Although the role of miR-200s in regulating E-cadherin expression and epithelial-to-mesenchymal transition is well established, their influence on metastatic colonization remains controversial. Here ...we have used clinical and experimental models of breast cancer metastasis to discover a pro-metastatic role of miR-200s that goes beyond their regulation of E-cadherin and epithelial phenotype. Overexpression of miR-200s is associated with increased risk of metastasis in breast cancer and promotes metastatic colonization in mouse models, phenotypes that cannot be recapitulated by E-cadherin expression alone. Genomic and proteomic analyses revealed global shifts in gene expression upon miR-200 overexpression toward that of highly metastatic cells. miR-200s promote metastatic colonization partly through direct targeting of Sec23a, which mediates secretion of metastasis-suppressive proteins, including Igfbp4 and Tinagl1, as validated by functional and clinical correlation studies. Overall, these findings suggest a pleiotropic role of miR-200s in promoting metastatic colonization by influencing E-cadherin-dependent epithelial traits and Sec23a-mediated tumor cell secretome.
SARS-COV-2 has roused the scientific community with a call to action to combat the growing pandemic. At the time of this writing, there are as yet no novel antiviral agents or approved vaccines ...available for deployment as a frontline defense. Understanding the pathobiology of COVID-19 could aid scientists in their discovery of potent antivirals by elucidating unexplored viral pathways. One method for accomplishing this is the leveraging of computational methods to discover new candidate drugs and vaccines
. In the last decade, machine learning-based models, trained on specific biomolecules, have offered inexpensive and rapid implementation methods for the discovery of effective viral therapies. Given a target biomolecule, these models are capable of predicting inhibitor candidates in a structural-based manner. If enough data are presented to a model, it can aid the search for a drug or vaccine candidate by identifying patterns within the data. In this review, we focus on the recent advances of COVID-19 drug and vaccine development using artificial intelligence and the potential of intelligent training for the discovery of COVID-19 therapeutics. To facilitate applications of deep learning for SARS-COV-2, we highlight multiple molecular targets of COVID-19, inhibition of which may increase patient survival. Moreover, we present CoronaDB-AI, a dataset of compounds, peptides, and epitopes discovered either
or
that can be potentially used for training models in order to extract COVID-19 treatment. The information and datasets provided in this review can be used to train deep learning-based models and accelerate the discovery of effective viral therapies.
Microglia are resident immune cells of the CNS that are activated by infection, neuronal injury, and inflammation. Here, we utilize flow cytometry and deep RNA sequencing of acutely isolated spinal ...cord microglia to define their activation in vivo. Analysis of resting microglia identified 29 genes that distinguish microglia from other CNS cells and peripheral macrophages/monocytes. We then analyzed molecular changes in microglia during neurodegenerative disease activation using the SOD1G93A mouse model of amyotrophic lateral sclerosis (ALS). We found that SOD1G93A microglia are not derived from infiltrating monocytes, and that both potentially neuroprotective and toxic factors, including Alzheimer’s disease genes, are concurrently upregulated. Mutant microglia differed from SOD1WT, lipopolysaccharide-activated microglia, and M1/M2 macrophages, defining an ALS-specific phenotype. Concurrent messenger RNA/fluorescence-activated cell sorting analysis revealed posttranscriptional regulation of microglia surface receptors and T cell-associated changes in the transcriptome. These results provide insights into microglia biology and establish a resource for future studies of neuroinflammation.
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•Identification of specific marker genes for acutely isolated microglia•Progressive resident microglia transcriptome changes reveal in vivo activation phenotype•Microglial ALS disease activation signature distinct from M1/M2 macrophages•Parallel transcriptome and FACS analyses reveal T cell/microglia crosstalk
Microglia are resident immune cells of the brain that are activated by infection or tissue damage. In this study, Maniatis and colleagues report the acute isolation, transcriptional profiling, and immunological analysis of microglia during disease activation in an ALS mouse model. A neurodegeneration-specific gene-expression signature is identified that includes induction of both neuroprotective and toxic factors and is distinct from that associated with M1/M2 macrophages. The data also provide a resource for future studies of microglia activation in neurodegenerative diseases.
Posttranscriptional regulatory programs governing diverse aspects of RNA biology remain largely uncharacterized. Understanding the functional roles of RNA cis-regulatory elements is essential for ...decoding complex programs that underlie the dynamic regulation of transcript stability, splicing, localization, and translation. Here, we describe a combined experimental/computational technology to reveal a catalog of functional regulatory elements embedded in 3' UTRs of human transcripts. We used a bidirectional reporter system coupled with flow cytometry and high-throughput sequencing to measure the effect of short, noncoding, vertebrate-conserved RNA sequences on transcript stability and translation. Information-theoretic motif analysis of the resulting sequence-to-gene-expression mapping revealed linear and structural RNA cis-regulatory elements that positively and negatively modulate the posttranscriptional fates of human transcripts. This combined experimental/computational strategy can be used to systematically characterize the vast landscape of posttranscriptional regulatory elements controlling physiological and pathological cellular state transitions.
Here we performed a systematic search to identify breast-cancer-specific small noncoding RNAs, which we have collectively termed orphan noncoding RNAs (oncRNAs). We subsequently discovered that one ...of these oncRNAs, which originates from the 3' end of TERC, acts as a regulator of gene expression and is a robust promoter of breast cancer metastasis. This oncRNA, which we have named T3p, exerts its prometastatic effects by acting as an inhibitor of RISC complex activity and increasing the expression of the prometastatic genes NUPR1 and PANX2. Furthermore, we have shown that oncRNAs are present in cancer-cell-derived extracellular vesicles, raising the possibility that these circulating oncRNAs may also have a role in non-cell autonomous disease pathogenesis. Additionally, these circulating oncRNAs present a novel avenue for cancer fingerprinting using liquid biopsies.
Our understanding of the beads-on-a-string arrangement of nucleosomes has been built largely on high-resolution sequence-agnostic imaging methods and sequence-resolved bulk biochemical techniques. To ...bridge the divide between these approaches, we present the single-molecule adenine methylated oligonucleosome sequencing assay (SAMOSA). SAMOSA is a high-throughput single-molecule sequencing method that combines adenine methyltransferase footprinting and single-molecule real-time DNA sequencing to natively and nondestructively measure nucleosome positions on individual chromatin fibres. SAMOSA data allows unbiased classification of single-molecular 'states' of nucleosome occupancy on individual chromatin fibres. We leverage this to estimate nucleosome regularity and spacing on single chromatin fibres genome-wide, at predicted transcription factor binding motifs, and across human epigenomic domains. Our analyses suggest that chromatin is comprised of both regular and irregular single-molecular oligonucleosome patterns that differ subtly in their relative abundance across epigenomic domains. This irregularity is particularly striking in constitutive heterochromatin, which has typically been viewed as a conformationally static entity. Our proof-of-concept study provides a powerful new methodology for studying nucleosome organization at a previously intractable resolution and offers up new avenues for modeling and visualizing higher order chromatin structure.
Synonymous codon usage has long been known as a factor that affects average expression level of proteins in fast-growing microorganisms, but neither its role in dynamic changes of expression in ...response to environmental changes nor selective factors shaping it in the genomes of higher eukaryotes have been fully understood. Here, we propose that codon usage is ubiquitously selected to synchronize the translation efficiency with the dynamic alteration of protein expression in response to environmental and physiological changes. Our analysis reveals that codon usage is universally correlated with gene function, suggesting its potential contribution to synchronized regulation of genes with similar functions. We directly show that coexpressed genes have similar synonymous codon usages within the genomes of human, yeast, Caenorhabditis elegans and Escherichia coli. We also demonstrate that perturbing the codon usage directly affects the level or even direction of changes in protein expression in response to environmental stimuli. Perturbing tRNA composition also has tangible phenotypic effects on the cell. By showing that codon usage is universally function-specific, our results expand, to almost all organisms, the notion that cells may need to dynamically alter their intracellular tRNA composition in order to adapt to their new environment or physiological role.
The major cap-binding protein eukaryotic translation initiation factor 4E (eIF4E), an ancient protein required for translation of all eukaryotic genomes, is a surprising yet potent oncogenic driver. ...The genetic interactions that maintain the oncogenic activity of this key translation factor remain unknown. In this study, we carry out a genome-wide CRISPRi screen wherein we identify more than 600 genetic interactions that sustain eIF4E oncogenic activity. Our data show that eIF4E controls the translation of Tfeb, a key executer of the autophagy response. This autophagy survival response is triggered by mitochondrial proteotoxic stress, which allows cancer cell survival. Our screen also reveals a functional interaction between eIF4E and a single anti-apoptotic factor, Bcl-xL, in tumor growth. Furthermore, we show that eIF4E and the exon-junction complex (EJC), which is involved in many steps of RNA metabolism, interact to control the migratory properties of cancer cells. Overall, we uncover several cancer-specific vulnerabilities that provide further resolution of the cancer translatome.
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•Genome-wide CRISPRi screen reveals more than 600 synthetic lethal partners of eIF4E•Functional interaction between eIF4E and Bcl-xL is important for tumor growth•Mitochondrial dysfunction triggers an eIF4E-dependent adaptive stress response•Interaction between eIF4E and EJC controls the migratory capacity of cancer cells
Kuzuoglu-Ozturk et al. identify more than 600 genetic interactions that sustain oncogenic activity of the major cap-binding protein eIF4E by a genome-wide CRIPSRi screen. Their data reveal interactions among distinct cellular processes and eIF4E, uncovering several cancer-specific vulnerabilities.
The original version of this Article contained an error in Figure 3, where panel d was inadvertently replaced with a duplicate of panel c during typesetting. Also, the legend of Figure 5f incorrectly ...read '310 AD patients (blue dots, r = -0.4) and 157 non-demented individuals (green dots, r = -0.1)', and should have read '310 AD patients (blue dots, r = -0.1) and 157 non-demented individuals (green dots, r = -0.4)'. Both of these errors have now been corrected in both the PDF and HTML versions of the Article.
Understanding the genetic basis of adaptation is a central problem in biology. However, revealing the underlying molecular mechanisms has been challenging as changes in fitness may result from ...perturbations to many pathways, any of which may contribute relatively little. We have developed a combined experimental/computational framework to address this problem and used it to understand the genetic basis of ethanol tolerance in Escherichia coli. We used fitness profiling to measure the consequences of single‐locus perturbations in the context of ethanol exposure. A module‐level computational analysis was then used to reveal the organization of the contributing loci into cellular processes and regulatory pathways (e.g. osmoregulation and cell‐wall biogenesis) whose modifications significantly affect ethanol tolerance. Strikingly, we discovered that a dominant component of adaptation involves metabolic rewiring that boosts intracellular ethanol degradation and assimilation. Through phenotypic and metabolomic analysis of laboratory‐evolved ethanol‐tolerant strains, we investigated naturally accessible pathways of ethanol tolerance. Remarkably, these laboratory‐evolved strains, by and large, follow the same adaptive paths as inferred from our coarse‐grained search of the fitness landscape.
Synopsis
Elucidating the genetic basis of complex phenotypes remains a fundamental challenge in biology. We have developed a systematic framework for comprehensive genetic analysis of microbial phenotypes. Our approach combines the power of fitness profiling (Girgis et al, 2007; Amini et al, 2009) with the sensitivity of module‐level analysis (Goodarzi et al, 2009a) to identify key genetic modules that directly affect a phenotype under study. We applied our technology to ethanol tolerance, a complex phenotype with broad industrial relevance. Ethanol affects a variety of cellular components and pathways, including but not limited to membrane integrity (Dombek and Ingram, 1984), enzyme activities (Millar et al, 1982), and proton flux (D'Amore et al, 1990). Given the diversity of targets, the emergence of ethanol tolerance requires modifications to multiple pathway (D'Amore and Stewart, 1987).
To reveal the genetic basis of ethanol tolerance in Escherichia coli, we used two high‐coverage mutant libraries (a transposon library and an overexpression library) to assess the fitness consequences of single‐locus perturbations. Each cell in our transposon library contains a random transposon insertion in its genome (Girgis et al, 2007); whereas the cells in the overexpression library carry 1–3 kb genomic fragments cloned into a cloning vector (Amini et al, 2009). We grew these libraries under mild (4% v/v) and harsh (5.5% v/v) ethanol concentrations. On growth, the abundance of each transposon insertion or overexpression mutant changes as a function of its fitness, a process that can be monitored through parallel genetic footprinting and microarray hybridization (Figure 1A). This results in a global fitness profile, where the contribution of each genetic locus to ethanol tolerance can be quantified in parallel. However, in the context of ethanol tolerance and other complex phenotypes, single‐locus perturbations typically result in modest changes in fitness. Although these small differences can be amplified through multiple rounds of selection, the number of generations is limited as spontaneous beneficial mutations emerge in the population and cause strong biases in the resulting fitness profiles. To boost our analytical power without introducing these biases in the data, we used a module‐level computational method to discover the pathways and components that are strongly associated with the data as opposed to focusing on the genes individually (Goodarzi et al, 2009a). Genes function in the context of pathways and modules and module‐level analyses increase statistical power through combining information from multiple genes functioning as part of a given pathway (Subramanian et al, 2005).
The module‐level analysis of the fitness scores from both libraries revealed a diverse set of pathways that have a direct function in ethanol tolerance. Some of these pathways, including heat‐shock stress response and osmoregulation, are known modifiers of ethanol tolerance; whereas others such as acid‐stress response and fimbrial structures are novel pathways. Among our findings was the important function of three regulatory proteins: FNR, ArcA, and CafA. Knocking out FNR/ArcA that upregulates aerobic respiration proteins and TCA cycle components results in a marked increase in ethanol tolerance. Similarly, knocking out CafA, a post‐transcriptional regulator of alcohol dehydrogenase, is beneficial for tolerance. Given these observations, we hypothesized that selection for ethanol tolerance can result in higher ethanol degradation.
As a large fraction of discovered pathways belonged to central metabolism, we used metabolomics to evaluate our findings. To directly assess the metabolic consequences of adaptation to ethanol, we evolved ethanol‐tolerant strains in minimal media plus glucose for ∼30 and 160 generations. We then compared the steady‐state level of metabolites in these strains to that of the wild type (Figure 1B). In agreement with our fitness profiling results, we observed a significant increase in TCA cycle metabolites in one of our ethanol‐tolerant strains. Higher concentrations of TCA cycle components along with a high free coenzyme A (CoA) to acetyl‐coenzyme A (acetyl‐CoA) ratio hinted at the capacity of this strain to metabolize ethanol. To test this hypothesis, we performed stable‐isotope labeling on our ethanol‐tolerant strain versus wild type. After growth on labeled ethanol, we measured the fraction of metabolites that were labeled at each timepoint (Figure 1B). Our results confirmed that the ethanol‐tolerant strain has the capacity to consume ethanol through its conversion into acetyl‐CoA and further assimilation in the TCA cycle.
By using a variety of systems‐level approaches, we have been able to genetically dissect ethanol tolerance in E. coli. We have shown that fitness profiling, in combination with module‐level analysis tools, can serve as a powerful approach for revealing the genetic basis of complex phenotypes. The fact that laboratory evolution ended up using the very modules that we discovered, highlights the biological and adaptive relevance of the proposed framework.
We have designed an experimental/computational framework for studying complex phenotypes in bacteria.
Our framework relies on whole‐genome fitness profiling coupled with a module‐level analysis to discover pathways that directly affect fitness.
As a proof‐of‐principle, we studied ethanol tolerance in Escherichia coli and we identified key pathways that contribute to this phenotype.
We then validated our findings through genetic manipulations, gene‐expression profiling, metabolite‐level measurements, and stable‐isotope labeling.
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