Relative Abundance of Proteins in Blood Plasma Samples from Patients with Chronic Cerebral Ischemia

An Orphan Gene Enhances Male Reproductive Success in Plutella xylostella

Plutella xylostella exhibits exceptional reproduction ability, yet the genetic basis underlying the high reproductive capacity remains unknown. Here, we demonstrate that an orphan gene, lushu, which encodes a sperm protein, plays a crucial role in male reproductive success. Lushu is located on the Z chromosome and is prevalent across different P. xylostella populations worldwide. We subsequently generated lushu mutants using transgenic CRISPR/Cas9 system. Knockout of Lushu results in reduced male mating efficiency and accelerated death in adult males. Furthermore, our findings highlight that the deficiency of lushu reduced the transfer of sperms from males to females, potentially resulting in hindered sperm competition. Additionally, the knockout of Lushu results in disrupted gene expression in energy-related pathways and elevated insulin levels in adult males. Our findings reveal that male reproductive performance has evolved through the birth of a newly evolved, lineage-specific gene with enormous potentiality in fecundity success. These insights hold valuable implications for identifying the target for genetic control, particularly in relation to species-specific traits that are pivotal in determining high levels of fecundity.

Convolutional neural network in proteomics and metabolomics for determination of comorbidity between cancer and schizophrenia

The association between cancer risk and schizophrenia is widely debated. Despite many epidemiological studies, there is still no strong evidence regarding the molecular basis for the comorbidity between these two pathological conditions. The vast majority of assays have been performed using clinical records of schizophrenic patients or those undergoing cancer treatment and monitored for sufficient time to find shared features between the considered conditions. We performed mass spectrometry-based proteomic and metabolomic investigations of patients with different cancer phenotypes (breast, ovarian, renal, and prostate) and patients with schizophrenia. The resulting vast quantity of proteomic and metabolomic data were then processed using systems biology and one-dimensional (1D) convolutional neural network (1DCNN) machine learning approaches. Traditional systematic approaches permit the segregation of schizophrenia and cancer phenotypes on the level of biological processes, while 1DCNN recognized "signatures" that could segregate distinct cancer phenotypes and schizophrenia at the comorbidity level. The designed network efficiently discriminated unrelated pathologies with a model accuracy of 0.90 and different subtypes of oncophenotypes with an accuracy of 0.94. The proposed strategy integrates systematic analysis of identified compounds and application of 1DCNN model for unidentified ones to reveal the similarity between distinct phenotypes.

Is It Possible to Find Needles in a Haystack? Meta-Analysis of 1000+ MS/MS Files Provided by the Russian Proteomic Consortium for Mining Missing Proteins

Despite direct or indirect efforts of the proteomic community, the fraction of blind spots on the protein map is still significant. Almost 11% of human genes encode missing proteins; the existence of which proteins is still in doubt. Apparently, proteomics has reached a stage when more attention and curiosity need to be exerted in the identification of every novel protein in order to expand the unusual types of biomaterials and/or conditions. It seems that we have exhausted the current conventional approaches to the discovery of missing proteins and may need to investigate alternatives. Here, we present an approach to deciphering missing proteins based on the use of non-standard methodological solutions and encompassing diverse MS/MS data, obtained for rare types of biological samples by members of the Russian Proteomic community in the last five years. These data were re-analyzed in a uniform manner by three search engines, which are part of the SearchGUI package. The study resulted in the identification of two missing and five uncertain proteins detected with two peptides. Moreover, 149 proteins were detected with a single proteotypic peptide. Finally, we analyzed the gene expression levels to suggest feasible targets for further validation of missing and uncertain protein observations, which will fully meet the requirements of the international consortium. The MS data are available on the ProteomeXchange platform (PXD014300).

Proteome data of serum samples from patients with schizophrenia

Schizophrenia is a complex chronic disease. The molecular determinants and neuropathology of schizophrenia are multifaceted; an important role in the pathogenesis is played by the dysregulation of molecular and epigenetic mechanisms. However, the molecular mechanisms of the development of the disease have not yet been studied.

An important task is the accumulation and systematization of “OMICS”-knowledge of the molecular profiles (transcriptome, proteome, metabolome) of blood specific to pathology. Thereby the development and improvement of mass spectrometric methods for the detection of biological molecules has become increasingly important in biomedical research. In the field of applied problems in biomedical research, the most prevalent issue involves the identification of serological protein markers associated with the development of schizophrenia, which account for the diseases that cause the a life-shortening illness, disability, decreased of functioning and quality of life and wellbeing or health status.

OMICS approaches are designed to detect genes (genomics), mRNA (transcriptomics), proteins (proteomics) and metabolites (metabolomics) in a specific biological sample.

We report the proteomic datasets on the serum samples from patients with schizophrenia (series “SCZ”) and healthy volunteers (series “CNT”). Data were acquired using shotgun ultra-high resolution mass spectrometry.

Revelation of Proteomic Indicators for Colorectal Cancer in Initial Stages of Development

Background: Colorectal cancer (CRC) at a current clinical level is still hardly diagnosed, especially with regard to nascent tumors, which are typically asymptotic. Searching for reliable biomarkers of early diagnosis is an extremely essential task. Identification of specific post-translational modifications (PTM) may also significantly improve net benefits and tailor the process of CRC recognition. We examined depleted plasma samples obtained from 41 healthy volunteers and 28 patients with CRC at different stages to conduct comparative proteome-scaled analysis. The main goal of the study was to establish a constellation of protein markers in combination with their PTMs and semi-quantitative ratios that may support and realize the distinction of CRC until the disease has a poor clinical manifestation. Results: Proteomic analysis revealed 119 and 166 proteins for patients in stages I–II and III–IV, correspondingly. Plenty of proteins (44 proteins) reflected conditions of the immune response, lipid metabolism, and response to stress, but only a small portion of them were significant (p < 0.01) for distinguishing stages I–II of CRC. Among them, some cytokines (Clusterin (CLU), C4b-binding protein (C4BP), and CD59 glycoprotein (CD59), etc.) were the most prominent and the lectin pathway was specifically enhanced in patients with CRC. Significant alterations in Inter-alpha-trypsin inhibitor heavy chains (ITIH1, ITIH2, ITIH3, and ITIH4) levels were also observed due to their implication in tumor growth and the malignancy process. Other markers (Alpha-1-acid glycoprotein 2 (ORM2), Alpha-1B-glycoprotein (A1BG), Haptoglobin (HP), and Leucine-rich alpha-2-glycoprotein (LRG1), etc.) were found to create an ambiguous core involved in cancer development but also to exactly promote tumor progression in the early stages. Additionally, we identified post-translational modifications, which according to the literature are associated with the development of colorectal cancer, including kininogen 1 protein (T327-p), alpha-2-HS-glycoprotein (S138-p) and newly identified PTMs, i.e., vitamin D-binding protein (K75-ac and K370-ac) and plasma protease C1 inhibitor (Y294-p), which may also contribute and negatively impact on CRC progression. Conclusions: The contribution of cytokines and proteins of the extracellular matrix is the most significant factor in CRC development in the early stages. This can be concluded since tumor growth is tightly associated with chronic aseptic inflammation and concatenated malignancy related to loss of extracellular matrix stability. Due attention should be paid to Apolipoprotein E (APOE), Apolipoprotein C1 (APOC1), and Apolipoprotein B-100 (APOB) because of their impact on the malfunction of DNA repair and their capability to regulate mTOR and PI3K pathways. The contribution of the observed PTMs is still equivocal, but a significant decrease in the likelihood between modified and native proteins was not detected confidently.

Proteome dataset of mouse macrophage cell line infected with tick-borne encephalitis virus

We report the proteomic datasets on the mouse macrophage cell line PMJ2R infected with tick-borne encephalitis virus (TBEV) for two and six days. Data were acquired using shotgun ultra-high resolution mass spectrometry. Peptide identifications were done using the Mascot version 2.4 (Matrix Science), and quantification was performed by a label-free approach. Protein profiles of early (two days) and late (six days) stages of infection were compared between each other and the respective control samples. Protein profiles of infected and control samples differed in the number of identified proteins and their relative abundances. Proteins detected in the TBEV-infected cells were involved in various processes related to the infection, including defense response against the virus, regulation of viral process, negative regulation of viral genome replication, RNA binding, or innate immune response. Also, proteins specific for the early and late stages of infection were identified.

Pilot data of serum proteins from children with autism spectrum disorders

Protein profiles of 13 serum samples from children with autism spectrum disorders (ASD) and 11 serum samples from healthy volunteers was obtained using panoramic ultra-high resolution mass spectrometry. The analysis of measurements was performed using the proteomics search engine. We identified a group of 74 proteins which we term a "protein fingerprint" specific for serum samples collected from children with autism. Components of the protein fingerprint are involved in hemostasis maintenance including biological regulation, the response to stimulus, regulation of metabolism, and proteins of the immune system.

Comparative Analysis of Blood Plasma Proteome in Patients with Renal Cell Carcinoma

Comparative mass spectrometric analysis of protein composition was carried out in 36 blood plasma specimens from patients with renal cell carcinoma and 20 specimens from donors. Analysis of protein composition of plasma specimens devoid of the major protein fractions showed a 20-50% higher level of protein identifications in patient' specimens. Specimens of the control and experimental series were similar by protein composition, 70-80% identifications in experimental and control series coinciding. High similarity of biological processes with participation of the proteins identified in both series was observed. The greater part of proteins in both series were located extracellularly and were exosomal (specimens from renal cancer patients) or vesicular (specimens from healthy volunteers).

Proteomic Analysis of Cerebral Cortex Extracts from Sus scrofa with Induced Hemorrhagic Stroke

Cerebrovascular diseases, including stroke and micro stroke, are the main causes of death in contemporary society. Hemorrhagic stroke is the fast emerging defficiency in the brain function resulting from disturbance of blood supply to the brain caused by the rapture of blood vessels (Lopez et al. in Proteomics Clin Appl 6:190-200, 2012). The influence of a model hemorrhagic stroke on white pigs with the change in the protein profile of their cortical samples 24 h and 2 months after the stroke was examined using mass-spectrometric analysis. Different proteins (n = 30) were identified, and their content was elevated. These proteins are involved in the mechanisms of neuroprotection, including compensation of oxidative stress (TXN, SNCA, PRDX6, ENO1), prevention of unwanted protein aggregation and apoptosis (PTMA, SNCA, SNCB), release of neurotransmitters (GAPDH, PEBP1) and assembly of the cytoskeleton (ACTA2, PTMA, TUBA4A, TUBA1D), etc. Also, a group of seven Ras family proteins involved in the regulation of cell proliferation and differentiation was found in the samples taken 24 h following the stroke. The relative concentrations of most of the proteins in the samples taken 2 months after the stroke demonstrate intermediate values between the control sample and the sample taken in 24 h, indicating the extinction of change in the protein profile with time. During the first 24 h after the stroke, there is an increase in protein fractions participating in exocytosis, synaptic plasticity/signaling, and support of neurotransmitter transport. Such shift in the weight of protein functional clusters can be attributed to activation of compensatory mechanisms in the body focused on neuroprotection.