Conformational transitions induced by in vitro macromolecular crowding lead to the amyloidogenesis of buffalo heart cystatin

The biological cells and extracellular matrix exhibit a highly crowded environment, called as macromolecular crowding. Crowding significantly influences protein structure and may lead to its aggregation. In the present study, buffalo heart cystatin (BHC), after purification from buffalo heart tissue, has been used as a model protein for studying effect of macromolecular crowding in the presence of high concentrations of bovine serum albumin (BSA), poly‐ethylene glycol‐1000 (PEG‐1000), and poly‐ethylene glycol‐4000 (PEG‐4000). Cystatins are thiol protease inhibitors and found to be involved in various important physiological processes. Functional inactivation of BHC was observed upon crowding, which varied as a function of concentration and molecular weight of crowding agents as well as incubation time. Structural changes of BHC at tertiary and secondary level were detected with the help of fluorescence and CD spectroscopy. CD analysis showed changes of α‐helix to β‐sheet, which could be due to aggregation. The ANS‐fluorescence study suggested the unfolding and presence of some partially folded intermediates. Increase in ThT‐fluorescence and absorption of Congo red spectra with red shift, confirmed the amyloid type aggregation of BHC in the presence of various crowding agents. Finally, electron microscopy provided the physical evidence about the formation of amyloid fibrils. Results suggested that among the various crowding agents used, amyloidogenesis of BHC was maximal in case of BSA followed by PEG‐4000 and least for PEG‐1000. The present work makes an important contribution in crowding mediated protein aggregation, which can have implications of potential interest. Copyright © 2015 John Wiley & Sons, Ltd.     

Chemoresistance of cancer floating cells is independent of their ability to form 3D structures: Implications for anticancer drug screening

Three-dimensional (3D) culture systems such as floating spheroids (FSs) and floating tumorspheres (FTs) are widely used as tumor models of chemoresistance. FTs are considered to be enriched in cancer stem-like cells (CS-LCs). In this study, we used cancer cell lines (lung H460, prostate LnCAP, and breast MCF-7) able to form FSs under anchorage-independent conditions and compared with cell lines (prostate PC3 and breast MDA-MB-231) that cannot form FSs under similar conditions. Independent of their ability to form FTs all cell lines growing under anchorage-independent conditions become highly resistant to obatoclax, colchicine, and hydroxyurea. We used anti-E-cadherin antibody (that blocked the formation of FSs) and demonstrated that floating LnCAP cells showed similar chemoresistance regardless of the formation of spheroids. Our results demonstrate that the development of chemoresistance is not because of the formation of a complex 3D structure and/or enrichment of CS-LCs but is likely the result of cell detachment per se and their ability to survive under anchorage-independent conditions. We propose that FSs and FTs could be useful models to study chemoresistance of cancer cells associated with cell detachment (e.g., circulating tumor cells) but they may not be representative of other types of chemoresistance that arise in vivo in attached cells.     

Probabilistic methods of identifying genes in prokaryotic genomes: connections to the HMM theory

In this paper, we review developments in probabilistic methods of gene recognition in prokaryotic genomes with the emphasis on connections to the general theory of hidden Markov models (HMM). We show that the Bayesian method implemented in GeneMark, a frequently used gene-finding tool, can be augmented and reintroduced as a rigorous forward-backward (FB) algorithm for local posterior decoding described in the HMM theory. Another earlier developed method, prokaryotic GeneMark.hmm, uses a modification of the Viterbi algorithm for HMM with duration to identify the most likely global path through hidden functional states given the DNA sequence. GeneMark and GeneMark.hmm programs are worth using in concert for analysing prokaryotic DNA sequences that arguably do not follow any exact mathematical model. The new extension of GeneMark using the FB algorithm was implemented in the software program GeneMark.fba. Given the DNA sequence, this program determines an a posteriori probability for each nucleotide to belong to coding or non-coding region. Also, for any open reading frame (ORF), it assigns a score defined as a probabilistic measure of all paths through hidden states that traverse the ORF as a coding region. The prediction accuracy of GeneMark.fba determined in our tests was compared favourably to the accuracy of the initial (standard) GeneMark program. Comparison to the prokaryotic GeneMark.hmm has also demonstrated a certain, yet species-specific, degree of improvement in raw gene detection, ie detection of correct reading frame (and stop codon). The accuracy of exact gene prediction, which is concerned about precise prediction of gene start (which in a prokaryotic genome unambiguously defines the reading frame and stop codon, thus, the whole protein product), still remains more accurate in GeneMarkS, which uses more elaborate HMM to specifically address this task.     

Voting-Based Cancer Module Identification by Combining Topological and Data-Driven Properties

Recently, computational approaches integrating copy number aberrations (CNAs) and gene expression (GE) have been extensively studied to identify cancer-related genes and pathways. In this work, we integrate these two data sets with protein-protein interaction (PPI) information to find cancer-related functional modules. To integrate CNA and GE data, we first built a gene-gene relationship network from a set of seed genes by enumerating all types of pairwise correlations, e.g. GE-GE, CNA-GE, and CNA-CNA, over multiple patients. Next, we propose a voting-based cancer module identification algorithm by combining topological and data-driven properties (VToD algorithm) by using the gene-gene relationship network as a source of data-driven information, and the PPI data as topological information. We applied the VToD algorithm to 266 glioblastoma multiforme (GBM) and 96 ovarian carcinoma (OVC) samples that have both expression and copy number measurements, and identified 22 GBM modules and 23 OVC modules. Among 22 GBM modules, 15, 12, and 20 modules were significantly enriched with cancer-related KEGG, BioCarta pathways, and GO terms, respectively. Among 23 OVC modules, 19, 18, and 23 modules were significantly enriched with cancer-related KEGG, BioCarta pathways, and GO terms, respectively. Similarly, we also observed that 9 and 2 GBM modules and 15 and 18 OVC modules were enriched with cancer gene census (CGC) and specific cancer driver genes, respectively. Our proposed module-detection algorithm significantly outperformed other existing methods in terms of both functional and cancer gene set enrichments. Most of the cancer-related pathways from both cancer data sets found in our algorithm contained more than two types of gene-gene relationships, showing strong positive correlations between the number of different types of relationship and CGC enrichment -values (0.64 for GBM and 0.49 for OVC). This study suggests that identified modules containing both expression changes and CNAs can explain cancer-related activities with greater insights.     

The Major Brain Endocannabinoid 2-AG Controls Neuropathic Pain and Mechanical Hyperalgesia in Patients with Neuromyelitis Optica

Recurrent myelitis is one of the predominant characteristics in patients with neuromyelitis optica (NMO). While paresis, visual loss, sensory deficits, and bladder dysfunction are well known symptoms in NMO patients, pain has been recognized only recently as another key symptom of the disease. Although spinal cord inflammation is a defining aspect of neuromyelitis, there is an almost complete lack of data on altered somatosensory function, including pain. Therefore, eleven consecutive patients with NMO were investigated regarding the presence and clinical characteristics of pain. All patients were examined clinically as well as by Quantitative Sensory Testing (QST) following the protocol of the German Research Network on Neuropathic Pain (DFNS). Additionally, plasma endocannabinoid levels and signs of chronic stress and depression were determined. Almost all patients (10/11) suffered from NMO-associated neuropathic pain for the last three months, and 8 out of 11 patients indicated relevant pain at the time of examination. Symptoms of neuropathic pain were reported in the vast majority of patients with NMO. Psychological testing revealed signs of marked depression. Compared to age and gender-matched healthy controls, QST revealed pronounced mechanical and thermal sensory loss, strongly correlated to ongoing pain suggesting the presence of deafferentation-induced neuropathic pain. Thermal hyperalgesia correlated to MRI-verified signs of spinal cord lesion. Heat hyperalgesia was highly correlated to the time since last relapse of NMO. Patients with NMO exhibited significant mechanical and thermal dysesthesia, namely dynamic mechanical allodynia and paradoxical heat sensation. Moreover, they presented frequently with either abnormal mechanical hypoalgesia or hyperalgesia, which depended significantly on plasma levels of the endogenous cannabinoid 2-arachidonoylglycerole (2-AG). These data emphasize the high prevalence of neuropathic pain and hyperalgesia in patients with NMO. The degree of mechanical hyperalgesia reflecting central sensitization of nociceptive pathways seems to be controlled by the major brain endocannabinoid 2-AG.     

Hepatitis C Virus Envelope Glycoprotein Fitness Defines Virus Population Composition following Transmission to a New Host

Genetic variability is a hallmark of RNA virus populations. However, transmission to a new host often results in a marked decrease in population diversity. This genetic bottlenecking is observed during hepatitis C virus (HCV) transmission and can arise via a selective sweep or through the founder effect. To model HCV transmission, we utilized chimeric SCID/Alb-uPA mice with transplanted human hepatocytes and infected them with a human serum HCV inoculum. E1E2 glycoprotein gene sequences in the donor inoculum and recipient mice were determined following single-genome amplification (SGA). In independent experiments, using mice with liver cells grafted from different sources, an E1E2 variant undetectable in the source inoculum was selected for during transmission. Bayesian coalescent analyses indicated that this variant arose in the inoculum pretransmission. Transmitted variants that established initial infection harbored key substitutions in E1E2 outside HVR1. Notably, all posttransmission E1E2s had lost a potential N-linked glycosylation site (PNGS) in E2. In lentiviral pseudoparticle assays, the major posttransmission E1E2 variant conferred an increased capacity for entry compared to the major variant present in the inoculum. Together, these data demonstrate that increased envelope glycoprotein fitness can drive selective outgrowth of minor variants posttransmission and that loss of a PNGS is integral to this improved phenotype. Mathematical modeling of the dynamics of competing HCV variants indicated that relatively modest differences in glycoprotein fitness can result in marked shifts in virus population composition. Overall, these data provide important insights into the dynamics and selection of HCV populations during transmission.