A Click-Chemistry-Based Enrichable Crosslinker for Structural and Protein Interaction Analysis by Mass Spectrometry

Mass spectrometry is the method of choice for the characterisation of proteomes. Most proteins operate in protein complexes, in which their close association modulates their function. However, with standard MS analysis, information on protein–protein interactions is lost and no structural information is retained. To gain structural and interactome data, new crosslinking reagents are needed that freeze inter- and intramolecular interactions. Herein, the development of a new reagent, which has several features that enable highly sensitive crosslinking MS, is reported. The reagent enables enrichment of crosslinked peptides from the majority of background peptides to facilitate efficient detection of low-abundant crosslinked peptides. Due to the special cleavable properties, the reagent can be used for MS² and potentially for MS³ experiments. Thus, the new crosslinking reagent, in combination with high-end MS, should enable sensitive analysis of interactomes, which will help researchers to obtain important insights into cellular states in health and diseases.     

Bubble point pressures of binary methanol/water mixtures in fine‐mesh screens

Binary methanol/water mixture bubble point tests involving three samples of fine‐mesh, stainless steel screens as porous liquid acquisition devices are presented in this article. Contact angles are measured as a function of methanol mass fraction using the Sessile Drop technique. Pretest predictions are based on a Langmuir isotherm fit. Predictions and data match for methanol mole fractions greater than 50% when pore diameters are based on pure liquid tests. For all three screens, bubble point is shown to be a maximum at a methanol mole fraction of 50%. Model and data are in disagreement for mole fractions less than 50%, which is attributed to variations between surface and bulk fluid properties. A critical Zisman surface tension value of 23.2 mN/m is estimated, below which contact angles can be assumed to be zero. Solid/vapor and solid/liquid interfacial tensions are also estimated using the equation of state analysis from Neumann and Good. Published 2013 American Institute of Chemical Engineers AIChE J 60: 730–739, 2014     

Molecular Biology in Treatment Decision Processes—Neuro-Oncology Edition

Computational approaches including machine learning, deep learning, and artificial intelligence are growing in importance in all medical specialties as large data repositories are increasingly being optimised. Radiation oncology as a discipline is at the forefront of large-scale data acquisition and well positioned towards both the production and analysis of large-scale oncologic data with the potential for clinically driven endpoints and advancement of patient outcomes. Neuro-oncology is comprised of malignancies that often carry poor prognosis and significant neurological sequelae. The analysis of radiation therapy mediated treatment and the potential for computationally mediated analyses may lead to more precise therapy by employing large scale data. We analysed the state of the literature pertaining to large scale data, computational analysis, and the advancement of molecular biomarkers in neuro-oncology with emphasis on radiation oncology. We aimed to connect existing and evolving approaches to realistic avenues for clinical implementation focusing on low grade gliomas (LGG), high grade gliomas (HGG), management of the elderly patient with HGG, rare central nervous system tumors, craniospinal irradiation, and re-irradiation to examine how computational analysis and molecular science may synergistically drive advances in personalised radiation therapy (RT) and optimise patient outcomes.     

Residue–Residue Interaction Prediction via Stacked Meta-Learning

Protein–protein interactions (PPIs) are the basis of most biological functions determined by residue–residue interactions (RRIs). Predicting residue pairs responsible for the interaction is crucial for understanding the cause of a disease and drug design. Computational approaches that considered inexpensive and faster solutions for RRI prediction have been widely used to predict protein interfaces for further analysis. This study presents RRI-Meta, an ensemble meta-learning-based method for RRI prediction. Its hierarchical learning structure comprises four base classifiers and one meta-classifier to integrate predictive strengths from different classifiers. It considers multiple feature types, including sequence-, structure-, and neighbor-based features, for characterizing other properties of a residue interaction environment to better distinguish between noninteracting and interacting residues. We conducted the same experiments using the same data as previously reported in the literature to demonstrate RRI-Meta’s performance. Experimental results show that RRI-Meta is superior to several current prediction tools. Additionally, to analyze the factors that affect the performance of RRI-Meta, we conducted a comparative case study using different protein complexes.     

Chitosan–Graphene Oxide Composite Membranes for Solid-Phase Extraction of Pesticides

Solid-phase extraction (SPE) coupled to LC/MS/MS analysis is a valid approach for the determination of organic micropollutants (OMPs) in liquid samples. To remove the greatest number of OMPs from environmental matrices, the development of innovative sorbent materials is crucial. Recently, much attention has been paid to inorganic nanosystems such as graphite-derived materials. Graphene oxide has been employed in water-purification processes, including the removal of several micropollutants such as dyes, flame retardants, or pharmaceutical products. Polysaccharides have also been widely used as convenient media for the dispersion of sorbent materials, thanks to their unique properties such as biodegradability, biocompatibility, nontoxicity, and low cost. In this work, chitosan–graphene oxide (CS_GO) composite membranes containing different amounts of GO were prepared and used as sorbents for the SPE of pesticides. To improve their dimensional stability in aqueous medium, the CS_GO membranes were surface crosslinked with glutaraldehyde. The composite systems were characterized by Fourier transform infrared spectroscopy, scanning electron microscopy, thermogravimetric analysis, swelling degree, contact angle, and mechanical measurements. As the GO content increased, a decrease in surface homogeneity, an improvement of mechanical properties, and a reduction of thermal stability of the CS-based membranes were observed. The increased dimensional stability in water, together with the presence of high GO amounts, made the prepared composite membranes more efficacious than the ones based just on CS in isolating and preconcentrating different hydrophilic/hydrophobic pollutants.     

Updated Information on Antimicrobial Activity of Hydrazide–Hydrazones

Hydrazide–hydrazones possess a wide spectrum of bioactivity, including antibacterial, antitubercular, antifungal, anticancer, anti-inflammatory, anticonvulsant, antidepressant, antiviral, and antiprotozoal properties. This review is focused on the latest scientific reports regarding antibacterial, antimycobacterial, and antifungal activities of hydrazide–hydrazones published between 2017 and 2021. The molecules and their chemical structures presented in this article are the most active derivatives, with discussed activities having a hydrazide–hydrazone moiety as the main scaffold or as a side chain. Presented information constitute a concise summary, which may be used as a practical guide for further design of new molecules with antimicrobial activity.     

Circulating miRNAs as Potential Biomarkers Distinguishing Relapsing–Remitting from Secondary Progressive Multiple Sclerosis. A Review

Multiple sclerosis (MS) is a debilitating neurodegenerative, highly heterogeneous disease with a variable course. The most common MS subtype is relapsing–remitting (RR), having interchanging periods of worsening and relative stabilization. After a decade, in most RR patients, it alters into the secondary progressive (SP) phase, the most debilitating one with no clear remissions, leading to progressive disability deterioration. Among the greatest challenges for clinicians is understanding disease progression molecular mechanisms, since RR is mainly characterized by inflammatory processes, while in SP, the neurodegeneration prevails. This is especially important because distinguishing RR from the SP subtype early will enable faster implementation of appropriate treatment. Currently, the MS course is not well-correlated with the biomarkers routinely used in clinical practice. Despite many studies, there are still no reliable indicators correlating with the disease stage and its activity degree. Circulating microRNAs (miRNAs) may be considered valuable molecules for the MS diagnosis and, presumably, helpful in predicting disease subtype. MiRNA expression dysregulation is commonly observed in the MS course. Moreover, knowledge of diverse miRNA panel expression between RRMS and SPMS may allow for deterring disability progression through successful treatment. Therefore, in this review, we address the current state of research on differences in miRNA panel expression between the phases.     

Meßdatenerfassung,Steuerung und Automatisierung bei Apparaten der Mechanischen Flüssigkeitsabtrennung

Data acquisition, control, and automation in equipment for mechanical separation of liquids. Mass- and energy flow in a production process are accompanied by a flow of information. While data processing has reached a high standard, there is a lack of suitable sensors for acquisition of measured data in solid/liquid separation processes. Methods used for acquisition of data relevant to separation engineering are presented for selected separation equipment, possibilities of control with changing product properties are discussed, and unsolved problems of sensor technology are considered.     

New insights in pharmaceutical analysis

The research unit of pharmaceutical analytical chemistry (PAC) has been active in the field of separation sciences for many years. Liquid chromatography (LC) and its latest improvements such as ultra-high performance chromatography (UHPLC) and supercritical fluid chromatography (SFC) are deeply and thoroughly studied, from a fundamental viewpoint to its various application capabilities. Electro-driven separations such as capillary electrophoresis (CE) are also a major field of interest, especially for macromolecules, and low cost. All these techniques are investigated with various detection modes including mass spectrometry (MS) for various applications where high sensitivity and selectivity is needed. Extracting the relevant information from the overwhelming amount of data generated by modern analytical platforms has become an important issue for knowledge discovery in various research fields. The appropriate treatment of such data is therefore of crucial importance to provide valuable information. Numerous works in our research group have demonstrated the usefulness of statistical and mathematical methodologies to improve quality of the results. Therefore, well-established chemometric approaches (e.g. design of experiments, multivariate data analysis, etc.) are implemented to optimize the analytical process from method development to data analysis.     

Application of High Throughput Technologies in the Development of Acute Myeloid Leukemia Therapy: Challenges and Progress

Acute myeloid leukemia (AML) is a complex hematological malignancy characterized by extensive heterogeneity in genetics, response to therapy and long-term outcomes, making it a prototype example of development for personalized medicine. Given the accessibility to hematologic malignancy patient samples and recent advances in high-throughput technologies, large amounts of biological data that are clinically relevant for diagnosis, risk stratification and targeted drug development have been generated. Recent studies highlight the potential of implementing genomic-based and phenotypic-based screens in clinics to improve survival in patients with refractory AML. In this review, we will discuss successful applications as well as challenges of most up-to-date high-throughput technologies, including artificial intelligence (AI) approaches, in the development of personalized medicine for AML, and recent clinical studies for evaluating the utility of integrating genomics-guided and drug sensitivity testing-guided treatment approaches for AML patients.     

Urinary Protein and Peptide Markers in Chronic Kidney Disease

Chronic kidney disease (CKD) is a non-specific type of kidney disease that causes a gradual decline in kidney function (from months to years). CKD is a significant risk factor for death, cardiovascular disease, and end-stage renal disease. CKDs of different origins may have the same clinical and laboratory manifestations but different progression rates, which requires early diagnosis to determine. This review focuses on protein/peptide biomarkers of the leading causes of CKD: diabetic nephropathy, IgA nephropathy, lupus nephritis, focal segmental glomerulosclerosis, and membranous nephropathy. Mass spectrometry (MS) approaches provided the most information about urinary peptide and protein contents in different nephropathies. New analytical approaches allow urinary proteomic–peptide profiles to be used as early non-invasive diagnostic tools for specific morphological forms of kidney disease and may become a safe alternative to renal biopsy. MS studies of the key pathogenetic mechanisms of renal disease progression may also contribute to developing new approaches for targeted therapy.     

Application of ESI and MALDI‐TOF MS for triacylglycerols analysis in edible oils

Triacylglycerols (TAG) are the most important group of compounds present in vegetable oils. These biomolecules, determining the physical, chemical and nutritional properties of the oils, are considered to be good fingerprints for quality and authenticity control. Therefore, TAGs characterization is a very important task in edible oil field, which has been undertaken by different analytical methods. The analysis of vegetable oils is still dominated by classic determinations, which are however laborious and time‐consuming and cannot be used routinely. More recently, advances in MS instrumentations coupled with online separation techniques and data processing have contributed to great expansion of MS in oil study, allowing the development of innovative analytical approaches that exhibit higher sensitivity, accuracy and rapidity in vegetable oils investigations. In the present contribution, a review of the most relevant applications of novel mass spectrometric techniques, such as ESI and MALDI, both alone and hyphenated with HPLC, used for analysis of the complex TAGs mixture of edible oils is illustrated.     

The Interaction of Human and Epstein–Barr Virus miRNAs with Multiple Sclerosis Risk Loci

Although the causes of Multiple Sclerosis (MS) still remain largely unknown, multiple lines of evidence suggest that Epstein–Barr virus (EBV) infection may contribute to the development of MS. Here, we aimed to identify the potential contribution of EBV-encoded and host cellular miRNAs to MS pathogenesis. We identified differentially expressed host miRNAs in EBV infected B cells (LCLs) and putative host/EBV miRNA interactions with MS risk loci. We estimated the genotype effect of MS risk loci on the identified putative miRNA:mRNA interactions in silico. We found that the protective allele of MS risk SNP rs4808760 reduces the expression of hsa-mir-3188-3p. In addition, our analysis suggests that hsa-let-7b-5p may interact with ZC3HAV1 differently in LCLs compared to B cells. In vitro assays indicated that the protective allele of MS risk SNP rs10271373 increases ZC3HAV1 expression in LCLs, but not in B cells. The higher expression for the protective allele in LCLs is consistent with increased IFN response via ZC3HAV1 and so decreased immune evasion by EBV. Taken together, this provides evidence that EBV infection dysregulates the B cell miRNA machinery, including MS risk miRNAs, which may contribute to MS pathogenesis via interaction with MS risk genes either directly or indirectly.     

Bridging Radiotherapy to Immunotherapy: The IFN–JAK–STAT Axis

The unprecedented successes of immunotherapies (IOs) including immune checkpoint blockers (ICBs) and adoptive T-cell therapy (ACT) in patients with late-stage cancer provide proof-of-principle evidence that harnessing the immune system, in particular T cells, can be an effective approach to eradicate cancer. This instills strong interests in understanding the immunomodulatory effects of radiotherapy (RT), an area that was actually investigated more than a century ago but had been largely ignored for many decades. With the “newly” discovered immunogenic responses from RT, numerous endeavors have been undertaken to combine RT with IOs, in order to bolster anti-tumor immunity. However, the underlying mechanisms are not well defined, which is a subject of much investigation. We therefore conducted a systematic literature search on the molecular underpinnings of RT-induced immunomodulation and IOs, which identified the IFN–JAK–STAT pathway as a major regulator. Our further analysis of relevant studies revealed that the signaling strength and duration of this pathway in response to RT and IOs may determine eventual immunological outcomes. We propose that strategic targeting of this axis can boost the immunostimulatory effects of RT and radiosensitizing effects of IOs, thereby promoting the efficacy of combination therapy of RT and IOs.     

Towards Personalized Medicine Mediated by in Vitro Virus-Based Interactome Approaches

We have developed a simple in vitro virus (IVV) selection system based on cell-free co-translation, using a highly stable and efficient mRNA display method. The IVV system is applicable to the high-throughput and comprehensive analysis of proteins and protein–ligand interactions. Huge amounts of genomic sequence data have been generated over the last decade. The accumulated genetic alterations and the interactome networks identified within cells represent a universal feature of a disease, and knowledge of these aspects can help to determine the optimal therapy for the disease. The concept of the “integrome” has been developed as a means of integrating large amounts of data. We have developed an interactome analysis method aimed at providing individually-targeted health care. We also consider future prospects for this system.     

Dietary Influences on the Microbiota–Gut–Brain Axis

Over unimaginable expanses of evolutionary time, our gut microbiota have co-evolved with us, creating a symbiotic relationship in which each is utterly dependent upon the other. Far from confined to the recesses of the alimentary tract, our gut microbiota engage in complex and bi-directional communication with their host, which have far-reaching implications for overall health, wellbeing and normal physiological functioning. Amongst such communication streams, the microbiota–gut–brain axis predominates. Numerous complex mechanisms involve direct effects of the microbiota, or indirect effects through the release and absorption of the metabolic by-products of the gut microbiota. Proposed mechanisms implicate mitochondrial function, the hypothalamus–pituitary–adrenal axis, and autonomic, neuro-humeral, entero-endocrine and immunomodulatory pathways. Furthermore, dietary composition influences the relative abundance of gut microbiota species. Recent human-based data reveal that dietary effects on the gut microbiota can occur rapidly, and that our gut microbiota reflect our diet at any given time, although much inter-individual variation pertains. Although most studies on the effects of dietary macronutrients on the gut microbiota report on associations with relative changes in the abundance of particular species of bacteria, in broad terms, our modern-day animal-based Westernized diets are relatively high in fats and proteins and impoverished in fibres. This creates a perfect storm within the gut in which dysbiosis promotes localized inflammation, enhanced gut wall permeability, increased production of lipopolysaccharides, chronic endotoxemia and a resultant low-grade systemic inflammatory milieu, a harbinger of metabolic dysfunction and many modern-day chronic illnesses. Research should further focus on the colony effects of the gut microbiota on health and wellbeing, and dysbiotic effects on pathogenic pathways. Finally, we should revise our view of the gut microbiota from that of a seething mass of microbes to one of organ-status, on which our health and wellbeing utterly depends. Future guidelines on lifestyle strategies for wellbeing should integrate advice on the optimal establishment and maintenance of a healthy gut microbiota through dietary and other means. Although we are what we eat, perhaps more importantly, we are what our gut microbiota thrive on and they thrive on what we eat.     

Reviewing the Significance of Blood–Brain Barrier Disruption in Multiple Sclerosis Pathology and Treatment

The disruption of blood–brain barrier (BBB) for multiple sclerosis (MS) pathogenesis has a double effect: early on during the onset of the immune attack and later for the CNS self-sustained ‘inside-out’ demyelination and neurodegeneration processes. This review presents the characteristics of BBB malfunction in MS but mostly highlights current developments regarding the impairment of the neurovascular unit (NVU) and the metabolic and mitochondrial dysfunctions of the BBB’s endothelial cells. The hypoxic hypothesis is largely studied and agreed upon recently in the pathologic processes in MS. Hypoxia in MS might be produced per se by the NVU malfunction or secondary to mitochondria dysfunction. We present three different but related terms that denominate the ongoing neurodegenerative process in progressive forms of MS that are indirectly related to BBB disruption: progression independent of relapses, no evidence of disease activity and smoldering demyelination or silent progression. Dimethyl fumarate (DMF), modulators of S1P receptor, cladribine and laquinimode are DMTs that are able to cross the BBB and exhibit beneficial direct effects in the CNS with very different mechanisms of action, providing hope that a combined therapy might be effective in treating MS. Detailed mechanisms of action of these DMTs are described and also illustrated in dedicated images. With increasing knowledge about the involvement of BBB in MS pathology, BBB might become a therapeutic target in MS not only to make it impenetrable against activated immune cells but also to allow molecules that have a neuroprotective effect in reaching the cell target inside the CNS.     

Sphingosine Kinase Interacting Protein is an A‐Kinase Anchoring Protein Specific for Type I cAMP‐Dependent Protein Kinase

The compartmentalization of kinases and phosphatases plays an important role in the specificity of second‐messenger‐mediated signaling events. Localization of the cAMP‐dependent protein kinase is mediated by interaction of its regulatory subunit (PKA‐R) with the versatile family of A‐kinase‐anchoring proteins (AKAPs). Most AKAPs bind avidly to PKA‐RII, while some have dual specificity for both PKA‐RI and PKA‐RII; however, no mammalian PKA‐RI‐specific AKAPs have thus far been assigned. This has mainly been attributed to the observation that PKA‐RI is more cytosolic than the more heavily compartmentalized PKA‐RII. Chemical proteomics screens of the cAMP interactome in mammalian heart tissue recently identified sphingosine kinase type 1‐interacting protein (SKIP, SPHKAP) as a putative novel AKAP. Biochemical characterization now shows that SPHKAP can be considered as the first mammalian AKAP that preferentially binds to PKA‐RIα. Recombinant human SPHKAP functions as an RI‐specific AKAP that utilizes the characteristic AKAP amphipathic helix for interaction. Further chemical proteomic screening utilizing differential binding characteristics of specific cAMP resins confirms SPHKAPs endogenous specificity for PKA‐RI directly in mammalian heart and spleen tissue. Immunolocalization studies revealed that recombinant SPHKAP is expressed in the cytoplasm, where PKA‐RIα also mainly resides. Alignment of SPHKAPs' amphipathic helix with peptide models of PKA‐RI‐ or PKA‐RII‐specific anchoring domains shows that it has largely only PKA‐RIα characteristics. Being the first mammalian PKA‐RI‐specific AKAP with cytosolic localization, SPHKAP is a very promising model for studying the function of the less explored cytosolic PKA‐RI signaling nodes.     

Secondary Structures of Proteins Follow Menzerath–Altmann Law

This article examines the presence of the empirical tendency known as the Menzerath–Altmann Law (MAL) on protein secondary structures. MAL is related to optimization principles observed in natural languages and in genetic information on chromosomes or protein domains. The presence of MAL is examined on a non-redundant dataset of 4728 proteins by verifying significant, negative correlations and testing classical and newly proposed formulas by fitting the observed trend. We conclude that the lengths of secondary structures are specifically dependent on their number inside the protein sequence, while possibly reflecting the formula proposed in this paper. This behavior is observed on average but is individually avoidable and possibly driven by a latent cost function. The data suggest that MAL could provide a useful guiding principle in protein design.