Biology subjects

Deem, M.

Publications and source records attributed to Deem, M..

5 recordsLinked to original sources

bioRxiv2017

Static and dynamic measures of human brain connectivity predict complementary aspects of human cognitive performance

In cognitive network neuroscience, the connectivity and community structure of the brain network is related to cognition. Much of this research has focused on two measures of connectivity - modularity and flexibility - which frequently have been examined in isolation. By using resting state fMRI data from 52 young adults, we investigate the relationship between modularity, flexibility and performance on cognitive tasks. We show that flexibility and modularity are highly negatively correlated. However, we also demonstrate that flexibility and modularity make unique contributions to explain task performance, with modularity predicting performance for simple tasks and flexibility predicting performance on complex tasks that require cognitive control and executive functioning. The theory and results presented here allow for stronger links between measures of brain network connectivity and cognitive processes.

neuroscience↗

bioRxiv2017

Modularity of the metabolic gene network as a prognostic biomarker for hepatocellular carcinoma

Abnormal metabolism is an emerging hallmark of cancer. Cancer cells utilize both aerobic glycolysis and oxidative phosphorylation (OXPHOS) for energy production and biomass synthesis. Understanding the metabolic reprogramming in cancer can help design therapies to target metabolism and thereby to improve prognosis. We have previously argued that more malignant tumors are usually characterized by a more modular expression pattern of cancer-associated genes. In this work, we analyzed the expression patterns of metabolism genes in terms of modularity for 371 hepatocellular carcinoma (HCC) samples from the Cancer Genome Atlas (TCGA). We found that higher modularity significantly correlated with glycolytic phenotype, later tumor stages, higher metastatic potential, and cancer recurrence, all of which contributed to poorer prognosis. Among patients with recurred tumor, we found the correlation of higher modularity with worse prognosis during early to mid-progression. Furthermore, we developed metrics to calculate individual modularity, which was shown to be predictive of cancer recurrence and patients survival and therefore may serve as a prognostic biomarker. Our overall conclusion is that more aggressive HCC tumors, as judged by decreased host survival probability, had more modular expression patterns of metabolic genes. These results may be used to identify cancer driver genes and for drug design.

cancer biology↗

bioRxiv2017

The standard genetic code facilitates exploration of the space of functional nucleotide sequences

The standard genetic code is well known to be optimized for minimizing the phenotypic effects of single nucleotide substitutions, a property that was likely selected for during the emergence of a universal code. Given the fitness advantage afforded by high standing genetic diversity in a population in a dynamic environment, it is possible that selection to explore a large fraction of the space of functional proteins also occurred. To determine whether selection for such a property played a role during the emergence of the nearly universal genetic code, we investigated the number of functional variants of the Escherichia coli PhoQ protein explored at different time scales under translation using different genetic codes. We found that the standard genetic code is highly optimal for exploring a large fraction of the space of functional PhoQ variants at intermediate time scales as compared to random codes. Environmental changes, in response to which genetic diversity in a population provides a fitness advantage, are likely to have occurred at these intermediate time scales. Our results indicate that the ability of the standard code to explore a large fraction of the space of functional sequence variants arises from a balance between robustness and flexibility and is largely independent of the property of the standard code to minimize the phenotypic effects of mutations. We propose that selection to explore a large fraction of the functional sequence space while minimizing the phenotypic effects of mutations contributed towards the emergence of the standard code as the universal genetic code.

evolutionary biology↗

bioRxiv2017

Resequencing the Escherichia coli genome by GenoCare single molecule sequencing platform

Next generation sequencing (NGS) has revolutionized life sciences research. Recently, a new class of third-generation sequencing platforms has arrived to meet increasing demands in the clinic, capable of directly measuring DNA and RNA sequences at the single-molecule level without amplification. Here, we use the new GenoCare single molecule sequencing platform from Direct Genomics to resequence the E. coli genome and show comparable performance to the Illumina MiSeq system. Our platform detects single-molecule fluorescence by total internal reflection microscopy, with sequencing-by-synthesis chemistry. With a consensus sequence of 99.71% nucleotide identity to that of the Illumina MiSeq systems, GenoCare was determined to be a reliable platform for single-molecule sequencing, with strong potential for clinical applications.

genomics↗

bioRxiv2017

Single Molecule Sequencing Of M13 Virus Genome Without Amplification

Third generation sequencing is a direct measurement of DNA/RNA sequences at the single molecule level without amplification. In this study, we report sequencing of the genome of the M13 virus by a new single molecule sequencing platform. Our platform detects single molecule fluorescence by the total internal reflection microscope technique, with sequencing-by-synthesis chemistry. We sequenced the genome of M13 to a depth of 316x and 100% coverage. The consensus sequence accuracy is 100%. We demonstrated that single molecule sequencing has no significant GC bias.

genomics↗