RefSeq annotations) were identified. Peptides and proteins identified in the other B73 genome annotations will improve maize genome annotation. We also illustrate the high-confidence detection of unique W22 proteins. N-terminal acetylation, phosphorylation, ubiquitination, and three lysine acylations (K-acetyl, K-malonyl, and K-hydroxyisobutyryl) were identified and can be inspected through a PTM viewer in PeptideAtlas. All matched MS/MS-derived peptide data are linked to spectral, technical, and biological metadata. This new PeptideAtlas is integrated in MaizeGDB with a peptide track in JBrowse.

Cytoplasmic poly(A)-binding protein (PABPC; Pab1 in yeast) is thought to be involved in multiple steps of post-transcriptional control, including translation initiation, translation termination, and mRNA decay. To understand both the direct and indirect roles of PABPC in more detail, we have employed mass spectrometry to assess the abundance of the components of the yeast proteome, as well as RNA-Seq and Ribo-Seq to analyze changes in the abundance and translation of the yeast transcriptome, in cells lacking the PAB1 gene. We find that pab1Δ cells manifest drastic changes in the proteome and transcriptome, as well as defects in translation initiation and termination. Defects in translation initiation and the stabilization of specific classes of mRNAs in pab1Δ cells appear to be partly indirect consequences of reduced levels of specific initiation factors, decapping activators, and components of the deadenylation complex in addition to the general loss of Pab1's direct role in these processes. Cells devoid of Pab1 also manifested a nonsense codon readthrough phenotype indicative of a defect in translation termination. Collectively, our results indicate that, unlike the loss of simpler regulatory proteins, elimination of cellular Pab1 is profoundly pleiotropic and disruptive to numerous aspects of post-transcriptional regulation., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: A.J. is co-founder, director, and consultant for PTC Therapeutics Inc., (Copyright: © 2024 Mangkalaphiban et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

We performed a series of integrative analyses including transcriptome-wide association studies (TWASs) and proteome-wide association studies (PWASs) of renal cell carcinoma (RCC) to nominate and prioritize molecular targets for laboratory investigation. On the basis of a genome-wide association study (GWAS) of 29,020 affected individuals and 835,670 control individuals and prediction models trained in transcriptomic reference models, our TWAS across four kidney transcriptomes (GTEx kidney cortex, kidney tubules, TCGA-KIRC [The Cancer Genome Atlas kidney renal clear-cell carcinoma], and TCGA-KIRP [TCGA kidney renal papillary cell carcinoma]) identified 38 gene associations (false-discovery rate <5%) in at least two of four transcriptomic panels and identified 12 genes that were independent of GWAS susceptibility regions. Analyses combining TWAS associations across 48 tissues from GTEx identified associations that were replicable in tumor transcriptomes for 23 additional genes. Analyses by the two major histologic types (clear-cell RCC and papillary RCC) revealed subtype-specific associations, although at least three gene associations were common to both subtypes. PWAS identified 13 associated proteins, all mapping to GWAS-significant loci. TWAS-identified genes were enriched for active enhancer or promoter regions in RCC tumors and hypoxia-inducible factor binding sites in relevant cell lines. Using gene expression correlation, common cancers (breast and prostate) and RCC risk factors (e.g., hypertension and BMI) display genetic contributions shared with RCC. Our work identifies potential molecular targets for RCC susceptibility for downstream functional investigation., Competing Interests: Declaration of interests K.S. receives research support from Gilead, GSK, Novo Nordisk, Bayer, Regeneron, Calico, and Novartis. K.S. is on the advisory board of Jnana Therapeutics and has received consulting fees from Pfizer and Jnana. Declaration of interests for the Renal Cancer Genetics Consortium, which contributed toward acquisition of the individual-level data, can be referred to as a part of Purdue et al.(10) The other named authors in the manuscript have no competing interests to declare. It is to be noted that the individual-level data contributed by The Renal Cancer Genetics Consortium is now publicly available, and the current analyses were conducted with summary-level data only., (Published by Elsevier Inc.)

Transcriptome-wide association study (TWAS) tools have been applied to conduct proteome-wide association studies (PWASs) by integrating proteomics data with genome-wide association study (GWAS) summary data. The genetic effects of PWAS-identified significant genes are potentially mediated through genetically regulated protein abundance, thus informing the underlying disease mechanisms better than GWAS loci. However, existing TWAS/PWAS tools are limited by considering only one statistical model. We propose an omnibus PWAS pipeline to account for multiple statistical models and demonstrate improved performance by simulation and application studies of Alzheimer disease (AD) dementia. We employ the Aggregated Cauchy Association Test to derive omnibus PWAS (PWAS-O) p values from PWAS p values obtained by three existing tools assuming complementary statistical models-TIGAR, PrediXcan, and FUSION. Our simulation studies demonstrated improved power, with well-calibrated type I error, for PWAS-O over all three individual tools. We applied PWAS-O to studying AD dementia with reference proteomic data profiled from dorsolateral prefrontal cortex of postmortem brains from individuals of European ancestry. We identified 43 risk genes, including 5 not identified by previous studies, which are interconnected through a protein-protein interaction network that includes the well-known AD risk genes TOMM40, APOC1, and APOC2. We also validated causal genetic effects mediated through the proteome for 27 (63%) PWAS-O risk genes, providing insights into the underlying biological mechanisms of AD dementia and highlighting promising targets for therapeutic development. PWAS-O can be easily applied to studying other complex diseases., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

Whole genome and exome sequencing are reporting on hundreds of thousands of missense mutations. Taking a pan-disease approach, we explored how mutations in intrinsically disordered regions (IDRs) break or generate protein interactions mediated by short linear motifs. We created a peptide-phage display library tiling ~57,000 peptides from the IDRs of the human proteome overlapping 12,301 single nucleotide variants associated with diverse phenotypes including cancer, metabolic diseases and neurological diseases. By screening 80 human proteins, we identified 366 mutation-modulated interactions, with half of the mutations diminishing binding, and half enhancing binding or creating novel interaction interfaces. The effects of the mutations were confirmed by affinity measurements. In cellular assays, the effects of motif-disruptive mutations were validated, including loss of a nuclear localisation signal in the cell division control protein CDC45 by a mutation associated with Meier-Gorlin syndrome. The study provides insights into how disease-associated mutations may perturb and rewire the motif-based interactome., (© 2024. The Author(s).)

Evolutionary relationships among parasites of the subfamily Leishmaniinae, which comprises pathogen agents of leishmaniasis, were inferred based on differential protein expression profiles from mass spectrometry-based quantitative data using the PhyloQuant method. Evolutionary distances following identification and quantification of protein and peptide abundances using Proteome Discoverer and MaxQuant software were estimated for 11 species from six Leishmaniinae genera. Results clustered all dixenous species of the genus Leishmania, subgenera L. (Leishmania), L. (Viannia), and L. (Mundinia), sister to the dixenous species of genera Endotrypanum and Porcisia. Placed basal to the assemblage formed by all these parasites were the species of genera Zelonia, Crithidia, and Leptomonas, so far described as monoxenous of insects although eventually reported from humans. Inferences based on protein expression profiles were congruent with currently established phylogeny using DNA sequences. Our results reinforce PhyloQuant as a valuable approach to infer evolutionary relationships within Leishmaniinae, which is comprised of very tightly related trypanosomatids that are just beginning to be phylogenetically unraveled. In addition to evolutionary history, mapping of species-specific protein expression is paramount to understand differences in infection processes, tissue tropisms, potential to jump from insects to vertebrates including humans, and targets for species-specific diagnostic and drug development., (© 2024 Wiley‐VCH GmbH.)

The GET pathway is associated with post-translational delivery of tail-anchored (TA) proteins to the endoplasmic reticulum (ER) in yeast, as well as other eukaryotes. Moreover, dysfunction of the GET pathway has been associated with various pathological conditions (i.e., neurodegenerative disorders, cardiovascular ailments, and protein misfolding diseases). In this study, we used yeast deletion strains of Get complex members (specifically, Get1, Get2, Get3, Get4, and Get5) coupled with sample multiplexing-based quantitative mass spectrometry to profile protein abundance on a proteome-wide scale across the five individual deletion strains. Our dataset consists of over 4500 proteins, which corresponds to >75% of the yeast proteome. The data reveal several dozen proteins that are differentially abundant in one or more deletion strains, some of which are membrane-associated, yet the abundance of many TA proteins remained unchanged. This study provides valuable insights into the roles of these Get genes, and the potential for alternative pathways which help maintain cellular function despite the disruption of the GET pathway., (© 2023 Wiley‐VCH GmbH.)

Large yellow croaker (Larimichthys crocea) is susceptible to oxidative denaturation during storage. This work is to investigate the quality alterations by analyzing its physicochemical changes and proteomics throughout preservation under refrigeration, frozen, and slurry ice (SI) conditions. Results revealed that the freshness of large yellow croaker, as evaluated by indicators such as total volatile basic nitrogen, total viable count, and thiobarbituric acid reactive substances, was well maintained while stored in the SI group. Meanwhile, the water distribution in the muscle tissue of group SI exhibited slower fluctuations, thereby preserving the integrity of fish muscle cells. Based on label-free proteomic analysis, a considerable downregulation was observed in the mitogen-activated protein kinase (MAPK) signaling pathway, indicating that SI decelerated this metabolic pathway and effectively delayed the deterioration of muscle. Therefore, the application of SI provides potential for maintaining the quality stability of large yellow croaker., Competing Interests: Declaration of competing interest The authors declare that they do not possess any recognized competing financial interests or personal relationships that could have potentially influenced the findings presented in this research., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Microbial sample analysis has received growing attention within the last decade, driven by important findings in microbiome research and promising applications in the biotechnological field. Modern mass spectrometry-based methodology has been established in this context, providing sufficient sensitivity, resolution, dynamic range, and throughput to analyze the so-called metaproteome of complex microbial mixtures from clinical or environmental samples. While proteomic analyses were previously restricted to common model organisms, next-generation sequencing technologies nowadays allow for the rapid and cost-efficient characterization of whole metagenomes of microbial consortia and specific genomes from non-model organisms to which microbes contribute by significant amounts. This proteogenomic approach, meaning the combined application of genomic and proteomic methods, enables researchers to create a protein database that presents a tailored blueprint of the microbial sample under investigation. This contribution provides an overview of the computational challenges and opportunities in proteogenomics and metaproteomics as of January 2018. For practical application, we first showcase an integrative proteogenomic method that circumvents existing reference databases by creating sample-specific transcripts. The underlying algorithm uses a graph network approach that combines RNA-Seq and peptide information. As a second example, we provide a tutorial for a simulation tool that estimates the computational limits of detecting microbial non-model organisms. This method evaluates the potential influence of error-tolerant searches and proteogenomic approaches on databases of interest. Finally, we discuss recommendations for developing future strategies that may help overcome present limitations by combining the strengths of genome- and proteome-based methods and moving toward an integrated metaproteogenomics approach., (© 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

During the last three decades, technological advancements in high-throughput next-generation sequencing have resulted in an increased understanding of proteomic and genomic data, aptly termed proteogenomics. Efforts in developing such approaches have not only been limited but also focused on protein identification and subcellular localization. These approaches, however, have also been explored for their broad understanding of how genomics/transcriptomics data have yielded measures, for example, gene expression regulation/signal cascading and diseasome studies. In this review, we discuss methods and tools developed through sequence-centric integrative modeling of proteogenomic approaches., (© 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Osteoblasts dysfunction, induced by oxidative stress (OS), is a significant contributor to the pathogenesis of osteoporosis. However, the genes implicated in regulating osteoblast dysfunction remain unclear. Here, we employed the hydrogen peroxide (H 2 O 2 )-induced osteoblast dysfunction model to assess its impact on osteoblast phenotype and to conduct transcriptome and proteome analyses in osteoblasts under OS. We identified 164 genes and 186 proteins with altered expression (differentially expressed genes (DEGs) and differentially expressed proteins (DEPs), respectively). Functional analysis using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways revealed enrichment in pathways associated with apoptosis and osteoblast differentiation. We constructed a protein-protein interaction (PPI) network of DEPs, which comprised 175 DEPs as nodes. Furthermore, seven key DEGs and DEPs with positive correlation (cor-DEGs-DEPs genes) were characterized based on the integrated analysis of mRNA-protein expression. Among these seven genes, Ho-1, Fosl1, and Fosl2 were shown to be upregulated, associated with OS-induced cell differentiation impairment and apoptosis. Conversely, Ccnd2, Col1α1, Col12α1, and Fgfr2 were shown to be downregulated, linked to OS-induced cell cycle delay, apoptosis, impaired mineralization, and differentiation. PPI analysis revealed interactions between these key genes. Lastly, we validated these genes at both mRNA and protein levels using qRT-PCR and Western blot experiments. This study identified seven candidate genes potentially involved in the detrimental effects of OS on MC3T3-E1 apoptosis and dysfunction. These findings offer new insights into how OS disrupts bone formation and may contribute to the development of osteoporosis., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Understanding how gene-environment interactions (GEIs) influence the circulating proteome could aid in biomarker discovery and validation. The presence of GEIs can be inferred from single nucleotide polymorphisms that associate with phenotypic variability - termed variance quantitative trait loci (vQTLs). Here, vQTL association studies are performed on plasma levels of 1463 proteins in 52,363 UK Biobank participants. A set of 677 independent vQTLs are identified across 568 proteins. They include 67 variants that lack conventional additive main effects on protein levels. Over 1100 GEIs are identified between 101 proteins and 153 environmental exposures. GEI analyses uncover possible mechanisms that explain why 13/67 vQTL-only sites lack corresponding main effects. Additional analyses also highlight how age, sex, epistatic interactions and statistical artefacts may underscore associations between genetic variation and variance heterogeneity. This study establishes the most comprehensive database yet of vQTLs and GEIs for the human proteome., (© 2024. The Author(s).)

Metamonads are a diverse group of heterotrophic microbial eukaryotes adapted to living in hypoxic environments. All metamonads but one harbour metabolically altered 'mitochondrion-related organelles' (MROs) with reduced functions, however the degree of reduction varies. Here, we generate high-quality draft genomes, transcriptomes, and predicted proteomes for five recently discovered free-living metamonads. Phylogenomic analyses placed these organisms in a group we name the 'BaSk' (Barthelonids+Skoliomonads) clade, a deeply branching sister group to the Fornicata, a phylum that includes parasitic and free-living flagellates. Bioinformatic analyses of gene models shows that these organisms are predicted to have extremely reduced MRO proteomes in comparison to other free-living metamonads. Loss of the mitochondrial iron-sulfur cluster assembly system in some organisms in this group appears to be linked to the acquisition in their common ancestral lineage of a SUF-like minimal system Fe/S cluster pathway by lateral gene transfer. One of the isolates, Skoliomonas litria, appears to have lost all other known MRO pathways. No proteins were confidently assigned to the predicted MRO proteome of this organism suggesting that the organelle has been lost. The extreme mitochondrial reduction observed within this free-living anaerobic protistan clade demonstrates that mitochondrial functions may be completely lost even in free-living organisms., (© 2024. The Author(s).)

A computational analysis of mass spectrometry data was performed to uncover alternative splicing derived protein variants across chambers of the human heart. Evidence for 216 non-canonical isoforms was apparent in the atrium and the ventricle, including 52 isoforms not documented on SwissProt and recovered using an RNA sequencing derived database. Among non-canonical isoforms, 29 show signs of regulation based on statistically significant preferences in tissue usage, including a ventricular enriched protein isoform of tensin-1 (TNS1) and an atrium-enriched PDZ and LIM Domain 3 (PDLIM3) isoform 2 (PDLIM3-2/ALP-H). Examined variant regions that differ between alternative and canonical isoforms are highly enriched with intrinsically disordered regions. Moreover, over two-thirds of such regions are predicted to function in protein binding and RNA binding. The analysis here lends further credence to the notion that alternative splicing diversifies the proteome by rewiring intrinsically disordered regions, which are increasingly recognized to play important roles in the generation of biological function from protein sequences.

Lactylation (Kla), a recently discovered post-translational modification derived from lactate, plays crucial roles in various cellular processes. However, the specific influence of lactylation on the biological processes underlying hypertrophic scar formation remains unclear. In this study, we present a comprehensive profiling of the lactylome and proteome in both hypertrophic scars and adjacent normal skin tissues. A total of 1023 Kla sites originating from 338 nonhistone proteins were identified based on lactylome analysis. Proteome analysis in hypertrophic scar and adjacent skin samples revealed the identification of 2008 proteins. It is worth noting that Kla exhibits a preference for genes associated with ribosome function as well as glycolysis/gluconeogenesis in both normal skin and hypertrophic scar tissues. Furthermore, the functional enrichment analysis demonstrated that differentially lactyled proteins are primarily involved in proteoglycans, HIF-1, and AMPK signaling pathways. The combined analysis of the lactylome and proteome data highlighted a significant upregulation of 14 lactylation sites in hypertrophic scar tissues. Overall, our investigation unveiled the significant involvement of protein lactylation in the regulation of ribosome function as well as glycolysis/gluconeogenesis, potentially contributing to the formation of hypertrophic scars.

Genome-wide association studies (GWAS) have become increasingly popular for detecting numerous loci associated with intracranial aneurysm (IA), but how these loci function remains unclear. In this study, we employed an integrative analytical pipeline to efficiently transform genetic associations and identify novel genes for IA. Using multidimensional high-throughput data, we integrated proteome-wide association studies (PWAS), transcriptome-wide association studies (TWAS), Mendelian randomization (MR) and Bayesian co-localization analyses to prioritize genes that can increase IA risk by altering their expression and protein abundances in the brain and blood. Moreover, single-cell RNA sequencing (scRNA-seq) of the circle of Willis was performed to enrich filtered genes in cells, and gene set enrichment analysis (GSEA) was conducted for each gene using bulk RNA-seq data for IA. No significant genes with cis-regulated plasma protein levels were proven to be associated with IA. The protein abundances of five genes in the brain were found to be associated with IA. According to cellular enrichment analysis, these five genes were expressed mainly in the endothelium, fibroblasts and vascular smooth muscle cells. Only three genes, CNNM2, GPRIN3 and UFL1, passed MR and Bayesian co-localization analyses. While UFL1 was not validated in confirmation PWAS as it was not profiled, it was validated in TWAS. GSEA suggested these three genes are associated with the cell cycle. In addition, the protein abundance of CNNM2 was found to be associated with IA rupture (based on PWAS, MR and co-localization analyses). Our findings indicated that CNNM2, GPRIN3 and UFL1 (CNNM2 correlated with IA rupture) are potential IA risk genes that may provide a broad hint for future research on possible mechanisms and therapeutic targets for IA., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

The missense tolerance ratio (MTR) was developed as a novel approach to assess the deleteriousness of variants. Its three-dimensional successor, MTR3D, was demonstrated powerful at discriminating pathogenic from benign variants. However, its reliance on experimental structures and homologs limited its coverage of the proteome. We have now utilized AlphaFold2 models to develop MTR3D-AF2, which covers 89.31% of proteins and 85.39% of residues across the human proteome. This work has improved MTR3D's ability to distinguish clinically established pathogenic from benign variants. MTR3D-AF2 is freely available as an interactive web server at https://biosig.lab.uq.edu.au/mtr3daf2/., (© 2024 The Author(s). Protein Science published by Wiley Periodicals LLC on behalf of The Protein Society.)

Background: The underlying pathogenesis of anxiety remain elusive, making the pinpointing of potential therapeutic and diagnostic biomarkers for anxiety paramount to its efficient treatment., Methods: We undertook a proteome-wide association study (PWAS), fusing human brain proteomes from both discovery (ROS/MAP; N = 376) and validation cohorts (Banner; N = 152) with anxiety genome-wide association study (GWAS) summary statistics. Complementing this, we executed transcriptome-wide association studies (TWAS) leveraging human brain transcriptomic data from the Common Mind Consortium (CMC) to discern the confluence of genetic influences spanning both proteomic and transcriptomic levels. We further scrutinized significant genes through a suite of methodologies., Results: We discerned 14 genes instrumental in the genesis of anxiety through their specific cis-regulated brain protein abundance. Out of these, 6 were corroborated in the confirmatory PWAS, with 4 also showing associations with anxiety via their cis-regulated brain mRNA levels. A heightened confidence level was attributed to 5 genes (RAB27B, CCDC92, BTN2A1, TMEM106B, and DOC2A), taking into account corroborative evidence from both the confirmatory PWAS and TWAS, coupled with insights from mendelian randomization analysis and colocalization evaluations. A majority of the identified genes manifest in brain regions intricately linked to anxiety and predominantly partake in lysosomal metabolic processes., Limitations: The limited scope of the brain proteome reference datasets, stemming from a relatively modest sample size, potentially curtails our grasp on the entire gamut of genetic effects., Conclusion: The genes pinpointed in our research present a promising groundwork for crafting therapeutic interventions and diagnostic tools for anxiety., Competing Interests: Declaration of competing interest None., (Copyright © 2024 Elsevier B.V. All rights reserved.)

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 N 6 -methyladenosine (m 6 A) across the rice major tissues shows that m 6 A 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 m 6 A methylation. Taken together, this study provides a paradigm for further research into rice proteogenome., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

Clonal hematopoiesis of indeterminate potential (CHIP), whereby somatic mutations in hematopoietic stem cells confer a selective advantage and drive clonal expansion, not only correlates with age but also confers increased risk of morbidity and mortality. Here, we leverage genetically predicted traits to identify factors that determine CHIP clonal expansion rate. We used the passenger-approximated clonal expansion rate method to quantify the clonal expansion rate for 4,370 individuals in the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) cohort and calculated polygenic risk scores for DNA methylation aging, inflammation-related measures and circulating protein levels. Clonal expansion rate was significantly associated with both genetically predicted and measured epigenetic clocks. No associations were identified with inflammation-related lab values or diseases and CHIP expansion rate overall. A proteome-wide search identified predicted circulating levels of myeloid zinc finger 1 and anti-Müllerian hormone as associated with an increased CHIP clonal expansion rate and tissue inhibitor of metalloproteinase 1 and glycine N-methyltransferase as associated with decreased CHIP clonal expansion rate. Together, our findings identify epigenetic and proteomic patterns associated with the rate of hematopoietic clonal expansion., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)