A computer adaptable method for finding similarities in the amino acid sequences of two proteins has been developed. From these findings i t is possible to determine
whether significant homology exists between the proteins. This information is used to trace their possible evolutionary development.

The maximum match is a number dependent’ upon the similarity of the sequences. One of its definitions is the largest number of amino acids of one protein that can be matched with those of a second protein allowing for all possible
interruptions in either of the sequences. While the interruptions give rise to a very large number of comparisons, the method efficiently excludes from consideration those comparisons that cannot contribute to the maximum match.

Comparisons are made from the smallest unit of significance, a pair of amino acids, one from each protein. All possible pairs are represented by a two-dimensional array, and all possible comparisons are representod by pathways through the array. For this maximum match only cerhain of the possible pathways must be evaluated. A numerical value, one in this case. is assigned to every cell in the array representing like amino acids. The maximum match is the largest number that would result from summing the cell values of every pathway.

J. Mol. Biol.(1970) 48, 443-453 | Full Article

Summary

Background

The recent flood of data from genome sequences and functional genomics has given rise to
new field, bioinformatics, which combines elements of biology and computer science.

Objectives

Here we propose a definition for this new field and review some of the research that is
being pursued, particularly in relation to transcriptional regulatory systems.

Methods

Our definition is as follows: Bioinformatics is conceptualizing biology in terms of macromolecules (in the sense of physical-chemistry) and then applying “informatics” techniques (derived from disciplines such as applied maths, computer science, and statistics) to understand and organize the information associated with these molecules, on a large-scale.

Results and Conclusions

Analyses in bioinformatics predominantly focus on three types of large datasets available in molecular biology: macromolecular structures, genome sequences, and the results of functional genomics experiments (eg expression data).
Additional information includes the text of scientific papers and “relationship data” from metabolic athways, taxonomy trees, and protein-protein interaction networks. Bioinformatics employs a wide range
of computational techniques including sequence and structural alignment, database design and data mining, macromolecular geometry, phylogenetic tree construction, prediction of protein structure and function, gene finding, and expression data clustering.
The emphasis is on approaches integrating a variety of computational methods and heterogeneous data sources. Finally, bioinformatics is a practical discipline.
We survey some representative applications, such as finding homologues, designing drugs, and performing nlarge-scale censuses. Additional information pertinent to the review is available over the web at
http://bioinfo.mbb.yale.edu/what-is-it.

Keywords

Bioinformatics, Genomics, Introduction, Transcription Regulation
Method Inform Med 2001; 40: 346–5 | Full Article

Abstract

Background

HIV vaccine development must address the genetic diversity and plasticity of the virus that permits the presentation of diverse genetic forms to the immune system and subsequent escape from immune pressure. Assessment of potential HIV strain coverage by candidate T cell-based vaccines (whether natural sequence or computationally optimized products) is now a critical component in interpreting candidate vaccine suitability.

Methods

We have utilized an N-mer identity algorithm to represent T cell epitopes and explore potential coverage of the global HIV pandemic using natural sequences derived from candidate HIV vaccines. Breadth (the number of T cell epitopes generated) and depth (the variant coverage within a T cell epitope) analyses have been incorporated into the model to explore vaccine coverage requirements in terms of the number of discrete T cell epitopes generated.

Results

We show that when multiple epitope generation by a vaccine product is considered a far more nuanced appraisal of the potential HIV strain coverage of the vaccine product emerges. By considering epitope breadth and depth several important observations were made: (1) epitope breadth requirements to reach particular levels of vaccine coverage, even for natural sequence-based vaccine products is not necessarily an intractable problem for the immune system; (2) increasing the valency (number of T cell epitope variants present) of vaccine products dramatically decreases the epitope requirements to reach particular coverage levels for any epidemic; (3) considering multiple-hit models (more than one exact epitope match with an incoming HIV strain) places a significantly higher requirement upon epitope breadth in order to reach a given level of coverage, to the point where low valency natural sequence based products would not practically be able to generate sufficient epitopes.

Conclusions

When HIV vaccine sequences are compared against datasets of potential incoming viruses important metrics such as the minimum epitope count required to reach a desired level of coverage can be easily calculated. We propose that such analyses can be applied early in the planning stages and during the execution phase of a vaccine trial to explore theoretical and empirical suitability of a vaccine product to a particular epidemic setting.

PMID: 22152192 | Full Article

Abstract

Background

Treatment failure for breast cancer is frequently due to lymph node metastasis and invasion to neighboring organs. The aim of the present study was to investigate invasion- and metastasis-related genes in breast cancer cells in vitro and in vivo. Identification of new targets will facilitate the developmental pace of new techniques in screening and early diagnosis. Improved abilities to predict progression and metastasis, therapeutic response and toxicity will help to increase survival of breast cancer patients.

Methods

Differential protein expression in two breast cancer cell lines, one with high and the other with low metastatic potential, was analyzed using two-dimensional liquid phase chromatographic fractionation (ProteomeLab PF 2D system) followed by matrix-assisted laser desorption/time-of-flight mass spectrometry (MALDI-TOF/MS).

Results

Upregulation of alpha-subunit of ATP synthase was identified in high metastatic cells compared with low metastatic cells. Immunohistochemical analysis of 168 human breast cancer specimens on tissue microarrays revealed a high frequency of ATP synthase alpha-subunit expression in breast cancer (94.6%) compared to normal (21.2%) and atypical hyperplasia (23%) breast tissues. Levels of ATP synthase expression levels strongly correlated with large tumor size, poor tumor differentiation and advanced tumor stages (P < 0.05). ATP synthase alpha-subunit over-expression was detected on the surface of a highly invasive breast cancer cell line. An antibody against the ATP synthase alpha-subunit inhibited proliferation, migration and invasion in these breast cancer cells but not that of a non-tumor derived breast cell line.

Conclusions

Over-expression of ATP synthase alpha-subunit may be involved in the progression and metastasis of breast cancer, perhaps representing a potential biomarker for diagnosis, prognosis and a therapeutic target for breast cancer. This findings of this study will help us to better understand the molecular mechanism of tumor metastasis and to improve the screening, diagnosis, as well as prognosis and/or prediction of responses to therapy for breast cancer.

PMID: 22152132 | Full Article

Abstract

Background

T cell receptor (TCR) reflects the status and function of T cells. We previously developed a gene melting spectral pattern (GMSP) assay, which rapidly detects clonal expansion of the T cell receptor beta variable gene (TCRBV) in patients with HBV by using quantitative real-time reverse transcription PCR (qRT-PCR) with DNA melting curve analysis. However, the molecular profiles of TCRBV in peripheral blood mononuclear cells (PBMCs) and CD8+, CD8- cell subsets from chronic severe hepatitis B (CSHB) patients have not been well described.

Methods

Human PBMCs were separated and sorted into CD8+ and CD8- cell subsets using density gradient centrifugation and magnetic activated cell sorting (MACS). The molecular features of the TCRBV CDR3 motif were determined using GMSP analysis; the TCRBV families were cloned and sequenced when the GMSP profile showed a single-peak, indicative of a monoclonal population.

Results

The number of skewed TCRBV in the CD8+ cell subset was significantly higher than that of the CD8- cell subset as assessed by GMSP analysis. The TCRBV11 and BV7 were expressed more frequently than other members of TCRBV family in PBMCs and CD8+, CD8- subsets. Also the relatively conserved amino acid motifs were detected in the TCRBV22, BV18 and BV11 CDR3 in PBMCs among patients with CSHB.

Conclusions

The molecular features of the TCRBV CDR3 were markedly different among PBMCs and CD8+, CD8- cell subsets derived from CSHB patients. Analysis of the TCRBV expression in the CD8+ subset was more accurate in assessing the status and function of circulating T cells. The expression of TCRBV11, BV7 and the relatively conserved CDR3 amino acid motifs could also help to predict and treat patients with CSHB.

PMID: 22152113 | Full Article

Abstract

Background

Breast cancer stem cells (BCSCs) are the source of breast tumors. Compared with other cancer cells, cancer stem cells show high resistance to both chemotherapy and radiotherapy. Targeting of BCSCs is thus a potentially promising and effective strategy for breast cancer treatment. Differentiation therapy represents one type of cancer stem-cell-targeting therapy, aimed at attacking the stemness of cancer stem cells, thus reducing their chemo- and radioresistance. In a previous study, we showed that down-regulation of CD44 sensitized BCSCs to the anti-tumor agent doxorubicin. This study aimed to determine if CD44 knockdown caused BCSCs to differentiate into breast cancer non-stem cells (non-BCSCs).

Methods

We isolated a breast cancer cell population (CD44+CD24- cells) from primary cultures of malignant breast tumors. These cells were sorted into four sub-populations based on their expression of CD44 and CD24 surface markers. CD44 knockdown in the BCSC population was achieved using small hairpin RNA lentivirus particles. The differentiated status of CD44 knock-down BCSCs was evaluated on the basis of changes in CD44+CD24- phenotype, tumorigenesis in NOD/SCID mice, and gene expression in relation to renewal status, metastasis, and cell cycle in comparison with BCSCs and non-BCSCs.

Results

Knockdown of CD44 caused BCSCs to differentiate into non-BCSCs with lower tumorigenic potential, and altered the cell cycle and expression profiles of some stem cell-related genes, making them more similar to those seen in non-BCSCs.

Conclusions

Knockdown of CD44 is an effective strategy for attacking the stemness of BCSCs, resulting in a loss of stemness and an increase in susceptibility to chemotherapy or radiation. The results of this study highlight a potential new strategy for breast cancer treatment through the targeting of BCSCs.

PMID: 22152097 | Full Article

Abstract

Background

The efficacy of the CTL component of a future HIV-1 vaccine will depend on the induction of responses with the most potent antiviral activity and broad HLA class I restriction. However, current HIV vaccine designs are largely based on viral sequence alignments only, not incorporating experimental data on T cell function and specificity.

Methods

Here, 950 untreated HIV-1 clade B or -C infected individuals were tested for responses to sets of 410 overlapping peptides (OLP) spanning the entire HIV-1 proteome. For each OLP, a “protective ratio” (PR) was calculated as the ratio of median viral loads (VL) between OLP non-responders and responders.

Results

For both clades, there was a negative relationship between the PR and the entropy of the OLP sequence. There was also a significant additive effect of multiple responses to beneficial OLP. Responses to beneficial OLP were of significantly higher functional avidity than responses to non-beneficial OLP. They also had superior in-vitro antiviral activities and, importantly, were at least as predictive of individuals’ viral loads than their HLA class I genotypes.

Conclusions

The data thus identify immunogen sequence candidates for HIV and provide an approach for T cell immunogen design applicable to other viral infections.

PMID: 22152067 | Full Article

Abstract

Background

The success rate of timely translation of genome-based technologies to commercially feasible products/services with applicability in health care systems is significantly low. We identified both industry and scientists neglect health policy aspects when commercializing their technology, more specifically, Public Health Assessment Tools (PHAT) and early on involvement of decision makers through which market authorization and reimbursements are dependent. While Technology Transfer (TT) aims to facilitate translation of ideas into products, Health Technology Assessment, one component of PHAT, for example, facilitates translation of products/processes into healthcare services and eventually comes up with recommendations for decision makers. We aim to propose a new model of valorization to optimize integration of genome-based technologies into the healthcare system.

Methods

The method used to develop our model is an adapted version of the Fish Trap Model and the Basic Design Cycle.

Results

We found although different, similarities exist between TT and PHAT. Realizing the potential of being mutually beneficial justified our proposal of their relative parallel initiation. We observed that the Public Health Genomics Wheel should be included in this relative parallel activity to ensure all societal/policy aspects are dealt with preemptively by both stakeholders. On further analysis, we found out this whole process is dependent on the Value of Information. As a result, we present our LAL (Learning Adapting Leveling) model which proposes, based on market demand; TT and PHAT by consultation/bi-lateral communication should advocate for relevant technologies. This can be achieved by public-private partnerships (PPPs). These widely defined PPPs create the innovation network which is a developing, consultative/collaborative-networking platform between TT and PHAT. This network has iterations and requires learning, assimilating and using knowledge developed and is called absorption capacity. We hypotheses that the higher absorption capacity, higher success possibility. Our model however does not address the phasing out of technology although we believe the same model can be used to simultaneously phase out a technology.

Conclusions

This model proposes to facilitate optimization/decreases the timeframe of integration in healthcare. It also helps industry and researchers to come to a strategic decision at an early stage, about technology being developed thus, saving on resources, hence minimizing failures.

PMID: 22142533 | Full Article

Abstract

Astrocyte elevated gene-1 (AEG-1) is associated with tumorigenesis and progression in diverse human cancers. The present study was aimed to investigate the clinical and prognostic significance of AEG-1 in salivary gland carcinomas (SGC). Methods: Real-time PCR and western blot analyses were employed to examine AEG-1 expression in two normal salivary gland tissues, eight SGC tissues of various clinical stages, and five pairs of primary SGC and adjacent salivary gland tissues from the same patient. Immunohistochemistry (IHC) was performed to examine AEG-1 protein expression in paraffin-embedded tissues from 141 SGC patients. Statistical analyses was applies to evaluate the diagnostic value and associations of AEG-1 expression with clinical parameters. Results: AEG-1 expression was evidently up-regulated in SGC tissues compared with that in the normal salivary gland tissues and in matched adjacent salivary gland tissues. AEG-1 protein level was positively correlated with clinical stage (P < 0.001), T classification (P = 0.008), N classification (P = 0.008) and M classifications (P = 0.006). Patients with higher AEG-1 expression had shorter overall survival time, whereas those with lower tumor AEG-1 expression had longer survival time. Conclusions: Our results suggest that AEG-1 expression is associated with SGC progression and may represent a novel and valuable predictor for prognostic evaluation of SGC patients.

PMID: 22133054 | Full Article

Hospitals increasingly use genetic testing to determine whether people are at risk for developing toxic levels of certain drugs in their bloodstreams due to common genetic variants that cause slower clearance of medication by the liver. A study published today in Genome Research strengthens the case for health providers to incorporate tests for rare variants that also influence how the body clears medications from the blood.

The study focused on the medication methotrexate, used to treat acute lymphoblastic leukemia as well as autoimmune disorders such as rheumatoid arthritis. (The drug is sold as Trexall, Rheumatrex by Teva Pharmaceutical Industries and DAVA Pharmaceuticals, respectively.) The SLCO1B1 gene encodes a transporter in the liver that is important for clearing the drug from the body.

Scientists already know that about 10% of the population possesses a common variant of the SLCO1B1 gene that causes methotrexate to be cleared from the body more slowly. As a result, doctors personalize the dosage of methotrexate based on common gene variants to avoid increased side-effects in those patients with low drug clearance. The side–effects of methotrexate are far from trivial — a build-up of medication in the blood can lead to mouth and intestinal sores and kidney failure.

However, in the report published today researchers report that in an additional 2% of people with low clearance, rare gene variants in SLCO1B1 are to blame.