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Pikeperch (Sander lucioperca) is a freshwater species and an internationally highly demanded fish in aquaculture. Despite intensive research efforts on this species, fundamental knowledge of skeletal muscle biology and structural characteristics is missing. Therefore, we conducted a comprehensive analysis of skeletal muscle parameters in adult pikeperch from two different origins, wild-caught specimens from a lake and those reared in a recirculating aquaculture system. The analyses comprised the biochemical characteristics (nucleic acid, protein content), enzyme activities (creatine kinase, lactate dehydrogenase, NADP-dependent isocitrate dehydrogenase), muscle-specific gene and protein expression (related to myofibre formation, regeneration and permanent growth, muscle structure), and muscle fibre structure. The findings reveal distinct differences between the skeletal muscle of wild and farmed pikeperch. Specifically, nucleic acid content, enzyme activity, and protein expression varied significantly. The higher enzyme activity observed in wild pikeperch suggests greater metabolically activity in their muscles. Conversely, farmed pikeperch indicated a potential for pronounced muscle growth. As the data on pikeperch skeletal muscle characteristics is sparse, the purpose of our study is to gain fundamental insights into the characteristics of adult pikeperch muscle. The presented data serve as a foundation for further research on percids' muscle biology and have the potential to contribute to advancements and adaptations in aquaculture practices. [ABSTRACT FROM AUTHOR]

Rapid advancements in technology refine our understanding of intricate biological processes, but a crucial emphasis remains on understanding the assumptions and sources of uncertainty underlying biological measurements. This is particularly critical in cell signaling research, where a quantitative understanding of the fundamental mechanisms governing these transient events is essential for drug development, given their importance in both homeostatic and pathogenic processes. Western blotting, a technique developed decades ago, remains an indispensable tool for investigating cell signaling, protein expression, and protein–protein interactions. While improvements in statistical analysis and methodology reporting have undoubtedly enhanced data quality, understanding the underlying assumptions and limitations of visual inspection in Western blotting can provide valuable additional information for evaluating experimental conclusions. Using the example of agonist-induced receptor post-translational modification, we highlight the theoretical and experimental assumptions associated with Western blotting and demonstrate how raw blot data can offer clues to experimental variability that may not be fully captured by statistical analyses and reported methodologies. This article is not intended as a comprehensive technical review of Western blotting. Instead, we leverage an illustrative example to demonstrate how assumptions about experimental design and data normalization can be revealed within raw data and subsequently influence data interpretation.

We previously constructed TheCellVision.org , a central repository for visualizing and mining data from yeast high-content imaging projects. At its inception, TheCellVision.org housed two high-content screening (HCS) projects providing genome-scale protein abundance and localization information for the budding yeast Saccharomyces cerevisiae , as well as a comprehensive analysis of the morphology of its endocytic compartments upon systematic genetic perturbation of each yeast gene. Here, we report on the expansion of TheCellVision.org by the addition of two new HCS projects and the incorporation of new global functionalities. Specifically, TheCellVision.org now hosts images from the Cell Cycle Omics project, which describes genome-scale cell cycle-resolved dynamics in protein localization, protein concentration, gene expression, and translational efficiency in budding yeast. Moreover, it hosts PIFiA, a computational tool for image-based predictions of protein functional annotations. Across all its projects, TheCellVision.org now houses >800,000 microscopy images along with computational tools for exploring both the images and their associated datasets. Together with the newly added global functionalities, which include the ability to query genes in any of the hosted projects using either yeast or human gene names, TheCellVision.org provides an expanding resource for single-cell eukaryotic biology. [ABSTRACT FROM AUTHOR]

Protein Protein low complexity regions (LCRs) are compositionally biased amino acid sequences, many of which have significant evolutionary impacts on the proteins which contain them. They are mutationally unstable experiencing higher rates of indels and substitutions than higher complexity regions. LCRs also impact the expression of their proteins, likely through multiple effects along the path from gene transcription, through translation, and eventual protein degradation. It has been observed that proteins which contain LCRs are associated with elevated transcript abundance (TAb), despite having lower protein abundance. We have gathered and integrated human data to investigate the co-evolution of TAb and LCRs through ancestral reconstructions and model inference using an approximate Bayesian calculation based method. We observe that on short evolutionary timescales TAb evolution is significantly impacted by changes in LCR length, with insertions driving TAb down. But in contrast, the observed data is best explained by indel rates in LCRs which are unaffected by shifts in TAb. Our work demonstrates a coupling between LCR and TAb evolution, and the utility of incorporating multiple responses into evolutionary analyses. [ABSTRACT FROM AUTHOR]

Bovine nucleoprotein transitions (TNPs), specifically TNP1 and TNP2, are essential molecules in sperm nucleus rich in arginine and lysine. These molecules act in the phase between histone expulsion and before incorporation of protamine in the spermatid nucleus. Therefore, this study aimed to analyze genes and protein abundance of TNP1 and TNP2 in sperm to determine the potential as motility markers and correlation with fertility in the field. An objective evaluation method, CASA-Sperm Vision, was used to separate 22 bulls into two groups (mg-A and mg-B) based on their increasing motility. Sperm quality parameters were also examined including velocity, mitochondrial membrane potential (MMP) by the JC-1 method, head defects using William staining, and DNA fragmentation by Halomax. TNPs genes abundance was performed using the RT-qPCR method, and the protein abundance was examined with the EIA approach. The fertility rate was also analyzed based on the conception rate generated from each bull in the field, with the data obtained from iSIKHNAS. The results showed that TNPs genes and protein abundance were significantly higher (P < 0.05) in mg-A compared to mg-B, followed by various sperm quality parameters and fertility rates (P < 0.05). Positive correlations were found in TNPs genes and protein abundance with motility, velocity, MMP, and fertility (P < 0.01). Meanwhile, a negative correlation (P < 0.01) was found between head defects and DNA fragmentation. These results showed the potential of TNPs as sperm motility markers and bull fertility. • Gene expression and protein abundance of Tnp1 and Tnp2 are associated with bull sperm motility. • The abundance of Tnp1 and Tnp2 mRNA and protein is closely related to the fertility rate of bulls in the field. • Tnp1 and Tnp2 may play an active role in the functional regulation of sperm motility and nucleus chromatin condensation. • Disruption of Tnp1 and Tnp2 gene expression and protein abundance may impact changes in sperm head morphology. • Bovine Tnp1 and Tnp2 genes and proteins have the potential to be developed as markers of sperm motility and bull fertility. [ABSTRACT FROM AUTHOR]

The hypoxia-inducible factor (HIF) is considered key in the transcriptional response to low oxygen. Yet, the role of HIF in the absence of oxygen (anoxia) and in preparation for reoxygenation remains unclear. Recent studies suggest that mounting a HIF response may be counterproductive for anoxia survival. We here studied one of the champions of anoxia survival, the crucian carp (Carassius carassius), and hypothesized that expression of prolyl hydroxylase domains (PHDs; the upstream regulators of HIF) are upregulated to circumvent an energy-costly activation of HIF in anoxia and to prepare for reoxygenation. We measured whole brain mRNA and protein levels of the three isoforms PHD1, PHD2, and PHD3, coded for by multiple paralogs of the genes egln2, egln1, and egln3, using quantitative PCR and Western blotting in the brain of crucian carps exposed to 5 days normoxia or anoxia, and 5 days anoxia followed by 3 or 24 h of reoxygenation. The mRNA levels of most egln paralogs were increased in anoxia and upon reoxygenation, with egln3 showing the largest increase in mRNA level (up to 17-fold) and highest relative mRNA abundance (up to 75% of expressed egln). The protein level of all PHDs was maintained in anoxia and increased upon reoxygenation. We then explored PHD distribution in different brain regions and found PHD immunoreactivity to be associated with axonal branches and showing region-specific changes during anoxia-reoxygenation. Our results support an overall upregulation of egln under prolonged anoxia and PHDs upon reoxygenation in crucian carp, likely aimed at suppressing HIF responses, although regional differences are apparent in such a complex organ as the brain. [ABSTRACT FROM AUTHOR]

Diapause, an adaptative strategy for survival under harsh conditions, is a dynamic multi-stage process. Bombus terrestris, an important agricultural pollinator, is declining in the wild, but artificial breeding is possible by imitating natural conditions. Mated queen bees enter reproductive diapause in winter and recover in spring, but the regulatory mechanisms remain unclear. Herein, we conducted a comparative 4D label-free proteomic analysis of queen bees during artificial breeding at seven timepoints, including pre-diapause, diapause, and post-diapause stages. Through bioinformatics analysis of proteomic and detection of substance content changes, our results found that, during pre-diapause stages, queen bees had active mitochondria with high levels of oxidative phosphorylation, high body weight, and glycogen and TAG content, all of which support energy consumption during subsequent diapause. During diapause stages, body weight and water content were decreased but glycerol increased, contributing to cold resistance. Dopamine content, immune defense, and protein phosphorylation were elevated, while fat metabolism, protein export, cell communication, signal transduction, and hydrolase activity decreased. Following diapause termination, JH titer, water, fatty acid, and pyruvate levels increased, catabolism, synaptic transmission, and insulin signaling were stimulated, ribosome and cell cycle proteins were upregulated, and cell proliferation was accelerated. Meanwhile, TAG and glycogen content decreased, and ovaries gradually developed. These findings illuminate changes occurring in queen bees at different diapause stages during commercial production. [ABSTRACT FROM AUTHOR]

Chronic pelvic pain (CPP) is generally defined as non-cyclic pain perceived in the pelvic area that has persisted from three to six months or longer and is unrelated to pregnancy. The etiology of CPP is complex, multifactorial, with heterogeneous presentation, and includes several diseases such as endometriosis, adenomyosis, and interstitial cystitis/bladder pain syndrome. It may also be associated with sexual dysfunction, musculoskeletal disorders, and comorbid psychiatric symptoms. Vulvar pain disorders (VPDs) are typically categorized separately from chronic pelvic pain; among all VPDs, vulvodynia is a chronic vulvar pain of unknown etiology, lasting at least 3 months and that might be associated with other potentially linked factors. Proteomics represents a useful approach to study the proteome profiles of clinical samples. In this review, we have considered a selection of articles that have analyzed the protein abundance and novel protein species from various biological samples, including eutopic/ectopic endometrium, urine, serum, follicular, peritoneal fluid, and cervical mucus, potentially involved in the pathogenesis and progression of CPP and VPDs. These findings could represent valuable targets for paving the way for the differential diagnosis and therapeutic management of CPP and VDPs, thereby optimizing both the prevention and treatment of these conditions. [ABSTRACT FROM AUTHOR]

Background: Malaria has remained a major health concern for decades among people living in tropical and sub-tropical countries. Plasmodium falciparum is one of the critical species that cause severe malaria and is responsible for major mortality. Moreover, the parasite has generated resistance against all WHO recommended drugs and therapies. Therefore, there is an urgent need for preventive measures in the form of reliable vaccines to achieve the target of a malaria-free world. Surface proteins are the preferable choice for subunit vaccine development because they are rapidly detected and engaged by host immune cells and vaccination-induced antibodies. Additionally, abundant surface or membrane proteins may contribute to the opsonization of pathogens by vaccine-induced antibodies. Results: In our study, we have listed all those surface proteins from the literature that could be functionally important and essential for infection and immune evasion of the malaria parasite. Eight Plasmodium surface and membrane proteins from the pre-erythrocyte and erythrocyte stages were shortlisted. Thirty-seven epitopes (B-cell, CTL, and HTL epitopes) from these proteins were predicted using immune-informatic tools and joined with suitable peptide linkers to design a vaccine construct. A TLR-4 agonist peptide adjuvant was added at the N-terminus of the multi-epitope series, followed by the PADRE sequence and EAAAK linker. The TLR-4 receptor was docked with the construct’s anticipated model structure. The complex of vaccine and TLR-4, with the lowest energy −1514, was found to be stable under simulated physiological settings. Conclusion: This study has provided a novel multi-epitope construct that may be exploited further for the development of an efficient vaccine for malaria.

Transcriptomics methods (RNA-Seq, PCR) today are more routine and reproducible than proteomics methods, i.e., both mass spectrometry and immunochemical analysis. For this reason, most scientific studies are limited to assessing the level of mRNA content. At the same time, protein content (and its post-translational status) largely determines the cell's state and behavior. Such a forced extrapolation of conclusions from the transcriptome to the proteome often seems unjustified. The ratios of "transcript-protein" pairs can vary by several orders of magnitude for different genes. As a rule, the correlation coefficient between transcriptome–proteome levels for different tissues does not exceed 0.3–0.5. Several characteristics determine the ratio between the content of mRNA and protein: among them, the rate of movement of the ribosome along the mRNA and the number of free ribosomes in the cell, the availability of tRNA, the secondary structure, and the localization of the transcript. The technical features of the experimental methods also significantly influence the levels of the transcript and protein of the corresponding gene on the outcome of the comparison. Given the above biological features and the performance of experimental and bioinformatic approaches, one may develop various models to predict proteomic profiles based on transcriptomic data. This review is devoted to the ability of RNA sequencing methods for protein abundance prediction. [ABSTRACT FROM AUTHOR]

Background and Objectives: Perilipins 1-5 (PLIN) are lipid droplet-associated proteins that participate in regulating lipid storage and metabolism, and the PLIN5 isoform is known to form a nuclear complex with peroxisome proliferator-activated receptor-gamma coactivator 1-alpha (PGC-1α) to regulate lipid metabolism gene expression. However, the changes in PLIN isoforms' expression in response to pregnancy-induced cardiac hypertrophy are not thoroughly studied. The aim of this study was to quantify the mRNA expression of PLIN isoforms and PGC-1α along with total triacylglycerol (TAG) and cholesterol levels during late pregnancy and the postpartum period in the rat left ventricle. Materials and Methods: Female Sprague-Dawley rats were divided into three groups: non-pregnant, late pregnancy, and postpartum. The mRNA and protein levels were evaluated using quantitative RT-PCR and Western blotting, respectively. TAG and total cholesterol content were evaluated using commercial colorimetric methods. Results: The expression of mRNAs for PLIN1, 2, and 5 increased during pregnancy and the postpartum period. PGC-1α mRNA and protein expression increased during pregnancy and the postpartum period. Moreover, TAG and total cholesterol increased during pregnancy and returned to basal levels after pregnancy. Conclusions: Our results demonstrate that pregnancy upregulates differentially the expression of PLIN isoforms along with PGC-1α, suggesting that together they might be involved in the regulation of the lipid metabolic shift induced by pregnancy.

Machine learning and modeling approaches have been used to classify protein sequences for a broad set of tasks including predicting protein function, structure, expression, and localization. Some recent studies have successfully predicted whether a given gene is expressed as mRNA or even translated to proteins potentially, but given that not all genes are expressed in every condition and tissue, the challenge remains to predict condition-specific expression. To address this gap, we developed a machine learning approach to predict tissue-specific gene expression across 23 different tissues in maize, solely based on DNA promoter and protein sequences. For class labels, we defined high and low expression levels for mRNA and protein abundance and optimized classifiers by systematically exploring various methods and combinations of k-mer sequences in a two-phase approach. In the first phase, we developed Markov model classifiers for each tissue and built a feature vector based on the predictions. In the second phase, the feature vector was used as an input to a Bayesian network for final classification. Our results show that these methods can achieve high classification accuracy of up to 95% for predicting gene expression for individual tissues. By relying on sequence alone, our method works in settings where costly experimental data are unavailable and reveals useful insights into the functional, evolutionary, and regulatory characteristics of genes.

Abstract Background Low temperature (LT) stress is one of the major environmental stress factors affecting the growth and yield of maize (Zea mays L.). Hence, it is important to unravel the molecular mechanisms behind LT stress tolerance to improve molecular breeding in LT tolerant genotypes. In the present study, two maize genotypes viz. Gurez local from Kashmir Himalaya and tropical grown GM6, were dissected for their LT stress response in terms of accumulation of differentially regulated proteins (DRPs). Leaf proteome analysis at three-leaf stage of maize seedlings subjected to LT stress of 6 °C for a total of 12 h duration was performed using two dimensional gel electrophoresis (2D-PAGE) followed by subsequent identification of the proteins involved. Results After MALDI-TOF (Matrix-assisted laser desorption/ionization-time of flight) and bioinformatics analysis, 19 proteins were successfully identified in Gurez local, while as 10 proteins were found to get successful identification in GM6. The interesting observations from the present investigation is the identification of three novel proteins viz. threonine dehydratase biosynthetic chloroplastic, thylakoidal processing peptidase 1 chloroplastic, and nodulin-like protein, whose role in abiotic stress tolerance, in general, and LT stress, in particular, has not been reported so far. It is important to highlight here that most of LT responsive proteins including the three novel proteins were identified from Gurez local only, owing to its exceptional LT tolerance. From the protein profiles, obtained in both genotypes immediately after LT stress perception, it was inferred that stress responsive protein accumulation and their expression fashion help the Gurez local in seedling establishment and withstand unfavorable conditions as compared to GM6. This was inferred from the findings of pathway enrichment analysis like regulation of seed growth, timing of floral transition, lipid glycosylation, and aspartate family amino acid catabolic processes, besides other key stress defense mechanisms. However, in GM6, metabolic pathways enriched were found to be involved in more general processes including cell cycle DNA replication and regulation of phenylpropanoid metabolism. Furthermore, majority of the qRT-PCR results of the selected proteins demonstrated positive correlation between protein levels and transcript abundance, thereby strengthening our findings. Conclusions In conclusion, our findings reported majority of the identified proteins in Gurez local exhibiting up-regulated pattern under LT stress as compared to GM6. Furthermore, three novel proteins induced by LT stress were found in Gurez local, requiring further functional validation. Therefore, our results offer more insights for elucidating the molecular networks mediating LT stress tolerance in maize.

BACKGROUND:Maize experienced a whole-genome duplication event approximately 5 to 12 million years ago. Because this event occurred after speciation from sorghum, the pre-duplication subgenomes can be partially reconstructed by mapping syntenic regions to the sorghum chromosomes. During evolution, maize has had uneven gene loss between each ancient subgenome. Fractionation and divergence between these genomes continue today, constantly changing genetic make-up and phenotypes and influencing agronomic traits. RESULTS:Here we regenerate the subgenome reconstructions for the most recent maize reference genome assembly. Based on both expression and abundance data for homeologous gene pairs across multiple tissues, we observed functional divergence of genes across subgenomes. Although the genes in the larger maize subgenome are often expressing more highly than their homeologs in the smaller subgenome, we observed cases where homeolog expression dominance switches in different tissues. We demonstrate for the first time that protein abundances are higher in the larger subgenome, but they also show tissue-specific dominance, a pattern similar to RNA expression dominance. We also find that pollen expression is uniquely decoupled from protein abundance. CONCLUSION:Our study shows that the larger subgenome has a greater range of functional assignments and that there is a relative lack of overlap between the subgenomes in terms of gene functions than would be suggested by similar patterns of gene expression and protein abundance. Our study also revealed that some reactions are catalyzed uniquely by the larger and smaller subgenomes. The tissue-specific, nonequivalent expression-level dominance pattern observed here implies a change in regulatory control which favors differentiated selective pressure on the retained duplicates leading to eventual change in gene functions.

In the setting of stroke, ischemia not only impairs neuronal function, but also detrimentally affects the different components of the neurovascular unit, which are shown to be involved in the transition from reversible to longlasting tissue damage. In this context, the glial proteins myelin basic protein (MBP) and the 20,30-cyclic-nucleotide 30-phosphodiesterase (CNP) as well as the vasculature-associated basement membrane proteins laminin and collagen IV have been identified as ischemia-sensitive elements. However, available data from immunofluorescence and Western blot analyses are often found to be contradictory, which renders interpretation of the respective data rather difficult. Therefore, the present study investigates the impact of tissue pretreatment and antibody clonality on immunofluorescence measurements of the mentioned proteins in a highly reproducible model of permanent middle cerebral artery occlusion. Here, immunofluorescence labeling using polyclonal antibodies revealed an increased immunofluorescence intensity of MBP, CNP, laminin and collagen IV in ischemic areas, although Western blot analyses did not reveal increased protein levels. Importantly, contrary to polyclonal antibodies, monoclonal ones did not provide increased fluorescence intensities in ischemic areas. Further, we were able to demonstrate that different ways of tissue pre-treatment including paraformaldehyde fixation and antigen retrieval may not only impact on fluorescence intensity measurements in general, but rather one-sidedly affect either ischemic or unaffected tissue. Therefore, immunofluorescence intensity measurements do not necessarily correlate with the actual protein levels, especially in ischemia-affected tissue and should always be complemented by different techniques to enhance reproducibility and to hopefully overcome the translational roadblock from bench to bedside. [ABSTRACT FROM AUTHOR]

Background: Low temperature (LT) stress is one of the major environmental stress factors affecting the growth and yield of maize (Zea mays L.). Hence, it is important to unravel the molecular mechanisms behind LT stress tolerance to improve molecular breeding in LT tolerant genotypes. In the present study, two maize genotypes viz. Gurez local from Kashmir Himalaya and tropical grown GM6, were dissected for their LT stress response in terms of accumulation of differentially regulated proteins (DRPs). Leaf proteome analysis at three-leaf stage of maize seedlings subjected to LT stress of 6 °C for a total of 12 h duration was performed using two dimensional gel electrophoresis (2D-PAGE) followed by subsequent identification of the proteins involved. Results: After MALDI-TOF (Matrix-assisted laser desorption/ionization-time of flight) and bioinformatics analysis, 19 proteins were successfully identified in Gurez local, while as 10 proteins were found to get successful identification in GM6. The interesting observations from the present investigation is the identification of three novel proteins viz. threonine dehydratase biosynthetic chloroplastic, thylakoidal processing peptidase 1 chloroplastic, and nodulin-like protein, whose role in abiotic stress tolerance, in general, and LT stress, in particular, has not been reported so far. It is important to highlight here that most of LT responsive proteins including the three novel proteins were identified from Gurez local only, owing to its exceptional LT tolerance. From the protein profiles, obtained in both genotypes immediately after LT stress perception, it was inferred that stress responsive protein accumulation and their expression fashion help the Gurez local in seedling establishment and withstand unfavorable conditions as compared to GM6. This was inferred from the findings of pathway enrichment analysis like regulation of seed growth, timing of floral transition, lipid glycosylation, and aspartate family amino acid catabolic processes, besides other key stress defense mechanisms. However, in GM6, metabolic pathways enriched were found to be involved in more general processes including cell cycle DNA replication and regulation of phenylpropanoid metabolism. Furthermore, majority of the qRT-PCR results of the selected proteins demonstrated positive correlation between protein levels and transcript abundance, thereby strengthening our findings. Conclusions: In conclusion, our findings reported majority of the identified proteins in Gurez local exhibiting up-regulated pattern under LT stress as compared to GM6. Furthermore, three novel proteins induced by LT stress were found in Gurez local, requiring further functional validation. Therefore, our results offer more insights for elucidating the molecular networks mediating LT stress tolerance in maize. [ABSTRACT FROM AUTHOR]

Accurate abundance measurements of cellular proteins are required to achieve a quantitative and predictive understanding of any biological process inside the cell. Existing methods to determine absolute protein abundances are labor-intensive and/or require sophisticated experimental and computational infrastructure (e.g., fluorescence correlation spectroscopy (FCS)-calibrated imaging and quantitative mass spectrometry). Here we detail a straightforward flow cytometry-based method to measure the absolute abundance of any Halo-tagged protein in live cells that uses a standard mammalian cell line with a known number of Halo-CTCF proteins recently characterized in our lab. The protocol only comprises a few steps. First, a cell line expressing the Halo-tagged protein of interest is grown and labeled side-by-side with our standard line. Then, average fluorescence intensities are measured by conventional flow cytometry analysis and finally a simple calculation is applied to estimate the absolute number of the Halo-tagged protein of interest per cell. Once the protein of interest has been endogenously tagged with HaloTag, which we routinely achieve by Cas9-mediated genome editing, the presented protocol is fast, convenient, reproducible, cost-effective and readily accessible.

Chemosensory pathways and two-component systems are important bacterial signal transduction systems. In the human pathogen Pseudomonas aeruginosa, these systems control many virulence traits. Previous studies showed that inorganic phosphate (Pi) deficiency induces virulence. We report here the abundance of chemosensory and two-component signaling proteins of P. aeruginosa grown in Pi deficient and sufficient media. The cellular abundance of chemoreceptors differed greatly, since a 2400-fold difference between the most and least abundant receptors was observed. For many chemoreceptors, their amount varied with the growth condition. The amount of chemoreceptors did not correlate with the magnitude of chemotaxis to their cognate chemoeffectors. Of the four chemosensory pathways, proteins of the Che chemotaxis pathway were most abundant and showed little variation in different growth conditions. The abundance of chemoreceptors and solute binding proteins indicates a sensing preference for amino acids and polyamines. There was an excess of response regulators over sensor histidine kinases in two-component systems. In contrast, ratios of the response regulators CheY and CheB to the histidine kinase CheA of the Che pathway were all below 1, indicative of different signaling mechanisms. This study will serve as a reference for exploring sensing preferences and signaling mechanisms of other bacteria. [ABSTRACT FROM AUTHOR]

In the setting of stroke, ischemia not only impairs neuronal function, but also detrimentally affects the different components of the neurovascular unit, which are shown to be involved in the transition from reversible to long-lasting tissue damage. In this context, the glial proteins myelin basic protein (MBP) and the 2′,3′-cyclic-nucleotide 3′-phosphodiesterase (CNP) as well as the vasculature-associated basement membrane proteins laminin and collagen IV have been identified as ischemia-sensitive elements. However, available data from immunofluorescence and Western blot analyses are often found to be contradictory, which renders interpretation of the respective data rather difficult. Therefore, the present study investigates the impact of tissue pre-treatment and antibody clonality on immunofluorescence measurements of the mentioned proteins in a highly reproducible model of permanent middle cerebral artery occlusion. Here, immunofluorescence labeling using polyclonal antibodies revealed an increased immunofluorescence intensity of MBP, CNP, laminin and collagen IV in ischemic areas, although Western blot analyses did not reveal increased protein levels. Importantly, contrary to polyclonal antibodies, monoclonal ones did not provide increased fluorescence intensities in ischemic areas. Further, we were able to demonstrate that different ways of tissue pre-treatment including paraformaldehyde fixation and antigen retrieval may not only impact on fluorescence intensity measurements in general, but rather one-sidedly affect either ischemic or unaffected tissue. Therefore, immunofluorescence intensity measurements do not necessarily correlate with the actual protein levels, especially in ischemia-affected tissue and should always be complemented by different techniques to enhance reproducibility and to hopefully overcome the translational roadblock from bench to bedside.

Diapause, an adaptative strategy for survival under harsh conditions, is a dynamic multi-stage process. Bombus terrestris, an important agricultural pollinator, is declining in the wild, but artificial breeding is possible by imitating natural conditions. Mated queen bees enter reproductive diapause in winter and recover in spring, but the regulatory mechanisms remain unclear. Herein, we conducted a comparative 4D label-free proteomic analysis of queen bees during artificial breeding at seven timepoints, including pre-diapause, diapause, and post-diapause stages. Through bioinformatics analysis of proteomic and detection of substance content changes, our results found that, during pre-diapause stages, queen bees had active mitochondria with high levels of oxidative phosphorylation, high body weight, and glycogen and TAG content, all of which support energy consumption during subsequent diapause. During diapause stages, body weight and water content were decreased but glycerol increased, contributing to cold resistance. Dopamine content, immune defense, and protein phosphorylation were elevated, while fat metabolism, protein export, cell communication, signal transduction, and hydrolase activity decreased. Following diapause termination, JH titer, water, fatty acid, and pyruvate levels increased, catabolism, synaptic transmission, and insulin signaling were stimulated, ribosome and cell cycle proteins were upregulated, and cell proliferation was accelerated. Meanwhile, TAG and glycogen content decreased, and ovaries gradually developed. These findings illuminate changes occurring in queen bees at different diapause stages during commercial production.

Chronic pelvic pain (CPP) is generally defined as non-cyclic pain perceived in the pelvic area that has persisted from three to six months or longer and is unrelated to pregnancy. The etiology of CPP is complex, multifactorial, with heterogeneous presentation, and includes several diseases such as endometriosis, adenomyosis, and interstitial cystitis/bladder pain syndrome. It may also be associated with sexual dysfunction, musculoskeletal disorders, and comorbid psychiatric symptoms. Vulvar pain disorders (VPDs) are typically categorized separately from chronic pelvic pain; among all VPDs, vulvodynia is a chronic vulvar pain of unknown etiology, lasting at least 3 months and that might be associated with other potentially linked factors. Proteomics represents a useful approach to study the proteome profiles of clinical samples. In this review, we have considered a selection of articles that have analyzed the protein abundance and novel protein species from various biological samples, including eutopic/ectopic endometrium, urine, serum, follicular, peritoneal fluid, and cervical mucus, potentially involved in the pathogenesis and progression of CPP and VPDs. These findings could represent valuable targets for paving the way for the differential diagnosis and therapeutic management of CPP and VDPs, thereby optimizing both the prevention and treatment of these conditions.

Transcriptomics methods (RNA-Seq, PCR) today are more routine and reproducible than proteomics methods, i.e., both mass spectrometry and immunochemical analysis. For this reason, most scientific studies are limited to assessing the level of mRNA content. At the same time, protein content (and its post-translational status) largely determines the cell’s state and behavior. Such a forced extrapolation of conclusions from the transcriptome to the proteome often seems unjustified. The ratios of “transcript-protein” pairs can vary by several orders of magnitude for different genes. As a rule, the correlation coefficient between transcriptome–proteome levels for different tissues does not exceed 0.3–0.5. Several characteristics determine the ratio between the content of mRNA and protein: among them, the rate of movement of the ribosome along the mRNA and the number of free ribosomes in the cell, the availability of tRNA, the secondary structure, and the localization of the transcript. The technical features of the experimental methods also significantly influence the levels of the transcript and protein of the corresponding gene on the outcome of the comparison. Given the above biological features and the performance of experimental and bioinformatic approaches, one may develop various models to predict proteomic profiles based on transcriptomic data. This review is devoted to the ability of RNA sequencing methods for protein abundance prediction.

Measuring changes in protein or organelle abundance in the cell is an essential, but challenging aspect of cell biology. Frequently-used methods for determining organelle abundance typically rely on detection of a very few marker proteins, so are unsatisfactory. In silico estimates of protein abundances from publicly available protein spectra can provide useful standard abundance values but contain only data from tissue proteomes, and are not coupled to organelle localization data. A new protein abundance score, the normalized protein abundance scale (NPAS), expands on the number of scored proteins and the scoring accuracy of lower-abundance proteins in Arabidopsis. NPAS was combined with subcellular protein localization data, facilitating quantitative estimations of organelle abundance during routine experimental procedures. A suite of targeted proteomics markers for subcellular compartment markers was developed, enabling independent verification of in silico estimates for relative organelle abundance. Estimation of relative organelle abundance was found to be reproducible and consistent over a range of tissues and growth conditions. In silico abundance estimations and localization data have been combined into an online tool, multiple marker abundance profiling, available in the SUBA4 toolbox (http://suba.live).

Low Complexity Regions (LCRs) are present in a surprisingly large number of eukaryotic proteins. These highly repetitive and compositionally biased sequences are often structurally disordered, bind promiscuously, and evolve rapidly. Frequently studied in terms of evolutionary dynamics, little is known about how LCRs affect the expression of the proteins which contain them. It would be expected that rapidly evolving LCRs are unlikely to be tolerated in strongly conserved, highly abundant proteins, leading to lower overall abundance in proteins which contain LCRs. To test this hypothesis and examine the associations of protein abundance and transcript abundance with the presence of LCRs, we have integrated high-throughput data from across mammals. We have found that LCRs are indeed associated with reduced protein abundance, but are also associated with elevated transcript abundance. These associations are qualitatively consistent across 12 human tissues and nine mammalian species. The differential impacts of LCRs on abundance at the protein and transcript level are not explained by differences in either protein degradation rates or the inefficiency of translation for LCR containing proteins. We suggest that rapidly evolving LCRs are a source of selective pressure on the regulatory mechanisms which maintain steady-state protein abundance levels. [ABSTRACT FROM AUTHOR]

In general, the evolutionary rate of proteins is not primarily related to protein and amino acid functions, and factors such as protein abundance, codon usage, and the protein's TM are more important. To better understand the factors that affect protein evolution, E. coli MG1655 orthologs were compared to those in closely related bacteria and to more distantly related prokaryotes, eukaryotes, and archaea. Also, the evolution of different types of proteins was studied. The analyses indicate that the amino acid conservation of enzymes that do not use macromolecules (e.g. DNA, RNA, and proteins) as substrates and that carry out metabolic processes involving small molecules (i.e. small molecule enzymes) is different than other enzymes. For example, the small molecule enzymes have a lower percent identity than other enzymes when sequences from closely related bacteria are compared. Analyses indicate the lower percent identity is not a result of the amino acid or codon usage of the small molecule enzymes. The small molecule enzymes also don't have a significantly lower protein abundance indicating that is also not likely an important factor driving differences in amino acid conservation. Analyses indicate different methods to measure the TM of proteins have different relationships between amino acid conservation over different evolutionary distances. In totality, the results demonstrate that the relationship between the factors thought to affect protein evolution (protein abundance, codon usage, and proteins TMs) and protein evolution are complex and depend on the factor, the organisms, and the type of proteins being analyzed. [ABSTRACT FROM AUTHOR]

STIM1 is an ER/SR transmembrane protein that interacts with ORAI1 to activate store operated Ca2+ entry (SOCE) upon ER/SR depletion of calcium. Normally highly expressed in skeletal muscle, STIM1 deficiency causes significant changes to mitochondrial ultrastructure that do not occur with loss of ORAI1 or other components of SOCE. The datasets in this article are from large-scale proteomics and phosphoproteomics experiments in an inducible mouse model of skeletal muscle-specific STIM1 knock out (KO). These data reveal statistically significant changes in the relative abundance of specific proteins and sites of protein phosphorylation in STIM1 KO gastrocnemius. Protein samples from five biological replicates of each condition (+/- STIM1) were enzymatically digested, the resulting peptides labeled with tandem mass tag (TMT) reagents, mixed, and fractionated. Phosphopeptides were enriched and a small amount of each input retained for protein abundance analysis. All phosphopeptide and input fractions were analyzed by nano LC-MS/MS on a Q Exactive Plus Orbitrap mass spectrometer, searched with Proteome Discoverer software, and processed with in-house R-scripts for data normalization and statistical analysis. Article published in Molecular Metabolism [1].

Machine learning and modeling approaches have been used to classify protein sequences for a broad set of tasks including predicting protein function, structure, expression, and localization. Some recent studies have successfully predicted whether a given gene is expressed as mRNA or even translated to proteins potentially, but given that not all genes are expressed in every condition and tissue, the challenge remains to predict condition-specific expression. To address this gap, we developed a machine learning approach to predict tissue-specific gene expression across 23 different tissues in maize, solely based on DNA promoter and protein sequences. For class labels, we defined high and low expression levels for mRNA and protein abundance and optimized classifiers by systematically exploring various methods and combinations of k-mer sequences in a two-phase approach. In the first phase, we developed Markov model classifiers for each tissue and built a feature vector based on the predictions. In the second phase, the feature vector was used as an input to a Bayesian network for final classification. Our results show that these methods can achieve high classification accuracy of up to 95% for predicting gene expression for individual tissues. By relying on sequence alone, our method works in settings where costly experimental data are unavailable and reveals useful insights into the functional, evolutionary, and regulatory characteristics of genes.

Abstract Background Cell-to-cell variation in gene expression strongly affects population behavior and is key to multiple biological processes. While codon usage is known to affect ensemble gene expression, how codon usage influences variation in gene expression between single cells is not well understood. Results Here, we used a Sort-seq based massively parallel strategy to quantify gene expression variation from a green fluorescent protein (GFP) library containing synonymous codons in Escherichia coli. We found that sequences containing codons with higher tRNA Adaptation Index (TAI) scores, and higher codon adaptation index (CAI) scores, have higher GFP variance. This trend is not observed for codons with high Normalized Translation Efficiency Index (nTE) scores nor from the free energy of folding of the mRNA secondary structure. GFP noise, or squared coefficient of variance (CV2), scales with mean protein abundance for low-abundant proteins but does not change at high mean protein abundance. Conclusions Our results suggest that the main source of noise for high-abundance proteins is likely not originating at translation elongation. Additionally, the drastic change in mean protein abundance with small changes in protein noise seen from our library implies that codon optimization can be performed without concerning gene expression noise for biotechnology applications.

Malaria has remained a major health concern for decades among people living in tropical and sub-tropical countries. Plasmodium falciparum is one of the critical species that cause severe malaria and is responsible for major mortality. Moreover, the parasite has generated resistance against all WHO recommended drugs and therapies. Therefore, there is an urgent need for preventive measures in the form of reliable vaccines to achieve the target of a malaria-free world. Surface proteins are the preferable choice for subunit vaccine development because they are rapidly detected and engaged by host immune cells and vaccination-induced antibodies. Additionally, abundant surface or membrane proteins may contribute to the opsonization of pathogens by vaccine-induced antibodies. In our study, we have listed all those surface proteins from the literature that could be functionally important and essential for infection and immune evasion of the malaria parasite. Eight Plasmodium surface and membrane proteins from the pre-erythrocyte and erythrocyte stages were shortlisted. Thirty-seven epitopes (B-cell, CTL, and HTL epitopes) from these proteins were predicted using immune-informatic tools and joined with suitable peptide linkers to design a vaccine construct. A TLR-4 agonist peptide adjuvant was added at the N-terminus of the multi-epitope series, followed by the PADRE sequence and EAAAK linker. The TLR-4 receptor was docked with the construct's anticipated model structure. The complex of vaccine and TLR-4, with the lowest energy −1514, was found to be stable under simulated physiological settings. This study has provided a novel multi-epitope construct that may be exploited further for the development of an efficient vaccine for malaria. [ABSTRACT FROM AUTHOR]

Chemosensory pathways and two-component systems are important bacterial signal transduction systems. In the human pathogen Pseudomonas aeruginosa, these systems control many virulence traits. Previous studies showed that inorganic phosphate (Pi) deficiency induces virulence. We report here the abundance of chemosensory and two-component signaling proteins of P. aeruginosa grown in Pi deficient and sufficient media. The cellular abundance of chemoreceptors differed greatly, since a 2400-fold difference between the most and least abundant receptors was observed. For many chemoreceptors, their amount varied with the growth condition. The amount of chemoreceptors did not correlate with the magnitude of chemotaxis to their cognate chemoeffectors. Of the four chemosensory pathways, proteins of the Che chemotaxis pathway were most abundant and showed little variation in different growth conditions. The abundance of chemoreceptors and solute binding proteins indicates a sensing preference for amino acids and polyamines. There was an excess of response regulators over sensor histidine kinases in two-component systems. In contrast, ratios of the response regulators CheY and CheB to the histidine kinase CheA of the Che pathway were all below 1, indicative of different signaling mechanisms. This study will serve as a reference for exploring sensing preferences and signaling mechanisms of other bacteria.

Functional genes and proteins in sperm play an essential role in bulls' reproductive processes. They are more accurate in determining bull fertility than conventional semen quality tests. Protamine-1 (PRM1) is a gene or protein crucial for packaging and protecting sperm DNA until fertilization affects normal sperm function. This study analyzes the genes and proteins potential from PRM1 as fertility markers for different breeds of bulls utilized in the artificial insemination programs, expected to be an accurate tool in interpreting bull fertility in Indonesia. This study used Limousin, Holstein, and Ongole Grade bulls divided into two groups based on fertility, high-fertility (HF) and low fertility (LF). The semen quality assessment included progressive motility (computer-assisted semen analysis), viability (eosin-nigrosine), and plasma membrane integrity (HOS test). Sperm DNA fragmentation (SDF) was assessed using the acridine orange staining and the Halomax test. Sperm PRM deficiency was evaluated with the chromomycin A3 method. Moreover, PRM1 gene expression was measured using qRT-PCR, and the PRM1 protein abundance was measured with the enzyme immunoassay method. Semen quality values, relative expression of PRM1 gene, and quantity of PRM1 protein were significantly higher (p < 0.05) in HF bulls than in LF bulls. The SDF and PRM deficiency values in LF bulls were significantly higher (p < 0.05) than HF bulls. Additionally, PRM1 at the gene and protein levels correlated significantly (p < 0.01) with fertility. Therefore, PRM1 is a potential candidate for fertility markers in bulls in Indonesia. [ABSTRACT FROM AUTHOR]

The present work explores the adaptive behavior of well-known ureolytic bacterial strain namely Sporosarcina pasteurii for microbial-induced calcite precipitation (MICP) under saline environment. MICP activity was observed up to 10% NaCl supplementation and confirmed by several characterization techniques viz. scanning electron microscopy (SEM), X-ray diffraction (XRD), and thermogravimetric analysis (TGA). The urease activity did not show appreciable decline till 5% NaCl supplementation but beyond that drastic reduction was observed though MICP process continued. These results indicated a shift in the biological pathway for MICP process at higher salt concentrations. This was confirmed by estimation of ammonium ion concentration which was approximately 4 µg/ml with 10% NaCl supplementation as compared to 8.5 µg/ml at 0% NaCl supplementation. To provide an insight on the nature of protein expressed, combination of liquid chromatography-mass spectrometry (nano-LC-MS) approaches along with an exponentially modified protein abundance index (emPAI) methodology were explored. The findings suggest the presence of significant number of osmoregulatory proteins substantiating halophilic adaptation of S. pasteurii and bicarbonate transport mediated precipitation at higher levels of salinity. Further, co-precipitation of CaCl2 and MgSO4 was also confirmed with SEM and energy dispersive spectroscopy (EDS) mapping under higher saline environment thus substantiating the efficacy of the strain. [ABSTRACT FROM AUTHOR]

Sperm mobility is a major determinant of sperm quality in the domesticated chicken (Gallus domesticus) and is therefore an area of interest for improving fertility. Sperm-associated antigen 6 (SPAG6) is an important flagellar protein implicated to be necessary for flagellar function but negatively associated with rooster fertility. This study was aimed to characterize the expression of SPAG6 and investigate its utility as a protein biomarker of sperm mobility. By western analysis, relative SPAG6 abundances were compared between the testicular, epididymal, and vasal tissues and in sequentially maturing sperm. Immunocytochemistry techniques were used to detect localization of SPAG6 in chicken sperm. Last, western analysis was used to compare relative SPAG6 abundances in sperm of differing mobility. SPAG6 was found in higher abundance in epididymal tissues and in highest abundance in vasal tissues, relative to that of the testis. SPAG6 was also found to sequentially increase in abundance in maturing sperm. SPAG6 localizes between the axonemal central pair of microtubules in the sperm flagella, but it is also found in lower concentration in the acrosomal region. SPAG6 was not a significant predictor of sperm mobility. SPAG6 abundance, alone, is not a strong predictor of sperm mobility. Its impact on rooster fertility is likely unrelated to its impact on sperm mobility.

Abstract Background Maize experienced a whole-genome duplication event approximately 5 to 12 million years ago. Because this event occurred after speciation from sorghum, the pre-duplication subgenomes can be partially reconstructed by mapping syntenic regions to the sorghum chromosomes. During evolution, maize has had uneven gene loss between each ancient subgenome. Fractionation and divergence between these genomes continue today, constantly changing genetic make-up and phenotypes and influencing agronomic traits. Results Here we regenerate the subgenome reconstructions for the most recent maize reference genome assembly. Based on both expression and abundance data for homeologous gene pairs across multiple tissues, we observed functional divergence of genes across subgenomes. Although the genes in the larger maize subgenome are often expressing more highly than their homeologs in the smaller subgenome, we observed cases where homeolog expression dominance switches in different tissues. We demonstrate for the first time that protein abundances are higher in the larger subgenome, but they also show tissue-specific dominance, a pattern similar to RNA expression dominance. We also find that pollen expression is uniquely decoupled from protein abundance. Conclusion Our study shows that the larger subgenome has a greater range of functional assignments and that there is a relative lack of overlap between the subgenomes in terms of gene functions than would be suggested by similar patterns of gene expression and protein abundance. Our study also revealed that some reactions are catalyzed uniquely by the larger and smaller subgenomes. The tissue-specific, nonequivalent expression-level dominance pattern observed here implies a change in regulatory control which favors differentiated selective pressure on the retained duplicates leading to eventual change in gene functions.

The availability of an increasingly large amount of public proteomics data sets presents an opportunity for performing combined analyses to generate comprehensive organism-wide protein expression maps across different organisms and biological conditions. Sus scrofa , a domestic pig, is a model organism relevant for food production and for human biomedical research. Here, we reanalyzed 14 public proteomics data sets from the PRIDE database coming from pig tissues to assess baseline (without any biological perturbation) protein abundance in 14 organs, encompassing a total of 20 healthy tissues from 128 samples. The analysis involved the quantification of protein abundance in 599 mass spectrometry runs. We compared protein expression patterns among different pig organs and examined the distribution of proteins across these organs. Then, we studied how protein abundances were compared across different data sets and studied the tissue specificity of the detected proteins. Of particular interest, we conducted a comparative analysis of protein expression between pig and human tissues, revealing a high degree of correlation in protein expression among orthologs, particularly in brain, kidney, heart, and liver samples. We have integrated the protein expression results into the Expression Atlas resource for easy access and visualization of the protein expression data individually or alongside gene expression data.

Background and Objectives: Perilipins 1–5 (PLIN) are lipid droplet-associated proteins that participate in regulating lipid storage and metabolism, and the PLIN5 isoform is known to form a nuclear complex with peroxisome proliferator-activated receptor-gamma coactivator 1-alpha (PGC-1α) to regulate lipid metabolism gene expression. However, the changes in PLIN isoforms’ expression in response to pregnancy-induced cardiac hypertrophy are not thoroughly studied. The aim of this study was to quantify the mRNA expression of PLIN isoforms and PGC-1α along with total triacylglycerol (TAG) and cholesterol levels during late pregnancy and the postpartum period in the rat left ventricle. Materials and Methods: Female Sprague-Dawley rats were divided into three groups: non-pregnant, late pregnancy, and postpartum. The mRNA and protein levels were evaluated using quantitative RT-PCR and Western blotting, respectively. TAG and total cholesterol content were evaluated using commercial colorimetric methods. Results: The expression of mRNAs for PLIN1, 2, and 5 increased during pregnancy and the postpartum period. PGC-1α mRNA and protein expression increased during pregnancy and the postpartum period. Moreover, TAG and total cholesterol increased during pregnancy and returned to basal levels after pregnancy. Conclusions: Our results demonstrate that pregnancy upregulates differentially the expression of PLIN isoforms along with PGC-1α, suggesting that together they might be involved in the regulation of the lipid metabolic shift induced by pregnancy.

Intestinal transporter proteins are known to affect the pharmacokinetics and in turn the efficacy and safety of many orally administered drugs in a clinically relevant manner. This knowledge is especially well-established for intestinal ATP-binding cassette transporters such as P-gp and BCRP. In contrast to this, information about intestinal uptake carriers is much more limited although many hydrophilic or ionic drugs are not expected to undergo passive diffusion but probably require specific uptake transporters. A transporter which is controversially discussed with respect to its expression, localization and function in the human intestine is the organic cation transporter 1 (OCT1). This review article provides an up-to-date summary on the available data from expression analysis as well as functional studies in vitro, animal findings and clinical observations. The current evidence suggests that OCT1 is expressed in the human intestine in small amounts (on gene and protein levels), while its cellular localization in the apical or basolateral membrane of the enterocytes remains to be finally defined, but functional data point to a secretory function of the transporter at the basolateral membrane. Thus, OCT1 should not be considered as a classical uptake transporter in the intestine but rather as an intestinal elimination pathway for cationic compounds from the systemic circulation.

The Göttingen Minipig is gaining ground as nonrodent species in safety testing of drugs for pediatric indications. Due to developmental changes in pharmacokinetics and pharmacodynamics, physiologically based pharmacokinetic (PBPK) models are built to better predict drug exposure in children and to aid species selection for nonclinical safety studies. These PBPK models require high quality physiological and ADME data such as protein abundance of drug metabolizing enzymes. These data are available for man and rat, but scarce for the Göttingen Minipig. The aim of this study was to assess hepatic cytochrome P450 (CYP) protein abundance in the developing Göttingen Minipig by using mass spectrometry. In addition, sex-related differences in CYP protein abundance and correlation of CYP enzyme activity with CYP protein abundance were assessed. The following age groups were included: gestational day (GD) 84–86 (n = 8), GD 108 (n = 6), postnatal day (PND) 1 (n = 8), PND 3 (n = 8), PND 7 (n = 8), PND 28 (n = 8) and adult (n = 8). Liver microsomes were extracted and protein abundance was compared to that in adult animals. Next, the CYP protein abundance was correlated to CYP enzyme activity in the same biological samples. In general, CYP protein abundance gradually increased during development. However, we observed a stable protein expression over time for CYP4A24 and CYP20A1 and for CYP51A1, a high protein expression during the fetal stages was followed by a decrease during the first month of life and an increase toward adulthood. Sex-related differences were observed for CYP4V2_2a and CYP20A1 at PND 1 with highest expression in females for both isoforms. In the adult samples, sex-related differences were detected for CYP1A1, CYP1A2, CYP2A19, CYP2E1_2, CYP3A22, CYP4V2_2a and CYP4V2_2b with higher values in female compared to male Göttingen Minipigs. The correlation analysis between CYP protein abundance and CYP enzyme activity showed that CYP3A22 protein abundance correlated clearly with the metabolism of midazolam at PND 7. These data are remarkably comparable to human data and provide a valuable step forward in the construction of a neonatal and juvenile Göttingen Minipig PBPK model.

An understanding of the forces shaping protein conservation is key, both for the fundamental knowledge it represents and to allow for optimal use of evolutionary information in practical applications. Sequence conservation is typically examined at one of two levels. The first is a residue-level, where intra-protein differences are analyzed and the second is a protein-level, where inter-protein differences are studied. At a residue level, we know that solvent-accessibility is a prime determinant of conservation. By inverting this logic, we inferred that disordered regions are slightly more solvent-accessible on average than the most exposed surface residues in domains. By integrating abundance information with evolutionary data within and across proteins, we confirmed a previously reported strong surface-core association in the evolution of structured regions, but we found a comparatively weak association between disordered and structured regions. The facts that disordered and structured regions experience different structural constraints and evolve independently provide a unique setup to examine an outstanding question: why is a protein’s abundance the main determinant of its sequence conservation? Indeed, any structural or biophysical property linked to the abundance-conservation relationship should increase the relative conservation of regions concerned with that property (e.g., disordered residues with mis-interactions, domain residues with misfolding). Surprisingly, however, we found the conservation of disordered and structured regions to increase in equal proportion with abundance. This observation implies that either abundance-related constraints are structure-independent, or multiple constraints apply to different regions and perfectly balance each other.

• Machine learning models can predict single-cell protein abundance from RNA-seq data. • Multimodal omics data of CITE-seq and REAP-seq have been used to train models. • Among 6 methods compared, Random Forest performed the best overall. • Data denoising and parameter tuning are crucial for prediction performance. Single-cell protein abundance is a fundamental type of information to characterize cell states. Due to high cost and technical barriers, however, direct quantification of proteins is difficult. Single-cell RNA sequencing (scRNA-seq) data, serving as a cost-effective substitute of single-cell proteomics, may not accurately reflect protein expression levels due to measurement error, noise, post-transcriptional and translational regulation, etc. The recently emerging single-cell multimodal omics data, e.g. CITE-seq and REAP-seq, can simultaneously profile RNA and protein abundances in single cells, providing labeled data for predictive modeling in a supervised learning framework. Deep neural network-based transfer learning method has been applied to imputation of surface protein abundances from single-cell transcriptomic data. However, it is unclear if the artificial neural network is the best model, and it is desirable to improve the prediction performance (e.g. accuracy, interpretability) of machine learning models. In this paper, we compared several tree-based ensemble learning methods with neural network models, and found that ensemble learning often performed better than neural network, and Random Forest (RF) performed the best overall. Moreover, we used the feature importance scores from RF to interpret biological mechanisms underlying the prediction. Our study demonstrates the effectiveness of ensemble learning for reliable protein abundances prediction using single-cell multimodal omics data, and paves the way for knowledge discovery by mining single-cell multi-omics data in large scale. [ABSTRACT FROM AUTHOR]

Diurnal rhythms and light availability affect transcription–translation feedback loops that regulate the synthesis of photosynthetic proteins. The CO2-fixing enzyme Rubisco is the most abundant protein in the leaves of major crop species and its activity depends on interaction with the molecular chaperone Rubisco activase (Rca). In Triticum aestivum L. (wheat), three Rca isoforms are present that differ in their regulatory properties. Here, we tested the hypothesis that the relative abundance of the redox-sensitive and redox-insensitive Rca isoforms could be differentially regulated throughout light–dark diel cycle in wheat. While TaRca1-β expression was consistently negligible throughout the day, transcript levels of both TaRca2-β and TaRca2-α were higher and increased at the start of the day, with peak levels occurring at the middle of the photoperiod. Abundance of TaRca-β protein was maximal 1.5 h after the peak in TaRca2-β expression, but the abundance of TaRca-α remained constant during the entire photoperiod. The redox-sensitive TaRca-α isoform was less abundant, representing 85% of the redox-insensitive TaRca-β at the transcript level and 12.5% at the protein level. Expression of Rubisco large and small subunit genes did not show a consistent pattern throughout the diel cycle, but the abundance of Rubisco decreased by up to 20% during the dark period in fully expanded wheat leaves. These results, combined with a lack of correlation between transcript and protein abundance for both Rca isoforms and Rubisco throughout the entire diel cycle, suggest that the abundance of these photosynthetic enzymes is post-transcriptionally regulated. [ABSTRACT FROM AUTHOR]

Large scale surveys in mammalian tissue culture cells suggest that the protein expressed at the median abundance is present at 8,000-16,000 molecules per cell and that differences in mRNA expression between genes explain only 10-40% of the differences in protein levels. We find, however, that these surveys have significantly underestimated protein abundances and the relative importance of transcription. Using individual measurements for 61 housekeeping proteins to rescale whole proteome data from Schwanhausser et al. (2011), we find that the median protein detected is expressed at 170,000 molecules per cell and that our corrected protein abundance estimates show a higher correlation with mRNA abundances than do the uncorrected protein data. In addition, we estimated the impact of further errors in mRNA and protein abundances using direct experimental measurements of these errors. The resulting analysis suggests that mRNA levels explain at least 56% of the differences in protein abundance for the 4,212 genes detected by Schwanhausser et al. (2011), though because one major source of error could not be estimated the true percent contribution should be higher. We also employed a second, independent strategy to determine the contribution of mRNA levels to protein expression. We show that the variance in translation rates directly measured by ribosome profiling is only 12% of that inferred by Schwanhausser et al. (2011), and that the measured and inferred translation rates correlate poorly (R(2) = 0.13). Based on this, our second strategy suggests that mRNA levels explain ∼81% of the variance in protein levels. We also determined the percent contributions of transcription, RNA degradation, translation and protein degradation to the variance in protein abundances using both of our strategies. While the magnitudes of the two estimates vary, they both suggest that transcription plays a more important role than the earlier studies implied and translation a much smaller role. Finally, the above estimates only apply to those genes whose mRNA and protein expression was detected. Based on a detailed analysis by Hebenstreit et al. (2012), we estimate that approximately 40% of genes in a given cell within a population express no mRNA. Since there can be no translation in the absence of mRNA, we argue that differences in translation rates can play no role in determining the expression levels for the ∼40% of genes that are non-expressed.

Objectives. Aldosterone rapidly enhances protein kinase C (PKC) alpha and beta1 proteins in the rat kidney. The G protein-coupled receptor 30 (GPR30)-mediated PKC pathway is involved in the inhibition of the potassium channel in HEK-239 cells. GPR30 mediates rapid actions of aldosterone in vitro. There are no reports available regarding the aldosterone action on other PKC isoforms and GPR30 proteins in vivo. The aim of the present study was to examine rapid actions of aldosterone on protein levels of phosphorylated PKC (p-PKC) delta, p-PKC epsilon, and GPR30 simultaneously in the rat kidney.

Background: Cell-to-cell variation in gene expression strongly affects population behavior and is key to multiple biological processes. While codon usage is known to affect ensemble gene expression, how codon usage influences variation in gene expression between single cells is not well understood. Results: Here, we used a Sort-seq based massively parallel strategy to quantify gene expression variation from a green fluorescent protein (GFP) library containing synonymous codons in Escherichia coli. We found that sequences containing codons with higher tRNA Adaptation Index (TAI) scores, and higher codon adaptation index (CAI) scores, have higher GFP variance. This trend is not observed for codons with high Normalized Translation Efficiency Index (nTE) scores nor from the free energy of folding of the mRNA secondary structure. GFP noise, or squared coefficient of variance (CV2), scales with mean protein abundance for low-abundant proteins but does not change at high mean protein abundance. Conclusions: Our results suggest that the main source of noise for high-abundance proteins is likely not originating at translation elongation. Additionally, the drastic change in mean protein abundance with small changes in protein noise seen from our library implies that codon optimization can be performed without concerning gene expression noise for biotechnology applications. [ABSTRACT FROM AUTHOR]

Functional genes and proteins in sperm play an essential role in bulls’ reproductive processes. They are more accurate in determining bull fertility than conventional semen quality tests. Protamine-1 (PRM1) is a gene or protein crucial for packaging and protecting sperm DNA until fertilization affects normal sperm function. This study analyzes the genes and proteins potential from PRM1 as fertility markers for different breeds of bulls utilized in the artificial insemination programs, expected to be an accurate tool in interpreting bull fertility in Indonesia. This study used Limousin, Holstein, and Ongole Grade bulls divided into two groups based on fertility, high-fertility (HF) and low fertility (LF). The semen quality assessment included progressive motility (computer-assisted semen analysis), viability (eosin-nigrosine), and plasma membrane integrity (HOS test). Sperm DNA fragmentation (SDF) was assessed using the acridine orange staining and the Halomax test. Sperm PRM deficiency was evaluated with the chromomycin A3 method. Moreover, PRM1 gene expression was measured using qRT-PCR, and the PRM1 protein abundance was measured with the enzyme immunoassay method. Semen quality values, relative expression of PRM1 gene, and quantity of PRM1 protein were significantly higher (p < 0.05) in HF bulls than in LF bulls. The SDF and PRM deficiency values in LF bulls were significantly higher (p < 0.05) than HF bulls. Additionally, PRM1 at the gene and protein levels correlated significantly (p < 0.01) with fertility. Therefore, PRM1 is a potential candidate for fertility markers in bulls in Indonesia.