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A novel tissue-selective β2-adrenoceptor agonist with minimized cardiovascular effects, 5-HOB, attenuates neuropathic pain in mice
No full textObjective: 5-HOB is a novel tissue selective, 5-hydroxybenzothiazolone-derived β2 adrenoceptor agonist with minimized cardiovascular effects while retaining efficacy on skeletal muscle in preclinical experiments unlike conventional β2 adrenoceptor agonists, however its effect on the nervous system has not been evaluated yet. Therefore, 5-HOB was evaluated in a mouse model of neuropathic pain.
Results: 5-HOB alleviated neuropathic allodynia in a dose dependent manner and reversed the changes in hind paw withdrawal thresholds to the sham control levels. The dose attenuating neuropathic allodynia was slightly lower than the dose inducing skeletal muscle hypertrophy. In conclusion, as reported with known β2 adrenoceptor agonists, 5-HOB was also effective in attenuating neuropathic pain in mice in addition to its effect on skeletal muscle
Metadata-Guided Visual Representation Learning for Biomedical Images
No full textMotivation:
The clustering of biomedical images according to their phenotype is an important step in early drug discovery. Modern high-content-screening devices easily produce thousands of cell images, but the resulting data is usually unlabelled and it requires extra effort to construct a visual representation that supports the grouping according to the presented morphological characteristics.
Results:
We introduce a novel approach to visual representation learning that is guided by metadata. In high-content-screening, meta-data can typically be derived from the experimental layout, which links each cell image of a particular assay to the tested chemical compound and corresponding compound concentration. In general, there exists a one-to-many relationship between phenotype and compound, since various molecules and different dosage can lead to one and the same alterations in biological cells.
Our empirical results show that metadata-guided visual representation learning is an effective approach for clustering biomedical images. We have evaluated our proposed approach on both benchmark and real- world biological data. Furthermore, we have juxtaposed implicit and explicit learning techniques, where both loss function and batch con- struction differ. Our experiments demonstrate that metadata-guided visual representation learning is able to identify commonalities and distinguish differences in visual appearance that lead to meaningful clusters, even without image-level annotations.
Note:
Please refer to the supplementary material for implementation details on metadata-guided visual representation learning strategies
Detection of CSF1 rearrangements deleting the 3′ UTR in tenosynovial giant cell tumors
No full textTenosynovial giant cell tumors (TGCTs) are characterized by rearrangements of CSF1, thought to drive overexpression of macrophage colony-stimulating factor (CSF1), thereby promoting tumor growth and recruitment of non-neoplastic mononuclear and multinucleated inflammatory cells. While fusions to collagen promoters have been described, the mechanism of CSF1 overexpression has been unclear in a majority of cases. Two cohorts of TGCT were investigated for CSF1 rearrangements using fluorescence in situ hybridization (FISH) and either RNA-seq or DNA-seq with Sanger validation. The study comprised 39 patients, including 13 localized TGCT, 21 diffuse TGCT, and five of unspecified type. CSF1 rearrangements were identified by FISH in 30 cases: 13 translocations, 17 3′ deletions. Sequencing confirmed CSF1 breakpoints in 28 cases; in all 28 the breakpoint was found to be downstream of exon 5, replacing or deleting a long 3′ UTR containing known miRNA and AU-rich element negative regulatory sequences. We also confirmed the presence of CBL exon 8-9 mutations in six of 21 cases. In conclusion, TGCT in our large cohort were characterized by variable alterations, all of which led to truncation of the 3′ end of CSF1, instead of the COL6A3-CSF1 fusions previously reported in some TGCTs. The diversity of fusion partners but consistent integrity of CSF1 functional domains encoded by exons 1-5 support a hypothesis that CSF1 overexpression results from transcription of a truncated form of CSF1 lacking 3′ negative regulatory sequences. The presence of CBL mutations affecting the linker and RING finger domain suggests an alternative mechanism for increased CSF1/CSF1R signaling in some cases
HDAC4 Controls Muscle Homeostasis through Deacetylation of Myosin Heavy Chain, PGC-1α, and Hsc70
No full textHDAC4, a class IIa histone deacetylase, is upregulated in skeletal muscle in response to denervation-induced atrophy. When HDAC4 is deleted postnatally, mice are partially protected from denervation. Despite the name “histone” deacetylase, HDAC4 demonstrably deacetylates cytosolic and non-histone nuclear proteins. We developed potent and selective class IIa HDAC inhibitors. Using these tools and genetic knockdown, we identified three previously unidentified substrates of HDAC4: myosin heavy chain, peroxisome proliferator-activated receptor gamma co-activator 1alpha (PGC-1α), and heat shock cognate 71 kDa protein (Hsc70). HDAC4 inhibition almost completely prevented denervation-induced loss of myosin heavy chain isoforms and blocked the action of their E3 ligase, MuRF1. PGC-1α directly interacts with class IIa HDACs; selective inhibitors increased PGC-1α protein in muscles. Hsc70 deacetylation by HDAC4 affects its chaperone activity. Through these endogenous HDAC4 substrates, we identified several muscle metabolic pathways that are regulated by class IIa HDACs, opening up new therapeutic options to treat skeletal muscle disorders and potentially other disease where these specific pathways are affected.
Luo et al. use class IIa HDAC inhibitors, along with skeletal-muscle-specific and whole-body inducible HDAC4 knockout mice, to demonstrate HDAC4 deacetylates three previously undescribed substrates: myosin heavy chain, PGC-1α, and Hsc70. Through these substrates, HDAC4 inhibition leads to rescue of muscle atrophy and increased succinate dehydrogenase activity
Near-infrared spectroscopy to determine residual moisture in freeze-dried products: model generation by statistical design of experiments
No full textMoisture content (MC) is a critical quality attribute of lyophilized biopharmaceuticals and can be
determined by near-infrared (NIR) spectroscopy as nondestructive alternative to Karl-Fischer
titration. In this study, we create NIR models to determine MC in monoclonal antibody
lyophilisates by use of statistical design of experiments (DoE) and multivariate data analysis
(MVDA). We varied the composition of the formulation as well as lyophilization parameters
covering a large range of representative conditions, which is commonly referred to as ‘robustness
testing’ according to quality-by-design concepts. We applied principles of chemometrics with
partial least squares and principal component analyses (PCA). The NIR model excluded samples
with complete collapse and MC > 6%. The two main components in the PCA were MC (91%)
and protein:sugar ratio (6%). The third component amounted to only 3% and remained
unspecified but may include variations in process parameters and cake structure. In contrast to
traditional approaches for NIR model creation, the DoE-based model can be used to monitor MC
during drug product development work including scale-up, and transfer without the need to
update the NIR model if protein:sugar ratio and MC stays within the tested limits and cake
structure remains macroscopically intact. The use of the DoE approach and MVDA ensures
product consistency and improves understanding of the manufacturing process
How computational chemistry develops: a tribute to Peter Goodford
No full textThis editorial discusses the foundation of aspects of computational chemistry and is a tribute to Peter Goodford, one of those founders, who recently passed away. Several colleagues describe Professor Goodford's work and the person himself
Reducing the Concepts of Data Science and Machine Learning to Tools for the Bench Chemist
No full textMachine Learning and Data Science have enjoyed a renaissance due to the availability of increased computational power and larger data sets. Many questions can be now asked and answered, that previously were beyond our scope. This does not translate instantly into new tools that can be used by those not skilled in the field, as many of the issues and traps still exist. In this paper, we look at some of the new tools that we have created, and some of the difficulties that still need to be taken care of during the transition from a project run by an expert, to a tool for the bench chemist
GEN: Highly Efficient SMILES Explorer Using Autodidactic Generative Examination Networks
No full textRecurrent neural networks have been widely used to generate millions of de novo molecules in a known chemical space. These deep generative models are typically setup with LSTM or GRU units and trained with canonical SMILES. In this study, we introduce a new robust architecture, Generatice Examination Network GEN, based on bidirectional RNNs with concatenated submodels to learn and generate molecular SMILES within a trained target space. GENs autonomously learn the target space in a few epochs while being subjected to online examination for quality on the generated set. Here we have used online statistical quality control (SQC) on the percentage of valid molecular SMILES as examination measure to select the earliest available stable model weights. Very high levels of valid SMILES (95-98%) can be generated using multiple parallel encoding layers in combination with SMILES augmentation using unrestricted SMILES randomization. Our architecture combine an excellent novelty rate (85-90%) while generating SMILES with strong conservation of the property space (95-99%). Our flexible examination mechanism is open to other quality criteria
A serum protein signature of APOE genotypes in centenarians.
No full textThe discovery of treatments to prevent or delay dementia and Alzheimer's disease is a priority. The gene APOE is associated with cognitive change and late-onset Alzheimer's disease, and epidemiological studies have provided strong evidence that the e2 allele of APOE has a neuroprotective effect, it is associated with increased longevity and an extended healthy lifespan in centenarians. In this study, we correlated APOE genotype data of 222 participants of the New England Centenarian Study, including 75 centenarians, 82 centenarian offspring, and 65 controls, comprising 55 carriers of APOE e2 , with aptamer-based serum proteomics (SomaLogic technology) of 4,785 human proteins corresponding to 4,137 genes. We discovered a signature of 16 proteins that associated with different APOE genotypes and replicated the signature in three independent studies. We also show that the protein signature tracks with gene expression profiles in brains of late-onset Alzheimer's disease versus healthy controls. Finally, we show that seven of these proteins correlate with cognitive function patterns in longitudinally collected data. This analysis in particular suggests that Baculoviral IAP repeat containing two (BIRC2) is a novel biomarker of neuroprotection that associates with the neuroprotective allele of APOE. Therefore, targeting APOE e2 molecularly may preserve cognitive function
First crystal structure of a nonstructural hepatitis E viral protein identifies a putative novel zinc-binding protein
No full textHepatitis E virus (HEV) is a 7.2-kb positive-sense, single-stranded RNA virus containing three partially overlapping reading frames, ORF1 to ORF3. All nonstructural proteins required for viral replication are encoded by ORF1 and are transcribed as a single transcript. Computational analysis of the complete ORF1 polyprotein identified a previously uncharacterized region of predicted secondary structure bordered by two disordered regions coinciding partially with a region predicted as a putative cysteine protease. Following successful cloning, expression, and purification of this region, the crystal structure of the identified protein was determined and identified to have considerable structural homology to a fatty acid binding domain. Further analysis of the structure revealed a metal binding site, shown unambiguously to specifically bind zinc via a nonclassical, potentially catalytic zinc-binding motif. Based on the structural homology of the HEV protein with known structures, along with the presence of a catalytic zinc-binding motif, it is possible that the identified protein corresponds to the HEV protease, which could require activation or repression through the binding of a fatty acid. This represents a significant step forward in the characterization and the understanding of the molecular mechanisms of the HEV genome. We present analysis for the first time of this identified nonstructural protein, expanding the knowledge and understanding of the complex mechanisms of HEV biology