The hitchhikers' guide to RNA sequencing and functional analysis.

DNA and RNA sequencing technologies have revolutionized biology and biomedical sciences, sequencing full genomes and transcriptomes at very high speeds and reasonably low costs. RNA sequencing (RNA-Seq) enables transcript identification and quantification, but once sequencing has concluded researchers can be easily overwhelmed with questions such as how to go from raw data to differential expression (DE), pathway analysis and interpretation. Several pipelines and procedures have been developed to this effect. Even though there is no unique way to perform RNA-Seq analysis, it usually follows these steps: 1) raw reads quality check, 2) alignment of reads to a reference genome, 3) aligned reads' summarization according to an annotation file, 4) DE analysis and 5) gene set analysis and/or functional enrichment analysis. Each step requires researchers to make decisions, and the wide variety of options and resulting large volumes of data often lead to interpretation challenges. There also seems to be insufficient guidance on how best to obtain relevant information and derive actionable knowledge from transcription experiments. In this paper, we explain RNA-Seq steps in detail and outline differences and similarities of different popular options, as well as advantages and disadvantages. We also discuss non-coding RNA analysis, multi-omics, meta-transcriptomics and the use of artificial intelligence methods complementing the arsenal of tools available to researchers. Lastly, we perform a complete analysis from raw reads to DE and functional enrichment analysis, visually illustrating how results are not absolute truths and how algorithmic decisions can greatly impact results and interpretation.

Pharmacologically controlling protein-protein interactions through epichaperomes for therapeutic vulnerability in cancer.

Cancer cell plasticity due to the dynamic architecture of interactome networks provides a vexing outlet for therapy evasion. Here, through chemical biology approaches for systems level exploration of protein connectivity changes applied to pancreatic cancer cell lines, patient biospecimens, and cell- and patient-derived xenografts in mice, we demonstrate interactomes can be re-engineered for vulnerability. By manipulating epichaperomes pharmacologically, we control and anticipate how thousands of proteins interact in real-time within tumours. Further, we can essentially force tumours into interactome hyperconnectivity and maximal protein-protein interaction capacity, a state whereby no rebound pathways can be deployed and where alternative signalling is supressed. This approach therefore primes interactomes to enhance vulnerability and improve treatment efficacy, enabling therapeutics with traditionally poor performance to become highly efficacious. These findings provide proof-of-principle for a paradigm to overcome drug resistance through pharmacologic manipulation of proteome-wide protein-protein interaction networks.

Optimization of western blotting for the detection of proteins of different molecular weight.

Protein samples electroblotted onto nitrocellulose membranes and quenched with a mixture of blocking agents produced a strong signal for cystic fibrosis transmembrane-conductance regulator (CFTR), a high-molecular-weight protein, in western blotting. Optimized conditions for CFTR were then extended to medium- and low-molecular-weight proteins (LAMP1 and Rab11a, respectively) to determine the effects of methanol concentration (0-20%) in Towbin's transfer buffer (TTB). Methanol in TTB appears to have little to no effect on CFTR signal. However, for medium-sized (LAMP1) and small (Rab11a) proteins, a lower concentration of methanol (10%) was sufficient to produce a maximal signal. Therefore, methanol, a toxic solvent, can be removed from or reduced in TTB without compromising signal strength. Here, we show modifications that may be useful in detecting and/or improving the signal of low-abundance proteins.