Biological Validation Research Articles

Proteomic Insights into Biology of Bipolar Disorder: Implications for Health Complexity and Mortality.

The present study has the following objectives: 1) identify differentially expressed proteins and pathways in blood samples of BD compared to healthy controls by employing high-throughput proteomics and bioinformatics and 2) characterize disease-related molecular signatures through in-depth analysis of the differentially expressed proteins and pathways. Blood samples from BD patients (n=10) classified into high (BD+) or poor functioning (BD-), based on functional and cognitive status, and healthy controls (n=5) were analyzed using mass spectrometry-based proteomic analysis. Bioinformatics was performed to detect biological processes, pathways, and diseases related to BD. Eight proteins exclusively characterized the molecular profile of patients with BD+ compared to HC, while 26 altered proteins were observed in the BD- group. These altered proteins were mainly enriched in biological processes related to lipid metabolism, complement system and coagulation cascade, and cardiovascular diseases; all these changes were more prominent in the BD- group. These findings may represent systemic alterations that occur with the progression of the illness and a possible link between BD and medical comorbidities. Such comprehensive understanding provides valuable insights for targeted interventions, addressing mental and physical health aspects in subjects with BD. Despite these promising findings, further research is warranted, encompassing larger sample cohorts and incorporating biological validation through molecular biology methods.

In Silico Models to Validate Novel Blood-Based Biomarkers.

Biological validation of preliminary findings is a key prerequisite in biomarker discovery. In recent years, the development of advanced large-scale sequencing technologies combined with high-throughput computational analysis methods led to the extraction of considerable amount of data from healthy and diseased tissues. Stored in large open-access repositories, these data can be accessed and interrogated by researchers aiming at understanding the biological rationale behind their results. These so called in silico analyses, in opposite to in vitro analyses, have gained increasing importance in recent years, becoming a major component of research projects and publications. However, making sense of the large amount of data available can be challenging and may lead to a misinterpretation of the data. To reduce the dimensionality of this data, recent years have seen the development of statistical m\ethods and advanced graph analytics which help researchers summarize the available data and draw appropriate conclusions. In this chapter we will describe three in silico methods to investigate the biological underpinnings of a panel of seven blood-based biomarkers of Alzheimer's disease.

Toward image-based personalization of glioblastoma therapy: A clinical and biological validation study of a novel, deep learning-driven tumor growth model.

The diffuse growth pattern of glioblastoma is one of the main challenges for accurate treatment. Computational tumor growth modeling has emerged as a promising tool to guide personalized therapy. Here, we performed clinical and biological validation of a novel growth model, aiming to close the gap between the experimental state and clinical implementation. One hundred and twenty-four patients from The Cancer Genome Archive (TCGA) and 397 patients from the UCSF Glioma Dataset were assessed for significant correlations between clinical data, genetic pathway activation maps (generated with PARADIGM; TCGA only), and infiltration (Dw) as well as proliferation (ρ) parameters stemming from a Fisher-Kolmogorov growth model. To further evaluate clinical potential, we performed the same growth modeling on preoperative magnetic resonance imaging data from 30 patients of our institution and compared model-derived tumor volume and recurrence coverage with standard radiotherapy plans. The parameter ratio Dw/ρ(P < .05 in TCGA) as well as the simulated tumor volume (P < .05 in TCGA/UCSF) were significantly inversely correlated with overall survival. Interestingly, we found a significant correlation between 11 proliferation pathways and the estimated proliferation parameter. Depending on the cutoff value for tumor cell density, we observed a significant improvement in recurrence coverage without significantly increased radiation volume utilizing model-derived target volumes instead of standard radiation plans. Identifying a significant correlation between computed growth parameters and clinical and biological data, we highlight the potential of tumor growth modeling for individualized therapy of glioblastoma. This might improve the accuracy of radiation planning in the near future.

On minimising tumoural growth under treatment resistance

Drug resistance is a major challenge for curative cancer treatment, representing the main reason of death in patients. Evolutionary biology suggests pauses between treatment rounds as a way to delay or even avoid resistance emergence. Indeed, this approach has already shown promising preclinical and early clinical results, and stimulated the development of mathematical models for finding optimal treatment protocols. Due to their complexity, however, these models do not lend themself to a rigorous mathematical analysis, hence so far clinical recommendations generally relied on numerical simulations and ad-hoc heuristics. Here, we derive two mathematical models describing tumour growth under genetic and epigenetic treatment resistance, respectively, which are simple enough for a complete analytical investigation. First, we find key differences in response to treatment protocols between the two modes of resistance. Second, we identify the optimal treatment protocol which leads to the largest possible tumour shrinkage rate. Third, we fit the “epigenetic model” to previously published xenograft experiment data, finding excellent agreement, underscoring the biological validity of our approach. Finally, we use the fitted model to calculate the optimal treatment protocol for this specific experiment, which we demonstrate to cause curative treatment, making it superior to previous approaches which generally aimed at stabilising tumour burden. Overall, our approach underscores the usefulness of simple mathematical models and their analytical examination, and we anticipate our findings to guide future preclinical and, ultimately, clinical research in optimising treatment regimes.

Do neutrophil extracellular traps implicate in atheromatous plaques from carotid endarterectomy? Re-analyzes of cDNA microarray data by surgeons.

Carotid artery stenosis is the cause of 15% of strokes. Neutrophil extracellular traps (NETs) and peptidyl arginine deiminase 4 (PAD4) are believed to be involved in thrombosis. This pilot study described the differential expression profile of NETs between atheromatous plaques and surrounding tissues. Microarray datasets of carotid plaques were obtained from Gene Expression Omnibus. The normalized data were processed into comma-separated value matrix files using spreadsheet software. Analyzes of microarray data were conducted using integrated differential expression and pathway analysis. The clustering results illustrated that the classifications of plaque and control had reasonable biological validity. Pathway analysis revealed the relevance of immune response, cell signaling, and other pathways. Differentially expressed genes were detected between carotid plaques and control specimens. However, enrichment analyzes did not reveal a difference in PAD4 expression between the groups and that NET implication was only found in one cDNA microarray dataset. This pilot study does not necessarily dismiss the possibility of a relationship between NETs and atherothrombotic stroke. Gene expression could differ between endothelial cells and atheromas, and further studies are needed.

Analysis of Stability, Sensitivity Index and Hopf Bifurcation of Eco-Epidemiological SIR Model under Pesticide Application

In this paper, a deterministic SIR plant mathematical model is proposed and analysed with the application of pesticides as a control measure. The primary purpose of this model is to study the role of pesticides in controlling disease prevalence in plant populations. The total plant population is subdivided into three categories: susceptible, infected, and recovered. Pesticides are considered to be applied to both susceptible and infected populations to prevent the spread of infection to unaffected plant populations. It is considered that plant populations can be recovered only through the use of pesticides. To ensure the biological validity and well-defined nature of the model, the positivity, boundedness, uniqueness and existence of solutions are analysed. The basic reproduction number (R0) of the infection is determined and observed that the disease-free equilibrium state is locally asymptotically stable whenever (R0) is less than unity and unstable otherwise. The sensitivity analysis of the basic reproduction number is carried out, and it is observed that the value of R0 decreases as the value of the death rate and the recovery rate of plants increases. Moreover, it is revealed that above a critical parameter value of the infective induce rate, the population starts oscillating periodically, and the endemic equilibrium state becomes unstable. Finally, numerical simulations are conducted in MATLAB software to compare the analytical findings. Overall, the results obtained from this analysis are both novel and significant, making them an intriguing and potentially valuable contribution to the field of theoretical ecology.

Early transcriptomic changes in peripheral blood 7 days after embryo transfer in dairy cattle

A common goal of the dairy industry is to shorten the calving interval to reap several benefits associated with improved fertility. Early pregnancy detection is crucial to shorten this interval, allowing for prompt re-insemination of cows that failed to conceive after the first service. Currently, the industry lacks a method to accurately predict pregnancy within the first 3 wk. The polypeptide cytokine IFN-tau (IFNT) is the primary signal for maternal recognition of pregnancy in ruminants. As IFNT is released from the early conceptus, it initiates a cascade of effects, including upregulation of IFN-stimulated genes (ISG). Expression of ISG can be detected in the peripheral blood. The present study aimed to characterize peripheral transcriptomic changes, including the ISG, as early as d 7 after embryo transfer. A total of 170 Holstein heifers received in vitro-produced embryos. Whole blood was collected from these heifers within 24 h of the embryo transfer (d 0), d 7, and d 14 after embryo transfer. The heifers were divided into 2 groups, pregnant and nonpregnant, based on pregnancy diagnosis on d 28 via ultrasound. Total RNA was extracted from the peripheral blood of pregnant and nonpregnant heifers, pooled and sequenced. Expression analysis on d 7 heifers resulted in 13 significantly differentially expressed genes mostly related to innate immunity. Differential expression analysis comparing pregnant heifers on d 0 to the same heifers on d 14 showed 51 significantly differentially expressed genes. Eight genes were further quantified through reverse-transcription quantitative real-time PCR for biological validation. On d 7 after embryo transfer, mRNA transcriptions of EDN1, CXCL3, CCL4, and IL1A were significantly upregulated in pregnant heifers (n = 14) compared with nonpregnant heifers (n = 14), with respective fold changes of 8.10, 18.12, 29.60, and 29.97. Although on d 14 after embryo transfer, mRNA transcriptions of ISG15, MX2, OASY1, and IFI6 were significantly upregulated in the blood of pregnant heifers (n = 14) compared with the same heifers on d 0, with respective fold changes of 5.09, 2.59, 3.89, and 3.08. These findings demonstrate that several immune-related genes and ISG are activated during the first 2 wk after embryo transfer, which may explain how the maternal immune system accommodates the allogenic conceptus. To further investigate the diagnostic potentials of these genes, future studies are warranted to analyze the specificity and sensitivity of these biomarkers to predict early pregnancy.

Inhibition of Protein Disulfide Isomerase Attenuates Osteoclast Differentiation and Function via the Readjustment of Cellular Redox State in Postmenopausal Osteoporosis.

Due to the accumulation of reactive oxygen species (ROS) and heightened activity of osteoclasts, postmenopausal osteoporosis could cause severe pathological bone destruction. Protein disulfide isomerase (PDI), an endoplasmic prototypic thiol isomerase, plays a central role in affecting cellular redox state. To test whether suppression of PDI could inhibit osteoclastogenesis through cellular redox regulation, bioinformatics network analysis was performed on the causative genes, followed by biological validation on the osteoclastogenesis in vitro and ovariectomy (OVX) mice model in vivo. The analysis identified PDI as one of gene targets for postmenopausal osteoporosis, which was positively expressed during osteoclastogenesis. Therefore, PDI expression inhibitor and chaperone activity inhibitor were used to verify the effects of PDI inhibitors on osteoclastogenesis. Results demonstrated that PDI inhibitors could reduce osteoclast number and inhibit resorption function via suppression on osteoclast marker genes. The mechanisms behind the scenes were the PDI inhibitors-caused intracellular ROS reduction via enhancement of the antioxidant system. Micro-CT and histological results indicated PDI inhibitors could effectively alleviate or even prevent bone loss in OVX mice. In conclusion, our findings unveiled the suppressive effects of PDI inhibitors on osteoclastogenesis by reducing intracellular ROS, providing new therapeutic options for postmenopausal osteoporosis.

Lymphoma Cell Sorting to Enrich Low Degree Infiltration Clonal B-Cells for Diagnostic Delineation of Molecular Genetic Background By NGS

Introduction: According to WHO 2022 classificationthe vast majority of B-cell lymphoma entities continue to be defined histologically and by their immunophenotype. However, genetics help to define these entities and provide prognostic information. NGS targeted panel sequencing with short turn-around time has emerged as powerful diagnostic tool to uncover relevant genes frequently mutated in B-cell lymphoma. Yet its sensitivity limits its application to lymphoma cases with clone sizes &amp;gt;5%. In case of clinical symptoms and need for therapy small B-cell clones (&amp;lt;5%) can be diagnostically challenging if tissue for histopathology is difficult to obtain and immunophenotype is not characteristic for a specific lymphoma entitiy such as splenic B-cell lymphoma. Aim: To enrich small B-cell clones (&amp;lt;5%) by cell sorting to &amp;gt;15% to enable lymphoma NGS panel sequencing for potential genetic characterization and risk stratification. Methods: Peripheral blood or bone marrow samples (n=43) that containedsmall B-cells clones (&amp;lt;5%) as identified by flow cytometric routine diagnostics were stained for cell sorting by a combination of monoclonal antibodies against 9 B-cell antigens, including CD5, CD19 and CD45. The gating strategy excludes debris and doublets and defines B- and T-cells out of CD45-positive events. By application of cell sorting (SRT Cytoflex, Beckman Coulter) we aimed at &amp;gt;500,000 cells consisting of B- and T-cells, containing &amp;gt;15% clonal B-cells to ensure sufficient NGS sensitivity but minimize sort time. DNA was extracted from sorted samples and lymphoma NGS panel sequencing (66 genes ~ 1,500x coverage, min. 400x) was performed (NovaSeq 6000, Illumina). For technical and biological validation, native sample material was sequenced in parallel for a subset of patient samples (n=30). Results: In total43samples with &amp;lt;5% clonal B-cells were applied for cell sorting and clonal B-cells were enriched to a median of 17% (range, 3-97%) in a median of 800,000 cells (range, 127,400-3,780,000). In a pilot study to optimize the sorting procedure 8 of 13 sorted samples (62%) revealed quantitatively sufficient and good quality DNA and could be successfully analyzed by NGS. In 4 of these 8 samples (50%) mutations were identified. Optimized processing regarding cell sorting and DNA extraction was applied to further 30 samples all of which could be successfully sequenced. Overall, in 50% of all samples with NGS analysis after cell sorting (19/38) one or more mutations (total number of mutations: n=30) associated with B-cell lymphoma were detected. Mutations comprised genes frequently affected in B-cell lymphoma: MYD88 (30% of detected mutations, 9/30), CXCR4 (20%, 6/30), NOTCH1 (7%, 2/30), NOTCH2 (3%, 1/30), KMT2D (7%, 2/30), TP53 (13%, 4/30), BRAF (7%, 2/30), KLF2 (10%, 3/30) and DNMT3A (3%, 1/30). All of the cases negative for NGS after sorting (n=15) were negative also by NGS on matched native sample material without prior enrichment of clonal B-cells. Out of the 15 cases with positive NGS results after sorting NGS on native sample material revealed mutations in only 4 (27%) cases (only 1 case with detection of all mutations) while in 11 (73%) cases no mutations were detected. In the 15 cases positive after sorting median variant allele frequency (VAF) was 8.8% (range 3.4-38.3%). As anticipated, in the 4 cases with mutations detected also without sorting VAF was lower in native sample material (median 3.1%, range 1.98-4.9%) as compared to sorted sample material (median 8.9%, range 6.8-23.2%, p=0.0412). Conclusion: Cell sorting can overcome the diagnostic challenge of low degree infiltration B-cell lymphoma that require molecular genetics for further classification and risk prediction prior to therapy. Analysis of 30 samples and comparison with matched native material revealed that enrichment targeting 15% of clonal B-cells in a total of &amp;gt;500.000 cells are desirable for successful and sensitive NGS panel sequencing. In 50% of such cases mutations are detectable after sorting. Cell sorting to enrich clonal B-cells thus is a powerful tool to enable NGS panel sequencing for low percentage mature B-cell lymphoma and should be considered for routine diagnostics of B-cell lymphoma.

Investigating the Cell States and Prognostic Impact of Tumor Microenvironment Ecosystems in Classic Hodgkin Lymphoma

Introduction: Classic Hodgkin lymphoma (cHL) is distinguished from other cancers by the minute fraction of Hodgkin/Reed-Sternberg (HRS) cells (1-5%) in the tumor mass. Therefore, the immune-cell rich microenvironment plays a pivotal role in cHL's development, progression, and response to therapy. However, comprehensive characterization of the malignant and immune cell states in cHL has posed significant challenges due to the rarity of HRS cells and the intrinsic heterogeneity of the disease. To address this challenge, we aimed to characterize the immune cell states of cHL using a combination of experimental and bioinformatics approaches. Methods: We identified patients diagnosed with cHL from 2013-19 managed at participating institutions with either formalin-fixed, paraffin-embedded (FFPE, n = 89) or cryopreserved cell suspensions prepared from excisional biopsies (n = 5). FACS purification of cell populations was performed on our cryopreserved tissues, followed by single cell RNA-seq (scRNA-seq) using 10X Genomics. Subsequently, we used CIBERSORTx to construct a customized signature matrix encompassing the gene expression profiles of 11 distinct cell types, including HRS cells. For our FFPE samples, we performed bulk RNA-seq with digital deconvolution using our signature matrix. Next, we combined the data and employed the EcoTyper machine learning framework to discover dominant cell states and ecosystems using deconvolved transcriptomics data. Validation of our identified cell states was performed in the remaining 2 samples with available scRNA-seq data, and a previously published cHL cohort (n = 110, Steidl et al. NEJM 2010). Results: Using our discovery set of scRNA-seq samples (n = 45,405 cells), we identified distinct cell phenotypes including the HRS cells. Using these data, we developed a gene expression signature matrix allowing for the deconvolution of bulk transcriptomes from 89 cHL cases. Among 18 patients with tumor and cell-free somatic tumor mutation data, we observed a positive correlation between the HRS cell proportion and Variant Allele Frequencies (VAFs) from tumor or plasma DNA sequencing ( R = 0.71 and 0.69, respectively). Employing the EcoTyper framework, we discovered 26 unique cell states and three conserved cellular communities or “ecotypes” in the 68 bulk transcriptomes (Figure A). To validate the cell states identified in our EcoTyper model, we employed additional scRNA-seq on two validation samples (n = 31,195 cells) and spatial transcriptomics using Visium 10X (n = 4 tumors). We recovered the three established ecotypes and 26 cell states in these datasets and found a significant spatial autocorrelation of the ecotypes, verifying the biological validity of our EcoTyper model. Finally, we observed Ecotype-3 is associated with adverse progression-free survival in our cohort as well as an independent validation cohort (Figure B) after adjustment for clinical stage in a multivariable model (HR = 8.78, P = 0.02). Conclusions: Our study represents a significant advancement in unraveling the complexities of the cHL cellular landscape. By leveraging innovative methodologies, we have shed light on key cell states and ecotypes within the tumor microenvironment that have prognostic significance, laying the groundwork for potential novel therapeutic avenues. Further validation in additional cohorts may allow ecotypes to risk stratify patients at diagnosis and aid in identification of poor performing subgroups within current low, intermediate, and high-risk groups.

A network-based transcriptomic landscape of HepG2 cells uncovering causal gene-cytotoxicity interactions underlying drug-induced liver injury.

Drug-induced liver injury (DILI) remains the main reason for drug development attritions largely due to poor mechanistic understanding. Toxicogenomic to interrogate the mechanism of DILI has been broadly performed. Gene coregulation network-based transcriptome analysis is a bioinformatics approach that potentially contributes to improve mechanistic interpretation of toxicogenomic data. Here we performed an extensive concentration time course response-toxicogenomic study in the HepG2 cell line exposed to 20 DILI compounds, 7 reference compounds for stress response pathways, and 10 agonists for cytokines and growth factor receptors. We performed whole transcriptome targeted RNA sequencing to more than 500 conditions and applied weighted gene coregulated network analysis to the transcriptomics data followed by the identification of gene coregulated networks (modules) that were strongly modulated upon the exposure of DILI compounds. Preservation analysis on the module responses of HepG2 and PHH demonstrated highly preserved adaptive stress response gene coregulated networks. We correlated gene coregulated networks with cell death onset and causal relationships of 67 critical target genes of these modules with the onset of cell death was evaluated using RNA interference screening. We identified GTPBP2, HSPA1B, IRF1, SIRT1, and TSC22D3 as essential modulators of DILI compound-induced cell death. These genes were also induced by DILI compounds in PHH. Altogether, we demonstrate the application of large transcriptome datasets combined with network-based analysis and biological validation to uncover the candidate determinants of DILI.

Diagnostic validity of premenstrual dysphoric disorder: revisited

The World Health Organization (WHO) and American Psychiatric Association (APA) have recognised premenstrual dysphoric disorder (PMDD) as an independent diagnostic entity, legitimising the distress and socio-occupational impairment experienced by affected women. However, the biological validity of this diagnosis remains inexplicit. This illness has also been criticised for a feminist-led, sympathetic reaction to the modern cultural challenges of urban, literate, employed, high-functioning women. This article systematically reviews existing literature on PMDD using the criteria established by Robins and Guze for the validity of a psychiatric diagnosis (clinical description, laboratory study, exclusion of other disorders, follow-up study, and family study). Despite the early recognition of premenstrual syndrome (PMS) in the 1950s, the research has encountered challenges due to two groups of proponents viewing it with psychologising bias and medicalising bias. PMDD is currently understood as the most severe form of PMS, characterised by the presence of psychological features. Recent evidence suggests that PMDD perhaps has neurodevelopmental underpinnings (attention deficit hyperactive disorder, adverse childhood experiences) affecting the fronto-limbic circuit that regulates the emotions. In addition, the affected individuals exhibit an increased sensitivity to gonadal hormonal fluctuations as observed during premenstrual, pregnancy, and perimenopausal phases of life. The prevalence is comparable between high-income countries and low- and middle-income countries (LAMIC), refuting the notion that it mostly affects modern women. Instead, a greater prevalence is observed in LAMIC. Despite the fact that educated women possess knowledge regarding the importance of getting help, there is a prevalent issue of inadequate help-seeking behaviour. This can be attributed to the perception of seeking help as an isolating experience, which is influenced by profound internalised stigma and discrimination in the workplace. Future studies must aim to develop culturally validated assessment tools and more research to understand the life course of the illness, in addition to systematically examining for more biological validators (animal models, genetics, imaging, neurotransmitters).

Probable targets and mechanism of ginsenoside Rg1 for non-alcoholic fatty liver disease: a study integrating network pharmacology, molecular docking, and molecular dynamics simulation

Ginsenoside Rg1 (GRg1), a key bioactive component of medicinal herbs, has shown beneficial effects on non-alcoholic fatty liver disease (NAFLD) and numerous other conditions. Nevertheless, the specific targets that are actively involved and the potential mechanisms underlying NAFLD treatment remain unclear. This study aimed to elucidate the therapeutic effects and mechanism of GRg1 in alleviating NAFLD using a combined approach of network pharmacology and molecular biology validation. The analysis yielded 294 targets for GRg1 and 1293 associated with NAFLD, resulting in 89 overlapping targets. Through protein-protein interactions (PPI) network topology analysis, 10 key targets were identified. Upon evaluating the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) analysis, GRg1 may exert therapeutic effects on NAFLD by negatively regulating the apoptotic process, insulin and endocrine resistance, the AGE-RAGE signaling pathway in diabetic complications, and the Estrogen, PI3K/Akt, and MAPK pathways. The three differential gene targets for Akt1, EGFR, and IGF1 were identified through the compound-target network in conjunction with the aforementioned methods. The molecular docking and molecular dynamics (MD) simulations showed that AKT1 and EGFR had a strong binding affinity with GRg1. Overall, our findings point to a novel therapeutic strategy involving NAFLD, with further in vivo and in vitro studies promising to deepen our comprehension and validate its potential advantages. Communicated by Ramaswamy H. Sarma

LDA-VGHB: identifying potential lncRNA-disease associations with singular value decomposition, variational graph auto-encoder and heterogeneous Newton boosting machine.

Long noncoding RNAs (lncRNAs) participate in various biological processes and have close linkages with diseases. In vivo and in vitro experiments have validated many associations between lncRNAs and diseases. However, biological experiments are time-consuming and expensive. Here, we introduce LDA-VGHB, an lncRNA-disease association (LDA) identification framework, by incorporating feature extraction based on singular value decomposition and variational graph autoencoder and LDA classification based on heterogeneous Newton boosting machine. LDA-VGHB was compared with four classical LDA prediction methods (i.e. SDLDA, LDNFSGB, IPCARF and LDASR) and four popular boosting models (XGBoost, AdaBoost, CatBoost and LightGBM) under 5-fold cross-validations on lncRNAs, diseases, lncRNA-disease pairs and independent lncRNAs and independent diseases, respectively. It greatly outperformed the other methods with its prominent performance under four different cross-validations on the lncRNADisease and MNDR databases. We further investigated potential lncRNAs for lung cancer, breast cancer, colorectal cancer and kidney neoplasms and inferred the top 20 lncRNAs associated with them among all their unobserved lncRNAs. The results showed that most of the predicted top 20 lncRNAs have been verified by biomedical experiments provided by the Lnc2Cancer 3.0, lncRNADisease v2.0 and RNADisease databases as well as publications. We found that HAR1A, KCNQ1DN, ZFAT-AS1 and HAR1B could associate with lung cancer, breast cancer, colorectal cancer and kidney neoplasms, respectively. The results need further biological experimental validation. We foresee that LDA-VGHB was capable of identifying possible lncRNAs for complex diseases. LDA-VGHB is publicly available at https://github.com/plhhnu/LDA-VGHB.

Metacaspase (Pf MCA-1) as antimalarial drug target: An in silico approach and their biological validation

AimsAcquired drug resistance of Plasmodium is a global issue for the treatment of malaria. There are various proteases in the genome of Plasmodium falciparum (P. falciparum) including metacaspase-1 (PfMCA-1) that are essential and are being considered as an attractive drug target. It is aimed to identify novel therapeutics against malaria and their action on PfMCA-1 along with other apoptotic pathway events. Main methodsHigh throughput virtual screening of 55,000 compounds derived from Maybridge library was performed against PfMCA-1. Based on the docking score, sixteen compounds were selected for in vitro antimalarial screening against drug sensitive and resistant strains of P. falciparum using SYBR green-based assay. Subsequently, three lead molecules were selected and subjected to the evaluation of cytotoxicity, caspase like protease activity, mitochondrial membrane potential, ROS generation and DNA fragmentation via TUNEL assay. Key findingsThe in silico and in vitro approaches have brought forward some Maybridge library compounds with antiplasmodial activity most likely by enhancing the metacaspase activity. The compound CD11095 has shown better antimalarial efficacy, and KM06591 depicted higher caspase mediated killing, elevated TUNEL positive cells and moderate ROS generation. Mitochondrial membrane depolarization was augmented by RJC0069. Exposure of P. falciparum to CD11095, KM06591 and RJC0069 has ended up in parasite growth arrest via multiple mechanisms. SignificanceIt is proposed that the Maybridge molecules CD11095, KM06591 and RJC0069 have antimalarial activity. Their mechanism of action was found to be by enhancing the metacaspases-like protease activity, mitochondrial depolarization and DNA fragmentation which stipulates significant insights towards promising candidates for drug development.

Metalloproteinases in Restorative Dentistry: An In Silico Study toward an Ideal Animal Model.

In dentistry, various animal models are used to evaluate adhesive systems, dental caries and periodontal diseases. Metalloproteinases (MMPs) are enzymes that degrade collagen in the dentin matrix and are categorized in over 20 different classes. Collagenases and gelatinases are intrinsic constituents of the human dentin organic matrix fibrillar network and are the most abundant MMPs in this tissue. Understanding such enzymes' action on dentin is important in the development of approaches that could reduce dentin degradation and provide restorative procedures with extended longevity. This in silico study is based on dentistry's most used animal models and intends to search for the most suitable, evolutionarily close to Homo sapiens. We were able to retrieve 176,077 mammalian MMP sequences from the UniProt database. These sequences were manually curated through a three-step process. After such, the remaining 3178 sequences were aligned in a multifasta file and phylogenetically reconstructed using the maximum likelihood method. Our study inferred that the animal models most evolutionarily related to Homo sapiens were Orcytolagus cuniculus (MMP-1 and MMP-8), Canis lupus (MMP-13), Rattus norvegicus (MMP-2) and Orcytolagus cuniculus (MMP-9). Further research will be needed for the biological validation of our findings.

NIMG-17. TOWARDS IMAGE - BASED PERSONALIZATION OF GLIOBLASTOMA THERAPY: A COMPREHENSIVE VALIDATION STUDY OF A NOVEL, DEEP LEARNING - DRIVEN TUMOR GROWTH MODEL FOR INDIVIDUALIZED RADIOTHERAPY

Abstract BACKGROUND The diffuse growth pattern of glioblastoma is one of the main challenges for accurate treatment planning. Computational tumor growth modeling has emerged as a promising tool to guide personalized therapy by estimating the tumor cell density in the whole brain. Here, we performed biological and clinical validation of a novel growth model, aiming to close the gap between the experimental state and clinical implementation. METHODS 124 patients from The Cancer Genome Archive (TCGA) and 397 patients from the UCSF Glioma Dataset were assessed for significant correlations between clinical data, genetic pathway activation maps (generated with PARADIGM; TCGA only), and infiltration (Dw) as well as proliferation (ρ) parameters stemming from a Fisher-Kolmogorov growth model that is adjusted to the patient’s anatomy using deep learning. To further evaluate clinical potential, we performed the same growth modeling on preoperative MRI data from 30 patients of our institution and compared model-derived tumor volumes (that serve as alternative clinical target volumes) and recurrence coverage with standard radiotherapy plans. RESULTS The parameter ratio Dw/ρ (p &amp;lt; 0.05 in TCGA) as well as the simulated tumor volume (0.5 tumor cell density) (p &amp;lt; 0.05 in TCGA/UCSF) were significantly inversely correlated with overall survival. We found a significant correlation between 11 proliferation pathways and the estimated proliferation parameter ρ, proving its biological plausibility. Depending on the cutoff value for tumor cell density, we observed significant improvement of recurrence coverage without significantly increased total radiation volume utilizing model-derived target volumes instead of standard radiation plans. More precisely, a cutoff value of 0.5 tumor cell density yielded the best results here. CONCLUSIONS Identifying a significant correlation between computed growth parameters, and clinical and biological data, we highlight the potential of tumor growth modeling for individualized therapy of glioblastoma. This might improve accuracy of radiation planning in the near future.

Identification of informative genes and sub-pathways using Improved Differential Expression Analysis for Pathways (iDEAP) for cancer classification

Background: Pathway-based analysis primarily focuses on sub-pathway-based analysis, which aids in understanding biological reactions. Several studies have found abnormalities in pathways caused by certain regions based on the etiology of diseases. The Differential Expression Analysis for Pathways (DEAP) method is one such sub-pathway-based analysis method, that identifies a local region perturbed by complex diseases based on larger pathway data. However, the method has low performance in identifying informative pathways and sub-pathways. Methods: In this paper we propose an improved DEAP (iDEAP) method for enhanced identification of perturbed sub-pathways that achieves higher performance in the detection of differentially expressed pathways. Firstly, a search algorithm adapted from the Detect Module from Seed Protein (DMSP) algorithm was implemented as part of the DEAP method to search for informative sub-pathways. Secondly, the relation among sub-pathways was taken into account by averaging the maximum absolute value for the DEAP score for the reaction among sub-pathways to support the efficient identification of informative pathways. Three gene expression data sets were applied to this research. Results: The proposed improved method shows a better performance over the previous methods. In fact, when the identified genes from the results were assessed using 10-fold cross validation to classify cancer based on accuracy, the improved method shows higher accuracy for colorectal cancer (90%) and breast cancer (94%). Conclusions: This shows that the proposed method effectively identifies informative genes related to the targeted phenotype. A biological validation was also conducted on the top five significant pathways and selected genes based on biological literature. The results from this analysis will be useful especially in the medical field for disease detection. In 10 years and beyond, computational biology will become ever more entwined with biomedical research and medicine.

Inhibition of chloride intracellular channel protein 1 (CLIC1) ameliorates liver fibrosis phenotype by activating the Ca2+-dependent Nrf2 pathway

Persistent damage to liver cells leads to liver fibrosis, which is characterized by the accumulation of scar tissue in the liver, ultimately leading to cirrhosis and serious complications. Because it is difficult to reverse cirrhosis once it has progressed, the primary focus has been on preventing the progression of liver fibrosis. However, studies on therapeutic agents for liver fibrosis are still lacking. Here, we investigated that the natural dipeptide cyclic histidine-proline (CHP, also known as diketopiperazine) shows promising potential as a therapeutic agent in models of liver injury by inhibiting the progression of fibrosis through activation of the Nrf2 pathway. To elucidate the underlying biological mechanism of CHP, we used the Cellular Thermal Shift Assay (CETSA)-LC–MS/MS, a label-free compound-based target identification platform. Chloride intracellular channel protein 1 (CLIC1) was identified as a target whose thermal stability is increased by CHP treatment. We analyzed the direct interaction of CHP with CLIC1 which revealed a potential interaction between CHP and the E228 residue of CLIC1. Biological validation experiments showed that knockdown of CLIC1 mimicked the antioxidant effect of CHP. Further investigation using a mouse model of CCl4-induced liver fibrosis in wild-type and CLIC1 KO mice revealed the critical involvement of CLIC1 in mediating the effects of CHP. Taken together, our results provide evidence that CHP exerts its anti-fibrotic effects through specific binding to CLIC1. These insights into the mechanism of action of CHP may pave the way for the development of novel therapeutic strategies for fibrosis-related diseases.

Circulating miR-133a-3p defines a low-risk subphenotype in patients with heart failure and central sleep apnea: a decision tree machine learning approach

BackgroundPatients with heart failure with reduced ejection fraction (HFrEF) and central sleep apnea (CSA) are at a very high risk of fatal outcomes.ObjectiveTo test whether the circulating miRNome provides additional information for risk stratification on top of clinical predictors in patients with HFrEF and CSA.MethodsThe study included patients with HFrEF and CSA from the SERVE-HF trial. A three-step protocol was applied: microRNA (miRNA) screening (n = 20), technical validation (n = 60), and biological validation (n = 587). The primary outcome was either death from any cause, lifesaving cardiovascular intervention, or unplanned hospitalization for worsening of heart failure, whatever occurred first. MiRNA quantification was performed in plasma samples using miRNA sequencing and RT-qPCR.ResultsCirculating miR-133a-3p levels were inversely associated with the primary study outcome. Nonetheless, miR-133a-3p did not improve a previously established clinical prognostic model in terms of discrimination or reclassification. A customized regression tree model constructed using the Classification and Regression Tree (CART) algorithm identified eight patient subphenotypes with specific risk patterns based on clinical and molecular characteristics. MiR-133a-3p entered the regression tree defining the group at the lowest risk; patients with log(NT-proBNP) ≤ 6 pg/mL (miR-133a-3p levels above 1.5 arbitrary units). The overall predictive capacity of suffering the event was highly stable over the follow-up (from 0.735 to 0.767).ConclusionsThe combination of clinical information, circulating miRNAs, and decision tree learning allows the identification of specific risk subphenotypes in patients with HFrEF and CSA.