Bioinformatics Modeling Research Articles

Should I stay, or should I go? Gene retention in organellar genomes.

Mitochondria and plastids retain their own small but essential genomes. However, the evolutionary pressures that determine whether a gene is retained in organellar DNA or exported to the "host" nuclear genome remain unclear. A new study in Cell Systems addresses this knowledge gap using bioinformatic data and modeling to identify universal "rules" that determine organellar gene retention.

Multiscale Modeling of Protein-RNA Condensation in and Out of Equilibrium.

A vast number of intracellular membraneless bodies also known as biomolecular condensates form through a liquid-liquid phase separation (LLPS) of biomolecules. To date, phase separation has been identified as the main driving force for a membraneless organelles such as nucleoli, Cajal bodies, stress granules, and chromatin compartments. Recently, the protein-RNA condensation is receiving increased attention, because it is closely related to the biological function of cells such as transcription, translation, and RNA metabolism. Despite the multidisciplinary efforts put forth to study the biophysical properties of protein-RNA condensates, there are many fundamental unanswered questions regarding the mechanism of formation and regulation of protein-RNA condensates in eukaryotic cells. Major challenges in studying protein-RNA condensation stem from (i) the molecular heterogeneity and conformational flexibility of RNA and protein chains and (ii) the nonequilibrium nature of transcription and cellular environment. Computer simulations, bioinformatics, and mathematical models are uniquely positioned for shedding light on the microscopic nature of protein-RNA phase separation. To this end, there is an urgent need for innovative models with the right spatiotemporal resolution for confronting the experimental observables in a comprehensive and physics-based manner. In this chapter, we will summarize the currently emerging research efforts, which employ atomistic and coarse-grained molecular models and field theoretical models to understand equilibrium and nonequilibrium aspects of protein-RNA condensation.

Biomarkers discovery for endometrial cancer: A graph convolutional sample network method

BackgroundEndometrial carcinoma is the sixth most common cancer in women worldwide. Importantly, endometrial cancer is among the few types of cancers with patient mortality that is still increasing, which indicates that the improvement in its diagnosis and treatment is still urgent. Moreover, biomarker discovery is essential for precise classification and prognostic prediction of endometrial cancer. MethodsA novel graph convolutional sample network method was used to identify and validate biomarkers for the classification of endometrial cancer. The sample networks were first constructed for each sample, and the gene pairs with high frequencies were identified to construct a subtype-specific network. Putative biomarkers were then screened using the highest degrees in the subtype-specific network. Finally, simplified sample networks are constructed using the biomarkers for the graph convolutional network (GCN) training and prediction. ResultsPutative biomarkers (23) were identified using the novel bioinformatics model. These biomarkers were then rationalised with functional analyses and were found to be correlated to disease survival with network entropy characterisation. These biomarkers will be helpful in future investigations of the molecular mechanisms and therapeutic targets of endometrial cancers. ConclusionsA novel bioinformatics model combining sample network construction with GCN modelling is proposed and validated for biomarker discovery in endometrial cancer. The model can be generalized and applied to biomarker discovery in other complex diseases.

T cells dominate peripheral inflammation in a cross-sectional analysis of obesity-associated diabetes.

Myeloid cells dominate metabolic disease-associated inflammation (metaflammation) in mouse obesity, but the contributions of myeloid cells to the peripheral inflammation that fuels sequelae of human obesity are untested. This study used unbiased approaches to rank contributions of myeloid and T cells to peripheral inflammation in people with obesity across the spectrum of metabolic health. Peripheral blood mononuclear cells (PBMCs) from people with obesity with or without prediabetes or type 2 diabetes were stimulated with T cell-targeting CD3/CD28 or myeloid-targeting lipopolysaccharide for 20 to 72 hours to assess cytokine production using Bio-Plex. Bioinformatic modeling ranked cytokines with respect to their predictive power for metabolic health. Intracellular tumor necrosis factor α was quantitated as a classical indicator of metaflammation. Cytokines increased over 72 hours following T cell-, but not myeloid-, targeted stimulation to indicate that acute myeloid inflammation may shift to T cell inflammation over time. T cells contributed more tumor necrosis factor α to peripheral inflammation regardless of metabolic status. Bioinformatic combination of cytokines from all cohorts, stimuli, and time points indicated that T cell-targeted stimulation was most important for differentiating inflammation in diabetes, consistent with previous identification of a mixed T helper type 1/T helper type 17 cytokine profile in diabetes. T cells dominate peripheral inflammation in obesity; therefore, targeting T cells may be an effective approach for prevention/management of metaflammation.

Identification of key candidate genes for IgA nephropathy using machine learning and statistics based bioinformatics models

Immunoglobulin-A-nephropathy (IgAN) is a kidney disease caused by the accumulation of IgAN deposits in the kidneys, which causes inflammation and damage to the kidney tissues. Various bioinformatics analysis-based approaches are widely used to predict novel candidate genes and pathways associated with IgAN. However, there is still some scope to clearly explore the molecular mechanisms and causes of IgAN development and progression. Therefore, the present study aimed to identify key candidate genes for IgAN using machine learning (ML) and statistics-based bioinformatics models. First, differentially expressed genes (DEGs) were identified using limma, and then enrichment analysis was performed on DEGs using DAVID. Protein-protein interaction (PPI) was constructed using STRING and Cytoscape was used to determine hub genes based on connectivity and hub modules based on MCODE scores and their associated genes from DEGs. Furthermore, ML-based algorithms, namely support vector machine (SVM), least absolute shrinkage and selection operator (LASSO), and partial least square discriminant analysis (PLS-DA) were applied to identify the discriminative genes of IgAN from DEGs. Finally, the key candidate genes (FOS, JUN, EGR1, FOSB, and DUSP1) were identified as overlapping genes among the selected hub genes, hub module genes, and discriminative genes from SVM, LASSO, and PLS-DA, respectively which can be used for the diagnosis and treatment of IgAN.

Using Advanced Bioinformatics Tools to Identify Novel Therapeutic Candidates for Age-Related Macular Degeneration.

PurposeAge-related macular degeneration (AMD) is the most common cause of aging-related blindness in the developing world. Although medications can slow progressive wet AMD, currently, no drugs to treat dry-AMD are available. We use a systems or in silico biology analysis to identify chemicals and drugs approved by the Food and Drug Administration for other indications that can be used to treat and prevent AMD.MethodsWe queried National Center for Biotechnology Information to identify genes associated with AMD, wet AMD, dry AMD, intermediate AMD, and geographic atrophy to date. We combined genes from various AMD subtypes to reflect distinct stages of disease. Enrichment analysis using the ToppGene platform predicted molecules that can influence AMD genes. Compounds without clinical indications or with deleterious effects were manually filtered.ResultsWe identified several drug/chemical classes that can affect multiple genes involved in AMD. The drugs predicted from this analysis include antidiabetics, lipid-lowering agents, and antioxidants, which could theoretically be repurposed for AMD. Metformin was identified as the drug with the strongest association with wet AMD genes and is among the top candidates in all dry AMD subtypes. Curcumin, statins, and antioxidants are also among the top drugs correlating with AMD-risk genes.ConclusionsWe use a systematic computational process to discover potential therapeutic targets for AMD. Our systematic and unbiased approach can be used to guide targeted preclinical/clinical studies for AMD and other ocular diseases.Translational RelevanceAdvanced bioinformatics models identify novel chemicals and approved drug candidates that can be efficacious for different subtypes of AMD.

Molecular Mimicry of the Rheumatoid Arthritis-Related Immunodominant T-Cell Epitope within Type II Collagen (CII260-270) by the Bacterial L-Asparaginase

The etiology of most autoimmune diseases, including rheumatoid arthritis (RA), remains unclear. Both genetic and environmental factors are believed to be involved in pathogenesis. Molecular mimicry is considered one of the mechanisms for the occurrence of autoimmune diseases. The aim of the study was to determine whether the bacterial peptide L-ASNase67-81, which mimics the immunodominant T-cell epitope CII259-273, can induce T-cell reactivity in blood samples from RA patients and healthy subjects through molecular mimicry. Using bioinformatic molecular modeling methods, we first determined whether the L-ASNase67-81 peptide binds to the HLA-DRB1*04:01 molecule and whether the formed MHCII–peptide complex interacts with the corresponding T-cell receptor. To validate the obtained results, leukocytes isolated from early RA patients and healthy individuals were stimulated in vitro with L-ASNase67-81 and CII259-273 peptides as well as with bacterial L-asparaginase or human type II collagen (huCII). The activated T cells (CD4+CD154+) were analyzed by flow cytometry (FACS), and the levels of cytokines produced (IL-2, IL-17A/F, and IFN-γ) were measured by ELISA. Our in silico analyses showed that the bacterial peptide L-ASNase67-81 binds better to HLA-DRB1*04:01 compared to the immunodominant T-cell epitope CII259-273, mimicking its structure and localization in the binding groove of MHCII. Six contact points were involved in the molecular interaction of the peptide with the TCR. FACS data showed that after in vitro stimulation with the L-ASNase67-81 peptide, the percentage of activated T cells (CD154+CD4+) was significantly increased in both cell cultures isolated from ERA patients and those isolated from healthy individuals, as higher values were observed for the ERA group (9.92 ± 0.23 vs. 4.82 ± 0.22). Furthermore, the ELISA assays revealed that after stimulation with L-ASNase67-81, a significant increase in the production of the cytokines IL-2, IL-17A/F, and IFN-γ was detected in the group of ERA patients. Our data showed that the bacterial L-ASNase67-81 peptide can mimic the immunodominant T-cell epitope CII259-273 and activate HLA-DRB1*04:01-restricted T cells as well as induce cytokine production in cells isolated from ERA patients. These results are the first to demonstrate that a specific bacterial antigen could play a role in the pathogenesis of RA, mimicking the immunodominant T-cell epitope from type II collagen.

Evaluation of recombinant toxin JFTX-23, an oral-effective anti-insect peptide from the spider Selenocosmia jiafu venom gland proteome

Excessive utilization of chemical pesticides for pest control can lead to adverse consequences for the health of humans and other organisms and may also cause irreversible ecological changes; therefore, the use of biologically derived insecticides can be a safe alternate strategy. Transcriptomic studies have shown JFTX- 23,a small peptide from the spider Selenocosmia jiafuis highly similar to U1-TRTX-Sp1, a well-characterized oral-effective insecticide toxin from the Australian tarantula Selenotypus plumipes. First, we evaluated the JFTX-23 peptide sequence using bioinformatics tools and modeling studies. Preliminary results showed a high similarity of JFTX-23 to JZTX-58 (91.67%) and U1-TRTX-Sp1 (86.11%). Superimposition of the α-carbons of the modeled JFTX-23 and U1-TRTX-Sp1 demonstrated a very high similarity of the 3-D structure of the two peptides (RMSD of 0.02 Å).The injection assay of JFTX-23 in Helicoverpa armigera indicated an LD50 of 0.077 and 0.423 nmol/insect after 24 and 120 h, respectively. JFTX-23 was toxic to H. armigera via oral administration with an LC50 of 1.16 nmol/g food after 5 days, which was comparable to the toxicity of the oral-effective toxin U1-TRTX-Sp1. Our studies have shown that JFTX-23 is a potent oral-effective toxin that can be considered an attractive candidate for the biological control of insect pests.

Whole cell response to receptor stimulation involves many deep and distributed subcellular biochemical processes

Neurite outgrowth is an integrated whole cell response triggered by the cannabinoid-1 receptor. We sought to identify the many different biochemical pathways that contribute to this whole cell response. To understand underlying mechanisms, we identified subcellular processes (SCPs) composed of one or more biochemical pathways and their interactions required for this response. Differentially expressed genes and proteins were obtained from bulk transcriptomics and proteomic analysis of extracts from cells stimulated with a cannabinoid-1 receptor agonist. We used these differentially expressed genes and proteins to build networks of interacting SCPs by combining the expression data with prior pathway knowledge. From these SCP networks, we identified additional genes that when ablated, experimentally validated the SCP involvement in neurite outgrowth. Our experiments and informatics modeling allowed us to identify diverse SCPs such as those involved in pyrimidine metabolism, lipid biosynthesis, and mRNA splicing and stability, along with more predictable SCPs such as membrane vesicle transport and microtubule dynamics. We find that SCPs required for neurite outgrowth are widely distributed among many biochemical pathways required for constitutive cellular functions, several of which are termed ‘deep’, since they are distal to signaling pathways and the key SCPs directly involved in extension of the neurite. In contrast, ‘proximal’ SCPs are involved in microtubule growth and membrane vesicle transport dynamics required for neurite outgrowth. From these bioinformatics and dynamical models based on experimental data, we conclude that receptor-mediated regulation of subcellular functions for neurite outgrowth is both distributed, that is, involves many different biochemical pathways, and deep.

GC6mA-Pred: A deep learning approach to identify DNA N6-methyladenine sites in the rice genome.

DNA N6-methyladenine (6mA) is a pivotal DNA modification for various biological processes. More accurate prediction of 6mA methylation sites plays an irreplaceable part in grasping the internal rationale of related biological activities. However, the existing prediction methods only extract information from a single dimension, which has some limitations. Therefore, it is very necessary to obtain the information of 6mA sites from different dimensions, so as to establish a reliable prediction method. In this study, a neural network based bioinformatics model named GC6mA-Pred is proposed to predict N6-methyladenine modifications in DNA sequences. GC6mA-Pred extracts significant information from both sequence level and graph level. In the sequence level, GC6mA-Pred uses a three-layer convolution neural network (CNN) model to represent the sequence. In the graph level, GC6mA-Pred employs graph neural network (GNN) method to integrate various information contained in the chemical molecular formula corresponding to DNA sequence. In our newly built dataset, GC6mA-Pred shows better performance than other existing models. The results of comparative experiments have illustrated that GC6mA-Pred is capable of producing a marked effect in accurately identifying DNA 6mA modifications.

Antimicrobial and Bioinformatic Modelling Studies of Isatin Mixed Ligand and Some Ternary Chelates

AbstractSynthesis of some ternary complexes with isatin (ISA) as primary ligand and N,N,N,N‐tetramethyl ethylene diamine (TMEDA) as secondary ligand with a series of metal ions (M) Cr(III), Mn(II), Fe(III), Co(II), Ni(II), Cu(II), Zn(II) and Cd(II) is presented. IR, elemental analysis, melting point, molar conductance, mass and thermal analysis were applied to characterize the prepared compounds. Conductometry measurements reveal that all complexes are non‐electrolytes except Cr(III), Mn(II), Fe(III) and Co(II) complexes. In the light of magnetic and solid reflectance techniques, all prepared complexes were suggested in an octahedral geometry. ESR study of Cu(II) complex prove its octahedral geometry. The mixed ligand complexes were found to have the formulae [M(ISA)(TMEDA)Cl2] where M=Ni(II), Cu(II) and Zn(II); [M(ISA)(TMEDA)Cl2]Cln.xH2O in case of M=Cr(III), Fe(III) and Cd(II) (where n=l, x=0 for Cr(III), n=l, x=0 for Fe(III) and n=0, x=2 for Cd(II) and [M(ISA)(TMEDA)Cl(H2O)]Cl in case of M=Mn(II) and Co(II). Antimicrobial activity of some complexes are higher than parent ligands. Molecular docking study confirmed the importance of Cu(II), Ni(II) and Cr(III) complexes as they have the highest interaction with the receptors of crystal structure of S.aureus nucleoside (PDB ID: 3Q8U), E. coli (PDB: 3T88) and protein phosphatase (PPZl) of C. albicans (PDB: 5JPE) with minimum binding energy of −2l.5, −7.2 and −45.6 kcal/mol, respectively.

Two Novel Mutations in Ectodysplasin-A Identified in Syndromic Tooth Agenesis.

To discover novel ectodysplasin-A (EDA) and wingless-type MMTV integration site family, member 10A (WNT10A) mutations in tooth agenesis (TA) patients. Case series. Guanghua School of Stomatology, Guangzhou, China, from March 2018 to August 2020. EDA and WNT10A were analysed in eleven TA families by PCR and Sanger sequencing. Bioinformatics and structure modelling analyses were performed after identifying different variants, to predict the resulting conformational alterations in WNT10A and EDA. Two novel mutations (c.796C>A (p.L266I), c.769G>A (p.G257R)) in EDA and two reported mutations (c.637G>A (p.G213S), c.511C>T (p.R171C))in WNT 10A were detected. Combined with the 3D structural analysis, we discovered a correlation between alterations in hydrogen bond formation and the observed phenotypes, potentially affecting protein binding. The mutations were predicted to be pathogenic through bioinformatics analyses. In addition, by identifying novel mutations, our knowledge regarding the TA spectrum and tooth development was considerably expanded. Anodontia, EDA, WNT 10A, Whole exome sequencing, Odontogenesis.

T cells dominate peripheral inflammation in obesity-associated diabetes

Abstract Many studies have examined obese/insulin-resistant mice to conclude that macrophages dominate pathogenic inflammation in metabolic disease, but few studies have ranked chronic sources of peripheral inflammation in obese people across a natural spectrum of metabolic health. We compared the relative contributions of myeloid and T cells to peripheral inflammation in samples from metabolically healthy and unhealthy obese people to address disappointing outcomes from clinical trials of anti-inflammatories in obesity-associated type 2 diabetes (T2D). We stimulated PBMCs from obese subjects with euglycemia, prediabetes, or T2D with T cell-targeting CD3/CD28 or myeloid-targeting LPS, and measured supernatant cytokines over time. Cytokines increased with time following CD3/CD28, but LPS responses were time-independent. A series of surface marker and intracellular staining assays taking into account these temporal differences in cytokine production unexpectedly showed that T cells contribute more TNFα to peripheral inflammation than do myeloid cells from all subject groups. Bioinformatic modeling combining cytokine outcomes from all subjects, stimuli, and time points, indicated IFNγ generated by CD3/CD28 stimulation was most important for differentiating peripheral inflammation in T2D compared to non-T2D samples. This result was consistent with the previously identified Th1/Th17 profile in T2D. We conclude that human T cells dominate peripheral inflammation in obesity-associated T2D, and thus targeting T cells may be an effective approach for prevention/management of obesity-associated metabolic diseases. Supported by NIH Training Grants T32 DK007778 and TL1 UL1TR001998, R01DK108056, the Shared Resource Facility of the University of Kentucky Markey Cancer Center P30 CA177558, University of Kentucky College of Medicine, Barnstable Brown Diabetes and Obesity Center, The Center for Clinical and Translational Research (UL1TR000117), and NIH National Center for Advancing Translational Sciences UL1TR001998.

Гибридная стратегия биоинформатического моделирования (in silico) для изучения биологически активных пептидов молочного белка

Bioinformatic analysis methods are an auxiliary tool in the preliminary stage of research into biocatalytic conversion of proteins with predicted release of biologically active peptides. However, there are a number of factors ignored in current strategies for designing biologically active peptides, which prevents the complete prediction of their biological properties. This determines the relevance of the research objective, i.e. developing a hybrid strategy for bioinformatic modeling to study biologically active peptides of milk protein. The new strategy ranks key criteria based on high-performance algorithms of proteomic database.
 The research featured the scientific publications on in silico methods applied to biologically active peptides. Modern taxonometric methods of information retrieval were applied using the RSCI, Scopus and Web of Science databases.
 The article introduces and describes step by step the optimal in silico hybrid strategy algorithm for studying biologically active milk protein peptides. The algorithm takes into account the safety assessment of all hydrolysis products, their physicochemical and technological properties. The strategy algorithm relies on analytical data on the protein profile, the amino acid sequence of proteins that make up the raw material, taking into account their polymorphism, and the subsequent identification of bioactive amino acid sites in the protein structure. The algorithm selects optimal enzyme preparations, as well as models the hydrolysis and assesses the peptide bioactivity using proteomic databases. 
 At the preliminary stage of protein hydrolysis, the new in silico strategy scientifically predicts the targeted release of stable peptide complexes of biologically active peptides with proven bioactivity, safety and sensory characteristics. The hybrid algorithm contributes to accumulation of the necessary primary data so as to reduce the time and cost of laboratory experiments.

SE-BLTCNN: A channel attention adapted deep learning model based on PSSM for membrane protein classification

Membrane protein classification is a key to inferring the function of uncharacterized membrane protein. To get around the time-consuming and expensive biochemical experiments in the wet lab, there has been a lot of research focusing on developing fast and reliable bioinformatics or computer modeling methods for membrane protein prediction. However, most research is inclined to incorporate as many types of protein data as possible, yet in many cases, the number of accessible protein data types is quite limited. To solve this challenge, a channel attention adapted deep learning model that takes the position-specific scoring matrix (PSSM) as only input and its simplified version without channel attention have been developed. They are named SE-BLTCNN and BLTCNN, respectively (the abbreviations for ”SE embedded BiLSTM-TextCNN” and ”plain BiLSTM-TextCNN”). The basic ideais to embed the Squeeze-and-Excitation (SE) block into the architecture of the text convolutional neural network (textCNN) and combine the resulting architecture with the bidirectional long short-term memory (BiLSTM) layer. An ablation experiment is also conducted to verify the effectiveness of using BiLSTM to extract high-level features from PSSM. On the benchmark sample set, the BLTCNN can achieve average precision as high as 96.2% and turns out to be state-of-the-art membrane protein type predictors based solely on PSSM data to the best of our knowledge; the SE-BLTCNN is second-best among all comparison methods, at a bit lower average precision of 95.7%. In addition, through empirical research, the excessive zero-padding in the training examples has been pinpointed as the major cause of performance loss of the SE-LTCNN; and by using the adjusted sample set, it has been confirmed that the SE-BLTCNN model outperforms the BLTCNN once this major cause is suppressed. The core code and dataset are available at https://github.com/Raymond-2017/membrane-protein-classifiction.

Type 1 T Helper Cell-Based Molecular Subtypes and Signature Are Associated with Clinical Outcome in Pancreatic Ductal Adenocarcinoma.

Lymph node metastasis in pancreatic ductal adenocarcinoma (PDAC) is shown to be related with poor prognosis. To construct an immune-related gene prognostic risk model for PDAC and clarify the molecular and immune characteristics and the benefit of immune checkpoint inhibitor (ICI) therapy in prognostic risk model-defined subgroups of PDAC, we analyze the association between the density of immune cell infiltration and lymph node metastatic status and further study the potential role of immune cells, immune cell–related genes, and immunotherapy outcomes in PDAC patients using bioinformatics models and machine learning methods. Based on The Cancer Genome Atlas (TCGA), PACA-AU and PACA-CA data sets, 62 immune-related hub genes were identified by weighted gene co-expression network analysis (WGCNA). Four genes were selected to construct a molecular subtype identification based on the type 1 T helper cell–related hub genes by using the Cox regression method. We found that lower type 1 T helper cell abundance was correlated with prolonged survival in PDAC patients. Further, prognostic risk score model constructed with the type 1 T helper cell-related signature showed high accuracy in predicting overall survival and response to immunotherapy. This study might improve the understanding of the role of type 1 T helper cells in PDAC patients and aid in the development of immunotherapy and personalized treatments for these patients.

Inhibition of ITGB1-DT expression delays the growth and migration of stomach adenocarcinoma and improves the prognosis of cancer patients using the bioinformatics and cell model analysis.

The long non-coding RNA, integrin subunit beta 1 (ITGB1) divergent transcript (ITGB1-DT), is known to be involved in cancer progression and associated with the poor prognosis of cancer patients. At present, the role of ITGB1-DT in stomach adenocarcinoma (STAD) has not been reported. The expression level of ITGB1-DT was detected in normal gastric and STAD tissues from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. A receiver operating characteristic (ROC) analysis was used to evaluate the role of ITGB1-DT in diagnosing STAD. The relationship between ITGB1-DT overexpression and clinicopathological features, prognosis, and immune-infiltrated cells in STAD were explored using correlation, survival, and Cox regression analyses. A cell model of ITGB1-DT interference was constructed to explore the roles of ITGB1-DT on STAD cell proliferation and migration, and the signaling mechanism was investigated using Gene Set Enrichment Analysis (GSEA). ITGB1-DT was expressed up-regulated in STAD tissues. ITGB1-DT overexpression was associated with the T stage, therapeutic effect, overall survival, progression-free interval status, and poor prognosis in STAD patients. ITGB1-DT overexpression was valuable in diagnosing STAD and a negative factor affecting the prognosis of STAD patients. Interference with ITGB1-DT expression inhibited STAD cell proliferation, invasion, and migration. GSEA results showed that ITGB1-DT may be involved in STAD progression through the insulin, p53, mechanistic target of rapamycin kinase (MTOR), and other signaling pathways. Overexpression of ITGB1-DT was significantly correlated with the levels of STAD B cells, T cells, T helper cells, CD8 T cells, cytotoxic cells, and other immune cells. ITGB1-DT was overexpressed and associated with poor prognosis in STAD. Interference with ITGB1-DT expression may delay the progression of STAD to improve the prognosis of STAD patients.

Iron Reduction in Dermacentor andersoni Tick Cells Inhibits Anaplasma marginale Replication.

Anaplasma spp. are obligate intracellular, tick-borne, bacterial pathogens that cause bovine and human anaplasmosis. We lack tools to prevent these diseases in part due to major knowledge gaps in our fundamental understanding of the tick–pathogen interface, including the requirement for and molecules involved in iron transport during tick colonization. We determine that iron is required for the pathogen Anaplasma marginale, which causes bovine anaplasmosis, to replicate in Dermacentor andersoni tick cells. Using bioinformatics and protein modeling, we identified three orthologs of the Gram-negative siderophore-independent iron uptake system, FbpABC. Am069, the A. marginale ortholog of FbpA, lacks predicted iron-binding residues according to the NCBI conserved domain database. However, according to protein modeling, the best structural orthologs of Am069 are iron transport proteins from Cyanobacteria and Campylobacter jejuni. We then determined that all three A. marginale genes are modestly differentially expressed in response to altered host cell iron levels, despite the lack of a Ferric uptake regulator or operon structure. This work is foundational for building a mechanistic understanding of iron uptake, which could lead to interventions to prevent bovine and human anaplasmosis.

Development of a Model System to Study Expression Profile of RAC2 Gene in Breast Cancer MDA-MB-231 Cell Line.

The RAC2 gene encoding GTPases involve cellular signaling of actin polymerization, cell migration, and formation of the phagocytic NADPH oxidase complex. Oncogenic mutations in the RAC2 gene have been identified in various cancers, and extensive research is in progress to delineate its signaling pathways and identify potential therapeutic targets in breast cancers. This paper explored developing a bioinformatics model system to understand the RAC2 gene expression pattern concerning estrogenic receptor status in breast cancers. We have used the MDA-MB-231 breast cancer cell line to identify RAC2 gene expression. To simplify the development of model system with one dataset, we retrieved the microarray dataset GSE27515 from the Gene Expression Omnibus (GEO) for the differential gene expression analysis. Then, network analysis, pathway enrichment analysis, volcano plot, ORA, and the up/downregulated genes were used to highlight genes involved in signaling network pathways. We observed that the RAC2 gene is upregulated in the GSM679722, GSM676923, and GSM679724 downregulated in the samples GSM676925, GSM676926, and GSM676927 from the GEO dataset. Our observation found that the RAC2 gene is upregulated in the estrogen receptor (ER) negative breast cancers and downregulated in ER-positive breast cancer, involving pathways such as focal adhesion, MAPK signaling, axon guidance, and VEGF signaling pathway.

Structural Consequence of Non-Synonymous Single-Nucleotide Variants in the N-Terminal Domain of LIS1.

Disruptive neuronal migration during early brain development causes severe brain malformation. Characterized by mislocalization of cortical neurons, this condition is a result of the loss of function of migration regulating genes. One known neuronal migration disorder is lissencephaly (LIS), which is caused by deletions or mutations of the LIS1 (PAFAH1B1) gene that has been implicated in regulating the microtubule motor protein cytoplasmic dynein. Although this class of diseases has recently received considerable attention, the roles of non-synonymous polymorphisms (nsSNPs) in LIS1 on lissencephaly progression remain elusive. Therefore, the present study employed combined bioinformatics and molecular modeling approach to identify potential damaging nsSNPs in the LIS1 gene and provide atomic insight into their roles in LIS1 loss of function. Using this approach, we identified three high-risk nsSNPs, including rs121434486 (F31S), rs587784254 (W55R), and rs757993270 (W55L) in the LIS1 gene, which are located on the N-terminal domain of LIS1. Molecular dynamics simulation highlighted that all variants decreased helical conformation, increased the intermonomeric distance, and thus disrupted intermonomeric contacts in the LIS1 dimer. Furthermore, the presence of variants also caused a loss of positive electrostatic potential and reduced dimer binding potential. Since self-dimerization is an essential aspect of LIS1 to recruit interacting partners, thus these variants are associated with the loss of LIS1 functions. As a corollary, these findings may further provide critical insights on the roles of LIS1 variants in brain malformation.