Human genetic defects in SRP19 and SRPRA cause severe congenital neutropenia with distinctive proteome changes.

The mechanisms of coordinated changes in proteome composition and their relevance for the differentiation of neutrophil granulocytes are not well studied. Here, we discover 2 novel human genetic defects in signal recognition particle receptor alpha (SRPRA) and SRP19, constituents of the mammalian cotranslational targeting machinery, and characterize their roles in neutrophil granulocyte differentiation. We systematically study the proteome of neutrophil granulocytes from patients with variants in the SRP genes, HAX1, and ELANE, and identify global as well as specific proteome aberrations. Using in vitro differentiation of human induced pluripotent stem cells and in vivo zebrafish models, we study the effects of SRP deficiency on neutrophil granulocyte development. In a heterologous cell-based inducible protein expression system, we validate the effects conferred by SRP dysfunction for selected proteins that we identified in our proteome screen. Thus, SRP-dependent protein processing, intracellular trafficking, and homeostasis are critically important for the differentiation of neutrophil granulocytes.

Higher-order modular regulation of the human proteome.

Operons are transcriptional modules that allow bacteria to adapt to environmental changes by coordinately expressing the relevant set of genes. In humans, biological pathways and their regulation are more complex. If and how human cells coordinate the expression of entire biological processes is unclear. Here, we capture 31 higher-order co-regulation modules, which we term progulons, by help of supervised machine-learning on proteomics data. Progulons consist of dozens to hundreds of proteins that together mediate core cellular functions. They are not restricted to physical interactions or co-localisation. Progulon abundance changes are primarily controlled at the level of protein synthesis and degradation. Implemented as a web app at www.proteomehd.net/progulonFinder, our approach enables the targeted search for progulons of specific cellular processes. We use it to identify a DNA replication progulon and reveal multiple new replication factors, validated by extensive phenotyping of siRNA-induced knockdowns. Progulons provide a new entry point into the molecular understanding of biological processes.

A Primer on Data Analytics in Functional Genomics: How to Move from Data to Insight?

High-throughput methodologies and machine learning have been central in developing systems-level perspectives in molecular biology. Unfortunately, performing such integrative analyses has traditionally been reserved for bioinformaticians. This is now changing with the appearance of resources to help bench-side biologists become skilled at computational data analysis and handling large omics data sets. Here, we show an entry route into the field of omics data analytics. We provide information about easily accessible data sources and suggest some first steps for aspiring computational data analysts. Moreover, we highlight how machine learning is transforming the field and how it can help make sense of biological data. Finally, we suggest good starting points for self-learning and hope to convince readers that computational data analysis and programming are not intimidating.

Prediction of In Vivo Pharmacokinetic Parameters and Time-Exposure Curves in Rats Using Machine Learning from the Chemical Structure.

Animal pharmacokinetic (PK) data as well as human and animal in vitro systems are utilized in drug discovery to define the rate and route of drug elimination. Accurate prediction and mechanistic understanding of drug clearance and disposition in animals provide a degree of confidence for extrapolation to humans. In addition, prediction of in vivo properties can be used to improve design during drug discovery, help select compounds with better properties, and reduce the number of in vivo experiments. In this study, we generated machine learning models able to predict rat in vivo PK parameters and concentration-time PK profiles based on the molecular chemical structure and either measured or predicted in vitro parameters. The models were trained on internal in vivo rat PK data for over 3000 diverse compounds from multiple projects and therapeutic areas, and the predicted endpoints include clearance and oral bioavailability. We compared the performance of various traditional machine learning algorithms and deep learning approaches, including graph convolutional neural networks. The best models for PK parameters achieved R2 = 0.63 [root mean squared error (RMSE) = 0.26] for clearance and R2 = 0.55 (RMSE = 0.46) for bioavailability. The models provide a fast and cost-efficient way to guide the design of molecules with optimal PK profiles, to enable the prediction of virtual compounds at the point of design, and to drive prioritization of compounds for in vivo assays.

Proteome Analysis of Human Neutrophil Granulocytes From Patients With Monogenic Disease Using Data-independent Acquisition.

Neutrophil granulocytes are critical mediators of innate immunity and tissue regeneration. Rare diseases of neutrophil granulocytes may affect their differentiation and/or functions. However, there are very few validated diagnostic tests assessing the functions of neutrophil granulocytes in these diseases. Here, we set out to probe omics analysis as a novel diagnostic platform for patients with defective differentiation and function of neutrophil granulocytes. We analyzed highly purified neutrophil granulocytes from 68 healthy individuals and 16 patients with rare monogenic diseases. Cells were isolated from fresh venous blood (purity >99%) and used to create a spectral library covering almost 8000 proteins using strong cation exchange fractionation. Patient neutrophil samples were then analyzed by data-independent acquisition proteomics, quantifying 4154 proteins in each sample. Neutrophils with mutations in the neutrophil elastase gene ELANE showed large proteome changes that suggest these mutations may affect maturation of neutrophil granulocytes and initiate misfolded protein response and cellular stress mechanisms. In contrast, only few proteins changed in patients with leukocyte adhesion deficiency (LAD) and chronic granulomatous disease (CGD). Strikingly, neutrophil transcriptome analysis showed no correlation with its proteome. In case of two patients with undetermined genetic causes, proteome analysis guided the targeted genetic diagnostics and uncovered the underlying genomic mutations. Data-independent acquisition proteomics may help to define novel pathomechanisms in neutrophil diseases and provide a clinically useful diagnostic dimension.

Epigenetic Variability Confounds Transcriptome but Not Proteome Profiling for Coexpression-based Gene Function Prediction.

Genes are often coexpressed with their genomic neighbors, even if these are functionally unrelated. For small expression changes driven by genetic variation within the same cell type, non-functional mRNA coexpression is not propagated to the protein level. However, it is unclear if protein levels are also buffered against any non-functional mRNA coexpression accompanying large, regulated changes in the gene expression program, such as those occurring during cell differentiation. Here, we address this question by analyzing mRNA and protein expression changes for housekeeping genes across 20 mouse tissues. We find that a large proportion of mRNA coexpression is indeed non-functional and does not lead to coexpressed proteins. Chromosomal proximity of genes explains a proportion of this nonfunctional mRNA coexpression. However, the main driver of non-functional mRNA coexpression across mouse tissues is epigenetic similarity. Both factors together provide an explanation for why monitoring protein coexpression outperforms mRNA coexpression data in gene function prediction. Furthermore, this suggests that housekeeping genes translocating during evolution within genomic subcompartments might maintain their broad expression pattern.

Pervasive coexpression of spatially proximal genes is buffered at the protein level.

Genes are not randomly distributed in the genome. In humans, 10% of protein-coding genes are transcribed from bidirectional promoters and many more are organised in larger clusters. Intriguingly, neighbouring genes are frequently coexpressed but rarely functionally related. Here we show that coexpression of bidirectional gene pairs, and closeby genes in general, is buffered at the protein level. Taking into account the 3D architecture of the genome, we find that co-regulation of spatially close, functionally unrelated genes is pervasive at the transcriptome level, but does not extend to the proteome. We present evidence that non-functional mRNA coexpression in human cells arises from stochastic chromatin fluctuations and direct regulatory interference between spatially close genes. Protein-level buffering likely reflects a lack of coordination of post-transcriptional regulation of functionally unrelated genes. Grouping human genes together along the genome sequence, or through long-range chromosome folding, is associated with reduced expression noise. Our results support the hypothesis that the selection for noise reduction is a major driver of the evolution of genome organisation.

Multiclassifier combinatorial proteomics of organelle shadows at the example of mitochondria in chromatin data.

Subcellular localization is an important aspect of protein function, but the protein composition of many intracellular compartments is poorly characterized. For example, many nuclear bodies are challenging to isolate biochemically and thus remain inaccessible to proteomics. Here, we explore covariation in proteomics data as an alternative route to subcellular proteomes. Rather than targeting a structure of interest biochemically, we target it by machine learning. This becomes possible by taking data obtained for one organelle and searching it for traces of another organelle. As an extreme example and proof-of-concept we predict mitochondrial proteins based on their covariation in published interphase chromatin data. We detect about ⅓ of the known mitochondrial proteins in our chromatin data, presumably most as contaminants. However, these proteins are not present at random. We show covariation of mitochondrial proteins in chromatin proteomics data. We then exploit this covariation by multiclassifier combinatorial proteomics to define a list of mitochondrial proteins. This list agrees well with different databases on mitochondrial composition. This benchmark test raises the possibility that, in principle, covariation proteomics may also be applicable to structures for which no biochemical isolation procedures are available.

The Impact of Blood Morphological Parameters on Treatment Outcomes in Tennis Elbow Patients Receiving Platelet-Rich Plasma (PRP) Therapy: A Prospective Study.

Platelet-rich plasma (PRP) therapy holds substantial promise for the treatment of tennis elbow, a complex and challenging musculoskeletal condition. The aim of the study was to assess whether there are correlations between the levels of individual morphotic elements determined in whole blood and the outcomes of tennis elbow treatment with PRP injection, as measured using patient-reported outcome measures (PROMs) such as the Visual Analog Scale (VAS), Quick Disabilities of the Arm, Shoulder, and Hand (QDASH), and Patient-Rated Tennis Elbow Evaluation (PRTEE). A prospective analysis was conducted on 107 patients (132 elbows) undergoing lateral epicondylitis treatment with (PRP) injections. Patients completed VAS, PRTEE, and QDASH questionnaires on the day of PRP administration and at established checkpoints (2, 4, 8, 12, 24, 52, and 104 weeks). Minimal clinically important difference (MCID) was employed to assess the treatment effects. Then, correlations were measured within each PROM, and the impact of the concentration of individual blood parameters on the MCID outcomes was assessed. Analyzing the relationships between the MCID+ and MCID- groups, significant correlations for the VAS and QDASH scales were observed. The level of individual morphotic elements in the blood may have influenced the treatment outcome, as measured using specific patient-reported outcome measures (PROMs). Regarding the VAS scale, factors favoring a positive treatment outcome included higher values of eosinophils (EOS) and basophils (BASO). For the QDASH scale, these factors were a lower value of mean corpuscular volume (MCV) and a higher mean corpuscular hemoglobin (MCH). The levels of certain blood parameters, such as EOS and BASO, in the current study influenced the classification of patients into MCID+ or MCID- groups, based on the VAS and QDASH scales.

Quiz. Correct answer to the quiz. Check your diagnosis. Mesenchymal chondrosarcoma of the orbit: two cases and a brief review of the literature.

Mesenchymal chondrosarcoma (MChS) is a rare, high-grade malignant tumor which occurs both in the bone and soft tissue. The extraskeletal location comprises one third of all MChS and in review of the up-to-date literature, about 30 cases of the orbital involvement were found. The authors present clinical, radiological and pathological findings of two cases of MChS of the orbit occurring in young adult females: primary extraskeletal MChS of the orbit and skeletal MChS of the ethmomaxillary complex with secondary orbit involvement. The histopathological examination revealed a characteristic biphasic pattern composed of small round to spindle-shaped cells, mimicking Ewing sarcoma family of tumors, with areas of a haemangiopericytoma-like pattern and admixed cartilage foci. One of the patients had local recurrence 3 years after initial surgical removal. Subsequently, she underwent enucleation followed by chemotherapy. The other patient had a biopsy and debulking resection of the tumor and started chemotherapy. Ten months follow-up of this patient show no evidence of metastasis.

Transforming early pharmaceutical assessment of genotoxicity: applying statistical learning to a high throughput, multi end point in vitro micronucleus assay.

To provide a comprehensive analysis of small molecule genotoxic potential we have developed and validated an automated, high-content, high throughput, image-based in vitro Micronucleus (IVM) assay. This assay simultaneously assesses micronuclei and multiple additional cellular markers associated with genotoxicity. Acoustic dosing (≤ 2 mg) of compound is followed by a 24-h treatment and a 24-h recovery period. Confocal images are captured [Cell Voyager CV7000 (Yokogawa, Japan)] and analysed using Columbus software (PerkinElmer). As standard the assay detects micronuclei (MN), cytotoxicity and cell-cycle profiles from Hoechst phenotypes. Mode of action information is primarily determined by kinetochore labelling in MN (aneugencity) and γH2AX foci analysis (a marker of DNA damage). Applying computational approaches and implementing machine learning models alongside Bayesian classifiers allows the identification of, with 95% accuracy, the aneugenic, clastogenic and negative compounds within the data set (Matthews correlation coefficient: 0.9), reducing analysis time by 80% whilst concurrently minimising human bias. Combining high throughput screening, multiparametric image analysis and machine learning approaches has provided the opportunity to revolutionise early Genetic Toxicology assessment within AstraZeneca. By multiplexing assay endpoints and minimising data generation and analysis time this assay enables complex genotoxicity safety assessments to be made sooner aiding the development of safer drug candidates.

Functional brain imaging in larval zebrafish for characterising the effects of seizurogenic compounds acting via a range of pharmacological mechanisms.

BACKGROUND AND PURPOSE: Functional brain imaging using genetically encoded Ca2+ sensors in larval zebrafish is being developed for studying seizures and epilepsy as a more ethical alternative to rodent models. Despite this, few data have been generated on pharmacological mechanisms of action other than GABAA antagonism. Assessing larval responsiveness across multiple mechanisms is vital to test the translational power of this approach, as well as assessing its validity for detecting unwanted drug-induced seizures and testing antiepileptic drug efficacy. EXPERIMENTAL APPROACH: Using light-sheet imaging, we systematically analysed the responsiveness of 4 days post fertilisation (dpf; which are not considered protected under European animal experiment legislation) transgenic larval zebrafish to treatment with 57 compounds spanning more than 12 drug classes with a link to seizure generation in mammals, alongside eight compounds with no such link. KEY RESULTS: We show 4dpf zebrafish are responsive to a wide range of mechanisms implicated in seizure generation, with cerebellar circuitry activated regardless of the initiating pharmacology. Analysis of functional connectivity revealed compounds targeting cholinergic and monoaminergic reuptake, in particular, showed phenotypic consistency broadly mapping onto what is known about neurotransmitter-specific circuitry in the larval zebrafish brain. Many seizure-associated compounds also exhibited altered whole brain functional connectivity compared with controls. CONCLUSIONS AND IMPLICATIONS: This work represents a significant step forward in understanding the translational power of 4dpf larval zebrafish for use in neuropharmacological studies and for studying the events driving transition from small-scale pharmacological activation of local circuits, to the large network-wide abnormal synchronous activity associated with seizures.

Co-regulation map of the human proteome enables identification of protein functions.

Assigning functions to the vast array of proteins present in eukaryotic cells remains challenging. To identify relationships between proteins, and thereby enable functional annotation of proteins, we determined changes in abundance of 10,323 human proteins in response to 294 biological perturbations using isotope-labeling mass spectrometry. We applied the machine learning algorithm treeClust to reveal functional associations between co-regulated human proteins from ProteomeHD, a compilation of our own data and datasets from the Proteomics Identifications database. This produced a co-regulation map of the human proteome. Co-regulation was able to capture relationships between proteins that do not physically interact or colocalize. For example, co-regulation of the peroxisomal membrane protein PEX11ß with mitochondrial respiration factors led us to discover an organelle interface between peroxisomes and mitochondria in mammalian cells. We also predicted the functions of microproteins that are difficult to study with traditional methods. The co-regulation map can be explored at www.proteomeHD.net .

LP533401 restores bone health in 5/6 nephrectomized rats by a decrease of gut-derived serotonin and regulation of serum phosphate through the inhibition of phosphate co-transporters expression in the kidneys.

LP533401 is an orally bioavailable small molecule that inhibits tryptophan hydroxylase-1, an enzyme responsible for the synthesis of gut-derived serotonin (GDS). Recently, we showed that increased GDS in rats with chronic kidney disease (CKD) affected bone strength and metabolism. We tested the hypothesis that treatment with LP533401 could reverse CKD-induced bone loss in uremia. Sixteen weeks after 5/6 nephrectomy, rats were randomized into untreated (CKD), treated with vehicle (VEH) and LP533401 at a dose of 30 or 100 mg/kg daily for 8 weeks. Treatment with LP533401 decreased serotonin turnover and restored bone mineral status, microarchitecture, and strength in CKD rats to the values observed in the controls. In parallel with the reduction of serotonin, serum phosphate levels also decreased, particularly in the LP533401, 100 mg/kg group. The mechanism underlying this phenomenon resulted from decreased expression of the renal VDR/FGF1R/Klotho/Npt2a/Npt2c axis, leading to elevated phosphate excretion in the kidneys. The elevated urinary phosphate excretion resulted in improved bone mineral status and strength in LP533401-treated rats. Unexpectedly, the standard VEH used in this model was able to reduce renal VDR/FGF1R/Klotho/Npt2a expression, leading to a compensatory increase in Npt2c mRNA levels, secondary disturbances in phosphate-regulated hormones and partial improvement in the mineral status of the trabecular bone. The decrease of serotonin synthesis together with the simultaneous reduction of renal Npt2a and Npt2c expression in rats treated with LP533401, 100 mg/kg led to an increase in 1,25(OH)2D3 levels; this mechanism seems to be particularly beneficial in relation to the mineral status of cortical bone.