CyclinPred: A SVM-Based Method for Predicting Cyclin Protein Sequences

Functional annotation of protein sequences with low similarity to well characterized protein sequences is a major challenge of computational biology in the post genomic era. The cyclin protein family is once such important family of proteins which consists of sequences with low sequence similarity making discovery of novel cyclins and establishing orthologous relationships amongst the cyclins, a difficult task. The currently identified cyclin motifs and cyclin associated domains do not represent all of the identified and characterized cyclin sequences. We describe a Support Vector Machine (SVM) based classifier, CyclinPred, which can predict cyclin sequences with high efficiency. The SVM classifier was trained with features of selected cyclin and non cyclin protein sequences. The training features of the protein sequences include amino acid composition, dipeptide composition, secondary structure composition and PSI-BLAST generated Position Specific Scoring Matrix (PSSM) profiles. Results obtained from Leave-One-Out cross validation or jackknife test, self consistency and holdout tests prove that the SVM classifier trained with features of PSSM profile was more accurate than the classifiers based on either of the other features alone or hybrids of these features. A cyclin prediction server- CyclinPred has been setup based on SVM model trained with PSSM profiles. CyclinPred prediction results prove that the method may be used as a cyclin prediction tool, complementing conventional cyclin prediction methods

The Mechanism of Enhanced Insulin Amyloid Fibril Formation by NaCl Is Better Explained by a Conformational Change Model

The high propensity of insulin to fibrillate causes severe biomedical and biotechnological complications. Insulin fibrillation studies attain significant importance considering the prevalence of diabetes and the requirement of functional insulin in each dose. Although studied since the early years of the 20th century, elucidation of the mechanism of insulin fibrillation has not been understood completely. We have previously, through several studies, shown that insulin hexamer dissociates into monomer that undergoes partial unfolding before converting into mature fibrils. In this study we have established that NaCl enhances insulin fibrillation mainly due to subtle structural changes and is not a mere salt effect. We have carried out studies both in the presence and absence of urea and Gdn.HCl and compared the relationship between conformation of insulin induced by urea and Gdn.HCl with respect to NaCl at both pH 7.4 (hexamer) and pH 2 (monomer). Fibril formation was followed with a Thioflavin T assay and structural changes were monitored by circular dichroism and size-exclusion chromatography. The results show salt-insulin interactions are difficult to classify as commonly accepted Debye-Hückel or Hofmeister series interactions but instead a strong correlation between the association states and conformational states of insulin and their propensity to fibrillate is evident

MicroRNA-22 Regulates Hypoxia Signaling in Colon Cancer Cells

MicroRNAs (MiRNAs) are short, non-coding RNA that regulate a variety of cellular functions by suppressing target protein expression. We hypothesized that a set of microRNA regulate tumor responses to hypoxia by inhibiting components of the hypoxia signaling pathway. We found that miR-22 expression in human colon cancer is lower than in normal colon tissue. We also found that miR-22 controls hypoxia inducible factor 1α (HIF-1α) expression in the HCT116 colon cancer cell line. Over-expression of miR-22 inhibits HIF-1α expression, repressing vascular endothelial growth factor (VEGF) production during hypoxia. Conversely, knockdown of endogenous miR-22 enhances hypoxia induced expression of HIF-1α and VEGF. The conditioned media from cells over-expressing miR-22 contain less VEGF protein than control cells, and also induce less endothelial cell growth and invasion, suggesting miR-22 in adjacent cells influences endothelial cell function. Taken together, our data suggest that miR-22 might have an anti-angiogenic effect in colon cancer

Functionalization of Carbon Nanomaterial Surface by Doxorubicin and Antibodies to Tumor Markers

The actual task of oncology is effective treatment of cancer while causing a minimum harm to the patient. The appearance of polymer nanomaterials and technologies launched new applications and approaches of delivery and release of anticancer drugs. The goal of work was to test ultra dispersed diamonds (UDDs) and onion-like carbon (OLCs) as new vehicles for delivery of antitumor drug (doxorubicin (DOX)) and specific antibodies to tumor receptors. Stable compounds of UDDs and OLCs with DOX were obtained. As results of work, an effectiveness of functionalization was 2.94 % w/w for OLC-DOX and 2.98 % w/w for UDD-DOX. Also, there was demonstrated that UDD-DOX and OLC-DOX constructs had dose-dependent cytotoxic effect on tumor cells in the presence of trypsin. The survival of adenocarcinoma cells reduced from 52 to 28 % in case of incubation with the UDD-DOX in concentrations from 8.4–2.5 to 670–20 μg/ml and from 72 to 30 % after incubation with OLC-DOX. Simultaneously, antibodies to epidermal growth factor maintained 75 % of the functional activity and specificity after matrix-assisted pulsed laser evaporation deposition. Thus, the conclusion has been made about the prospects of selected new methods and approaches for creating an antitumor agent with capabilities targeted delivery of drugs

Comparison of Epithelial Differentiation and Immune Regulatory Properties of Mesenchymal Stromal Cells Derived from Human Lung and Bone Marrow

Mesenchymal stromal cells (MSCs) reside in many organs including lung, as shown by their isolation from fetal lung tissues, bronchial stromal compartment, bronchial-alveolar lavage and transplanted lung tissues. It is still controversial whether lung MSCs can undergo mesenchymal-to-epithelial-transition (MET) and possess immune regulatory properties. To this aim, we isolated, expanded and characterized MSCs from normal adult human lung (lung-hMSCs) and compared with human bone marrow-derived MSCs (BM-hMSCs). Our results show that lung-MSCs reside at the perivascular level and do not significantly differ from BM-hMSCs in terms of immunophenotype, stemness gene profile, mesodermal differentiation potential and modulation of T, B and NK cells. However, lung-hMSCs express higher basal level of the stemness-related marker nestin and show, following in vitro treatment with retinoic acid, higher epithelial cell polarization, which is anyway partial when compared to a control epithelial bronchial cell line. Although these results question the real capability of acquiring epithelial functions by MSCs and the feasibility of MSC-based therapeutic approaches to regenerate damaged lung tissues, the characterization of this lung-hMSC population may be useful to study the involvement of stromal cell compartment in lung diseases in which MET plays a role, such as in chronic obstructive pulmonary disease and idiopathic pulmonary fibrosis

Accumulation of poly(A) RNA in nuclear granules enriched in Sam68 in motor neurons from the SMNA7 mouse model of SMA

Spinal muscular atrophy (SMA) is a severe motor neuron (MN) disease caused by the deletion or mutation of the survival motor neuron 1 (SMN1) gene, which results in reduced levels of the SMN protein and the selective degeneration of lower MNs. The best-known function of SMN is the biogenesis of spliceosomal snRNPs, the major components of the pre-mRNA splicing machinery. Therefore, SMN deficiency in SMA leads to widespread splicing abnormalities. We used the SMN?7 mouse model of SMA to investigate the cellular reorganization of polyadenylated mRNAs associated with the splicing dysfunction in MNs. We demonstrate that SMN deficiency induced the abnormal nuclear accumulation in euchromatin domains of poly(A) RNA granules (PARGs) enriched in the splicing regulator Sam68. However, these granules lacked other RNA-binding proteins, such as TDP43, PABPN1, hnRNPA12B, REF and Y14, which are essential for mRNA processing and nuclear export. These effects were accompanied by changes in the alternative splicing of the Sam68-dependent Bcl-x and Nrnx1 genes, as well as changes in the relative accumulation of the intron-containing Chat, Chodl, Myh9 and Myh14 mRNAs, which are all important for MN functions. PARG-containing MNs were observed at presymptomatic SMA stage, increasing their number during the symptomatic stage. Moreover, the massive accumulations of poly(A) RNA granules in MNs was accompanied by the cytoplasmic depletion of polyadenylated mRNAs for their translation. We suggest that the SMN-dependent abnormal accumulation of polyadenylated mRNAs and Sam68 in PARGs reflects a severe dysfunction of both mRNA processing and translation, which could contribute to SMA pathogenesis.This work was supported by grants from: “Dirección General de Investigación” of Spain (BFU2014-54754-P and SAF2015-70801-R, cofinanced by FEDER) and “Instituto de Investigación Marqués de Valdecilla-IDIVAL (NVAL17/22). Dr. Tapia is the recipient of a grant from SMA Europe and FundAME (Spain)

Classification and Lateralization of Temporal Lobe Epilepsies with and without Hippocampal Atrophy Based on Whole-Brain Automatic MRI Segmentation

Brain images contain information suitable for automatically sorting subjects into categories such as healthy controls and patients. We sought to identify morphometric criteria for distinguishing controls (n = 28) from patients with unilateral temporal lobe epilepsy (TLE), 60 with and 20 without hippocampal atrophy (TLE-HA and TLE-N, respectively), and for determining the presumed side of seizure onset. The framework employs multi-atlas segmentation to estimate the volumes of 83 brain structures. A kernel-based separability criterion was then used to identify structures whose volumes discriminate between the groups. Next, we applied support vector machines (SVM) to the selected set for classification on the basis of volumes. We also computed pairwise similarities between all subjects and used spectral analysis to convert these into per-subject features. SVM was again applied to these feature data. After training on a subgroup, all TLE-HA patients were correctly distinguished from controls, achieving an accuracy of 96 ± 2% in both classification schemes. For TLE-N patients, the accuracy was 86 ± 2% based on structural volumes and 91 ± 3% using spectral analysis. Structures discriminating between patients and controls were mainly localized ipsilaterally to the presumed seizure focus. For the TLE-HA group, they were mainly in the temporal lobe; for the TLE-N group they included orbitofrontal regions, as well as the ipsilateral substantia nigra. Correct lateralization of the presumed seizure onset zone was achieved using hippocampi and parahippocampal gyri in all TLE-HA patients using either classification scheme; in the TLE-N patients, lateralization was accurate based on structural volumes in 86 ± 4%, and in 94 ± 4% with the spectral analysis approach. Unilateral TLE has imaging features that can be identified automatically, even when they are invisible to human experts. Such morphometric image features may serve as classification and lateralization criteria. The technique also detects unsuspected distinguishing features like the substantia nigra, warranting further study