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PubMed2004

[Necessity and usefulness of bioinformatic methods for microarray data analysis].

Data emerging from DNA microarray experiments are usually difficult to interpret. While the level of expression of several thousand genes can be measured in a single experiment, only a few dozen experiments are normally carried out, leading to data sets of very high dimensionality and low cardinality. The computational analysis of gene expression data makes significant usage of machine learning and statistical methods. Nevertheless, caution should be used in the blind adoption of these methods, as this usually leads to an over-interpretation of the expression profiles. The following presentation provides an overview of up-to-date principles of biostatistical analysis. A potential application for the analysis of high-dimensional expression profiles of prostate cancer is given.

Chromosome Aberrations↗
PubMed2004

Recognition of HLA-A2-restricted mammaglobin-A-derived epitopes by CD8+ cytotoxic T lymphocytes from breast cancer patients.

A breast cancer-associated antigen, mammaglobin-A, is specifically expressed in 80% of primary breast tumors. The definition of immune responses against this highly expressed breast cancer-specific antigen should be of great value in the development of new therapeutic strategies for breast cancer. Thus, the purpose of this study was to identify HLA-A2-restricted mammaglobin-A-derived epitopes recognized by CD8+ cytotoxic T lymphocytes (CTL). We identified seven mammaglobin-A-derived candidate epitopes that bind the HLA-A2 molecule (Mam-A2.1-7) by means of a HLA class I-peptide binding computer algorithm from the Bioinformatics & Molecular Analysis Section of the National Institutes of Health. Subsequently, we determined that CD8+ CTLs from breast cancer patients reacted to the Mam-A2.1 (83-92, LIYDSSLCDL), Mam-A2.2 (2-10, KLLMVLMLA), Mam-A2.3 (4-12, LMVLMLAAL), Mam-A2.4 (66-74, FLNQTDETL), and Mam-A2.7 (32-40, TINPQVSKT) epitopes using an IFN-gamma ELISPOT assay. Interestingly, healthy individuals also showed high reactivity to the Mam-A2.2 epitope. Two CD8+ CTL lines generated in vitro against TAP-deficient T2 cells loaded with the candidate epitopes showed significant cytotoxic activity against the Mam-A2.1-4 epitopes. These CD8+CTL lines recognized a HLA-A2+breast cancer cell line expressing the Mam-A2.1 epitope. In addition, DNA vaccination of HLA-A2+/human CD8+ double-transgenic mice with a DNA construct encoding the Mam-A2.1 epitope and the HLA-A2 molecule induced a significant expansion of epitope-specific CD8+ CTLs that recognize the same HLA- A2+/Mam-A2.1+ breast cancer cell line. In conclusion, these results demonstrate the immunotherapeutic potential of mammaglobin-A for the treatment and prevention of breast cancer.

Adenocarcinoma↗
PubMed2024

Identification of RBP binding sites using RNA deaminases.

RNA-binding proteins (RBPs) are critical regulators of gene expression and RNA processing. Identification of their binding sites has important implications for their physiological and disease-related functions. Crosslinking and immunoprecipitation, followed by sequencing (CLIP-seq) and its derivatives, are the most commonly used methods to identify RBP binding sites, but are laborious and require a large amount of starting material. Recent advancements harnessing RNA deaminases in fusion to any RBP of interest, allow for the profiling of RBP binding sites from low-input samples in simpler procedures. Among these efforts, we developed STAMP (Surveying Targets by APOBEC-Mediated Profiling), which efficiently detects RBP-RNA interactions. This chapter describes the detailed protocol for the STAMP method, including plasmid construction, delivery and sorting, library preparation and bioinformatic data analysis.

RNA-Binding Proteins↗
PubMed2004

Nucleocapsid protein of SARS coronavirus tightly binds to human cyclophilin A.

Severe acute respiratory syndrome coronavirus (SARS-CoV) is responsible for SARS infection. Nucleocapsid protein (NP) of SARS-CoV (SARS_NP) functions in enveloping the entire genomic RNA and interacts with viron structural proteins, thus playing important roles in the process of virus particle assembly and release. Protein-protein interaction analysis using bioinformatics tools indicated that SARS_NP may bind to human cyclophilin A (hCypA), and surface plasmon resonance (SPR) technology revealed this binding with the equilibrium dissociation constant ranging from 6 to 160nM. The probable binding sites of these two proteins were detected by modeling the three-dimensional structure of the SARS_NP-hCypA complex, from which the important interaction residue pairs between the proteins were deduced. Mutagenesis experiments were carried out for validating the binding model, whose correctness was assessed by the observed effects on the binding affinities between the proteins. The reliability of the binding sites derived by the molecular modeling was confirmed by the fact that the computationally predicted values of the relative free energies of the binding for SARS_NP (or hCypA) mutants to the wild-type hCypA (or SARS_NP) are in good agreement with the data determined by SPR. Such presently observed SARS_NP-hCypA interaction model might provide a new hint for facilitating the understanding of another possible SARS-CoV infection pathway against human cell.

Amino Acid Sequence↗
PubMed2026

Integrated exome and mitochondrial genome sequencing reveals the genetic landscape of primary mitochondrial diseases: findings from a large Tunisian cohort.

Primary mitochondrial diseases are a heterogeneous group of neurometabolic disorders recognized as the most common metabolic genetic diseases. They manifest at any age, affecting any tissue or organ, especially those with high energy demands, and are caused by pathogenic variants in both mitochondrial and nuclear genomes. Here, we aimed to describe the genetic spectrum of a Tunisian pediatric cohort with suspected mitochondrial diseases. We recruited 47 unrelated families who underwent exome sequencing as a first-tier test followed by whole mitochondrial genome sequencing for unsolved cases. Dedicated bioinformatic pipelines and prediction tools were used to determine the potential disease-causing variants. Sanger sequencing confirmed the presence and segregation within parents. For the newly identified variants, structural modeling was conducted to study the impact of these variants on protein structure and motions. Dual genome sequencing yielded a molecular diagnosis in 33/47 families (70%) and 18/47 (38%) showed disease-causing variants in genes encoding mitochondrial proteins. Among them, four families disclosed novel variants in FASTKD2, SERAC1 and GATB, which were supported by in-depth in silico and structural analyses demonstrating their deleterious effect. The remaining families (32%, 15/47) disclosed other metabolic and neurological disorders. An exome-first strategy delivers a high diagnostic yield in Tunisia, where consanguinity remains high and simultaneously captures mitochondrial and non-mitochondrial etiologies. Mitochondrial sequencing remains indispensable in the case of an inconclusive exome. Thus, our data expand the clinical and genetic spectrum of primary mitochondrial diseases in Tunisia, an underrepresented and admixed population.

Humans↗
PubMed2026

XsiAMT1.1a was identified as a novel ammonium uptake functional gene and its overexpression combined with GA4 application significantly increased yield in Arabidopsis thaliana.

Nitrogen (N) is a key limiting factor for plant yield. Ammonium is one of the main N forms absorbed by plants. Overexpression of ammonium uptake functional genes, such as ammonium transporter (AMT), can increase yield. However, the AMTs reported to enhance yield significantly is still limited. No researches have focused on the effect of overexpressing AMT combined with hormone application on yield improvement. In this study, we first investigated the role of XsiAMT1.1a, a potential ammonium uptake functional gene in an ammonium preference plant Xanthium sibiricum, in ammonium uptake by the analysis of bioinformatics, gene expression and subcellular localization, and the determination of ammonium uptake rate in endogenous silencing and heterologous overexpression plants. Subsequently, the effect of XsiAMT1.1a overexpression combined with hormone application on yield increase was further investigated in model plant Arabidopsis thaliana. Our results showed that XsiAMT1.1a shared the same conserved domains with AtAMT1 subfamily members and localized on the plasma membrane. XsiAMT1.1a was induced by N deficiency and highly expressed during the reproductive period. XsiAMT1.1a endogenous silencing and heterologous overexpression significantly decreased and increased ammonium uptake rates in X. sibiricum and A. thaliana, respectively. Overexpression of XsiAMT1.1a significantly improved total N accumulation, biomass and yield in A. thaliana, while XsiAMT1.1a overexpression combined with GA4 application had a stronger promoting effect on the above indicators. Our research identified a novel ammonium uptake functional gene, XsiAMT1.1a, and provided a new yield-increasing strategy which was verified in A. thaliana.

Arabidopsis↗
PubMed2024

A deep learning framework for denoising and ordering scRNA-seq data using adversarial autoencoder with dynamic batching.

Single-cell RNA sequencing (scRNA-seq) provides high resolution of cell-to-cell variation in gene expression and offers insights into cell heterogeneity, differentiating dynamics, and disease mechanisms. However, technical challenges such as low capture rates and dropout events can introduce noise in data analysis. Here, we present a deep learning framework, called the dynamic batching adversarial autoencoder (DB-AAE), for denoising scRNA-seq datasets. First, we describe steps to set up the computing environment, training, and tuning. Then, we depict the visualization of the denoising results. For complete details on the use and execution of this protocol, please refer to Ko et al.1.

Deep Learning↗
PubMed2005

Using proteomic approaches to identify new biomarkers for detection and monitoring of ovarian cancer.

OBJECTIVES: Early detection and monitoring the treatment remain the most important factors in improving long-term survival of ovarian cancer patients. New biomarkers that individually or in combination improve the diagnostic performance of existing tumor markers are critically needed. This study uses proteomic approaches to identify new biomarkers for detection and monitoring of ovarian cancer. METHODS: We analyzed protein profiles of three sets of sera using surface enhanced laser desorption and ionization time-of-light mass spectroscopy (SELDI-TOF-MS) on IMAC ProteinChip arrays and ProPeak software for bioinformatics data analysis. The first set of patients included 21 ovarian cancers, 18 benign diseases, and 20 normal patients. The second set included 32 ovarian cancers, 30 benign ovarian diseases, and 30 age-matched healthy controls. The third set included samples collected before and after chemotherapy from 18 ovarian cancer patients. All samples were collected at the Gynecology and Obstetrics Hospital of Fudan University in Shanghai, China. The datasets from low-intensity and high-intensity spectra were analyzed separately. RESULTS: Seven peaks were selected for their contribution to the separation of ovarian cancers from controls using the first and second set of samples. The same dysregulation trends were confirmed for six of the seven peaks in independent validation using the third set of samples. CONCLUSIONS: Using SELDI-TOF analysis of 195 unique specimens, we discovered with preliminary validation six distinct peaks that may potentially be useful in the detection and monitoring of ovarian cancer. Additional studies are required to determine the protein identities of these peaks and to further validate their performance as biomarkers.

Adult↗
PubMed2002

Complete nucleotide sequence and likely recombinatorial origin of bacteriophage T3.

We report the complete genome sequence (38,208 bp) of bacteriophage T3 and provide a bioinformatic comparative analysis with other completely sequenced members of the T7 group of phages. This comparison suggests that T3 has evolved from a recombinant between a T7-like coliphage and a yersiniophage. To assess this, recombination between T7 and the Yersinia enterocolitica serotype O:3 phage phiYeO3-12 was accomplished in vivo; coliphage progeny from this cross were selected that had many biological properties of T3. This represents the first experimentally observed recombination between lytic phages whose normal hosts are different bacterial genera.

Amino Acid Sequence↗
PubMed1996

STRUCTURELAB: a heterogeneous bioinformatics system for RNA structure analysis.

STRUCTURELAB is a computational system that has been developed to permit the use of a broad array of approaches for the analysis of the structure of RNA. The goal of the development is to provide a large set of tools that can be well integrated with experimental biology to aid in the process of the determination of the underlying structure of RNA sequences. The approach taken views the structure determination problem as one of dealing with a database of many computationally generated structures and provides the capability to analyze this data set from different perspectives. Many algorithms are integrated into one system that also utilizes a heterogeneous computing approach permitting the use of several computer architectures to help solve the posed problems. These different computational platforms make it relatively easy to incorporate currently existing programs as well as newly developed algorithms and to best match these algorithms to the appropriate hardware. The system has been written in Common Lisp running on SUN or SGI Unix workstations, and it utilizes a network of participating machines defined in reconfigurable tables. A window-based interface makes this heterogeneous environment as transparent to the user as possible.

Algorithms↗
PubMed2001

Application of industrial scale genomics to discovery of therapeutic targets in heart failure.

In recent years intense activity in both academic and industrial sectors has provided a wealth of information on the human genome with an associated impressive increase in the number of novel gene sequences deposited in sequence data repositories and patent applications. This genomic industrial revolution has transformed the way in which drug target discovery is now approached. In this article we discuss how various differential gene expression (DGE) technologies are being utilized for cardiovascular disease (CVD) drug target discovery. Other approaches such as sequencing cDNA from cardiovascular derived tissues and cells coupled with bioinformatic sequence analysis are used with the aim of identifying novel gene sequences that may be exploited towards target discovery. Additional leverage from gene sequence information is obtained through identification of polymorphisms that may confer disease susceptibility and/or affect drug responsiveness. Pharmacogenomic studies are described wherein gene expression-based techniques are used to evaluate drug response and/or efficacy. Industrial-scale genomics supports and addresses not only novel target gene discovery but also the burgeoning issues in pharmaceutical and clinical cardiovascular medicine relative to polymorphic gene responses.

Databases, Genetic↗
PubMedDate not supplied

Proteome analysis of rhoptry-enriched fractions isolated from Plasmodium merozoites.

The rhoptries of Plasmodium species participate in merozoite invasion and modification of the host erythrocyte. However, only a few rhoptry proteins have been identified using conventional gene identification protocols. To investigate the protein organization of this organelle and to identify new rhoptry proteins, merozoite rhoptries from three different Plasmodium rodent species were enriched by sucrose density gradient fractionation, and subjected to proteome analysis using multidimensional protein identification technology (MudPIT); 148 proteins were identified. To distinguish abundant cellular contaminants from bona fide organellar proteins, a differential analysis comparing the proteins in the rhoptry-enriched fractions to proteins identified from whole cell lysates of P. berghei mixed asexual blood stages was undertaken. In addition, the proteins detected were analyzed for the presence of transmembrane domains, secretory signal peptide, cell adhesion motifs, and/or rhoptry-specific tyrosine-sorting motifs. Combining the differential analysis and bioinformatic approaches, a set of 36 proteins was defined as being potentially located to the Plasmodium rhoptries. Among these potential rhoptry proteins were homologues of known rhoptry proteins, proteases, and enzymes involved in lipid metabolism. Molecular characterization and understanding of the supramolecular organization of these novel potential rhoptry proteins may assist in the identification of new intervention targets for the asexual blood stages of malaria.

Animals↗
PubMed2003

Chromosomal abnormalities and mental illness.

Linkage studies of mental illness have provided suggestive evidence of susceptibility loci over many broad chromosomal regions. Pinpointing causative gene mutations by conventional linkage strategies alone is problematic. The breakpoints of chromosomal abnormalities occurring in patients with mental illness may be more direct pointers to the relevant gene locus. Publications that describe patients where chromosomal abnormalities co-exist with mental illness are reviewed along with supporting evidence that this may amount to an association. Chromosomal abnormalities are considered to be of possible significance if (a) the abnormality is rare and there are independent reports of its coexistence with psychiatric illness, or (b) there is colocalisation of the abnormality with a region of suggestive linkage findings, or (c) there is an apparent cosegregation of the abnormality with psychiatric illness within the individual's family. Breakpoints have been described within many of the loci suggested by linkage studies and these findings support the hypothesis that shared susceptibility factors for schizophrenia and bipolar disorder may exist. If these abnormalities directly disrupt coding regions, then combining molecular genetic breakpoint cloning with bioinformatic sequence analysis may be a method of rapidly identifying candidate genes. Full karyotyping of individuals with psychotic illness especially where this coexists with mild learning disability, dysmorphism or a strong family history of mental disorder is encouraged.

Bipolar Disorder↗
PubMed2002

A combined bioinformatic approach oriented to the analysis and design of peptides with high affinity to MHC class I molecules.

We report on a new method to compute the antigenic degree of peptides from available experimental data on peptide binding affinity to class I MHC molecules. The methodology is a combination of two strategies at different levels of information. The first, at the primary structure level, consists in expressing the peptides binding activity as a profile of amino acid contributions, amino acid similarity being accounted for by their characteristic physicochemical properties and their position within the sequence. The higher level of the strategy is based on a meticulous analysis of the contact interface of the peptides with the cleft constituting the receptor region of a particular class I MHC molecule. Interaction interfaces are inferred by docking the peptide onto the receptor groove of the MHC molecule; evaluation of the affinity of the peptide to the receptor is then performed by analysis of the electrostatic and hydrophobic energies on points of the interaction interface. The result is a robust system for analysis of peptide affinity to class I MHC molecules since while the first analysis dictates the composition of active sequences at the amino acid level, the second translates this information to the atomic level, where the molecular interaction can be analyzed in terms of the intrinsic interatomic forces and energies. Evaluation results for the methodology are encouraging since high affinity peptides are reflected by high scores at both levels of information, and are proportionally lower for peptides of medium and lower affinity for which interaction surfaces show relatively lower electrostatic complementarity and hydrophobic correlation than for the former.

Algorithms↗
PubMed2006

Lectin capture strategies combined with mass spectrometry for the discovery of serum glycoprotein biomarkers.

The application of mass spectrometry to identify disease biomarkers in clinical fluids like serum using high throughput protein expression profiling continues to evolve as technology development, clinical study design, and bioinformatics improve. Previous protein expression profiling studies have offered needed insight into issues of technical reproducibility, instrument calibration, sample preparation, study design, and supervised bioinformatic data analysis. In this overview, new strategies to increase the utility of protein expression profiling for clinical biomarker assay development are discussed with an emphasis on utilizing differential lectin-based glycoprotein capture and targeted immunoassays. The carbohydrate binding specificities of different lectins offer a biological affinity approach that complements existing mass spectrometer capabilities and retains automated throughput options. Specific examples using serum samples from prostate cancer and hepatocellular carcinoma subjects are provided along with suggested experimental strategies for integration of lectin-based methods into clinical fluid expression profiling strategies. Our example workflow incorporates the necessity of early validation in biomarker discovery using an immunoaffinity-based targeted analytical approach that integrates well with upstream discovery technologies.

Amino Acid Sequence↗
PubMed2002

Exploring the pathway structure of metabolism: decomposition into subnetworks and application to Mycoplasma pneumoniae.

MOTIVATION: Reconstructing and analyzing the metabolic map of microorganisms is an important challenge in bioinformatics. Pathway analysis of large metabolic networks meets with the problem of combinatorial explosion of pathways. Therefore, appropriate algorithms for an automated decomposition of these networks into smaller subsystems are needed. RESULTS: A decomposition algorithm for metabolic networks based on the local connectivity of metabolites is presented. Interrelations of this algorithm with alternative methods proposed in the literature and the theory of small world networks are discussed. The applicability of our method is illustrated by an analysis of the metabolism of Mycoplasma pneumoniae, which is an organism of considerable medical interest. The decomposition gives rise to 19 subnetworks. Three of these are here discussed in biochemical terms: arginine degradation, the tetrahydrofolate system, and nucleotide metabolism. The interrelations of pathway analysis of biochemical networks with Petri net theory are outlined.

Algorithms↗