Upper Brachial Plexus Injuries: Grafts vs Ulnar Fascicle Transfer to Restore Biceps Muscle Function

BACKGROUND: Nerve transfers or graft repairs in upper brachial plexus palsies are 2 available options for elbow flexion recovery. OBJECTIVE: To assess outcomes of biceps muscle strength when treated either by grafts or nerve transfer. METHODS: A standard supraclavicular approach was performed in all patients. When roots were available, grafts were used directed to proximal targets. Otherwise, a distal ulnar nerve fascicle was transferred to the biceps branch. Elbow flexion strength was measured with a dynamometer, and an index comparing the healthy arm and the operated-on side was developed. Statistical analysis to compare both techniques was performed. RESULTS: Thirty-five patients (34 men) were included in this series. Mean age was 28.7 years (standard deviation, 8.7). Twenty-two patients (62.8%) presented with a C5-C6 injury, whereas 13 patients (37.2%) had a C5-C6-C7 lesion. Seventeen patients received reconstruction with grafts, and 18 patients were treated with a nerve transfer from the ulnar nerve to the biceps. The trauma to surgery interval (mean, 7.6 months in both groups), strength in the healthy arm, and follow-up duration were not statistically different. On the British Medical Research Council muscle strength scale, 8 of 17 (47%) patients with a graft achieved >= M3 biceps flexion postoperatively, vs 16 of 18 (88%) post nerve transfers (P = .024). This difference persisted when a muscle strength index assessing improvement relative to the healthy limb was used (P = .031). CONCLUSION: The results obtained from ulnar nerve fascicle transfer to the biceps branch were superior to those achieved through reconstruction with grafts.

Simcluster: clustering enumeration gene expression data on the simplex space

Abstract Background Transcript enumeration methods such as SAGE, MPSS, and sequencing-by-synthesis EST "digital northern", are important high-throughput techniques for digital gene expression measurement. As other counting or voting processes, these measurements constitute compositional data exhibiting properties particular to the simplex space where the summation of the components is constrained. These properties are not present on regular Euclidean spaces, on which hybridization-based microarray data is often modeled. Therefore, pattern recognition methods commonly used for microarray data analysis may be non-informative for the data generated by transcript enumeration techniques since they ignore certain fundamental properties of this space. Results Here we present a software tool, Simcluster, designed to perform clustering analysis for data on the simplex space. We present Simcluster as a stand-alone command-line C package and as a user-friendly on-line tool. Both versions are available at: http://xerad.systemsbiology.net/simcluster. Conclusion Simcluster is designed in accordance with a well-established mathematical framework for compositional data analysis, which provides principled procedures for dealing with the simplex space, and is thus applicable in a number of contexts, including enumeration-based gene expression data.

Retroviral transfer of the p16INK4a cDNA inhibits C6 glioma formation in Wistar rats

Abstract Background The p16INK4A gene product halts cell proliferation by preventing phosphorylation of the Rb protein. The p16INK4a gene is often deleted in human glioblastoma multiforme, contributing to unchecked Rb phosphorylation and rapid cell division. We show here that transduction of the human p16INK4a cDNA using the pCL retroviral system is an efficient means of stopping the proliferation of the rat-derrived glioma cell line, C6, both in tissue culture and in an animal model. C6 cells were transduced with pCL retrovirus encoding the p16INK4a, p53, or Rb genes. These cells were analyzed by a colony formation assay. Expression of p16INK4a was confirmed by immunohistochemistry and Western blot analysis. The altered morphology of the p16-expressing cells was further characterized by the senescence-associated β-galactosidase assay. C6 cells infected ex vivo were implanted by stereotaxic injection in order to assess tumor formation. Results The p16INK4a gene arrested C6 cells more efficiently than either p53 or Rb. Continued studies with the p16INK4a gene revealed that a large portion of infected cells expressed the p16INK4a protein and the morphology of these cells was altered. The enlarged, flat, and bi-polar shape indicated a senescence-like state, confirmed by the senescence-associated β-galactosidase assay. The animal model revealed that cells infected with the pCLp16 virus did not form tumors. Conclusion Our results show that retrovirus mediated transfer of p16INK4a halts glioma formation in a rat model. These results corroborate the idea that retrovirus-mediated transfer of the p16INK4a gene may be an effective means to arrest human glioma and glioblastoma.

Modeling gene expression regulatory networks with the sparse vector autoregressive model

Abstract Background To understand the molecular mechanisms underlying important biological processes, a detailed description of the gene products networks involved is required. In order to define and understand such molecular networks, some statistical methods are proposed in the literature to estimate gene regulatory networks from time-series microarray data. However, several problems still need to be overcome. Firstly, information flow need to be inferred, in addition to the correlation between genes. Secondly, we usually try to identify large networks from a large number of genes (parameters) originating from a smaller number of microarray experiments (samples). Due to this situation, which is rather frequent in Bioinformatics, it is difficult to perform statistical tests using methods that model large gene-gene networks. In addition, most of the models are based on dimension reduction using clustering techniques, therefore, the resulting network is not a gene-gene network but a module-module network. Here, we present the Sparse Vector Autoregressive model as a solution to these problems. Results We have applied the Sparse Vector Autoregressive model to estimate gene regulatory networks based on gene expression profiles obtained from time-series microarray experiments. Through extensive simulations, by applying the SVAR method to artificial regulatory networks, we show that SVAR can infer true positive edges even under conditions in which the number of samples is smaller than the number of genes. Moreover, it is possible to control for false positives, a significant advantage when compared to other methods described in the literature, which are based on ranks or score functions. By applying SVAR to actual HeLa cell cycle gene expression data, we were able to identify well known transcription factor targets. Conclusion The proposed SVAR method is able to model gene regulatory networks in frequent situations in which the number of samples is lower than the number of genes, making it possible to naturally infer partial Granger causalities without any a priori information. In addition, we present a statistical test to control the false discovery rate, which was not previously possible using other gene regulatory network models.

Constraint-based analysis of gene interactions using restricted boolean networks and time-series data

Abstract Background A popular model for gene regulatory networks is the Boolean network model. In this paper, we propose an algorithm to perform an analysis of gene regulatory interactions using the Boolean network model and time-series data. Actually, the Boolean network is restricted in the sense that only a subset of all possible Boolean functions are considered. We explore some mathematical properties of the restricted Boolean networks in order to avoid the full search approach. The problem is modeled as a Constraint Satisfaction Problem (CSP) and CSP techniques are used to solve it. Results We applied the proposed algorithm in two data sets. First, we used an artificial dataset obtained from a model for the budding yeast cell cycle. The second data set is derived from experiments performed using HeLa cells. The results show that some interactions can be fully or, at least, partially determined under the Boolean model considered. Conclusions The algorithm proposed can be used as a first step for detection of gene/protein interactions. It is able to infer gene relationships from time-series data of gene expression, and this inference process can be aided by a priori knowledge available.

MTP -493G/T gene polymorphism is associated with steatosis in hepatitis C-infected patients

The reduction of hepatic microsomal transfer protein (MTP) activity results in fatty liver, worsening hepatic steatosis and fibrosis in chronic hepatitis C (CHC). The G allele of the MTP gene promoter, -493G/T, has been associated with lower transcriptional activity than the T allele. We investigated this association with metabolic and histological variables in patients with CHC. A total of 174 untreated patients with CHC were genotyped for MTP -493G/T by direct sequencing using PCR. All patients were negative for markers of Wilson’s disease, hemochromatosis and autoimmune diseases and had current and past daily alcohol intake lower than 100 g/week. The sample distribution was in Hardy-Weinberg equilibrium. Among subjects with genotype 1, 56.8% of the patients with fibrosis grade 3+4 presented at least one G allele versus 34.3% of the patients with fibrosis grade 1+2 (OR = 1.8; 95%CI = 1.3-2.3). Logistic regression analysis with steatosis as the dependent variable identified genotypes GG+GT as independent protective factors against steatosis (OR = 0.4, 95%CI = 0.2-0.8; P = 0.01). The results suggest that the presence of the G allele of MTP -493G/T associated with lower hepatic MTP expression protects against steatosis in our CHC patients.