Transcriptional variability accelerates preleukemia by cell diversification and perturbation of protein synthesis.

Transcriptional variability facilitates stochastic cell diversification and can in turn underpin adaptation to stress or injury. We hypothesize that it may analogously facilitate progression of premalignancy to cancer. To investigate this, we initiated preleukemia in mouse cells with enhanced transcriptional variability due to conditional disruption of the histone lysine acetyltransferase gene Kat2a. By combining single-cell RNA sequencing of preleukemia with functional analysis of transformation, we show that Kat2a loss results in global variegation of cell identity and accumulation of preleukemic cells. Leukemia progression is subsequently facilitated by destabilization of ribosome biogenesis and protein synthesis, which confer a transient transformation advantage. The contribution of transcriptional variability to early cancer evolution reflects a generic role in promoting cell fate transitions, which, in the case of well-adapted malignancies, contrastingly differentiates and depletes cancer stem cells. That is, transcriptional variability confers forward momentum to cell fate systems, with differential multistage impact throughout cancer evolution.

Novel open reading frames in human accelerated regions and transposable elements reveal new leads to understand schizophrenia and bipolar disorder.

Schizophrenia (SCZ) and bipolar disorder are debilitating neuropsychiatric disorders arising from a combination of environmental and genetic factors. Novel open reading frames (nORFs) are genomic loci that give rise to previously uncharacterized transcripts and protein products. In our previous work, we have shown that nORFs can be biologically regulated and that they may play a role in cancer and rare diseases. More importantly, we have shown that nORFs may emerge in accelerated regions of the genome giving rise to species-specific functions. We hypothesize that nORFs represent a potentially important group of biological factors that may contribute to SCZ and bipolar disorder pathophysiology. Human accelerated regions (HARs) are genomic features showing human-lineage-specific rapid evolution that may be involved in biological regulation and have additionally been found to associate with SCZ genes. Transposable elements (TEs) are another set of genomic features that have been shown to regulate gene expression. As with HARs, their relevance to SCZ has also been suggested. Here, nORFs are investigated in the context of HARs and TEs. This work shows that nORFs whose expression is disrupted in SCZ and bipolar disorder are in close proximity to HARs and TEs and that some of them are significantly associated with SCZ and bipolar disorder genomic hotspots. We also show that nORF encoded proteins can form structures and potentially constitute novel drug targets.

Pan-cancer analysis of transcripts encoding novel open-reading frames (nORFs) and their potential biological functions.

Uncharacterized and unannotated open-reading frames, which we refer to as novel open reading frames (nORFs), may sometimes encode peptides that remain unexplored for novel therapeutic opportunities. To our knowledge, no systematic identification and characterization of transcripts encoding nORFs or their translation products in cancer, or in any other physiological process has been performed. We use our curated nORFs database (nORFs.org), together with RNA-Seq data from The Cancer Genome Atlas (TCGA) and Genotype-Expression (GTEx) consortiums, to identify transcripts containing nORFs that are expressed frequently in cancer or matched normal tissue across 22 cancer types. We show nORFs are subject to extensive dysregulation at the transcript level in cancer tissue and that a small subset of nORFs are associated with overall patient survival, suggesting that nORFs may have prognostic value. We also show that nORF products can form protein-like structures with post-translational modifications. Finally, we perform in silico screening for inhibitors against nORF-encoded proteins that are disrupted in stomach and esophageal cancer, showing that they can potentially be targeted by inhibitors. We hope this work will guide and motivate future studies that perform in-depth characterization of nORF functions in cancer and other diseases.

PhosphoEffect: Prioritizing Variants On or Adjacent to Phosphorylation Sites through Their Effect on Kinase Recognition Motifs.

Phosphorylation sites often have key regulatory functions and are central to many cellular signaling pathways, so mutations that modify them have the potential to contribute to pathological states such as cancer. Although many classifiers exist for prioritization of coding genomic variants, to our knowledge none of them explicitly account for the alteration or creation of kinase recognition motifs that alter protein structure, function, regulation of activity, and interaction networks through modifying the pattern of phosphorylation. We present a novel computational pipeline that uses a random forest classifier to predict the pathogenicity of a variant, according to its direct or indirect effect on local phosphorylation sites and the predicted functional impact of perturbing a phosphorylation event. We call this classifier PhosphoEffect and find that it compares favorably and with increased accuracy to the existing classifier PolyPhen 2.2.2 when tested on a dataset of known variants enriched for phosphorylation sites and their neighbors.

Big data in digital healthcare: lessons learnt and recommendations for general practice.

Big Data will be an integral part of the next generation of technological developments-allowing us to gain new insights from the vast quantities of data being produced by modern life. There is significant potential for the application of Big Data to healthcare, but there are still some impediments to overcome, such as fragmentation, high costs, and questions around data ownership. Envisioning a future role for Big Data within the digital healthcare context means balancing the benefits of improving patient outcomes with the potential pitfalls of increasing physician burnout due to poor implementation leading to added complexity. Oncology, the field where Big Data collection and utilization got a heard start with programs like TCGA and the Cancer Moon Shot, provides an instructive example as we see different perspectives provided by the United States (US), the United Kingdom (UK) and other nations in the implementation of Big Data in patient care with regards to their centralization and regulatory approach to data. By drawing upon global approaches, we propose recommendations for guidelines and regulations of data use in healthcare centering on the creation of a unique global patient ID that can integrate data from a variety of healthcare providers. In addition, we expand upon the topic by discussing potential pitfalls to Big Data such as the lack of diversity in Big Data research, and the security and transparency risks posed by machine learning algorithms.

Loss of Kat2a enhances transcriptional noise and depletes acute myeloid leukemia stem-like cells.

Acute Myeloid Leukemia (AML) is an aggressive hematological malignancy with abnormal progenitor self-renewal and defective white blood cell differentiation. Its pathogenesis comprises subversion of transcriptional regulation, through mutation and by hijacking normal chromatin regulation. Kat2a is a histone acetyltransferase central to promoter activity, that we recently associated with stability of pluripotency networks, and identified as a genetic vulnerability in AML. Through combined chromatin profiling and single-cell transcriptomics of a conditional knockout mouse, we demonstrate that Kat2a contributes to leukemia propagation through preservation of leukemia stem-like cells. Kat2a loss impacts transcription factor binding and reduces transcriptional burst frequency in a subset of gene promoters, generating enhanced variability of transcript levels. Destabilization of target programs shifts leukemia cell fate out of self-renewal into differentiation. We propose that control of transcriptional variability is central to leukemia stem-like cell propagation, and establish a paradigm exploitable in different tumors and distinct stages of cancer evolution.

Less than 30% of patients with acute myeloid leukaemia ­ an aggressive cancer of the white blood cells ­ survive five years post-diagnosis. This disease disrupts the maturation of white blood cells, resulting in the accumulation of immature cells that multiply and survive but are incapable of completing their maturation process. Amongst these, a group of cancer cells known as leukemic stem cells is responsible for continually replenishing the leukaemia, thus perpetuating its growth. Cancers develop when cells in the body acquire changes or mutations to their genetic makeup. The mutations that lead to acute myeloid leukaemia often affect the activity of genes known as epigenetic regulators. These genes regulate which proteins and other molecules cells make by controlling the way in which cells 'read' their genetic instructions. The epigenetic regulator Kat2a is thought to 'tune' the frequency at which cells read their genetic instructions. This tuning mechanism decreases random fluctuations in the execution of the instructions cells receive to make proteins and other molecules. In turn, this helps to ensure that individual cells of the same type behave in a similar way, for example by keeping leukaemia cells in an immature state. Here, Domingues, Kulkarni et al. investigated whether interfering with Kat2a can make acute myeloid leukaemia less aggressive by allowing the immature white blood cells to mature. Domingues, Kulkarni et al. genetically engineered mice to remove Kat2a from blood cells on demand and then inserted a mutation that causes acute myeloid leukaemia. The experiments showed that the loss of Kat2a delayed the development of leukaemia in the mice and progressively depleted leukaemia stem cells, causing the disease to become less aggressive. The results also showed that loss of Kat2a caused more fluctuations in how the white blood cells read their genetic code, which resulted in more variability in the molecules they produced and increased the tendency of the cells to mature. These findings establish that loss of Kat2a causes leukaemia stem cells to mature and stop multiplying by untuning the frequency at which the cells read their genetic instructions. In the future, it may be possible to develop drugs that target human KAT2A to treat acute myeloid leukaemia.

Quantitative profiling of peptides from RNAs classified as noncoding.

Only a small fraction of the mammalian genome codes for messenger RNAs destined to be translated into proteins, and it is generally assumed that a large portion of transcribed sequences--including introns and several classes of noncoding RNAs (ncRNAs)--do not give rise to peptide products. A systematic examination of translation and physiological regulation of ncRNAs has not been conducted. Here we use computational methods to identify the products of non-canonical translation in mouse neurons by analysing unannotated transcripts in combination with proteomic data. This study supports the existence of non-canonical translation products from both intragenic and extragenic genomic regions, including peptides derived from antisense transcripts and introns. Moreover, the studied novel translation products exhibit temporal regulation similar to that of proteins known to be involved in neuronal activity processes. These observations highlight a potentially large and complex set of biologically regulated translational events from transcripts formerly thought to lack coding potential.

Paradoxical results in perturbation-based signaling network reconstruction.

Mathematical models are extensively employed to understand physicochemical processes in biological systems. In the absence of detailed mechanistic knowledge, models are often based on network inference methods, which in turn rely upon perturbations to nodes by biochemical means. We have discovered a potential pitfall of the approach underpinning such methods when applied to signaling networks. We first show experimentally, and then explain mathematically, how even in the simplest signaling systems, perturbation methods may lead to paradoxical conclusions: for any given pair of two components X and Y, and depending upon the specific intervention on Y, either an activation or a repression of X could be inferred. This effect is of a different nature from incomplete network identification due to underdetermined data and is a phenomenon intrinsic to perturbations. Our experiments are performed in an in vitro minimal system, thus isolating the effect and showing that it cannot be explained by feedbacks due to unknown intermediates. Moreover, our in vitro system utilizes proteins from a pathway in mammalian (and other eukaryotic) cells that play a central role in proliferation, gene expression, differentiation, mitosis, cell survival, and apoptosis. This pathway is the perturbation target of contemporary therapies for various types of cancers. The results presented here show that the simplistic view of intracellular signaling networks being made up of activation and repression links is seriously misleading, and call for a fundamental rethinking of signaling network analysis and inference methods.

Comparative analysis of Erk phosphorylation suggests a mixed strategy for measuring phospho-form distributions.

The functional impact of multisite protein phosphorylation can depend on both the numbers and the positions of phosphorylated sites-the global pattern of phosphorylation or 'phospho-form'-giving biological systems profound capabilities for dynamic information processing. A central problem in quantitative systems biology, therefore, is to measure the 'phospho-form distribution': the relative amount of each of the 2(n) phospho-forms of a protein with n-phosphorylation sites. We compared four potential methods-western blots with phospho-specific antibodies, peptide-based liquid chromatography (LC) and mass spectrometry (MS; pepMS), protein-based LC/MS (proMS) and nuclear magnetic resonance spectroscopy (NMR)-on differentially phosphorylated samples of the well-studied mitogen-activated protein kinase Erk2, with two phosphorylation sites. The MS methods were quantitatively consistent with each other and with NMR to within 10%, but western blots, while highly sensitive, showed significant discrepancies with MS. NMR also uncovered two additional phosphorylations, for which a combination of pepMS and proMS yielded an estimate of the 16-member phospho-form distribution. This combined MS strategy provides an optimal mixture of accuracy and coverage for quantifying distributions, but positional isomers remain a challenging problem.