In the last two decades, there have been massive advancements in high throughput technologies, which resulted in the exponential growth of public repositories of gene expression datasets for various phenotypes. It is possible to unravel biomarkers by comparing the gene expression levels under different conditions, such as disease vs. control, treated vs. not treated, drug A vs. drug B, etc. This problem refers to a well-studied problem in the machine learning domain, i.e., the feature selection problem. In biological data analysis, most of the computational feature selection methodologies were taken from other fields, without considering the nature of the biological data. For gene expression data analysis, most of the existing feature selection methods rely on expression values alone to select the genes; and biological knowledge is integrated at the end of the analysis in order to gain biological insights or to support the initial findings. Thus, integrative approaches that utilize the biological knowledge while performing feature selection are necessary for this kind of data. The main idea behind the integrative gene selection process is to generate a ranked list of genes considering both the statistical metrics that are applied to the gene expression data, and the biological background information which is provided as external datasets. Since the integrative approach attracted attention in the gene expression domain, lately the gene selection process shifted from being purely data-centric to more incorporative analysis with additional biological knowledge.

Eurotium rubrum is a halophilic marine ascomycete, which can bear the hypersalinities of the Red Sea and proliferate, while most living entities cannot bear this condition. Recently, a 26.2 Mb assembled genome of this fungus had become available. Marine fungi are fascinating organisms capable of harboring several biosynthetic gene clusters (BGCs), which enables them to produce several natural compounds with antibiotic and anticancerous properties. Understanding the BGCs are critically important for the development of biotechnological applications and the discovery of future drugs. There is no knowledge available on the BGCs of this halophilic marine ascomycete. Herein, we set out to explore and characterize BGCs and the corresponding genes from E. rubrum using bioinformatic methods. We deciphered 36 BGCs in the genome of E. rubrum. These 36 BGCs can be grouped into four non-ribosomal peptide synthetase (NRPS) clusters, eight NRPS-like (NRPSL) BGCs, eight type I polyketide synthase (T1PKS), 11 terpene BGCs including one β-lactone cluster, four hybrid BGCs, and two siderophore BGCs. This study is an example of marine genomics application into potential future drug-like compound discovery.

About 15% of colorectal cancer (CRC) patients have first-degree relatives affected by the same malignancy. However, for most families the cause of familial aggregation of CRC is unknown. In order to identify novel high-to-moderate penetrant germline variants underlying CRC susceptibility, we performed whole exome sequencing (WES) on four CRC cases and two unaffected family members of a Polish family without any mutation in known CRC predisposition genes. After WES, we used our in-house developed Familial Cancer Variant Prioritization Pipeline and identified two novel variants in the solute carrier family 15 member 4 (SLC15A4) gene. The heterozygous missense variant, p. Y444C, was predicted to affect the phylogenetically conserved PTR2/POT domain and to have a deleterious effect on the function of the encoded peptide/histidine transporter. The other variant was located in the upstream region of the same gene (GRCh37.p13, 12_129308531_C_T; 43bp upstream of transcription start site, ENST00000266771.5) and it was annotated to affect the promoter region of SLC15A4 as well as binding sites of 17 different transcription factors. Our findings of two distinct variants in the same gene may indicate a synergistic up-regulation of SLC15A4 as the underlying genetic cause and implicate this gene for the first time in genetic inheritance of familial CRC.

Colorectal cancer (CRC) shows one of the largest proportions of familial cases among different malignancies, but only 5-10% of all CRC cases are linked to mutations in established predisposition genes. Thus, familial CRC constitutes a promising target for the identification of novel, high- to moderate-penetrance germline variants underlying cancer susceptibility by next generation sequencing. In this study, we performed whole genome sequencing on 3 members of a family with CRC aggregation. Subsequent integrative in silico analysis using our in-house developed variant prioritization pipeline resulted in the identification of a novel germline missense variant in SRC gene (V177M), a proto-oncogene highly upregulated in CRC. Functional validation experiments in HT-29 cells showed that introduction of SRCV177M resulted in increased cell proliferation and enhanced protein expression of phospho-SRC (Y419), a potential marker for SRC activity. Upregulation of paxillin, β-Catenin and STAT3 mRNA levels, increased levels of phospho-ERK, CREB and CCND1 proteins and downregulation of the tumor suppressor p53 further proposed the activation of several pathways due to the SRCV177M variant. The findings of our pedigree-based study contribute to the exploration of the genetic background of familial CRC and bring insights into the molecular basis of upregulated SRC activity and downstream pathways in colorectal carcinogenesis.