Discovery Studies Research Articles

Big Data and Pharmaceutical Industry: Applications and Priorities

Background. Hospitals, patients, researchers, and healthcare organizations are producing enormous amounts of data in both the healthcare and drug detection sectors. With continued development of cheap data storage and availability of smart devices in the world, the influence of big data (BD) will continue to grow. This influence has also carried over to healthcare. The volumes of data available in the fields of pharmacology, toxicology, and pharmaceutics are constantly increasing. Therefore, the present study aimed to identify the applications of big data technology in the field of pharmaceutics. Methods. Using the mixed methods approach, this study was conducted in two phases in winter 2023. In the first phase, the applications of big data technology were identified by library search and assessed by content analysis. In the second phase, applications were ranked by a panel of experts, including 17 experts who worked in the Iranian pharmaceutical industry. The stepwise weight assessment ratio analysis (SWARA) method was used for ranking the application of big data in pharmaceutics. Results. The present study examined the importance of big data applications in pharmaceutics. The results showed that drug discovery (0.263) and clinical study analysis (0.224) had the highest importance, followed by drug efficacy and performance (0.197), drug safety (0.170) and drug personalization (0.146). Conclusion. The present study detailed a substantial attempt to review the existing literature regarding the implementation of big data and to rank big data applications in the pharmaceutical industry. Big data can help researchers to better discover and develop drugs and understand the effects of drugs and other chemicals on the human body. As a result, it can help improve the safety and efficacy of drugs and other chemicals. Also, big data can help to improve the accuracy of predictions regarding the effects of drugs and chemicals, which can improve safety in drug development and help to avoid potential adverse drug interactions. The applications considered in this study are ultimately necessary for humanity, and big data may significantly impact the betterment of these domains. Big data has a revolutionary potential, providing new ways to understand and predict the effects of drugs. BD will possibly play an important role in pharmaceutics in future, critically helping to drug discovery and improve drug safety and efficacy.

Novel metabolic biomarkers for diagnosis of Alzheimer’s disease

AbstractBackgroundThe need for biomarkers for the etiological identification of dementia syndromes can not be overemphasized. Blood metabolites are attractive targets for identifying biomarkers with predictive values for AD diagnosis and progression. This study aims to identify novel AD blood metabolic biomarkers and develop a high‐sensitivity and specificity blood‐based test for AD.MethodIn the discovery phase, the serum metabolome of AD patients (n = 10) and age, sex‐matched healthy controls (n = 10) were analyzed with a mass spectrometry (MS)‐based approach. Supervised machine learning was used to identify the biomarker candidates and develop a computational model for AD diagnosis. Finally, the computational model and biomarker candidates were verified with a second AD cohort in the verification phase, where the serum metabolome of AD patients (n = 27) and age, sex‐matched healthy controls (n = 30) were analyzed.ResultIn this discovery study, 7075 metabolic features were quantified. Among them, the abundance of 750 unique features significantly differed between AD and Controls (t‐test p‐value <0.05). Support vector machine (SVM) with radial basis function (RBF) kernel was used for features optimization and identified a panel of 14 metabolic features which predicted AD with 0.0% classification error rate (4‐fold cross‐validation). In the verification study, 10595 metabolic features were quantified. Among them, 908 unique features significantly differed between AD and Controls (t‐test with Permutation q‐value <0.05). Then, the computational model and biomarker panel identified in the discovery phase were tested with the verification dataset and confirmed that they could predict AD with high accuracy (0.0% classification error rate, 4‐fold cross‐validation).ConclusionThis study comprehensively profiled the AD serum metabolome and identified a panel of metabolic biomarkers and a computational model for AD diagnosis, which will serve as a foundation for a high‐performance, blood‐based test for clinical AD screening and diagnosis.

A new strategy for Alzheimer’s disease: low‐dose combination of radiotherapy and pharmacological epigenetic perturbation

AbstractBackgroundMost Alzheimer’s disease (AD) drug discovery studies focus exclusively on one specific target. This approach has to date shown limited success in AD, mainly because this complex disease is multifactorial and involves numerous pathways. In light of the large number of disappointing clinical trials, the National Institute on Aging (NIA) and the Alzheimer’s Association (AA) published, in 2018, new guidelines recommending focusing on biomarkers of the AD continuum. These guidelines advise targeting Amyloid‐β (A), neurofibrillary tangles (T) and neurodegeneration markers (N), designated “ATN”, to advance AD research. Using 2 different approaches, we have been able to both reverse and prevent ATN burden in mouse models of AD. Indeed, our group has previously demonstrated that histone deacetylase inhibition (HDACi) and cranial radiotherapy (RT) independently improve (AD)‐like pathogenesis in cell and animal models. In the present study, we investigate the synergistic potential of using both RT and HDACi as a low‐dose combination therapy (LDCT) to further improve molecular and behavioral AD‐like phenotypes in AD models.MethodLDCT consisted of daily administration of the HDAC3 inhibitor RGFP‐966 and/or bi‐weekly cranial x‐irradiation. Behavioral analyses, real‐time qPCR, NanoString technology, ELISAs and Western blots were used to assess the effects of RT, RGFP‐966 and LDCT on AD‐like pathogenesis in AD models. Additionally, innate immune response to LDCT was also assessed in vitro and in vivo using human and murine cell models and 3xTg‐AD mice.ResultLDCT induced a tolerant, anti‐inflammatory innate immune response in microglia and increased non‐amyloidogenic APP processing. Both RT and LDCT improved spatial learning and memory in the Barnes maze test. No sensorimotor deficits were observed. LDCT induced a unique anti‐AD gene expression profile in the brain, that included upregulation of neurotrophic genes and downregulation of inflammation‐related genes. Brain region specific observations included lowered Aβ42/40 ratio, decreased Bace1, and reduced p‐tau181 levels.ConclusionThis work shows that it is possible to attenuate AD‐related pathologies through the synergy of low doses of RT and HDACi treatment. This project supports a paradigm shift to a multipronged approach to AD therapeutics.

A structure‐based fluid biomarker for the differential diagnosis of early‐stage neurodegenerative diseases measured by the immuno‐infrared‐sensor (iRS)

AbstractBackgroundNeurodegenerative diseases like Alzheimer’s (AD) and Parkinson’s disease (PD) are accompanied by misfolding of the key proteins into beta‐sheet enriched structures resulting in high neurotoxicity in early phases. We have developed a diagnostic immuno‐infrared‐sensor (iRS) that monitors the secondary structure distribution of these key proteins (Abeta for AD, alpha‐synuclein for PD) as an early detectable biomarker before the clinical manifestation, which is shown exemplarily for AD.1 MethodThe iRS combines the secondary‐structure‐specific infrared spectroscopy in a flow system based attenuated total reflection (ATR) set‐up with methods of immuno‐detection by surface‐immobilized antibodies. The antibodies specifically capture disease‐related target proteins (e.g., Abeta) from complex media (cerebrospinal fluid or blood plasma) for measurements. The readout is the amide‐I band, which directly reflects the secondary structure distribution of the captured proteins in body fluids and thus, indicates the protein misfolding: The lower the amide‐I frequency, the higher the content of misfolded Abeta species in the sample present.2 ResultThe structure biomarker for Abeta has been validated in multiple independent clinical studies with strong correlation to CSF biomarkers and PET scans.1,3‐5 Especially, very early preclinical AD could be identified in symptom‐free individuals before plaque formation, up to 17 years before the diagnosis.6 ConclusionThe results demonstrate a highly specific and sensitive immune‐assay capable of tracking structural changes of Abeta before clinical manifestation of AD. In addition, alpha‐synuclein and TDP‐43 misfolding indicate PD and ALS in current discovery studies, providing a tool for differential diagnosis at early stages.

Blood DNA methylomic signatures associated with CSF biomarkers of Alzheimer’s disease in the EMIF‐AD Multimodal Biomarker Discovery study

AbstractBackgroundThe aim of this study was to identify DNA methylation signatures that were associated with 15 CSF biomarker measures of Alzheimer’s disease (AD) or neurodegeneration.MethodWe profiled DNA methylation in 886 blood samples from the EMIF‐AD study using the Illumina EPIC array, identifying differentially methylated loci, and regions consisting of multiple adjacent differentially methylated sites.ResultsWe identified Bonferroni‐significant differences in DNA methylation with respect to CSF t‐tau Z‐score (five loci), Aβ42 levels (one loci), YKL‐40 levels (six loci) and NFL (seven loci). Significant differentially methylated regions were identified in 13 of the 15 analyses, with overlapping regions featuring in multiple CSF measures(MX2, RHOH, ANKMY1, ZFP57) analyses, which was unsurprising given the high correlation between measures. Interestingly, we identified several regions that overlapped between the tau and amyloid (ZBTB22), tau and NFL (S100A13), NFL and neurogranin (SORD), and neurogranin and amyloid (STRA6) regional analyses.ConclusionIn this blood‐based epigenome‐wide association study of AD‐relevant CSF biomarkers we identified several interesting genomic loci and regions.

Tumor-Infiltrating Normal B Lymphocytes Have Remarkable Prognostic Effects and Are Crucial for Antitumor Immune Responses in Diffuse Large B-Cell Lymphoma

Introduction: In recent years, increasing evidence suggests an important role of tumor-infiltrating B lymphocytes (TIL-Bs) in cancer immunity. In various types of solid tumors, high density of TIL-Bs and presence of mature tertiary lymphoid structures consistently correlate with better prognosis and response to immune checkpoint blockade therapies. Diffuse large B-cell lymphoma (DLBCL) is a type of aggressive lymphoma arising from mature B cells. TIL-Bs have been identified in DLBCL based on single-cell gene-expression data in several previous studies, but their prognostic significance in DLBCL is unknown. In this study, we determined the clinical significance of TIL-B abundance and investigated the potential underlying mechanisms. Patients and Methods: This abstract includes a discovery study in which we distinguished TIL-Bs from malignant B-cells by immunoglobulin gene (IG) clonotype analysis and a validation study using flow cytometry. In the discovery study, a molecularly well-characterized DLBCL cohort assembled by the International DLBCL R-CHOP Consortium Program was analyzed. DNA samples from DLBCL patients were sequenced by high-throughput ultra-deep sequencing assays using multiplexed forward and reverse primers. Based on the IG heavy chain (IGH) sequencing results, malignant B-cell and TIL-B VDJ clonotypes were identified for 138 patients, and the frequencies and numbers of TIL-B clonotypes in individual sequence repertoires were calculated. In 79 DLBCL cases, a DJ-only sequence was predominant and no productive VDJ sequences met the criteria for diagnostic sequences. Total frequencies of productive VDJ sequences in the sequence repertoires were then used to access high and low TIL-B frequencies. TIL-B frequencies were also evaluated by IG light chain clonotypes for these 217 patients and additional 52 patients. The TIL-B frequencies were correlated with patient survival, gene-expression profiling (GEP) data GSE31312, and frequencies of DLBCL-infiltrating immune cells quantified by fluorescent multiplex immunohistochemistry (mIHC). To validate the prognostic significance of TIL-B frequencies, we assembled a validation cohort of 94 DLBCL patients treated at Duke University Medical Center. TIL-Bs in tissues were quantified by flow cytometry immunophenotyping. Ratios of TIL-Bs to abnormal B cells were assessed for prognostic effects. Results: First, we found thathigh frequencies of TIL-Bs and clonotypic diversity were associated with significantly favorable prognostic effects in 138 DLBCL patients with their malignant B-cell IGH-VDJ clonotypes identified (Figure A). The prognostic effects of TIL-Bs outweighed those of the germinal center B-cell-like/activated B-cell-like classification (Figure B). Second, patients with high frequencies of TIL-Bs compared with those without had significant upregulation of immune response genes by GEP (Figure C), higher CD4 T cell infiltration quantified by fluorescent mIHC (Figure D), and enrichment of EcoTyper-defined specific cell states, including significantly increased frequencies of B cell state S2 (pre-memory), CD4 T cell state S3 (naïve), follicular helper T cell state S3, and endothelial cell state S3, whereas lower frequencies of B cell state S5 (plasmablast) and follicular helper T cell state S1. Third, high TIL-B abundance in 79 ‘DJ-only’ clonotype cases and additional 52 cases assessed by light chain clonotype analysis showed similar favorable prognostic effects and associations with upregulated immune gene expression and CD4 T cell infiltration. Fourth, the identified GEP signatures, prominent with genes involved in T-dependent B cell activation and T cell activation and differentiation, showed significant prognostic effects in two GEO datasets. Fifth, TIL-B abundance showed significant prognostic effects in DLBCL cases infiltrated with T-cells in both the discovery study (Figure E) and validation study (Figure F, flow cytometry cohort). Conclusions: TIL-B frequencies and clonotypic diversity have remarkable prognostic and biological effects in DLBCL, which has significant implications for developing novel therapeutic strategies. Our study identified a new prognostic biomarker in DLBCL and an important player in anti-lymphoma immune responses, providing a new point of view to analyze the tumor biology in aggressive B-cell lymphoma and precisely identify prognostic determinants.

Assessment of Ultra-Deep Mass Spectrometry Based Proteomics Compared to Flow Cytometry and RNA-Based Methods for the Discovery and Validation of Therapeutic Targets in Immune Cells

Immuno-oncology (IO) has substantially improved the survival of cancer patients over the past several years encouraging the discovery of novel IO targets which are typically proteins expressed on the surface of immune cells. Sensitive quantification of proteins in complex biological samples is routinely achieved by immunoassays that use antibodies specific to target proteins. Such approaches can be a limitation in IO drug discovery since the development of de novo antibodies is associated with long lead times, high costs, and high failure rates, often resulting in the substitution of the protein readout by a surrogate transcript readout using RNA sequencing methods. Protein quantification using mass spectrometry (MS) is agnostic to any capture agent and removes the barriers of availability or specificity of antibody-based assays. Furthermore, MS proteomics workflows can support large scale discovery studies with wide proteome coverage, and also represent an attractive antibody-free alternative in targeted quantitative studies for protein measurement. The main purpose of this work was primarily to assess the sensitivity of DIA-based MS-proteomics platform for the deep proteome profiling of human primary immune cells by measuring the maximum protein coverage obtained across cell inputs ranging from 2 million down to single cells ( performance assessment). The secondary purpose of this work was to assess the accuracy of the quantification of targeted MS proteomics workflow in comparison with the gold standard QIFI® flow cytometry approach, and other methods using mRNA readouts in immortalized human hematological cell lines ( accuracy assessment). We found that our MS-based proteomics workflows achieve high sensitivity in detection of immune cell markers down to few immune cells as input. Additionally, we report a strong correlation of the quantitative data derived from our MS-based proteomics workflows compared to the flow cytometry gold standard (r 2= 0.7354). Notably, we observed that this correlation was significantly higher than using transcriptomics approaches, emphasizing the benefit of substituting immunoassays for MS-based proteomics platforms in IO target discovery and validation.

Prediction of Time-to-First Treatment (TTFT) in Favorable-Risk Chronic Lymphocytic Leukemia (CLL) Patients Using a Novel Stratification through Transcriptome Risk Scoring

Background: CLL is the most prevalent adult leukemia with a highly heterogenous genetic landscape leading to extremely varied clinical outcomes. Previously, CLL patients with immunoglobulin heavy chain variable region ( IGHV) mutated status and presence of deletion 13q14 [del(13q)] were identified as favorable-risk with most patients remaining on lifetime observation. However, other intrinsic factors contributing to CLL disease progression and leading to treatment initiation remain unknown. Our goal was to identify patients at high-risk of progression with added predictive value (APV) beyond IGHV-status and presence of del(13q). Methods: Flow cytometry was used to sort CD19+CD5+ malignant cells in peripheral blood from CLL patients at Huntsman Cancer Institute at University of Utah and triple sequencing generated (tumor whole transcriptome and paired tumor-normal whole exome data). We used SPECTRA, a novel dimension reduction transcriptome approach, to derive a set of independent, intrinsic, quantitative transcriptome tumor variables from 249 CLL patients with quality-controlled transcriptome sequence data. From this set, 182 treatment-naïve (TN) patients with quality-controlled clinical variables were used as a discovery cohort set to derive a SPECTRA-based risk score for TTFT using multivariable Cox proportional hazards modeling. Event time was date of patient specimen acquisition to initiation of frontline therapy with patients censored at last follow-up or death. Gaussian mixture modeling (GMM) was performed to identify evidence for distinct risk groups within the quantitative risk score. GMM-risk groups, and further stratifications by IGHV mutational and del(13q) status, were used in hazard ratios (HR) and to illustrate differences in Kaplan-Meier time-to-event curves. An independent publicly available dataset of 548 TN CLL patients with clinical and transcriptome data for CD19+ enriched cells was used for replication. Results: SPECTRA produced 19 quantitative spectra variables to describe most of the expression variation across the 249 CLL patients. In multivariable Cox modeling for TTFT, 7 spectra were significant and retained in a final model, which GMM separated into a bi-modal distribution and defined high (N=83) and low-risk (N=99) groups (HR=5.7, 95% confidence interval [CI] 3.4-9.7). In a post-hoc model including IGHV mutational and del(13q) status, the spectra-risk score showed significant APV beyond both factors (p<0.001 for both). The discovery study timeframe was 6.3 years (y) and included 109 IGHV-mutated and 75 del(13q) patients. In the IGHV-mutated patients, median TTFT (mTTFT) was significantly shorter for spectra-high risk patients (3.1y) vs. spectra-low risk patients (median not reached [NR]; p<0.001). Similarly, in the del(13q) patients, mTTFT was significantly shorter in high-risk patients (3.3y) compared to median NR for spectra-low (p<0.001) ( Figure). Importantly, stratification of patients into low- and high-risk SPECTRA scores provided significant additional information for prediction of TTFT than IGHV mutational and del(13q) status ( Figure). For validation, we applied the same spectra-risk score to the independent data set containing 301 IGHV-mutated and 278 del(13q) CLL patients. In the replication set, the mTTFT in the IGHV-mutated patients who were spectra-high was 3.3y vs. 11.2y in spectra-low (p<0.001). Similarly, in the replication set of del(13q) patients, spectra-high-risk patients had a mTTFT of 2.7y vs. 10.7y for spectra-low (p<0.001). Conclusion: A transcriptome risk score utilizing novel SPECTRA variables identified a substantial subset of CLL patients with IGHV-mutated status and presence of del(13q) who have uncharacteristically short TTFT, but who would be classified as favorable risk under current best practice stratification. Future directions include validating our results with external patient cohorts from Mayo Clinic and investigating the genetic and transcriptomic profile of SPECTRA low- and high-risk cohorts to determine targetable pathways for treatment.

Pretreatment MRI–Based Radiomics for Prediction of Rectal Cancer Outcome: A Discovery and Validation Study

Accurate prediction of local recurrence or distant metastasis is critical for developing individualized therapies for locally advanced rectal cancer (LARC) patients after standard therapy. This study aims to develop and validate a multiparameter MRI-based radiomics signature (RS) for prognostic prediction in LARC patients receiving neoadjuvant chemoradiotherapy (nCRT) and total mesorectal excision (TME) and to explore the ability of RS for personalized survival risk stratification. In this multi-center study, 454 patients who received nCRT and TME and completed 3 years of follow-up participated. RS was constructed for prognostic prediction based on features extracted from pretreatment multiparameter MRI in a training cohort (TC; n=298), which was tested in an internal validation cohort (IVC; n=75) and further validated in an independent external validation cohort (EVC; n=81). Furthermore, the ability of RS for personalized survival risk stratification was explored using the Kaplan-Meier survival curves. The RS model showed satisfactory accuracy for prognostic prediction with AUCs of 0.83, 0.81 and 0.82 in the TC, IVC and EVC, respectively. In addition, RS helped to refine risk stratification for LARC patients on the basis of significantly different 3-year disease-free survival rates, independent of their pathological stage, pre-surgery CEA, and even treatment modality. The proposed RS can be used not only to predict local recurrence or distant metastasis but also to serve as an effective postoperative survival risk stratification tool for clinicians to facilitate decision-making for LARC patients receiving standard treatment.

Reduced glutathione level in the aqueous humor of patients with primary open-angle glaucoma and normal-tension glaucoma

Glaucoma is a leading cause of blindness worldwide in older people. Profiling the aqueous humor, including the metabolites it contains, is useful to understand physiological and pathological conditions in the eye. In the current study, we used mass spectrometry (MS) to characterize the aqueous humor metabolomic profile and biological features of patients with glaucoma. Aqueous humor samples were collected during trabeculectomy surgery or cataract surgery and analyzed with global metabolomics. We included 40 patients with glaucoma (32 with POAG, 8 with NTG) and 37 control subjects in a discovery study. VIP analysis revealed five metabolites that were elevated and three metabolites that were reduced in the glaucoma patients. The identified metabolomic profile had an area under the receiver operating characteristic curve of 0.953. Among eight selected metabolites, the glutathione level was significantly decreased in association with visual field defects. Moreover, in a validation study to confirm the reproducibility of our findings, the glutathione level was reduced in NTG and POAG patients compared with a cataract control group. Our findings demonstrate that aqueous humor profiling can help to diagnose glaucoma and that various aqueous humor metabolites are correlated with clinical parameters in glaucoma patients. In addition, glutathione is clearly reduced in the aqueous humor of glaucoma patients with both IOP-dependent and IOP-independent disease subtypes. These findings indicate that antioxidant agents in the aqueous humor reflect glaucomatous optic nerve damage and that excessive oxidative stress may be involved in the pathogenesis of glaucoma.

Isolation and Characterization of Monomeric Human RAD51: A Novel Tool for Investigating Homologous Recombination in Cancer.

DNA repair protein RAD51 is a key player in the homologous recombination pathway. Upon DNA damage, RAD51 is transported into the nucleus by BRCA2, where it can repair DNA double-strand breaks. Due to the structural complexity and dynamics, researchers have not yet clarified the mechanistic details of every step of RAD51 recruitment and DNA repair. RAD51 possesses an intrinsic tendency to form oligomeric structures, which make it challenging to conduct biochemical and biophysical investigations. Here, for the first time, we report on the isolation and characterization of a human monomeric RAD51 recombinant form, obtained through a double mutation, which preserves the protein's integrity and functionality. We investigated different buffers to identify the most suitable condition needed to definitively stabilize the monomer. The monomer of human RAD51 provides the community with a unique biological tool for investigating RAD51-mediated homologous recombination, and paves the way for more reliable structural, mechanistic, and drug discovery studies.

Isolation and Characterization of Monomeric Human RAD51: A Novel Tool for Investigating Homologous Recombination in Cancer

AbstractDNA repair protein RAD51 is a key player in the homologous recombination pathway. Upon DNA damage, RAD51 is transported into the nucleus by BRCA2, where it can repair DNA double‐strand breaks. Due to the structural complexity and dynamics, researchers have not yet clarified the mechanistic details of every step of RAD51 recruitment and DNA repair. RAD51 possesses an intrinsic tendency to form oligomeric structures, which make it challenging to conduct biochemical and biophysical investigations. Here, for the first time, we report on the isolation and characterization of a human monomeric RAD51 recombinant form, obtained through a double mutation, which preserves the protein's integrity and functionality. We investigated different buffers to identify the most suitable condition needed to definitively stabilize the monomer. The monomer of human RAD51 provides the community with a unique biological tool for investigating RAD51‐mediated homologous recombination, and paves the way for more reliable structural, mechanistic, and drug discovery studies.

Lipid Bilayer Reformation Using the Wiping Blade for Improved Ion Channel Analysis.

The measurement of ion permeation activity across planar lipid bilayers is a useful technique for the functional analysis and drug evaluation of ion channels at the single-molecule level. To enhance the data throughput, parallelization of lipid bilayers is desirable. However, existing parallelized approaches face challenges in simultaneously and efficiently measuring ion channel activities under various conditions on one chip. In this study, we propose an approach to overcome these limitations by developing a device capable of repeated measurements of ion channels incorporated into individually arrayed lipid bilayers. Our device forms an array of a lipid bilayer at a micropore on a separator by merging two lipid monolayers assembled on the surface of aqueous droplets. We introduce a vertically moving, blade-shaped module─referred to as a "wiping blade"─which enables controlled disruption and reformation of the bilayer at the micropore. By optimizing the surface properties and clearance of the wiping blade, we successfully achieved repeated bilayer formation. The arrayed lipid bilayer device with the integrated wiping blade module demonstrates a 5-fold improvement in data throughput during ion channel activity measurements. Finally, we validate the practical utility of our device by evaluating the effects of an ion channel inhibitor. The developed device opens new avenues for high-throughput analysis and screening of ion channels, leading to significant advancements in drug discovery and functional studies of membrane proteins. It offers a powerful tool for researchers in the field and holds promise for accelerating drug development by targeting ion channels.

Proteomic investigations of adult polyglucosan body disease: insights into the pathobiology of a neurodegenerative disorder.

Inadequate glycogen branching enzyme 1 (GBE1) activity results in different forms of glycogen storage disease type IV, including adult polyglucosan body disorder (APBD). APBD is clinically characterized by adult-onset development of progressive spasticity, neuropathy, and neurogenic bladder and is histologically characterized by the accumulation of structurally abnormal glycogen (polyglucosan bodies) in multiple cell types. How insufficient GBE1 activity causes the disease phenotype of APBD is poorly understood. We hypothesized that proteomic analysis of tissue from GBE1-deficient individuals would provide insights into GBE1-mediated pathobiology. In this discovery study, we utilized label-free LC-MS/MS to quantify the proteomes of lymphoblasts from 3 persons with APBD and 15 age- and gender-matched controls, with validation of the findings by targeted MS. There were 531 differentially expressed proteins out of 3,427 detected between APBD subjects vs. controls, including pronounced deficiency of GBE1. Bioinformatic analyses indicated multiple canonical pathways and protein-protein interaction networks to be statistically markedly enriched in APBD subjects, including: RNA processing/transport/translation, cell cycle control/replication, mTOR signaling, protein ubiquitination, unfolded protein and endoplasmic reticulum stress responses, glycolysis and cell death/apoptosis. Dysregulation of these processes, therefore, are primary or secondary factors in APBD pathobiology in this model system. Our findings further suggest that proteomic analysis of GBE1 mutant lymphoblasts can be leveraged as part of the screening for pharmaceutical agents for the treatment of APBD.

RefMetaPlant: a reference metabolome database for plants across five major phyla.

Plants are unique with tremendous chemical diversity and metabolic complexity, which is highlighted by estimates that green plants collectively produce metabolites numbering in the millions. Plant metabolites play crucial roles in all aspects of plant biology, like growth, development, stress responses, etc. However, the lack of a reference metabolome for plants, and paucity of high-quality standard compound spectral libraries and related analytical tools, have hindered the discovery and functional study of phytochemicals in plants. Here, by leveraging an advanced LC-MS platform, we generated untargeted mass spectral data from>150 plant species collected across the five major phyla. Using a self-developed computation protocol, we constructed reference metabolome for 153 plant species. A 'Reference Metabolome Database for Plants' (RefMetaPlant) was built to encompass the reference metabolome, integrated standard compound mass spectral libraries for annotation, and related query and analytical tools like 'LC-MS/MS Query', 'RefMetaBlast' and 'CompoundLibBlast' for searches and profiling of plant metabolome and metabolite identification. Analogous to a reference genome in genomic research, RefMetaPlant provides a powerful platform to support plant genome-scale metabolite analysis to promote knowledge/data sharing and collaboration in the field of metabolomics. RefMetaPlant is freely available at https://www.biosino.org/RefMetaDB/.

Exploring the potential of Andrographis paniculata for developing novel HDAC inhibitors: an in silico approach

Cancer is one of the dreaded diseases of the twentieth century, emerging the major global causes of human morbidity. Cancer research in the last 15 years has provided unprecedented information on the role of epigenetics in cancer initiation and progression. Histone deacetylases (HDACs) are recognized as important epigenetic markers in cancer, whose overexpression leads to increased metastasis and angiogenesis. In the current study, thirty-four (34) compounds from Andrographis paniculata were screened for the identification of potential candidate drugs, targeting three Class I HDACs (Histone deacetylases), namely HDAC1 (PDB id 5ICN), HDAC3 (PDB id 4A69) and HDAC8 (PDB id 5FCW) through computer-assisted drug discovery study. Results showed that some of the phytochemicals chosen for this study exhibited significant drug-like properties. In silico molecular docking study further revealed that out of 34 compounds, the flavonoid Andrographidine E had the highest binding affinities towards HDAC1 (−9.261 Kcal mol−1) and 3 (−9.554 Kcal mol−1) when compared with the control drug Givinostat (-8.789 and −9.448 Kcal mol−1). The diterpenoid Andrographiside displayed the highest binding affinity (-9.588 Kcal mol−1) to HDAC8 compared to Givinostat (-8.947 Kcal mol−1). Statistical analysis using Principal Component Analysis tool revealed that all 34 phytocompounds could be clustered in four statistical groups. Most of them showed high or comparable inhibitory potentials towards HDAC target protein. Finally, the stability of top-ranked complexes (Andrographidine E-HDAC1 and HDAC3; Andrographiside-HDAC8) at the physiological condition was validated by Molecular Dynamic Simulation and MM-PBSA study. Communicated by Ramaswamy H. Sarma

Emotional dissociations in temporal associations: opposing effects of arousal on memory for details surrounding unpleasant events

ABSTRACT Research targeting emotion’s impact on relational episodic memory has largely focused on spatial aspects, but less is known about emotion’s impact on memory for an event’s temporal associations. The present research investigated this topic. Participants viewed a series of interspersed negative and neutral images with instructions to create stories linking successive images. Later, participants performed a surprise memory test, which measured temporal associations between pairs of consecutive pictures where one picture was negative and one was neutral. Analyses focused on how the order of negative and neutral images during encoding influenced retrieval accuracy. Converging results from a discovery study (N = 72) and pre-registered replication study (N = 150) revealed a “forward-favouring” effect of emotion in temporal memory encoding: Participants encoded associations between negative stimuli and subsequent neutral stimuli more strongly than associations between negative stimuli and preceding neutral stimuli. This finding may reflect a novel trade-off regarding emotion’s effects on memory and is relevant for understanding affective disorders, as key clinical symptoms can be conceptualised as maladaptive memory retrieval of temporal details.

Cardiovascular magnetic resonance imaging for sequential assessment of cardiac fibrosis in mice: technical advancements and reverse translation.

Cardiovascular magnetic resonance (CMR) imaging has become an essential technique for the assessment of cardiac function and morphology, and is now routinely used to monitor disease progression and intervention efficacy in the clinic. Cardiac fibrosis is a common characteristic of numerous cardiovascular diseases and often precedes cardiac dysfunction and heart failure. Hence, the detection of cardiac fibrosis is important for both early diagnosis and the provision of guidance for interventions/therapies. Experimental mouse models with genetically and/or surgically induced disease have been widely used to understand mechanisms underlying cardiac fibrosis and to assess new treatment strategies. Improving the appropriate applications of CMR to mouse studies of cardiac fibrosis has the potential to generate new knowledge, and more accurately examine the safety and efficacy of antifibrotic therapies. In this review, we provide 1) a brief overview of different types of cardiac fibrosis, 2) general background on magnetic resonance imaging (MRI), 3) a summary of different CMR techniques used in mice for the assessment of cardiac fibrosis including experimental and technical considerations (contrast agents and pulse sequences), and 4) provide an overview of mouse studies that have serially monitored cardiac fibrosis during disease progression and/or therapeutic interventions. Clinically established CMR protocols have advanced mouse CMR for the detection of cardiac fibrosis, and there is hope that discovery studies in mice will identify new antifibrotic therapies for patients, highlighting the value of both reverse translation and bench-to-bedside research.

DNA methylation is not associated with sensitization to or dietary introduction of highly allergenic foods in a subset of the CHILD cohort at age 1 year.

In the first year of life, DNA methylation (DNAm) patterns are established and are particularly susceptible to exposure-induced changes. Some of these changes may leave lasting effects by persistently altering gene expression or cell type composition or function, contributing to disease. In this discovery study, we investigated DNAm associations with sensitization to peanut, egg, or cow's milk and hypothesized that genes demonstrating DNAm differences in immune cells may play a role in the development of food sensitization. Infant sensitization (a skin prick test wheal size that is at least 2 mm greater than the negative control) was measured to peanut, egg, and cow's milk at age 1 year, and ages of food introduction were reported prospectively. PBMC DNAm was measured in blood samples at 1 year in 144 infants, oversampled for atopy or wheeze. Statistical analysis of Illumina 450k array DNAm data was conducted in R with adjustment for clinical and genetic covariables and a minimum effect size of 1%, false discovery rate of 5%, and medium-confidence false discovery rate threshold of 20%. There were no DNAm differences between infants with and without peanut, egg, or cow's milk sensitization. Borderline significant sites with high effect sizes were enriched for methylation quantitative trait loci, hinting at genetic factors influencing DNAm at these sites. DNAm patterns did not differ by peanut or egg introduction before or after 12 months. This small pilot study did not show differences inmethylation by food sensitization or introduction, but it diddemonstrate DNAm patterns linked to genetic variants.

Potential relevance of salivary legumain for the clinical diagnostic of hand, foot, and mouth disease.

The fight against hand, foot, and mouth disease (HFMD) remains an arduous challenge without existing point-of-care (POC) diagnostic platforms for accurate diagnosis and prompt case quarantine. Hence, the purpose of this salivary biomarker discovery study is to set the fundamentals for the realization of POC diagnostics for HFMD. Whole salivary proteome profiling was performed on the saliva obtained from children with HFMD and healthy children, using a reductive dimethylation chemical labeling method coupled with high-resolution mass spectrometry-based quantitative proteomics technology. We identified 19 upregulated (fold change = 1.5-5.8) and 51 downregulated proteins (fold change = 0.1-0.6) in the saliva samples of HFMD patients in comparison to that of healthy volunteers. Four upregulated protein candidates were selected for dot blot-based validation assay, based on novelty as biomarkers and exclusions in oral diseases and cancers. Salivary legumain was validated in the Singapore (n = 43 healthy, 28 HFMD cases) and Taiwan (n = 60 healthy, 47 HFMD cases) cohorts with an area under the receiver operating characteristic curve of 0.7583 and 0.8028, respectively. This study demonstrates the feasibility of a broad-spectrum HFMD POC diagnostic test based on legumain, a virus-specific host systemic signature, in saliva.