Gene expression rearrangements denoting changes in the biological state

In many situations, the gene expression signature is a unique marker of the biological state. We study the modification of the gene expression distribution function when the biological state of a system experiences a change. This change may be the result of a selective pressure, as in the Long Term Evolution Experiment with E. Coli populations, or the progression to Alzheimer disease in aged brains, or the progression from a normal tissue to the cancer state. The first two cases seem to belong to a class of transitions, where the initial and final states are relatively close to each other, and the distribution function for the differential expressions is short ranged, with a tail of only a few dozens of strongly varying genes. In the latter case, cancer, the initial and final states are far apart and separated by a low-fitness barrier. The distribution function shows a very heavy tail, with thousands of silenced and over-expressed genes. We characterize the biological states by means of their principal component representations, and the expression distribution functions by their maximal and minimal differential expression values and the exponents of the Pareto laws describing the tails

Estimating the number of available states for normal and tumor tissues in gene expression space

The topology of gene expression space for a set of 12 cancer types is studied by means of an entropy-like magnitude, which allows the characterization of the regions occupied by tumor and normal samples. The comparison indicates that the number of available states in gene expression space is much greater for tumors than for normal tissues, suggesting the irreversibility of the progression to the tumor phase. The entropy is nearly constant for tumors, whereas exhibits a higher variability in normal tissues, probably due to tissue differentiation. In addition, we show an interesting correlation between the fraction of available states and the overlapping between the tumor and normal sample clouds, interpreted as a way of reducing the decay rate to the tumor phase in more ordered or structured tissues

Scalable bio marker combinations for early stroke diagnosis: A systematic review

Background: Acute stroke treatment is a time-critical process in which every minute counts. Laboratory biomarkers are needed to aid clinical decisions in the diagnosis. Although imaging is critical for this process, these biomarkers may provide additional information to distinguish actual stroke from its mimics and monitor patient condition and the effect of potential neuroprotective strategies. For such biomarkers to be effectively scalable to public health in any economic setting, these must be cost-effective and non-invasive. We hypothesized that blood-based combinations (panels) of proteins might be the key to this approach and explored this possibility through a systematic review. Methods: We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines for systematic review. Initially, the broader search for biomarkers for early stroke diagnosis yielded 704 hits, and five were added manually. We then narrowed the search to combinations (panels) of the protein markers obtained from the blood. Results: Twelve articles dealing with blood-based panels of protein biomarkers for stroke were included in the systematic review. We observed that NR2 peptide (antibody against the NR2 fragment) and glial fibrillary acidic protein (GFAP) are brain-specific markers related to stroke. Von Willebrand factor (vWF), matrix metalloproteinase 9 (MMP-9), and S100β have been widely used as biomarkers, whereas others such as the ischemia-modified albumin (IMA) index, antithrombin III (AT-III), and fibrinogen have not been evaluated in combination. We herein propose the following new combination of biomarkers for future validation: panel 1 (NR2 + GFAP + MMP-9 + vWF + S100β), panel 2 (NR2 + GFAP + MMP-9 + vWF + IMA index), and panel 3 (NR2 + GFAP + AT-III + fibrinogen). Conclusions: More research is needed to validate, identify, and introduce these panels of biomarkers into medical practice for stroke recurrence and diagnosis in a scalable manner. The evidence indicates that the most promising approach is to combine different blood-based proteins to provide diagnostic precision for health interventions. Through our systematic review, we suggest three novel biomarker panels based on the results in the literature and an interpretation based on stroke pathophysiology