In the last decade, next-generation sequencing (NGS) technology, alternatively massive parallel sequencing (MPS), was applied to all fields of biological research. Its introduction to the field of forensics was slower, mainly due to lack of accredited sequencers, kits, and relatively higher sequencing error rates as compared with standardized Sanger sequencing. Currently, a majority of the problematic issues have been solved, which is proven by the body of reports in the literature. Here, we discuss the utility of NGS sequencing in forensics, emphasizing the advantages, issues, the technical aspects of the experiments, commercial solutions, and the potentially interesting applications of MPS.

VHL inactivation is the most established molecular characteristic of clear cell renal cell carcinoma (ccRCC), with only a few additional genes implicated in development of this kidney tumor. In recently published ccRCC gene expression meta-analysis study we identified a number of deregulated genes with limited information available concerning their biological role, represented by gene transcripts belonging to transmembrane proteins family (TMEMs). TMEMs are predicted to be components of cellular membranes, such as mitochondrial membranes, ER, lysosomes and Golgi apparatus. Interestingly, the function of majority of TMEMs remains unclear. Here, we analyzed expression of ten TMEM genes in the context of ccRCC progression and development, and characterized these proteins bioinformatically. The expression of ten TMEMs (RTP3, SLC35G2, TMEM30B, TMEM45A, TMEM45B, TMEM61, TMEM72, TMEM116, TMEM207 and TMEM213) was measured by qPCR. T-test, Pearson correlation, univariate and multivariate logistic and Cox regression were used in statistical analysis. The topology of studied proteins was predicted with Metaserver, together with PSORTII, Pfam and Localizome tools. We observed significant deregulation of expression of 10 analyzed TMEMs in ccRCC tumors. Cluster analysis of expression data suggested the down-regulation of all tested TMEMs to be a descriptor of the most advanced tumors. Logistic and Cox regression potentially linked TMEM expression to clinical parameters such as: metastasis, Fuhrman grade and overall survival. Topology predictions classified majority of analyzed TMEMs as type 3 and type 1 transmembrane proteins, with predicted localization mainly in ER. The massive down-regulation of expression of TMEM family members suggests their importance in the pathogenesis of ccRCC and the bioinformatic analysis of TMEM topology implies a significant involvement of ER proteins in ccRCC pathology.

In recent years, thyroid malignances have become more prevalent, especially among women. The most common sporadic types of thyroid tumors of follicular origin include papillary, follicular and anaplastic thyroid carcinomas. Although modern diagnosis methods enable the identification of tumors of small diameter, tumor subtype differentiation, which is imperative for the correct choice of treatment, is still troublesome. This review discusses the recent advances in the field of molecular marker identification via next-generation sequencing and microarrays. The potential use of these biomarkers to distinguish among the most commonly occurring sporadic thyroid cancers is presented and compared. Geographical heterogeneity might be a differentiator, although not necessarily a limiting factor, in biomarker selection. The available data advocate for a subset of mutations common for the three subtypes as well as mutations that are unique for a particular tumor subtype. Tumor heterogeneity, a known issue occurring within solid malignancies, is also discussed where applicable. Public databases with datasets derived from high-throughput experiments are a valuable source of information that aid biomarker research in general, including the identification of molecular hallmarks of thyroid cancer.

In this paper, we provide a comprehensive summary of available clear cell renal cell carcinoma (ccRCC) microarray data in the form of meta-analysis of genes differentially regulated in tumors as compared to healthy tissue, using effect size to measure the strength of a relationship between the disease and gene expression. We identified 725 differentially regulated genes, with a number of interesting targets, such as TMEM213, SMIM5, or ATPases: ATP6V0A4 and ATP6V1G3, of which limited or no information is available in terms of their function in ccRCC pathology. Downregulated genes tended to represent pathways related to tissue remodeling, blood clotting, vasodilation, and energy metabolism, while upregulated genes were classified into pathways generally deregulated in cancers: immune system response, inflammatory response, angiogenesis, and apoptosis. One hundred fifteen deregulated genes were included in network analysis, with EGLN3, AP-2, NR3C1, HIF1A, and EPAS1 (gene encoding HIF2-α) as points of functional convergence, but, interestingly, 610 genes failed to join previously identified molecular networks. Furthermore, we validated the expression of 14 top deregulated genes in independent sample set of 32 ccRCC tumors by qPCR and tested if it could serve as a marker of disease progression. We found a correlation of high fucosyltransferase 11 (FUT11) expression with non-symptomatic course of the disease, which suggests that FUT11's expression might be potentially used as a biomarker of disease progression.

In addition to the canonical ISGF3 and non-canonical STAT2/IRF9 complexes, evidence is emerging of the role of their unphosphorylated counterparts in IFN-dependent and -independent ISG transcription. To better understand the relation between ISGF3 and U-ISGF3 and STAT2/IRF9 and U-STAT2/IRF9 in IFN-I-stimulated transcriptional responses, we performed RNA-Seq and ChIP-Seq, in combination with phosphorylation inhibition and antiviral experiments. First, we identified a group of ISRE-containing ISGs that were commonly regulated in IFNα-treated WT and STAT1-KO cells. Thus, in 2fTGH and Huh7.5 WT cells, early and long-term IFNα-inducible transcription and antiviral activity relied on the DNA recruitment of the ISGF3 components STAT1, STAT2 and IRF9 in a phosphorylation- and time-dependent manner. Likewise, in ST2-U3C and Huh-STAT1KO cells lacking STAT1, delayed IFN responses correlated with DNA binding of phosphorylated STAT2/IRF9 but not U-STAT2/IRF9. In addition, comparative experiments in U3C (STAT1-KO) cells overexpressing all the ISGF3 components (ST1-ST2-IRF9-U3C) revealed U-ISGF3 (and possibly U-STAT2/IRF9) chromatin interactions to correlate with phosphorylation-independent ISG transcription and antiviral activity. Together, our data point to the dominant role of the canonical ISGF3 and non-canonical STAT2/IRF9, without a shift to U-ISGF3 or U-STAT2/IRF9, in the regulation of early and prolonged ISG expression and viral protection. At the same time, they suggest the threshold-dependent role of U-ISFG3, and potentially U-STAT2/IRF9, in the regulation of constitutive and possibly long-term IFNα-dependent responses.

Compared to other mammalian species, porcine oocytes and embryos are characterized by large amounts of lipids stored mainly in the form of droplets in the cytoplasm. The amount and the morphology of lipid droplets (LD) change throughout the preimplantation development, however, relatively little is known about expression of genes involved in lipid metabolism of early embryos. We compared porcine and bovine blastocyst stage embryos as well as dissected inner cell mass (ICM) and trophoblast (TE) cell populations with regard to lipid droplet storage and expression of genes functionally annotated to selected lipid gene ontology terms using RNA-seq. Comparing the number and the volume occupied by LD between bovine and porcine blastocysts, we have found significant differences both at the level of single embryo and a single blastomere. Aside from different lipid content, we found that embryos regulate the lipid metabolism differentially at the gene expression level. Out of 125 genes, we found 73 to be differentially expressed between entire porcine and bovine blastocyst, and 36 and 51 to be divergent between ICM and TE cell lines. We noticed significant involvement of cholesterol and ganglioside metabolism in preimplantation embryos, as well as a possible shift towards glucose, rather than pyruvate dependence in bovine embryos. A number of genes like DGAT1, CD36 or NR1H3 may serve as lipid associated markers indicating distinct regulatory mechanisms, while upregulated PLIN2, APOA1, SOAT1 indicate significant function during blastocyst formation and cell differentiation in both models.