ChIP on chip and ChIP-Seq assays: genome-wide analysis of transcription factor binding and histone modifications

A survey on algorithms to characterize transcription factor binding sites

Transcription factors (TFs) are key regulatory proteins that control the transcriptional rate of cells by binding short DNA sequences called transcription factor binding sites (TFBS) or motifs. Identifying and characterizing TFBS is fundamental to understanding the regulatory mechanisms governing the transcriptional state of cells. During the last decades, several experimental methods have been developed to recover DNA sequences containing TFBS. In parallel, computational methods have been proposed to discover and identify TFBS motifs based on these DNA sequences. This is one of the most widely investigated problems in bioinformatics and is referred to as the motif discovery problem. In this manuscript, we review classical and novel experimental and computational methods developed to discover and characterize TFBS motifs in DNA sequences, highlighting their advantages and drawbacks. We also discuss open challenges and future perspectives that could fill the remaining gaps in the field.

Genomics in medicine: A new era in medicine

The sequencing of complete human genome revolutionized the genomic medicine. However, the complex interplay of gene-environment-lifestyle and influence of non-coding genomic regions on human health remain largely unexplored. Genomic medicine has great potential for diagnoses or disease prediction, disease prevention and, targeted treatment. However, many of the promising tools of genomic medicine are still in their infancy and their application may be limited because of the limited knowledge we have that precludes its use in many clinical settings. In this review article, we have reviewed the evolution of genomic methodologies/tools, their limitations, and scope, for current and future clinical application.

MACF1 promotes osteoblast differentiation by sequestering repressors in cytoplasm

Osteoblast differentiation leading to bone formation requires a coordinated transcriptional program. Osteoblastic cells with low level of microtubule actin crosslinking factor 1 (MACF1) show reduced osteoblast differentiation ability, however, the comprehensive mechanism of MACF1's action remains unexplored. In the current study, we found that MACF1 knockdown suppressed osteoblast differentiation by altering the transcriptome dynamics. We further identified two MACF1-interacted proteins, cyclin-dependent kinase 12 (CDK12) and MYST/Esa1-associated factor 6 (MEAF6), and two MACF1-interacted transcription factors (TFs), transcription factor 12 (TCF12) and E2F transcription factor 6 (E2F6), which repress osteoblast differentiation by altering the expression of osteogenic TFs and genes. Moreover, we found that MACF1 regulated cytoplasmic-nuclear localization of itself, TCF12 and E2F6 in a concentration-dependent manner. MACF1 oppositely regulates the expression of TCF12 and transcription factor 7 (TCF7), two TFs that drive osteoblast differentiation to opposite directions. This study reveals that MACF1, a cytoskeletal protein, acts as a sponge for repressors of osteoblast differentiation to promote osteoblast differentiation and contributes to a novel mechanistic insight of osteoblast differentiation and transcription dynamics.

Protein-DNA/RNA Interactions: An Overview of Investigation Methods in the -Omics Era

The fields of application of functional proteomics are not limited to the study of protein–protein interactions; they also extend to those involving protein complexes that bind DNA or RNA. These interactions affect fundamental processes such as replication, transcription, and repair in the case of DNA, as well as transport, translation, splicing, and silencing in the case of RNA. Analytical or preparative experimental approaches, both in vivo and in vitro, have been developed to isolate and identify DNA/RNA binding proteins by exploiting the advantage of the affinity shown by these proteins toward a specific oligonucleotide sequence. The present review proposes an overview of the approaches most commonly employed in proteomics applications for the identification of nucleic acid-binding proteins, such as affinity purification (AP) protocols, EMSA, chromatin purification methods, and CRISPR-based chromatin affinity purification, which are generally associated with mass spectrometry methodologies for the unbiased protein identification.

Integrating Proteomics and Targeted Metabolomics to Understand Global Changes in Histone Modifications

The chromatin fiber is the control panel of eukaryotic cells. Chromatin is mostly composed of DNA, which contains the genetic instruction for cell phenotype, and histone proteins, which provide the scaffold for chromatin folding and part of the epigenetic inheritance. Histone writers/erasers "flag" chromatin regions by catalyzing/removing covalent histone post-translational modifications (PTMs). Histone PTMs chemically contribute to chromatin relaxation or compaction and recruit histone readers to modulate DNA readout. The precursors of protein PTMs are mostly small metabolites. For instance, acetyl-CoA is used for acetylation, ATP for phosphorylation, and S-adenosylmethionine for methylation. Interestingly, PTMs such as acetylation can occur at neutral pH also without their respective enzyme when the precursor is sufficiently concentrated. Therefore, it is essential to differentially quantify the contribution of histone writers/erasers versus the effect of local concentration of metabolites to understand the primary regulation of histone PTM abundance. Aberrant phenotypes such as cancer cells have misregulated metabolism and thus the composition and the modulation of chromatin is not only driven by enzymatic tuning. In this review, the latest advances in mass spectrometry (MS) to analyze histone PTMs and the most adopted quantification methods for related metabolites, both necessary to understand PTM relative changes, are discussed.

Towards Precision Medicine in the Clinic: From Biomarker Discovery to Novel Therapeutics

Precision medicine continues to be the benchmark to which we strive in cancer research. Seeking out actionable aberrations that can be selectively targeted by drug compounds promises to optimize treatment efficacy and minimize toxicity. Utilizing these different targeted agents in combination or in sequence may further delay resistance to treatments and prolong antitumor responses. Remarkable progress in the field of immunotherapy adds another layer of complexity to the management of cancer patients. Corresponding advances in companion biomarker development, novel methods of serial tumor assessments, and innovative trial designs act synergistically to further precision medicine. Ongoing hurdles such as clonal evolution, intra- and intertumor heterogeneity, and varied mechanisms of drug resistance continue to be challenges to overcome. Large-scale data-sharing and collaborative networks using next-generation sequencing (NGS) platforms promise to take us further into the cancer 'ome' than ever before, with the goal of achieving successful precision medicine.

Precision Medicine for Molecularly Targeted Agents and Immunotherapies in Early-Phase Clinical Trials

Precision medicine in oncology promises the matching of genomic, molecular, and clinical data with underlying mechanisms of a range of novel anticancer therapeutics to develop more rational and effective antitumor strategies in a timely manner. However, despite the remarkable progress made in the understanding of novel drivers of different oncogenic processes, success rates for the approval of oncology drugs remain low with substantial fiscal consequences. In this article, we focus on how recent rapid innovations in technology have brought greater clarity to the biological and clinical complexities of different cancers and advanced the development of molecularly targeted agents and immunotherapies in clinical trials. We discuss the key challenges of identifying and validating predictive biomarkers of response and resistance using both tumor and surrogate tissues, as well as the hurdles associated with intratumor heterogeneity. Finally, we outline evolving strategies employed in early-phase trial designs that incorporate omics-based technologies.