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PubMed2026

Human IL-34 Deficiency Primes Microglia Toward Alzheimer's Disease-Associated States.

BACKGROUND: Genome-wide association studies (GWAS), with independent replication in large European consortia, have identified a common nonsense variant in IL-34 (Y213X) as a genetic risk factor for late-onset Alzheimer's disease (AD). However, the biological consequences of this IL-34 mutation in humans, its prevalence in the population, and the mechanisms by which IL-34-Y213X alters microglial homeostasis, cerebrospinal fluid (CSF) proteomic networks, and amyloid pathology remain poorly understood. METHODS: We combined human genetics, cerebrospinal fluid (CSF) and serum proteomics, transcriptomics, large-scale phenome-wide association analyses, and preclinical experimental models to define the impact of human IL-34 deficiency. IL-34 concentrations were first quantified in CSF and serum from deeply phenotyped AD cohorts stratified by the common IL-34-Y213X nonsense variant. IL-34 levels and IL-34-Y213X status were then integrated with unbiased CSF proteomic networks and AD biomarkers. Transcriptomic profiling of purified microglia from IL-34 knockout mice was performed to assess disease-associated microglial programs. Using APP/PS1 mice lacking IL-34, we examined the effects of IL-34 deficiency on microglial survival, tiling, and plaque encapsulation. Finally, we performed postmortem analyses of temporal cortex from AD patients carrying IL-34-Y213X to assess microglial density, spatial organization, and plaque-associated responses. FINDINGS: IL-34-Y213X was a strong, dose-dependent loss-of-function (LOF) allele that reduced IL-34 levels by up to 2.5 standard deviations in CSF and serum and was common in multiple populations. IL-34 deficiency reshaped CSF proteomic networks, downregulating axon guidance and microglial support modules while upregulating inflammatory and extracellular matrix signatures, and showed pleiotropic associations with neurological, inflammatory, and metabolic traits. Transcriptomic analysis of sorted microglia from healthy 9-month-old IL-34KO compare to wild-type mice revealed a profound pro-inflammatory and disease-associated microglial transcriptional program enriched for disease-associated microglia (DAM) signatures, inflammatory pathways, and AD risk genes including APOE, CLU, and CASS4. In APP/PS1 mice, genetic IL-34 deletion selectively depleted homeostatic gray-matter microglia, disrupted microglial tiling, and impaired plaque encapsulation, resulting in altered amyloid structure and enhancing neuritic injury. Concordantly, AD patients homozygous for IL-34-Y213X displayed markedly reduced cortical microglial density and increased microglial spatial dispersion, indicating a breakdown of the microglial network organization in the human brain. INTERPRETATION: A common human IL-34 LOF variant creates a naturally occurring model of IL-34 deficiency that links microglial survival, CSF network signatures, and amyloid pathology in both mice and humans. Importantly, IL-34 deficiency alone is sufficient to induce inflammatory, AD-associated microglial states beyond simply reducing microglial number. These findings identify IL-34/CSF1R signaling as a critical determinant of microglial resilience and a potential upstream pathway linking human genetic variation to AD susceptibility, highlighting IL-34-dependent pathways as promising targets for disease modification. FUNDING: This work was supported by grants from the Spanish Ministerio de Ciencia, Innovación y Universidades/FEDER/UE (PID2024-157400OB-I00) and FORTALECE program (FORT23/00008; Instituto de Salud Carlos III, Spain) to RRL and JLV, ISCIII of Spain co-financed by FEDER funds (European Union) through grants PI24/00308 (JV) and CIBERNED collaborative grant 2022/01 to JV, PID2023-147125OB-I00 and CEX2023-001386-S (Severo Ochoa Programme) to SMTBC. A.R. is supported by STAR Award. University of Texas System. Tx, United States, The South Texas ADRC. National Institute of Aging. National Institutes of Health. USA. (P30AG066546), the Keith M. Orme and Pat Vigeon Orme Endowed Chair in Alzheimer's and Neurodegenerative Diseases (2024-2025) and Patricia Ruth Frederick Distinguished Chair for Precision Therapeutics in Alzheimer's and Neurodegenerative Diseases (2025-2028). AR is also supported by the Agency for Innovation and Entrepreneurship (VLAIO) grant N° PR067/21 for the HARPONE project and the ADAPTED project the EU/EFPIA Innovative Medicines Initiative Joint Undertaking Grant N° 115975 and CIBERNED (ISCIII).

Journal Article↗

PubMed2026

Proteomic and Metabolomic Analysis of Immune-Related Adverse Events in Patients Treated with PD-1 Inhibitors.

As a class of immune checkpoint inhibitors (ICIs), programmed cell death protein-1 (PD-1) blockade has demonstrated remarkable efficacy in the treatment of various malignancies. However, their clinical application is constrained by the high incidence of immune-related adverse events (irAEs), which arise from nonspecific immune activation and can affect multiple organ systems, with severe cases posing life-threatening risks. This study integrated high-throughput proteomic and metabolomic analyses to systematically characterize the molecular features associated with irAEs in cancer patients receiving PD-1 inhibitor therapy. The results showed that, following the first treatment, patients who developed irAEs exhibited potential involvement of the NF-κB pathway, along with lower baseline levels of SNRPA and higher expression of CD63. Metabolomic analyses further revealed that the kynurenine/tryptophan ratio was significantly elevated in the irAE group both at baseline and post-treatment compared with patients who did not develop irAEs. In addition, significant differences in the abundance of specific lipids were observed between the two groups prior to the administration of immunotherapy. Our findings provide exploratory insights into immune and metabolic alterations associated with PD-1 blockade treatment and may help generate hypotheses for future studies on early irAE risk assessment in cancer patients undergoing PD-1 blockade therapy.

Humans↗

PubMed2000

Human ERp29: isolation, primary structural characterisation and two-dimensional gel mapping.

Recently we characterised a novel 29 kDa endoplasmic reticulum protein that is widely expressed in rat tissues, and named it ERp29. Several ERp29-like gene products have been reported in human tissues but uncertainty surrounds their relationships with each other and rat ERp29. To clarify these issues, ERp29 was isolated from human liver and characterised by primary structural analysis and two-dimensional gel mapping. Comparisons with rat ERp29 revealed striking homologies both in sequence and physical properties. Characterisation of the isoelectric heterogeneity and anomalous mass on two-dimensional gels enabled two reported homologues (UL35 and ERp31) to be identified as ERp29. Resolution of a sequence discrepancy led to unequivocal correlation of human ERp29 with the cognate cDNA previously named ERp31 and ERp28. Consequent links established to human genome and proteome projects showed that ERp29 is encoded by a gene on chromosome 12 that is expressed universally in human tissues. Together, these findings unified various ERp29 homologues as products of a single gene orthologous to rat ERp29 and established ERp29 as the only known member of a new protein class. Investigations of ERp29 function in human health and disease should benefit from the integrated links between genome, proteome and murine model organisms established here.

Amino Acid Sequence↗

PubMed2006

Construction of quantitative proteome reference maps of mouse spleen and lymph node based on two-dimensional gel electrophoresis.

Quantitative features of the proteome are extremely useful for studying cellular processes at a molecular level. In this study, we attempted to construct quantitative reference proteome maps of the mouse spleen and lymph node based on 2-DE followed by protein identification using MS. We analyzed more than 1000 spots on the 2-DE images and consequently were able to determine that 919 spots were derived from 328 different genes. To obtain statistically reliable information of the protein levels from these 2-DE images, we measured the volumes of the respective spots on 2-DE images obtained by four to six independent experimental runs. These measurements were used to calculate the variability of the volumes of the respective spots on 2-DE following subcellular fractionation, which enabled us to discriminate differentially produced proteins from those within the range of intrinsic variability. More importantly, while the 2-DE data have been traditionally collected in a gel image-based manner, the resultant quantitative 2-DE data could be analyzed using the same procedure as that for mRNA expression profiles. This greatly assists in bridging the gap between the analyses of transcriptomes and proteomes and enables the integration of this data on the same informational platform.

Amino Acid Sequence↗

PubMed2004

Web-accessible proteome databases for microbial research.

The analysis of proteomes of biological organisms represents a major challenge of the post-genome era. Classical proteomics combines two-dimensional electrophoresis (2-DE) and mass spectrometry (MS) for the identification of proteins. Novel technologies such as isotope coded affinity tag (ICAT)-liquid chromatography/mass spectrometry (LC/MS) open new insights into protein alterations. The vast amount and diverse types of proteomic data require adequate web-accessible computational and database technologies for storage, integration, dissemination, analysis and visualization. A proteome database system (http://www.mpiib-berlin.mpg.de/2D-PAGE) for microbial research has been constructed which integrates 2-DE/MS, ICAT-LC/MS and functional classification data of proteins with genomic, metabolic and other biological knowledge sources. The two-dimensional polyacrylamide gel electrophoresis database delivers experimental data on microbial proteins including mass spectra for the validation of protein identification. The ICAT-LC/MS database comprises experimental data for protein alterations of mycobacterial strains BCG vs. H37Rv. By formulating complex queries within a functional protein classification database "FUNC_CLASS" for Mycobacterium tuberculosis and Helicobacter pylori the researcher can gather precise information on genes, proteins, protein classes and metabolic pathways. The use of the R language in the database architecture allows high-level data analysis and visualization to be performed "on-the-fly". The database system is centrally administrated, and investigators without specific bioinformatic competence in database construction can submit their data. The database system also serves as a template for a prototype of a European Proteome Database of Pathogenic Bacteria. Currently, the database system includes proteome information for six strains of microorganisms.

Bacterial Proteins↗

PubMed2006

New insights into the pathological mechanisms of cerebrotendinous xanthomatosis in the Taiwanese using genomic and proteomic tools.

Cerebrotendinous xanthomatosis (CTX) is an autosomal recessive lipid-storage disorder caused by a deficiency of the mitochondrial sterol 27-hydroxylase. Genetic analysis utilizing SSCP and direct DNA sequencing identified a new mutation. One base-pair of cytosine was deleted at codon 326 on exon 2 of CYP27 in all CTX patients while their father was heterozygotic. This novel point deletion predicts a frameshift in mRNA (Pro(102) -->Leu) and results in the appearance of a premature termination codon (TGA) to substitute for Val(106) (GTG). To characterize the pathological mechanism of CTX patients, the protein profiles of serum and leukocytes extracted from these subjects were presented by means of proteomic technologies including 2-DE and MALDI-TOF analysis. According to the results, the amount of vinculin, ABP-280, talin and vimentin in leukocytes of CTX patients had changed significantly, reflecting the changes in membrane dynamics concerning cholestanol accumulation. The expression of target proteins in CTX patients and control was further verified by western blotting which indicated the same tendency as 2-DE data. This is the first paper to integrate both genomic and proteomic concepts for analyzing the possible mechanism of CTX and provides more information for related study in the future.

Amino Acid Sequence↗

PubMed2003

Bioinformatics support for high-throughput proteomics.

In the "post-genome" era, mass spectrometry (MS) has become an important method for the analysis of proteome data. The rapid advancement of this technique in combination with other methods used in proteomics results in an increasing number of high-throughput projects. This leads to an increasing amount of data that needs to be archived and analyzed. To cope with the need for automated data conversion, storage, and analysis in the field of proteomics, the open source system ProDB was developed. The system handles data conversion from different mass spectrometer software, automates data analysis, and allows the annotation of MS spectra (e.g. assign gene names, store data on protein modifications). The system is based on an extensible relational database to store the mass spectra together with the experimental setup. It also provides a graphical user interface (GUI) for managing the experimental steps which led to the MS data. Furthermore, it allows the integration of genome and proteome data. Data from an ongoing experiment was used to compare manual and automated analysis. First tests showed that the automation resulted in a significant saving of time. Furthermore, the quality and interpretability of the results was improved in all cases.

Algorithms↗

PubMed2002

Proteomics-based anticancer drug discovery and development.

Proteins are important targets for drug discovery and this applied to cancer as well because there is a defect in the protein machinery of the cell in malignancy. Proteomic technologies are now being integrated with genomic approaches for cancer drug discovery and target validation. Among the large number of proteomic technologies available for this purpose, the most important ones are 3-D protein structure determination, protein microarrays, laser capture microdissection and study of protein-protein and protein-drug interactions. Cancer biomarkers and several cell pathways are important drug targets. Several companies are involved in using proteomic technologies for drug discovery. Finally, proteomic approaches will play an important role in the discovery and development of personalized medicines.

Antineoplastic Agents↗

PubMed2004

Structure-based functional discovery of proteins: structural proteomics.

The discovery of biochemical and cellular functions of unannotated gene products begins with a database search of proteins with structure/sequence homologues based on known genes. Very recently, a number of frontier groups in structural biology proposed a new paradigm to predict biological functions of an unknown protein on the basis of its three-dimensional structure on a genomic scale. Structural proteomics (genomics), a research area for structure-based functional discovery, aims to complete the protein-folding universe of all gene products in a cell. It would lead us to a complete understanding of a living organism from protein structure. Two major complementary experimental techniques, X-ray crystallography and NMR spectroscopy, combined with recently developed high throughput methods have played a central role in structural proteomics research; however, an integration of these methodologies together with comparative modeling and electron microscopy would speed up the goal for completing a full dictionary of protein folding space in the near future.

Animals↗

PubMed2025

PLAID: ultrafast single-sample gene set enrichment scoring.

SUMMARY: In recent years, computational methods have emerged that calculate enrichment of gene signatures within individual samples. These signatures offer critical insights into the coordinated activity of functionally related genes, proteins or metabolites, enabling the identification of unique molecular profiles in individual cells and patients. This strategy is pivotal for patient stratification and advancement of personalized medicine. However, the rise of large-scale datasets, including single-cell profiles and population biobanks, has exposed significant computational inefficiencies in existing methods. Current methods often demand excessive runtime and memory resources, becoming impractical for large datasets. Overcoming these limitations is a focus of current efforts by bioinformatics teams in academia and the pharmaceutical industry, as essential to support basic and clinical biomedical research. To address this critical need, we developed PLAID (Pathway Level Average Intensity Detection), an ultrafast and memory optimized single sample gene set enrichment algorithm that utilizes sparse matrix computation. PLAID delivers highly accurate gene set scoring and surpasses the performance of current methods in single-cell and bulk transcriptomics, and proteomics data. PLAID uniquely integrates the most widely used gene set scoring algorithms, enabling researchers to apply multiple methods for cross-validation with outstanding runtime efficiency and minimal memory requirement. AVAILABILITY AND IMPLEMENTATION: PLAID is implemented in the R language for statistical computing. PLAID source code and installation instructions are available with no restrictions at https://github.com/bigomics/plaid.

Algorithms↗

PubMed2002

Integrating interactome, phenome, and transcriptome mapping data for the C. elegans germline.

By integrating functional genomic and proteomic mapping approaches, biological hypotheses should be formulated with increasing levels of confidence. For example, yeast interactome and transcriptome data can be correlated in biologically meaningful ways. Here, we combine interactome mapping data generated for a multicellular organism with data from both large-scale phenotypic analysis ("phenome mapping") and transcriptome profiling. First, we generated a two-hybrid interactome map of the Caenorhabditis elegans germline by using 600 transcripts enriched in this tissue. We compared this map to a phenome map of the germline obtained by RNA interference (RNAi) and to a transcriptome map obtained by clustering worm genes across 553 expression profiling experiments. In this dataset, we find that essential proteins have a tendency to interact with each other, that pairs of genes encoding interacting proteins tend to exhibit similar expression profiles, and that, for approximately 24% of germline interactions, both partners show overlapping embryonic lethal or high incidence of males RNAi phenotypes and similar expression profiles. We propose that these interactions are most likely to be relevant to germline biology. Similar integration of interactome, phenome, and transcriptome data should be possible for other biological processes in the nematode and for other organisms, including humans.

Animals↗

PubMed2025

Development and Validation an Integrated Deep Learning Model to Assist Eosinophilic Chronic Rhinosinusitis Diagnosis: A Multicenter Study.

BACKGROUND: The assessment of eosinophilic chronic rhinosinusitis (eCRS) lacks accurate non-invasive preoperative prediction methods, relying primarily on invasive histopathological sections. This study aims to use computed tomography (CT) images and clinical parameters to develop an integrated deep learning model for the preoperative identification of eCRS and further explore the biological basis of its predictions. METHODS: A total of 1098 patients with sinus CT images were included from two hospitals and were divided into training, internal, and external test sets. The region of interest of sinus lesions was manually outlined by an experienced radiologist. We utilized three deep learning models (3D-ResNet, 3D-Xception, and HR-Net) to extract features from CT images and calculate deep learning scores. The clinical signature and deep learning score were inputted into a support vector machine for classification. The receiver operating characteristic curve, sensitivity, specificity, and accuracy were used to evaluate the integrated deep learning model. Additionally, proteomic analysis was performed on 34 patients to explore the biological basis of the model's predictions. RESULTS: The area under the curve of the integrated deep learning model to predict eCRS was 0.851 (95% confidence interval [CI]: 0.77-0.93) and 0.821 (95% CI: 0.78-0.86) in the internal and external test sets. Proteomic analysis revealed that in patients predicted to be eCRS, 594 genes were dysregulated, and some of them were associated with pathways and biological processes such as chemokine signaling pathway. CONCLUSIONS: The proposed integrated deep learning model could effectively predict eCRS patients. This study provided a non-invasive way of identifying eCRS to facilitate personalized therapy, which will pave the way toward precision medicine for CRS.

Humans↗

PubMed2026

AI-Supported, Integrative Prediction of Postoperative Delirium: Protocol for the CONFUSED Study.

BACKGROUND: Postoperative delirium (POD) is a frequent and serious complication in older surgical patients, characterized by acute cognitive dysfunction and fluctuating levels of consciousness. POD is associated with prolonged hospitalization, long-term cognitive decline, reduced quality of life, and increased mortality. Despite its clinical relevance, the underlying pathophysiological mechanisms remain poorly understood, and reliable biomarkers for early prediction and prevention are lacking. OBJECTIVE: The CONFUSED study aims to identify molecular and clinical predictors of POD by integrating clinical data with proteomic, transcriptomic, and epigenetic analyses. The primary objective is to develop predictive models for POD using multimodal data. Secondary objectives include the identification of delirium-associated genes, proteins, and epigenetic signatures, as well as the exploration of patient subgroups at increased risk for POD. METHODS: CONFUSED is a prospective observational cohort study conducted at a German university hospital. Adult patients undergoing major surgery under general anesthesia will be enrolled until 100 cases of POD have been observed, which is expected to require a total sample size of approximately 200 to 300 patients. Blood samples are collected at 4 predefined time points: before premedication, immediately after surgery, and on postoperative days 2 and 5. Samples undergo comprehensive proteomic profiling, transcriptomic analysis using RNA microarrays, DNA methylation analysis, and genotyping of selected polymorphisms. Clinical data, including demographics, comorbidities, perioperative variables, medications, and delirium assessments using the Confusion Assessment Method (CAM) and CAM for the intensive care unit, are systematically recorded. Statistical analyses include univariate and multivariate methods, as well as machine learning approaches such as random forests and support vector machines, to identify relevant biomarkers and develop predictive models. The study protocol follows STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) and TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) guidelines and was approved by the responsible ethics committees. RESULTS: The study was registered in the German Clinical Trials Register (DRKS00033854) on March 18, 2024. Recruitment started in January 2024 and is ongoing at the time of manuscript submission. As of now, 135 patients have been enrolled. Sample collection and laboratory analyses are ongoing. Data analysis began in January 2026, with first results anticipated in July 2026. Final data lock is anticipated after the completion of recruitment. CONCLUSIONS: By integrating multimodal molecular data with clinical parameters and applying advanced machine learning techniques, the CONFUSED study aims to improve the prediction and understanding of POD. The results are expected to support the development of personalized preventive strategies and contribute to improved perioperative care for patients at risk of POD.

Humans↗

PubMed2000

Global analysis of gene expression in cells of the immune system I. Analytical limitations in obtaining sequence information on polypeptides in two-dimensional gel spots.

We have outlined various aspects and limitations of the collective analysis of protein species of a cell (lymphocyte). We have indicated research directions that, in to our opinion, deserve more attention. We have evaluated mainly the approach used in our laboratory and we recognize that a bulk of important research on the interface of proteomics and genomics remains to be dealt with. It is of great value that we can proceed in our quest by trial and error. But as much as the human genome initiative was not implemented by trial and error, but by formulating new technological approaches, we hope that our approach can be incorporated in the mainstream of proteomics. We need several integrating research directions, some of which are outlined in this communication, namely the use of ordered cDNA libraries, cell-free expression systems, high density filter hybridization, identification of two-dimensional (2-D) gel spots in terms of their amino acid composition through biosynthetic labeling and identification of restriction sites in the corresponding coding sequences. In the accompanying paper the cDNA ordered library approach will be described in some detail.

B-Lymphocytes↗

PubMed2005

Digital microfluidics with in-line sample purification for proteomics analyses with MALDI-MS.

An in-line sample purification method for MALDI-MS, which relies on the electrowetting-on-dielectric (EWOD)-based technique for digital microfluidics, is reported. In this method, a droplet containing peptides and impurities is moved by EWOD and deposited onto a Teflon-AF surface. A droplet of water is subsequently moved over the spot, where it dissolves and removes the impurities. A droplet containing MALDI matrix is then moved to the spot, which is analyzed by MALDI-MS. This purification method reduces the number of salt adduct peaks caused by low concentrations of impurities (e.g., 20 mM sodium phosphate), and reduces or eliminates the catastrophic effects of high concentrations of impurities (e.g., 8 M urea). The method was used to purify spots made by depositing multiple droplets of contaminated peptides. Spectra from the purified spots showed an increase in the S/N ratio as a function of the number of droplets deposited; when not purified, the S/N ratio remained constant regardless of the number of droplets. Finally, the method was used to purify protein digests for peptide mass fragment (PMF) searches, and was shown to be more efficient than the conventional method of purification with reversed-phase-packed pipet tips. We anticipate this new, in-line sample purification technique for EWOD-MALDI-MS will enable development of integrated high-throughput proteomics analysis methodologies.

Analytic Sample Preparation Methods↗

PubMed2002

Analytical and biological variances associated with proteomic studies of Medicago truncatula by two-dimensional polyacrylamide gel electrophoresis.

Two-dimensional polyacrylamide gel electrophoresis (2-DE) and mass spectrometry are being used as proteomic tools in an integrated functional genomics program focused on the model legume Medicago truncatula. Due to the perceived high levels of indeterminate error associated with 2-DE we deemed it necessary to quantify the coefficient of variance (or relative standard deviation) for both analytical and biological sources associated with 2-DE of Medicago truncatula leaf protein extracts. Leaf protein extracts were chosen because of their biological significance and due to the more challenging nature of green tissues. Analytical variance was calculated for fifty proteins from ten replicate 2-DE gels of the same protein extract. Biological variance was calculated for the same fifty proteins from ten independent 2-DE gel analyses of ten independent but similar plants grown under identical conditions. Average analytical and biological variances were calculated for both data sets and represent the average variance of approximately 500 independent measurements of protein concentration. Analytical variance was determined to be 16.2% and biological variance was determined to be 24.2%. These average variances provide a quantified and statistical basis for evaluation of protein expression changes in future comparative proteomic investigations. It is proposed that 2-DE measured protein expression levels should differ by a minimum of 3.92sigma (i.e. /+/-2sigma/ and sigma = standard deviation), or 94.7% based on our measured variances, for the difference to be significant at the 95% confidence level.

Data Interpretation, Statistical↗

PubMed2006

A multivariate approach for integrating genome-wide expression data and biological knowledge.

MOTIVATION: Several statistical methods that combine analysis of differential gene expression with biological knowledge databases have been proposed for a more rapid interpretation of expression data. However, most such methods are based on a series of univariate statistical tests and do not properly account for the complex structure of gene interactions. RESULTS: We present a simple yet effective multivariate statistical procedure for assessing the correlation between a subspace defined by a group of genes and a binary phenotype. A subspace is deemed significant if the samples corresponding to different phenotypes are well separated in that subspace. The separation is measured using Hotelling's T(2) statistic, which captures the covariance structure of the subspace. When the dimension of the subspace is larger than that of the sample space, we project the original data to a smaller orthonormal subspace. We use this method to search through functional pathway subspaces defined by Reactome, KEGG, BioCarta and Gene Ontology. To demonstrate its performance, we apply this method to the data from two published studies, and visualize the results in the principal component space.

Algorithms↗

PubMed2006

MitoP2: the mitochondrial proteome database--now including mouse data.

The MitoP2 database (http://www.mitop.de) integrates information on mitochondrial proteins, their molecular functions and associated diseases. The central database features are manually annotated reference proteins localized or functionally associated with mitochondria supplied for yeast, human and mouse. MitoP2 enables (i) the identification of putative orthologous proteins between these species to study evolutionarily conserved functions and pathways; (ii) the integration of data from systematic genome-wide studies such as proteomics and deletion phenotype screening; (iii) the prediction of novel mitochondrial proteins using data integration and the assignment of evidence scores; and (iv) systematic searches that aim to find the genes that underlie common and rare mitochondrial diseases. The data and analysis files are referenced to data sources in PubMed and other online databases and can be easily downloaded. MitoP2 users can explore the relationship between mitochondrial dysfunctions and disease and utilize this information to conduct systems biology approaches on mitochondria.

Animals↗