Targeting ZNF638 activates antiviral immune responses and potentiates immune checkpoint inhibition in glioblastoma.

Viral mimicry refers to the activation of innate anti-viral immune responses due to the induction of endogenous retroelement (RE) expression. Viral mimicry has been previously described to augment anti-tumor immune responses and sensitize solid tumors to immunotherapy including colorectal cancer, melanoma, and clear renal cell carcinoma. Here, we found that targeting a novel, master epigenetic regulator, Zinc Finger Protein 638 (ZNF638), induces viral mimicry in glioblastoma (GBM) preclinical models and potentiates immune checkpoint inhibition (ICI).

MiR-449a antagonizes EMT through IL-6-mediated -signaling in laryngeal squamous cancer.

MicroRNAs (miRNAs) are involved in post-transcriptional gene expression regulation and in mechanisms of cancer growth and metastases. In this light, miRNAs could be promising therapeutic targets and biomarkers in clinical practice. Therefore, we investigated if specific miRNAs and their target genes contribute to laryngeal squamous cell carcinoma (LSCC) development.

Guadecitabine plus ipilimumab in unresectable melanoma: five-year follow-up and integrated multi-omic analysis in the phase 1b NIBIT-M4 trial.

Association with hypomethylating agents is a promising strategy to improve the efficacy of immune checkpoint inhibitors-based therapy. The NIBIT-M4 was a phase Ib, dose-escalation trial in patients with advanced melanoma of the hypomethylating agent guadecitabine combined with the anti-CTLA-4 antibody ipilimumab that followed a traditional 3 + 3 design (NCT02608437). Patients received guadecitabine 30, 45 or 60 mg/m/day subcutaneously on days 1 to 5 every 3 weeks starting on week 0 for a total of four cycles, and ipilimumab 3 mg/kg intravenously starting on day 1 of week 1 every 3 weeks for a total of four cycles.

A novel microRNA signature for the detection of melanoma by liquid biopsy.

Background: Melanoma is the deadliest form of skin cancer and metastatic disease is associated with a significant survival rate drop. There is an urgent need for consistent tumor biomarkers to scale precision medicine and reduce cancer mortality. Here, we aimed to identify a melanoma-specific circulating microRNA signature and assess its value as a diagnostic tool.

Loss of circadian rhythmicity in knockout zebrafish larvae.

Brain-derived neurotrophic factor (BDNF) plays a pivotal role in neuronal growth and differentiation, neuronal plasticity, learning, and memory. Using CRISPR/Cas9 technology, we generated a vital Bdnf null mutant line in zebrafish and carried out its molecular and behavioral characterization. Although no defects are evident on a morphological inspection, 66% of coding genes and 37% of microRNAs turned out to be differentially expressed in compared with wild type sibling embryos.

Prediction and pan-cancer analysis of mammalian transcripts involved in target directed miRNA degradation.

It is currently unknown how many RNA transcripts are able to induce degradation of microRNAs (miRNA) via the mechanism known as target-directed miRNA degradation (TDMD). We developed TDMDfinder, a computational pipeline that identifies 'high confidence' TDMD interactions in the Human and Mouse transcriptomes by combining sequence alignment and feature selection approaches. Our predictions suggested that TDMD is widespread, with potentially every miRNA controlled by endogenous targets.

Structural Model for Recruitment of RIT1 to the LZTR1 E3 Ligase: Evidences from an Integrated Computational Approach.

Leucine-zipper transcription regulator 1 (LZTR1) is a highly mutated tumor suppressor gene, involved in the pathogenesis of several cancer types and developmental disorders. In proteasomal degradation, it acts as an adaptor protein responsible for the recognition and recruitment of substrates to be ubiquitinated in Cullin3-RING ligase E3 (CRL3) machinery. LZTR1 belongs to the BTB-Kelch family, a multi-domain protein where the Kelch propeller plays as the substrate recognition region and for which no experimental structure has been solved.

Deep learning predicts short non-coding RNA functions from only raw sequence data.

Small non-coding RNAs (ncRNAs) are short non-coding sequences involved in gene regulation in many biological processes and diseases. The lack of a complete comprehension of their biological functionality, especially in a genome-wide scenario, has demanded new computational approaches to annotate their roles. It is widely known that secondary structure is determinant to know RNA function and machine learning based approaches have been successfully proven to predict RNA function from secondary structure information.

Retinoic Acid Induces Embryonic Stem Cells (ESCs) Transition to 2 Cell-Like State Through a Coordinated Expression of and .

Embryonic stem cells (ESCs) are derived from inner cell mass (ICM) of the blastocyst. In serum/LIF culture condition, they show variable expression of pluripotency genes that mark cell fluctuation between pluripotency and differentiation metastate. The ESCs subpopulation marked by zygotic genome activation gene (ZGA) signature, including , retains a wider differentiation potency than epiblast-derived ESCs.

Detection of long non-coding RNA homology, a comparative study on alignment and alignment-free metrics.

Background: Long non-coding RNAs (lncRNAs) represent a novel class of non-coding RNAs having a crucial role in many biological processes. The identification of long non-coding homologs among different species is essential to investigate such roles in model organisms as homologous genes tend to retain similar molecular and biological functions. Alignment-based metrics are able to effectively capture the conservation of transcribed coding sequences and then the homology of protein coding genes.

Identification of long non-coding transcripts with feature selection: a comparative study.

Background: The unveiling of long non-coding RNAs as important gene regulators in many biological contexts has increased the demand for efficient and robust computational methods to identify novel long non-coding RNAs from transcripts assembled with high throughput RNA-seq data. Several classes of sequence-based features have been proposed to distinguish between coding and non-coding transcripts. Among them, open reading frame, conservation scores, nucleotide arrangements, and RNA secondary structure have been used with success in literature to recognize intergenic long non-coding RNAs, a particular subclass of non-coding RNAs.