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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2018

The splicing factor U2AF1 contributes to cancer progression through a non-canonical role in translation regulation

Somatic mutations in the genes encoding components of the spliceosome occur frequently in human neoplasms, including myeloid dysplasias and leukemias and less often in solid tumors. One of the affected factors, U2AF1, is involved in splice site selection, and the most common change, S34F, alters a conserved nucleic acid binding domain, recognition of the 3-splice site, and alternative splicing of many mRNAs. However, the role that this mutation plays in oncogenesis is still unknown. Here, we have uncovered a non-canonical function of U2AF1, showing that it binds mature mRNA in the cytoplasm and negatively regulates mRNA translation. This splicing-independent role of U2AF1 is altered by the S34F mutation, and polysomal profiling indicates the mutation affects translation of hundreds of mRNA. One functional consequence is increased synthesis of the secreted chemokine interleukin 8 which contributes to metastasis, inflammation, and cancer progression in mice and humans.

cell biology↗

bioRxiv2018

Epigenetic inactivation of miR-203 as a key step in neural crest epithelial-to-mesenchymal transition

miR-203 is a tumor-suppressor microRNA with known functions in cancer metastasis. Here, we explore its normal developmental role in the context of neural crest development. As neural crest cells undergo an epithelial-to-mesenchymal transition to emigrate from the neural tube, miR-203 displays a reciprocal expression pattern with key regulators of neural crest delamination, Phf12 and Snail2, and interacts with their 3UTRs. Ectopic maintenance of miR-203 inhibits neural crest migration, whereas its functional inhibition using a \"sponge\" vector promotes premature neural crest delamination. Bisulfite sequencing further shows that epigenetic repression of miR-203 is mediated by the de novo DNA methyltransferase DNMT3B, whose recruitment to regulatory regions on the miR-203 locus is directed by SNAIL2 in a negative feedback loop. These findings reveal an important role for miR-203 in an epigenetic-microRNA regulatory network that influences the timing of neural crest delamination.\n\nSummary statementThe EMT is a highly conserved process, involving similar levels of regulation in both neural crest and cancer cells. Our work shows an epigenetic-miRNA-gene regulatory circuit, conserved in cancer, which controls the timing of neural crest EMT as well.

developmental biology↗

bioRxiv2018

Genome-wide disruption of DNA methylation by 5-aza-2’-deoxycytidine in the parasitioid wasp Nasonia vitripennis

DNA methylation of cytosine residues across the genome influences how genes and phenotypes are regulated in a wide range of organisms. As such, understanding the role of DNA methylation and other epigenetic mechanisms has become very much a part of mapping genotype to phenotype, a major question in evolutionary biology. Ideally, we would like to manipulate DNA methylation patterns on a genome-wide scale, to help us to elucidate the role that epigenetic modifications play in phenotypic expression. Recently, the demethylating agent 5-aza-2-deoxycytidine (5-aza-dC; commonly used in the epigenetic treatment of certain cancers), has been deployed to explore the epigenetic regulation of a number of traits of interest to evolutionary ecologists, including facultative sex allocation in the parasitoid wasp Nasonia vitripennis. In a recent study, we showed that treatment with 5-aza-dC did not ablate the facultative sex allocation response in Nasonia, but shifted the patterns of sex allocation in a way predicted by genomic conflict theory. This was the first (albeit indirect) experimental evidence for genomic conflict over sex allocation facilitated by DNA methylation. However, that work lacked direct evidence of the effects of 5-aza-dC on DNA methylation, and indeed the effect of the chemical has since been questioned in Nasonia. Here, using whole-genome bisulphite sequencing of more than 4 million CpGs, across more than 11,000 genes, we demonstrate unequivocally that 5-aza-dC disrupts methylation on a large scale across the Nasonia vitripennis genome. We show that the disruption can lead to both hypo- and hyper-methylation, may vary across tissues and time of sampling, and that the effects of 5-aza-dC are context- and sequence specific. We conclude that 5-aza-dC does indeed have the potential to be repurposed as a tool for studying the role of DNA methylation in evolutionary ecology, whilst many details of its action remain to be discovered. Author SummaryShedding light on the mechanistic basis of phenotypes is a major aim in the field of evolutionary biology. If we understand how phenotypes are controlled at the molecular level, we can begin to understand how evolution has shaped that phenotype and conversely, how genetic architecture may constrain trait evolution. Epigenetic markers (such as DNA methylation) also influence phenotypic expression by regulating how and when genes are expressed. Recently, 5-aza-2-deoxycytidine (5-aza-dC), a hypomethylating agent used in the treatment of certain cancers, has been used to explore the epigenetic regulation of traits of interest to evolutionary ecologists. Previously, we used 5-aza-dC to validate a role for DNA methylation in facultative sex allocation behaviour in the parasitoid wasp Nasonia vitripennis. However, the direct effects of the chemical were not examined at that point and its efficacy in insects was questioned. Here, we demonstrate that 5-aza-dC disrupts DNA methylation on a genome-wide scale in a context- and sequence-specific manner and results in both hypo- and hyper-methylation. Our work demonstrates that 5-aza-dC has the potential to be repurposed as a tool for studying the role of DNA methylation in phenotypic expression.

evolutionary biology↗

bioRxiv2018

A computational framework identifying concordant gene expression-neuropathology associations reveals Complex I as a potential Alzheimer’s disease therapeutic target

Identifying gene expression markers for Alzheimers disease (AD) neuropathology through meta-analysis is a complex undertaking because available data are often from different studies and/or brain regions involving study-specific confounders and/or region-specific biological processes. Here we introduce a novel probabilistic model-based framework, DECODER, leveraging these discrepancies to identify robust biomarkers for complex phenotypes. Our experiments present: (1) DECODERs potential as a general meta-analysis framework widely applicable to various diseases (e.g., AD and cancer) and phenotypes (e.g., Amyloid-{beta} (A{beta}) pathology, tau pathology, and survival), (2) our results from a meta-analysis using 1,746 human brain tissue samples from nine brain regions in three studies -- the largest expression meta-analysis for AD, to our knowledge --, and (3) in vivo validation of identified modifiers of A{beta} toxicity in a transgenic Caenorhabditis elegans model expressing AD-associated A{beta}, which pinpoints mitochondrial Complex I as a critical mediator of proteostasis and a promising pharmacological avenue toward treating AD.

systems biology↗

bioRxiv2018

Encircling the regions of the pharmacogenomic landscape that determine drug response

The integration of large-scale drug sensitivity screens and genome-wide experiments is changing the field of pharmacogenomics, revealing molecular determinants of drug response without the need for a priori, hypothesis-driven assumptions about drug action. In particular, transcriptomic signatures of drug sensitivity may guide drug repositioning, the discovery of synergistic drug combinations and suggest new therapeutic biomarkers. However, the inherent complexity of transcriptomic signatures, with thousands of genes differentially expressed, makes them hard to interpret, giving poor mechanistic insights and hampering translation to the clinics. Here we show how network biology can help simplify transcriptomic drug signatures, filtering out irrelevant genes, accounting for tissue-specific biases and ultimately yielding functionally-coherent, less noisy drug modules. We successfully analyzed 170 drugs tested in 637 cancer cell lines, proving a broad applicability of our approach and evincing an intimate relationship between modules gene expression levels and drugs mechanisms of action. Further, we have characterized multiple aspects of our transcriptomic modules. As a result, the drugs included in this study are now annotated well beyond the reductionist (target-centered) view.\n\nAuthor SummaryLarge scale pharmacogenomics studies performed with hundreds of cell lines offer a means to link the molecular features of the cells to their response to drug treatments. Unfortunately, simple drug-gene correlations are usually not enough to consistently identify what gene expression patterns will determine drug sensitivity, as the tissue of origin of the cells, together with the expression of e.g. membrane transporters can greatly confound the analysis. To ameliorate these biases, we have devised a network-based strategy that selects genes that are both well correlated to drug response and closeby in the human protein interaction network. Reassuringly, we have confirmed that our identified drug sensitivity modules are tightly connected to the mechanisms of action of the drugs. Moreover, while our modules have no more than 100 genes, they retain the predictive power of the much larger gene signatures that are typically obtained by drug-gene correlations alone. Here, we release the characteristic modules for almost 200 drugs, in a format that is suitable for downstream bioinformatics analyses such as gene-set enrichment analysis.

bioinformatics↗

bioRxiv2018

Predicting Methylation from Sequence and Gene Expression Using Deep Learning with Attention

DNA methylation has been extensively linked to alterations in gene expression, playing a key role in the manifestation of multiple diseases, most notably cancer. For this reason, researchers have long been measuring DNA methylation in living organisms. The relationship between methylation and expression, and between methylation in different genomic regions is of great theoretical interest from a molecular biology perspective. Therefore, several models have been suggested to support the prediction of methylation status in samples. These models, however, have two main limitations: (a) they heavily rely on partially measured methylation levels as input, somewhat defeating the object as one is required to collect measurements from the sample of interest before applying the model; and (b) they are largely based on human mediated feature engineering, thus preventing the model from unveiling its own representations. To address these limitations we used deep learning, with an attention mechanism, to produce a general model that predicts DNA methylation for a given sample in any CpG position based solely on the sample's gene expression profile and the sequence surrounding the CpG.\n\nWe show that our model is capable of generalizing to a completely separate test set of CpG positions and subjects. Depending on gene-CpG proximity conditions, our model can attain a Spearman correlation of up to 0.8 and MAE of 0.14 for thousands of CpG sites in the test data. We also identify and analyze several motifs and genes that our model suggests may be linked to methylation activity, such as Nodal and Hand1. Moreover, our approach, and most notably the use of attention mechanisms, offers a novel framework with which to extract valuable insights from gene expression data when combined with sequence information.\n\nThe code and trained models are available at: https://github.com/YakhiniGroup/Methylation

bioinformatics↗

bioRxiv2018

Leveraging protein dynamics to identify cancer mutational hotspots in 3D-structures

Large-scale exome sequencing of tumors has enabled the identification of cancer drivers using recurrence and clustering-based approaches. Some of these methods also employ three-dimensional protein structures to identify mutational hotspots in cancer-associated genes. In determining such mutational clusters in structures, existing approaches overlook protein dynamics, despite the essential role of dynamics in protein functionality. In this work, we present a framework to identify driver genes using a dynamics-based search of mutational hotspot communities. After partitioning 3D structures into distinct communities of residues using anisotropic network models, we map variants onto the partitioned structures. We then search for signals of positive selection among these residue communities to identify putative drivers. We applied our method using the TCGA pan-cancer atlas missense mutation catalog. Overall, our analyses predict one or more mutational hotspots within the resolved structures of 434 genes. Ontological and pathway enrichment analyses implicate genes with predicted hotspots to be enriched in biological processes associated with tumor progression. Additionally, a comparison between our approach and existing hotspot detection methods that use structural data suggests that the inclusion of dynamics significantly increases the sensitivity of driver detection.

bioinformatics↗

bioRxiv2017

Heterogeneity of normal human breast stem and progenitor cells as revealed by transcriptional profiling

During development and pregnancy, the human mammary gland undergoes extensive remodeling in processes driven by populations of stem and progenitor cells. We recently reported that breast cancers are also hierarchically organized and driven by distinct populations of cancer stem cells characterized as CD44+CD24low/- or by expression of Aldehyde dehydrogenase (ALDH). These sets of markers identify largely non-overlapping mesenchymal and epithelial populations, each of which is capable of tumor initiation when transplanted into immunosuppressed mice. Less is known about these two populations, individually or their overlap, in the normal human mammary gland. The goal of this study was to understand the biology of the ALDH+ and CD44+CD24- populations in the normal human breast, using flow cytometry based sorting paired with functional ex vivo analyses, RNA-sequencing, and single cell RNA expression profiling. ALDH+ cells and ALDH-CD44+CD24- cells, generally, have epithelial-like and mesenchymal-like characteristics, respectively. Despite this, there are substantial similarities in the biological pathways activated in both populations when compared to differentiated cells. Additionally, we found a substantial proportion of cells that simultaneously express ALDH+ and CD44+CD24- whose abundance varies between individuals. At the single cell level, these cells have the greatest mammosphere forming capacity and express high levels of stemness and EMT-associated genes including ID1, SOX2, TWIST1, and ZEB2. Through unbiased analysis of individual ALDH+ cells, we find cells with either epithelial or mesenchymal expression phenotypes. We also identify a subpopulation of cells with a hybrid epithelial/mesenchymal expression phenotype that overexpress genes associated with aggressive triple negative breast cancers. These results highlight the utility of single cell analyses to characterize tissue heterogeneity, even in marker enriched cell populations, and further identifies the genes and pathways that define this heterogeneity.

developmental biology↗

bioRxiv2016

Aggregation of biological particles under radial directional guidance.

Many biological environments display an almost radially-symmetric structure, allowing proteins, cells or animals to move in an oriented fashion. Motivated by specific examples of cell movement in tissues, pigment protein movement in pigment cells and animal movement near watering holes, we consider a class of radially-symmetric anisotropic diffusion problems, which we call the star problem. The corresponding diffusion tensor D(x) is radially symmetric with isotropic diffusion at the origin. We show that the anisotropic geometry of the environment can lead to strong aggregations and blow-up at the origin. We classify the nature of aggregation and blow-up solutions and provide corresponding numerical simulations. A surprising element of this strong aggregation mechanism is that it is entirely based on geometry and does not derive from chemotaxis, adhesion or other well known aggregating mechanisms. We use these aggregate solutions to discuss the process of pigmentation changes in animals, cancer invasion in an oriented fibrous habitat (such as collagen fibres), and sheep distributions around watering holes.\n\nJTB classification21.050, 21.160, 52.250, 71.060

systems biology↗

bioRxiv2018

The self-organization model reveals systematic characteristics of aging

Aging is a fundamental biological process, where key bio-markers interact with each other and synergistically regulate the aging process. Thus aging dysfunction will induce many disorders. Finding aging markers and re-constructing networks based on multi-omics data (i.e. methylation, transcriptional and so on) are informative to study the aging process. However, optimizing the model to predict aging have not been performed systemically, although it is critical to identify potential molecular mechanism of aging relative diseases.\n\nThis paper aims to model the aging self-organization system using a serious of supervised learning methods, and study complex molecular mechanism of aging at system level: i.e. optimizing the aging network; summarizing interactions between aging markers; accumulating patterns of aging markers within module; finding order-parameters of the aging self-organization system.\n\nIn this work, the normal aging process is modeled based on multi-omics profiles across tissues. In addition, the computational pipeline aims to model aging self-organizing systems and study the relationship between aging and related diseases (i.e. cancers), thus provide useful indexes of aging related diseases and improve diagnostic effects for both pre- and pro- gnosis.

systems biology↗

bioRxiv2017

A Deep Learning Model for Predicting Tumor Suppressor Genes and Oncogenes from PDB Structure

While cancer is a heterogeneous complex of distinct diseases, the common underlying mechanism for uncontrolled tumor growth is due to mutations in proto-oncogenes and the loss of the regulatory function of tumor suppression genes. In this paper we propose a novel deep learning model for predicting tumor suppression genes (TSGs) and proto-oncogenes (OGs) from their Protein Data Bank (PDB) three dimensional structures. Specifically, we develop a convolutional neural network (CNN) to classify the feature map sets extracted from the tertiary protein structures. Each feature map set represents particular biological features associated with the atomic coordinates appearing on the outer surface of proteins three dimensional structure. The experimental results on the collected dataset for classifying TSGs and OGs demonstrate promising performance with 82.57% accuracy and 0.89 area under ROC curve. The initial success of the proposed model warrants further study to develop a comprehensive model to identify the cancer driver genes or events using the principle cancer genes (TSG and OG).

bioinformatics↗

bioRxiv2016

Single-cell entropy for accurate estimation of differentiation potency from a cell’s transcriptome

The ability to quantify differentiation potential of single cells is a task of critical importance for single-cell studies. So far however, there is no robust general molecular correlate of differentiation potential at the single cell level. Here we show that differentiation potency of a single cell can be approximated by computing the signaling promiscuity, or entropy, of a cells transcriptomic profile in the context of a cellular interaction network, without the need for model training or feature selection. We validate signaling entropy in over 7,000 single cell RNA-Seq profiles, representing all main differentiation stages, including time-course data. We develop a novel algorithm called Single Cell Entropy (SCENT), which correctly identifies known cell subpopulations of varying potency, enabling reconstruction of cell-lineage trajectories. By comparing bulk to single cell data, SCENT reveals that expression heterogeneity within single cell populations is regulated, pointing towards the importance of cell-cell interactions. In the context of cancer, SCENT can identify drug resistant cancer stem-cell phenotypes, including those obtained from circulating tumor cells. In summary, SCENT can directly estimate the differentiation potency and plasticity of single-cells, allowing unbiased quantification of intercellular heterogeneity, and providing a means to identify normal and cancer stem cell phenotypes.\n\nSoftware AvailabilitySCENT is freely available as an R-package from github: https://github.com/aet21/SCENT

systems biology↗

bioRxiv2017

TraPS-VarI: a python module for the identification of STAT3 modulating germline receptor variants

MotivationHuman individuals differ because of variations in the DNA sequences of all the 46 chromosomes. Information on genetic variations altering the membrane-proximal binding sites for signal transducer of transcription 3 (STAT3) is valuable for understanding the genetic basis of cancer prognosis and disease progression (Ulaganathan et al, 2015). In this regard, non-synonymous coding region mutations resulting in the alteration of protein sequence in the juxtamembrane region of the type I membrane proteins are biologically and clinically relevant. The knowledge of such rare cell line- and individual-specific germline receptor variants is crucial for the investigation of cell-line specific biological mechanisms and genotype-centric therapeutic approaches.\n\nResultsHere we present TraPS-VarI (Transmembrane Protein Sequence Variant Identifier), a python module to rapidly identify human germline receptor variants modulating STAT3 binding sites by using the genetic variation datasets in the variant call format 4.0. For the found protein variants the module also checks for the availability of associated therapeutic agents and ongoing clinical trial studies.\n\nAvailabilityThe Source code and binaries are freely available for download at https://gitlab.com/VJ-Ulaganathan/TraPS-VarI and the documentation can be found at http://traps-vari.readthedocs.io/.\n\nContactulaganat@biochem.mpg.de & ulaganat@icloud.com\n\nSupplementary informationSupplementary data enclosed with the manuscript file.

bioinformatics↗

bioRxiv2017

A Yeast Global Genetic Screen Reveals that Metformin Induces an Iron Deficiency-Like State

We report here a simple and global strategy to map out gene functions and target pathways of drugs, toxins or other small molecules based on \"homomer dynamics\" Protein-fragment Complementation Assays (hdPCA). hdPCA measures changes in self-association (homomerization) of over 3,500 yeast proteins in yeast grown under different conditions. hdPCA complements genetic interaction measurements while eliminating confounding effects of gene ablation. We demonstrate that hdPCA accurately predicts the effects of two longevity and health-span-affecting drugs, immunosuppressant rapamycin and type II diabetes drug metformin, on cellular pathways. We also discovered an unsuspected global cellular response to metformin that resembles iron deficiency. This discovery opens a new avenue to investigate molecular mechanisms for the prevention or treatments of diabetes, cancers and other chronic diseases of aging.

systems biology↗

bioRxiv2018

6mer Seed Toxicity Determines Strand Selection in miRNAs

Many siRNAs and shRNAs are toxic to cancer cells through a 6mer seed sequence (position 2-7 of the guide strand). A siRNA screen with all 4096 possible 6mer seed sequences in a neutral RNA backbone revealed a preference for guanine in positions 1-3 and a GC content of >80% of the 6mer seed in the most toxic siRNAs. These 6mer seed containing siRNAs exert their toxicity by targeting survival genes which contain GC-rich 3UTRs. The master tumor suppressor miRNA miR-34a was found to be toxic through such a G-rich 6mer seed suggesting that certain tumor suppressive miRNAs use a toxic 6mer seed to kill cancer cells. An analysis of all mature miRNAs suggests that most miRNAs evolved to avoid guanine at the 5' end of the 6mer seed sequence of the predominantly expressed arm. In contrast, for many tumor suppressive miRNAs the predominant arm contains a G-rich toxic 6mer seed, presumably to eliminate cancer cells.

cell biology↗

bioRxiv2018

Molecular analysis of long non-coding RNA GAS5 and microRNA-34a expression signature in common solid tumors: A pilot study

Accumulating evidence indicates that non-coding RNAs including microRNAs (miRs) and long non-coding RNAs (lncRNAs) are aberrantly expressed in cancer, providing promising biomarkers for diagnosis, prognosis and/or therapeutic targets. We aimed in the current work to quantify the expression profile of miR-34a and one of its bioinformatically selected partner lncRNA growth arrest-specific 5 (GAS5) in a sample of Egyptian cancer patients, including three prevalent types of cancer in our region; renal cell carcinoma (RCC), hepatocellular carcinoma (HCC) and glioblastoma (GB) as well as to correlate these expression profiles with the available clinicopathological data in an attempt to clarify their roles in cancer. Quantitative real-time polymerase chain reaction analysis was applied. Different bioinformatics databases were searched to confirm the potential miRNAs-lncRNA interactions of the selected ncRNAs in cancer pathogenesis. GAS5 was significantly under-expressed in the three types of cancer. However, levels of miR-34a greatly varied according to the tumor type; it displayed an increased expression in RCC [4.05 (1.003-22.69), p <0.001] and a decreased expression in GB [0.35 (0.04-0.95), p <0.001]. A weak negative correlation was observed between levels of GAS5 and miR-34a in GB [r = -0.39, p =0.006]. Univariate analyses revealed a correlation of GAS5 downregulation with poor disease-free survival (r = 0.31, p =0.018) and overall survival (r = 0.28, p =0.029) in RCC but not in GB, and a marginal significance correlation with a higher number of lesions in HCC. Hierarchical clustering analysis showed RCC patients among others, could be clustered by GAS5 and miR-34a co-expression profile. Our results confirm the tumor suppressor role of GAS5 in cancer and suggest its potential applicability to be a predictor of bad outcomes with other conventional markers for various types of cancer. Further functional validation studies are warranted to confirm miR-34a/GAS5 interplay in cancer.

molecular biology↗

bioRxiv2018

Towards Decoding the Metabolic Plasticity in Cancer: Coupling of Gene Regulation and Metabolic Pathways

Metabolic plasticity enables cancer cells to switch their metabolism phenotypes between glycolysis and oxidative phosphorylation (OXPHOS) during tumorigenesis and metastasis. However, it is still largely unknown how cancer cells orchestrate gene regulation to balance their glycolysis and OXPHOS activities for better survival. Here, we establish a theoretical framework to model the coupling of gene regulation and metabolic pathways in cancer. Our modeling results demonstrate a direct association between the activities of AMPK and HIF-1, master regulators of OXPHOS and glycolysis respectively, with the activities of three metabolic pathways: glucose oxidation, glycolysis and fatty acid oxidation (FAO). Guided by the model, we develop metabolic pathway signatures to quantify the activities of glycolysis, FAO and the citric acid cycle of tumor samples by evaluating the expression levels of enzymes involved in corresponding processes. The association of AMPK/HIF-1 activity with metabolic pathway activity, predicted by the model and verified by analyzing the gene expression and metabolite abundance data of patient samples, is further validated by in vitro studies of aggressive triple negative breast cancer cell lines.

systems biology↗

bioRxiv2018

Cell rounding causes genomic instability by dissociation of single-stranded DNA-binding proteins

Genomic instability can cause a wide range of diseases, including cancer and cellular senescence, which is also a major challenge in stem cell therapy. However, how a single event can cause extremely high levels of genomic instability remains unclear. Using our developed method, cell in situ electrophoresis (CISE), and models of normal, cancer, and embryonic stem cells, we found that cell rounding as a catastrophic source event ubiquitously observed in vivo and in vitro might lead to large-scale DNA deprotection, genomic instability, chromosomal shattering, cell heterogeneity, and senescent crisis by dissociation of single-stranded DNA-binding proteins (SSBs). Understanding the mechanism may facilitate the development of clinical strategies for cancer therapy, improve the safety of stem cell therapy, and prevent pathological aging.

cell biology↗