Functional discovery via a compendium of expression profiles

Transcription-Driven Repurposing of Cardiotonic Steroids for Lithium Treatment of Severe Depression

Lithium is prescribed as a mood stabilizer for bipolar disorder and severe depression. However, the mechanism of action of lithium is unknown and there are major side effects associated with prolonged medication. This motivates a search for safer alternative drug repurposing candidates. Given that the drug mechanism may be encoded in transcriptional changes, we generated the gene expression profile for acute lithium treatment of cortical neuronal cultures. We found that the lithium-associated transcription response harbors a significant component that is the reverse of that seen in human brain samples from patients with major depression, bipolar disorder, and a mouse model of depression. Interrogating publicly available drug-driven expression data, we found that cardiotonic steroids drive gene expression in a correlated manner to our acute lithium profile. An analysis of the psychiatric medication cohort of the Norwegian Prescription Database showed that cardiotonic prescription is associated with a lower incidence of lithium prescription. Our transcriptional and epidemiological observations point towards cardiotonic steroids as possible repurposing candidates for lithium. These observations motivate a controlled trial to establish a causal connection and genuine therapeutic benefit in the context of depression.

DeleteomeTools: utilizing a compendium of yeast deletion strain transcriptomes to identify co-functional genes

We introduce DeleteomeTools, an R package that leverages the Deleteome compendium of yeast single-gene deletion transcriptomes to predict gene function. Primarily, the package provides functions for identifying similarities between the transcriptomic signatures of deletion strains, thereby associating genes of interest with others that may be functionally related. We describe how our software predicted a novel relationship between the yeast nucleoporin Nup170 and the Ctf18-RFC complex, which was confirmed experimentally, revealing a previously unknown link between nuclear pore complexes and the DNA replication machinery. To assess the package's broader predictive capabilities, we performed a systematic evaluation that tested how well it predicted Gene Ontology (GO) annotations already applied to the subset of genes deleted in Deleteome strains. We show that our package predicted a majority of reported GO:biological process annotations with semantic similarities ranging from moderate to identical. We also discuss how our strategy for quantifying similarity between deletion strains, which relies on differential expression signatures, differs from other approaches that use global expression profiles, and why it has the potential to identify functional relationships that might otherwise go undetected.

Identification of chemical inhibitors targeting long noncoding RNA through gene signature-based high throughput screening

Scalable methods for functionally high-throughput screening of RNA-targeting small molecules are currently limited. Here, an RNA knockdown gene signature and high-throughput sequencing-based high-throughput screening (HTS2) were integrated to identify RNA-targeting compounds. We first generated a gene signature characterizing the knockdown of the long non-coding RNA LINC00973. Then, screening of 8199 compounds by HTS2 assay identified that treatments of Hesperadin and GSK1070916 significantly mimic the expression pattern of the LINC00973 knockdown gene signature. Functionally, cell phenotype changes after treatments of these two compounds also mimic the losing function of LINC00973 in multiple types of cancer cells. Mechanistically, the inhibitory action of these two compounds on LINC00973 primarily operates via the AURKB-mediated MAPK signaling pathway, resulting in reduced expression of the transcription factor c-Jun. Consequently, this leads to the suppression of LINC00973 transcription. Moreover, these two compounds significantly inhibit xenograft tumor growth in vivo. Clinically, we further found that breast tumors with high expression of LINC00973 also show relatively high expression of AURKB or JUN, and vice versa. In summary, we established a novel high-throughput screening strategy to identify small molecules capable of targeting RNA, provided two promising compounds targeting LINC00973 and further shed light on the underlying transcriptional upregulation mechanism of LINC00973 within cancer cells.

Worm Perturb-Seq: massively parallel whole-animal RNAi and RNA-seq

The transcriptome provides a highly informative molecular phenotype to connect genotype to phenotype and is most frequently measured by RNA-sequencing (RNA-seq). Therefore, an ultimate goal is to perturb every gene and measure changes in the transcriptome. However, this remains challenging, especially in intact organisms due to different experimental and computational challenges. Here, we present 'Worm Perturb-Seq (WPS)', which provides high-resolution RNA-seq profiles for hundreds of replicate perturbations at a time in a living animal. WPS introduces multiple experimental advances that combine strengths of bulk and single cell RNA-seq, and that further provides an analytical framework, EmpirDE, that leverages the unique power of the large WPS datasets. EmpirDE identifies differentially expressed genes (DEGs) by using gene-specific empirical null distributions, rather than control conditions alone, thereby systematically removing technical biases and improving statistical rigor. We applied WPS to 103 Caenhorhabditis elegans nuclear hormone receptors (NHRs) to delineate a Gene Regulatory Network (GRN) and found that this GRN presents a striking 'pairwise modularity' where pairs of NHRs regulate shared target genes. We envision that the experimental and analytical advances of WPS should be useful not only for C. elegans, but will be broadly applicable to other models, including human cells.

Translation efficiency covariation across cell types is a conserved organizing principle of mammalian transcriptomes

Characterization of shared patterns of RNA expression between genes across conditions has led to the discovery of regulatory networks and novel biological functions. However, it is unclear if such coordination extends to translation, a critical step in gene expression. Here, we uniformly analyzed 3,819 ribosome profiling datasets from 117 human and 94 mouse tissues and cell lines. We introduce the concept of Translation Efficiency Covariation (TEC), identifying coordinated translation patterns across cell types. We nominate potential mechanisms driving shared patterns of translation regulation. TEC is conserved across human and mouse cells and helps uncover gene functions. Moreover, our observations indicate that proteins that physically interact are highly enriched for positive covariation at both translational and transcriptional levels. Our findings establish translational covariation as a conserved organizing principle of mammalian transcriptomes.

Oxyresveratrol as a novel ferroptosis inducer exhibits anticancer activity against breast cancer via the EGFR/PI3K/AKT/GPX4 signalling axis

Introduction

Oxyresveratrol (ORes) exhibits significant anticancer activity, particularly against breast cancer. However, its exact mechanism of action (MOA) remains unclear. This study aimed to investigate the pharmacological activity and underlying MOA.

Methods

The inhibitory effect of ORes on breast cancer cell growth was confirmed, and the effective concentrations were determined for further experiments. Gene expression profiles (GEPs) were collected from MDA-MB-231 cells treated with ORes at varying concentrations using HTS2. Bioinformatics tools were used to predict the anticancer activity and MOA of ORes. Ferroptosis markers (ferrous ions, reactive oxygen species, lipid peroxidation, and GPX4 expression) were assessed, and mitochondrial morphology was observed. The effect of ORes on tumour growth was evaluated in vivo, along with the analysis of ferroptosis in tissues. The MOA was explored using L1000, Drug Gene DataBase (DGDB), and Western blotting analyses.

Results

ORes significantly reduces breast cancer cell viability and proliferation in a concentration-dependent manner, with IC50 values of 104.8 μM, 150.2 μM, and 143.6 μM in MDA-MB-231, BT-549, and 4T1 cells, respectively. GEPs induced by ORes were significantly enriched in the ferroptosis and PI3K/AKT signalling pathways. ORes inhibited breast cancer cell growth, increased intracellular ferrous ion levels, reactive oxygen species, and lipid peroxidation, and induced ferroptosis-related mitochondrial alterations. These effects were associated with decreased GPX4 expression and suppression of EGFR, phosphorylated PI3K, and phosphorylated AKT. ORes inhibited tumour growth, enhanced iron deposition, and reduced GPX4 expression in tumour tissues in vivo. Notably, treatment with the ferroptosis inhibitor ferrostatin-1 (Ferr-1) attenuated the anticancer effects of ORes, confirming the pivotal role of ferroptosis in ORes-mediated breast cancer inhibition.

Conclusion

ORes inhibits breast cancer cell growth by inducing ferroptosis through suppression of the EGFR/PI3K/AKT/GPX4 signalling axis. This study suggests that ORes holds promise as a potential therapeutic agent for breast cancer and warrants further investigation into its clinical applications and potential integration into existing treatment regimens.

DeleteomeTools: Utilizing a compendium of yeast deletion strain transcriptomes to identify co-functional genes

We introduce DeleteomeTools, an R package that leverages the Deleteome compendium of yeast single-gene deletion transcriptomes to predict gene function. Primarily, the package provides functions for identifying similarities between the transcriptomic signatures of deletion strains, thereby associating genes of interest with others that may be functionally related. We describe how our software predicted a novel relationship between the yeast nucleoporin Nup170 and the Ctf18-RFC complex, which was confirmed experimentally, revealing a previously unknown link between nuclear pore complexes and the DNA replication machinery. To assess the package's broader predictive capabilities, we performed a systematic evaluation that tested how well it predicted Gene Ontology (GO) annotations already applied to the subset of genes deleted in Deleteome strains. We show that our package predicted a majority of reported GO:biological process annotations with semantic similarities ranging from moderate to identical. We also discuss how our strategy for quantifying similarity between deletion strains, which relies on differential expression signatures, differs from other approaches that use global expression profiles and why it has the potential to identify functional relationships that might otherwise go undetected.

Revisiting the model for coactivator recruitment: Med15 can select its target sites independent of promoter-bound transcription factors

Activation domains (ADs) within transcription factors (TFs) induce gene expression by recruiting coactivators such as the Mediator complex. Coactivators lack DNA binding domains (DBDs) and are assumed to passively follow their recruiting TFs. This is supported by direct AD-coactivator interactions seen in vitro but has not yet been tested in living cells. To examine that, we targeted two Med15-recruiting ADs to a range of budding yeast promoters through fusion with different DBDs. The DBD-AD fusions localized to hundreds of genomic sites but recruited Med15 and induced transcription in only a subset of bound promoters, characterized by a fuzzy-nucleosome architecture. Direct DBD-Med15 fusions shifted DBD localization towards fuzzy-nucleosome promoters, including promoters devoid of the endogenous Mediator. We propose that Med15, and perhaps other coactivators, possess inherent promoter preference and thus actively contribute to the selection of TF-induced genes.

Perturbation-specific transcriptional mapping for unbiased target elucidation of antibiotics

The rising prevalence of antibiotic resistance threatens human health. While more sophisticated strategies for antibiotic discovery are being developed, target elucidation of new chemical entities remains challenging. In the postgenomic era, expression profiling can play an important role in mechanism-of-action (MOA) prediction by reporting on the cellular response to perturbation. However, the broad application of transcriptomics has yet to fulfill its promise of transforming target elucidation due to challenges in identifying the most relevant, direct responses to target inhibition. We developed an unbiased strategy for MOA prediction, called perturbation-specific transcriptional mapping (PerSpecTM), in which large-throughput expression profiling of wild-type or hypomorphic mutants, depleted for essential targets, enables a computational strategy to address this challenge. We applied PerSpecTM to perform reference-based MOA prediction based on the principle that similar perturbations, whether chemical or genetic, will elicit similar transcriptional responses. Using this approach, we elucidated the MOAs of three molecules with activity against Pseudomonas aeruginosa by comparing their expression profiles to those of a reference set of antimicrobial compounds with known MOAs. We also show that transcriptional responses to small-molecule inhibition resemble those resulting from genetic depletion of essential targets by clustered regularly interspaced short palindromic repeats interference (CRISPRi) by PerSpecTM, demonstrating proof of concept that correlations between expression profiles of small-molecule and genetic perturbations can facilitate MOA prediction when no chemical entities exist to serve as a reference. Empowered by PerSpecTM, this work lays the foundation for an unbiased, readily scalable, systematic reference-based strategy for MOA elucidation that could transform antibiotic discovery efforts.

Most azole resistance mutations in the Candida albicans drug target confer cross-resistance without intrinsic fitness cost

Azole antifungals are the main drugs used to treat fungal infections. Amino acid substitutions in the drug target Erg11 (Cyp51) are a common resistance mechanism in pathogenic yeasts. How many and which mutations confer resistance is, however, largely unknown. Here we measure the impact of nearly 4,000 amino acid variants of Candida albicans Erg11 on the susceptibility to six clinical azoles. This was achieved by deep mutational scanning of CaErg11 expressed in Saccharomyces cerevisiae. We find that a large fraction of mutations lead to resistance (33%), most resistance mutations confer cross-resistance (88%) and only a handful of resistance mutations show a significant fitness cost (9%). Our results reveal that resistance to azoles can arise through a large set of mutations and this will probably lead to azole pan-resistance, with little evolutionary compromise. This resource will help inform treatment choices in clinical settings and guide the development of new drugs.

Pleiotropy, epistasis and the genetic architecture of quantitative traits

Pleiotropy (whereby one genetic polymorphism affects multiple traits) and epistasis (whereby non-linear interactions between genetic polymorphisms affect the same trait) are fundamental aspects of the genetic architecture of quantitative traits. Recent advances in the ability to characterize the effects of polymorphic variants on molecular and organismal phenotypes in human and model organism populations have revealed the prevalence of pleiotropy and unexpected shared molecular genetic bases among quantitative traits, including diseases. By contrast, epistasis is common between polymorphic loci associated with quantitative traits in model organisms, such that alleles at one locus have different effects in different genetic backgrounds, but is rarely observed for human quantitative traits and common diseases. Here, we review the concepts and recent inferences about pleiotropy and epistasis, and discuss factors that contribute to similarities and differences between the genetic architecture of quantitative traits in model organisms and humans.

Toward a foundation model of causal cell and tissue biology with a Perturbation Cell and Tissue Atlas

Comprehensively charting the biologically causal circuits that govern the phenotypic space of human cells has often been viewed as an insurmountable challenge. However, in the last decade, a suite of interleaved experimental and computational technologies has arisen that is making this fundamental goal increasingly tractable. Pooled CRISPR-based perturbation screens with high-content molecular and/or image-based readouts are now enabling researchers to probe, map, and decipher genetically causal circuits at increasing scale. This scale is now eminently suitable for the deployment of artificial intelligence and machine learning (AI/ML) to both direct further experiments and to predict or generate information that was not-and sometimes cannot-be gathered experimentally. By combining and iterating those through experiments that are designed for inference, we now envision a Perturbation Cell Atlas as a generative causal foundation model to unify human cell biology.

The RNA Helicase Ded1 from Yeast Is Associated with the Signal Recognition Particle and Is Regulated by SRP21

The DEAD-box RNA helicase Ded1 is an essential yeast protein involved in translation initiation that belongs to the DDX3 subfamily. The purified Ded1 protein is an ATP-dependent RNA-binding protein and an RNA-dependent ATPase, but it was previously found to lack substrate specificity and enzymatic regulation. Here we demonstrate through yeast genetics, yeast extract pull-down experiments, in situ localization, and in vitro biochemical approaches that Ded1 is associated with, and regulated by, the signal recognition particle (SRP), which is a universally conserved ribonucleoprotein complex required for the co-translational translocation of polypeptides into the endoplasmic reticulum lumen and membrane. Ded1 is physically associated with SRP components in vivo and in vitro. Ded1 is genetically linked with SRP proteins. Finally, the enzymatic activity of Ded1 is inhibited by SRP21 in the presence of SCR1 RNA. We propose a model where Ded1 actively participates in the translocation of proteins during translation. Our results provide a new understanding of the role of Ded1 during translation.

Perturbation-Specific Transcriptional Mapping for unbiased target elucidation of antibiotics

The rising prevalence of antibiotic resistance threatens human health. While more sophisticated strategies for antibiotic discovery are being developed, target elucidation of new chemical entities remains challenging. In the post-genomic era, expression profiling can play an important role in mechanism-of-action (MOA) prediction by reporting on the cellular response to perturbation. However, the broad application of transcriptomics has yet to fulfill its promise of transforming target elucidation due to challenges in identifying the most relevant, direct responses to target inhibition. We developed an unbiased strategy for MOA prediction, called Perturbation-Specific Transcriptional Mapping (PerSpecTM), in which large-throughput expression profiling of wildtype or hypomorphic mutants, depleted for essential targets, enables a computational strategy to address this challenge. We applied PerSpecTM to perform reference-based MOA prediction based on the principle that similar perturbations, whether chemical or genetic, will elicit similar transcriptional responses. Using this approach, we elucidated the MOAs of three new molecules with activity against Pseudomonas aeruginosa by comparing their expression profiles to those of a reference set of antimicrobial compounds with known MOAs. We also show that transcriptional responses to small molecule inhibition resemble those resulting from genetic depletion of essential targets by CRISPRi by PerSpecTM, demonstrating proof-of-concept that correlations between expression profiles of small molecule and genetic perturbations can facilitate MOA prediction when no chemical entities exist to serve as a reference. Empowered by PerSpecTM, this work lays the foundation for an unbiased, readily scalable, systematic reference-based strategy for MOA elucidation that could transform antibiotic discovery efforts.

The Construction of a Multi-Gene Risk Model for Colon Cancer Prognosis and Drug Treatments Prediction

In clinical practice, colon cancer is a prevalent malignant tumor of the digestive system, characterized by a complex and progressive process involving multiple genes and molecular pathways. Historically, research efforts have primarily focused on investigating individual genes; however, our current study aims to explore the collective impact of multiple genes on colon cancer and to identify potential therapeutic targets associated with these genes. For this research, we acquired the gene expression profiles and RNA sequencing data of colon cancer from TCGA. Subsequently, we conducted differential gene expression analysis using R, followed by GO and KEGG pathway enrichment analyses. To construct a protein-protein interaction (PPI) network, we selected survival-related genes using the log-rank test and single-factor Cox regression analysis. Additionally, we performed LASSO regression analysis, immune infiltration analysis, mutation analysis, and cMAP analysis, as well as an investigation into ferroptosis. Our differential expression and survival analyses identified 47 hub genes, and subsequent LASSO regression analysis refined the focus to 23 key genes. These genes are closely linked to cancer metastasis, proliferation, apoptosis, cell cycle regulation, signal transduction, cancer microenvironment, immunotherapy, and neurodevelopment. Overall, the hub genes discovered in our study are pivotal in colon cancer and are anticipated to serve as important biological markers for the diagnosis and treatment of the disease.

scDrugPrio: a framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases

BACKGROUND

Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs.

METHODS

Here, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs.

RESULTS

scDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive in vitro, in vivo, and in silico studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohn's disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohn's disease patients. The analysis showed great variations in drug predictions between patients, for example, assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment.

CONCLUSIONS

We propose a computational framework, scDrugPrio, for drug prioritisation based on scRNA-seq of IMID disease. Application to individual patients indicates scDrugPrio's potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package ( https://github.com/SDTC-CPMed/scDrugPrio ).

Secretory IgA reduced the ergosterol contents of Candida albicans to repress its hyphal growth and virulence

Candida albicans, one of the most prevalent conditional pathogenic fungi, can cause local superficial infections and lethal systemic infections, especially in the immunocompromised population. Secretory immunoglobulin A (sIgA) is an important immune protein regulating the pathogenicity of C. albicans. However, the actions and mechanisms that sIgA exerts directly against C. albicans are still unclear. Here, we investigated that sIgA directs against C. albicans hyphal growth and virulence to oral epithelial cells. Our results indicated that sIgA significantly inhibited C. albicans hyphal growth, adhesion, and damage to oral epithelial cells compared with IgG. According to the transcriptome and RT-PCR analysis, sIgA significantly affected the ergosterol biosynthesis pathway. Furthermore, sIgA significantly reduced the ergosterol levels, while the addition of exogenous ergosterol restored C. albicans hyphal growth and adhesion to oral epithelial cells, indicating that sIgA suppressed the growth of hyphae and the pathogenicity of C. albicans by reducing its ergosterol levels. By employing the key genes mutants (erg11Δ/Δ, erg3Δ/Δ, and erg3Δ/Δ erg11Δ/Δ) from the ergosterol pathway, sIgA lost the hyphal inhibition on these mutants, while sIgA also reduced the inhibitory effects of erg11Δ/Δ and erg3Δ/Δ and lost the inhibition of erg3Δ/Δ erg11Δ/Δ on the adhesion to oral epithelial cells, further proving the hyphal repression of sIgA through the ergosterol pathway. We demonstrated for the first time that sIgA inhibited C. albicans hyphal development and virulence by affecting ergosterol biosynthesis and suggest that ergosterol is a crucial regulator of C. albicans-host cell interactions. KEY POINTS: • sIgA repressed C. albicans hyphal growth • sIgA inhibited C. albicans virulence to host cells • sIgA affected C. albicans hyphae and virulence by reducing its ergosterol levels.

Fluorescent Phenotyping of Blood Cells Using a Differential Sensing Strategy: Differentiating Physiological Aging Stages and Neuro-Degenerative Disease Drugs

Blood continually contributes to the maintenance of homeostasis of the body and contains information regarding the health state of an individual. However, current hematological analyses predominantly rely on a limited number of CD markers and morphological analysis. In this work, differentially sensitive fluorescent compounds based on TCF scaffolds are introduced that are designed for fluorescent phenotyping of blood. Depending on their structures, TCF compounds displayed varied responses to reactive oxygen species, biothiols, redox-related biomolecules, and hemoglobin, which are the primary influential factors within blood. Contrary to conventional CD marker-based analysis, this unbiased fluorescent phenotyping method produces diverse fingerprints of the health state. Precise discrimination of blood samples from 37 mice was demonstrated based on their developmental stages, ranging from 10 to 19 weeks of age. Additionally, this fluorescent phenotyping method enabled the differentiation between drugs with distinct targets, serving as a simple yet potent tool for pharmacological analysis to understand the mode of action of various drugs.

From beer to breadboards: yeast as a force for biological innovation

The history of yeast Saccharomyces cerevisiae, aka brewer's or baker's yeast, is intertwined with our own. Initially domesticated 8,000 years ago to provide sustenance to our ancestors, for the past 150 years, yeast has served as a model research subject and a platform for technology. In this review, we highlight many ways in which yeast has served to catalyze the fields of functional genomics, genome editing, gene-environment interaction investigation, proteomics, and bioinformatics-emphasizing how yeast has served as a catalyst for innovation. Several possible futures for this model organism in synthetic biology, drug personalization, and multi-omics research are also presented.

Peroxisome population control by phosphoinositide signaling at the endoplasmic reticulum-plasma membrane interface

Phosphoinositides are lipid signaling molecules acting at the interface of membranes and the cytosol to regulate membrane trafficking, lipid transport and responses to extracellular stimuli. Peroxisomes are multicopy organelles that are highly responsive to changes in metabolic and environmental conditions. In yeast, peroxisomes are tethered to the cell cortex at defined focal structures containing the peroxisome inheritance protein, Inp1p. We investigated the potential impact of changes in cortical phosphoinositide levels on the peroxisome compartment of the yeast cell. Here we show that the phosphoinositide, phosphatidylinositol-4-phosphate (PI4P), found at the junction of the cortical endoplasmic reticulum and plasma membrane (cER-PM) acts to regulate the cell's peroxisome population. In cells lacking a cER-PM tether or the enzymatic activity of the lipid phosphatase Sac1p, cortical PI4P is elevated, peroxisome numbers and motility are increased, and peroxisomes are no longer firmly tethered to Inp1p-containing foci. Reattachment of the cER to the PM through an artificial ER-PM "staple" in cells lacking the cER-PM tether does not restore peroxisome populations to the wild-type condition, demonstrating that integrity of PI4P signaling at the cell cortex is required for peroxisome homeostasis.

Mistranslating the genetic code with leucine in yeast and mammalian cells

Translation fidelity relies on accurate aminoacylation of transfer RNAs (tRNAs) by aminoacyl-tRNA synthetases (AARSs). AARSs specific for alanine (Ala), leucine (Leu), serine, and pyrrolysine do not recognize the anticodon bases. Single nucleotide anticodon variants in their cognate tRNAs can lead to mistranslation. Human genomes include both rare and more common mistranslating tRNA variants. We investigated three rare human tRNALeu variants that mis-incorporate Leu at phenylalanine or tryptophan codons. Expression of each tRNALeu anticodon variant in neuroblastoma cells caused defects in fluorescent protein production without significantly increased cytotoxicity under normal conditions or in the context of proteasome inhibition. Using tRNA sequencing and mass spectrometry we confirmed that each tRNALeu variant was expressed and generated mistranslation with Leu. To probe the flexibility of the entire genetic code towards Leu mis-incorporation, we created 64 yeast strains to express all possible tRNALeu anticodon variants in a doxycycline-inducible system. While some variants showed mild or no growth defects, many anticodon variants, enriched with G/C at positions 35 and 36, including those replacing Leu for proline, arginine, alanine, or glycine, caused dramatic reductions in growth. Differential phenotypic defects were observed for tRNALeu mutants with synonymous anticodons and for different tRNALeu isoacceptors with the same anticodon. A comparison to tRNAAla anticodon variants demonstrates that Ala mis-incorporation is more tolerable than Leu at nearly every codon. The data show that the nature of the amino acid substitution, the tRNA gene, and the anticodon are each important factors that influence the ability of cells to tolerate mistranslating tRNAs.

Parallel Nonfunctionalization of CK1δ/ε Kinase Ohnologs Following a Whole-Genome Duplication Event

Whole-genome duplication (WGD) followed by speciation allows us to examine the parallel evolution of ohnolog pairs. In the yeast family Saccharomycetaceae, HRR25 is a rare case of repeated ohnolog maintenance. This gene has reverted to a single copy in Saccharomyces cerevisiae where it is now essential, but has been maintained as pairs in at least 7 species post-WGD. In S. cerevisiae, HRR25 encodes the casein kinase 1δ/ε and plays a role in a variety of functions through its kinase activity and protein-protein interactions (PPIs). We hypothesized that the maintenance of duplicated HRR25 ohnologs could be a result of repeated subfunctionalization. We tested this hypothesis through a functional complementation assay in S. cerevisiae, testing all pairwise combinations of 25 orthologs (including 7 ohnolog pairs). Contrary to our expectations, we observed no cases of pair-dependent complementation, which would have supported the subfunctionalization hypothesis. Instead, most post-WGD species have one ohnolog that failed to complement, suggesting their nonfunctionalization or neofunctionalization. The ohnologs incapable of complementation have undergone more rapid protein evolution, lost most PPIs that were observed for their functional counterparts and singletons from post-WGD and non-WGD species, and have nonconserved cellular localization, consistent with their ongoing loss of function. The analysis in Naumovozyma castellii shows that the noncomplementing ohnolog is expressed at a lower level and has become nonessential. Taken together, our results indicate that HRR25 orthologs are undergoing gradual nonfunctionalization.

An overview of recent advances and challenges in predicting compound-protein interaction (CPI)

Compound-protein interactions (CPIs) are critical in drug discovery for identifying therapeutic targets, drug side effects, and repurposing existing drugs. Machine learning (ML) algorithms have emerged as powerful tools for CPI prediction, offering notable advantages in cost-effectiveness and efficiency. This review provides an overview of recent advances in both structure-based and non-structure-based CPI prediction ML models, highlighting their performance and achievements. It also offers insights into CPI prediction-related datasets and evaluation benchmarks. Lastly, the article presents a comprehensive assessment of the current landscape of CPI prediction, elucidating the challenges faced and outlining emerging trends to advance the field.

scDrugPrio: A framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases

Background

Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs.

Methods

Here, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs.

Results

scDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive in vitro, in vivo, and in silico studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohn's disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohn's disease patients. The analysis showed great variations in drug predictions between patients, for example, assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment.

Conclusion

We propose a computational framework, scDrugPrio, for drug prioritisation based on scRNA-seq of IMID disease. Application to individual patients indicates scDrugPrio's potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package (https://github.com/SDTC-CPMed/scDrugPrio).

Parallel nonfunctionalization of CK1δ/ε kinase ohnologs following a whole-genome duplication event

Whole genome duplication (WGD) followed by speciation allows us to examine the parallel evolution of ohnolog pairs. In the yeast family Saccharomycetaceae, HRR25 is a rare case of repeated ohnolog maintenance. This gene has reverted to a single copy in S. cerevisiae where it is now essential, but has been maintained as pairs in at least 7 species post WGD. In S. cerevisiae, HRR25 encodes the casein kinase (CK) 1δ/ε and plays a role in a variety of functions through its kinase activity and protein-protein interactions (PPIs). We hypothesized that the maintenance of duplicated HRR25 ohnologs could be a result of repeated subfunctionalization. We tested this hypothesis through a functional complementation assay in S. cerevisiae, testing all pairwise combinations of 25 orthologs (including 7 ohnolog pairs). Contrary to our expectations, we observed no cases of pair-dependent complementation, which would have supported the subfunctionalization hypothesis. Instead, most post-WGD species have one ohnolog that failed to complement, suggesting their nonfunctionalization or neofunctionalization. The ohnologs incapable of complementation have undergone more rapid protein evolution, lost most PPIs that were observed for their functional counterparts and singletons from post and non-WGD species, and have non-conserved cellular localization, consistent with their ongoing loss of function. The analysis in N. castelli shows that the non-complementing ohnolog is expressed at a lower level and has become non-essential. Taken together, our results indicate that HRR25 orthologs are undergoing gradual nonfunctionalization.

SIR telomere silencing depends on nuclear envelope lipids and modulates sensitivity to a lysolipid

The nuclear envelope (NE) is important in maintaining genome organization. The role of lipids in communication between the NE and telomere regulation was investigated, including how changes in lipid composition impact gene expression and overall nuclear architecture. Yeast was treated with the non-metabolizable lysophosphatidylcholine analog edelfosine, known to accumulate at the perinuclear ER. Edelfosine induced NE deformation and disrupted telomere clustering but not anchoring. Additionally, the association of Sir4 at telomeres decreased. RNA-seq analysis showed altered expression of Sir-dependent genes located at sub-telomeric (0-10 kb) regions, consistent with Sir4 dispersion. Transcriptomic analysis revealed that two lipid metabolic circuits were activated in response to edelfosine, one mediated by the membrane sensing transcription factors, Spt23/Mga2, and the other by a transcriptional repressor, Opi1. Activation of these transcriptional programs resulted in higher levels of unsaturated fatty acids and the formation of nuclear lipid droplets. Interestingly, cells lacking Sir proteins displayed resistance to unsaturated-fatty acids and edelfosine, and this phenotype was connected to Rap1.

Transcriptional repression by the histone tails in budding yeast is mediated by Rpd3, Tup1-Ssn6, and Bur6/NC2

Chromatin-mediated transcriptional regulation is modulated by post-translational modifications of the core histones, particularly the H3 and H4 unstructured amino termini, or "tails". In budding yeast, the H3 and H4 tails can be deacetylated by Rpd3 to repress specific target genes, and hypoacetylated histones can facilitate recruitment of the Tup1-Ssn6 complex to effect gene repression. However, the extent to which these mechanisms are used to effect repression by the histone tails, and whether other factors similarly collaborate with the tails to facilitate gene repression, has not been determined. Here, a chromatin modifier compendium of 170 gene expression profiles from yeast strains mutated for chromatin-related genes was used to query the effect of the corresponding mutations on gene cohorts repressed by the histone H3 and H4 tails and/or by Rpd3. The resulting analysis reveals that repression of nearly all of the genes repressed by the histone tails requires Rpd3 and/or the Tup1-Ssn6 complex. Repression by Rpd3 occurs via the Rpd3-L complex, and TFIID-dominated genes are underrepresented among genes repressed by mutations or deletions of the H3 or H4 tails, in accord with previous work. In addition, Bur6, the yeast homolog of human NC2α, is required for repression at ∼50% of genes repressed by the H3 or H4 tail. These results illuminate genome-wide repression mechanisms utilized by the histone tails in yeast and raise new questions regarding the role of Bur6 in histone tail-mediated repression and whether parallels exist in metazoan cells.

yGPS-P: A Yeast-Based Peptidome Screen for Studying Quality Control-Associated Proteolysis

Quality control-associated proteolysis (QCAP) is a fundamental mechanism that maintains cellular homeostasis by eliminating improperly folded proteins. In QCAP, the exposure of normally hidden cis-acting protein sequences, termed degrons, triggers misfolded protein ubiquitination, resulting in their elimination by the proteasome. To identify the landscape of QCAP degrons and learn about their unique features we have developed an unbiased screening method in yeast, termed yGPS-P, which facilitates the determination of thousands of proteome-derived sequences that enhance proteolysis. Here we describe the fundamental features of the yGPS-P method and provide a detailed protocol for its use as a tool for degron search. This includes the cloning of a synthetic peptidome library in a fluorescence-based reporter system, and data acquisition, which entails the combination of Fluorescence-Activated Cell Sorting (FACS) and Next-Generation Sequencing (NGS). We also provide guidelines for data extraction and analysis and for the application of a machine-learning algorithm that established the evolutionarily conserved amino acid preferences and secondary structure propensities of QCAP degrons.

Profiling The Compendium Of Changes In Saccharomyces cerevisiae Due To Mutations That Alter Availability Of The Main Methyl Donor S-Adenosylmethionine

The SAM1 and SAM2 genes encode for S-AdenosylMethionine (AdoMet) synthetase enzymes, with AdoMet serving as the main methyl donor. We have previously shown that independent deletion of these genes alters chromosome stability and AdoMet concentrations in opposite ways in S. cerevisiae. To characterize other changes occurring in these mutants, we grew wildtype, sam1∆/sam1∆, and sam2∆/sam2∆ strains in 15 different Phenotypic Microarray plates with different components, equal to 1440 wells, and measured for growth variations. RNA-Sequencing was also carried out on these strains and differential gene expression determined for each mutant. In this study, we explore how the phenotypic growth differences are linked to the altered gene expression, and thereby predict the mechanisms by which loss of the SAM genes and subsequent AdoMet level changes, impact S. cerevisiae pathways and processes. We present six stories, discussing changes in sensitivity or resistance to azoles, cisplatin, oxidative stress, arginine biosynthesis perturbations, DNA synthesis inhibitors, and tamoxifen, to demonstrate the power of this novel methodology to broadly profile changes due to gene mutations. The large number of conditions that result in altered growth, as well as the large number of differentially expressed genes with wide-ranging functionality, speaks to the broad array of impacts that altering methyl donor abundance can impart, even when the conditions tested were not specifically selected as targeting known methyl involving pathways. Our findings demonstrate that some cellular changes are directly related to AdoMet-dependent methyltransferases and AdoMet availability, some are directly linked to the methyl cycle and its role is production of several important cellular components, and others reveal impacts of SAM gene mutations on previously unconnected pathways.

Differential Hsp90-dependent gene expression is strain-specific and common among yeast strains

Enhanced phenotypic diversity increases a population's likelihood of surviving catastrophic conditions. Hsp90, an essential molecular chaperone and a central network hub in eukaryotes, has been observed to suppress or enhance the effects of genetic variation on phenotypic diversity in response to environmental cues. Because many Hsp90-interacting genes are involved in signaling transduction pathways and transcriptional regulation, we tested how common Hsp90-dependent differential gene expression is in natural populations. Many genes exhibited Hsp90-dependent strain-specific differential expression in five diverse yeast strains. We further identified transcription factors (TFs) potentially contributing to variable expression. We found that on Hsp90 inhibition or environmental stress, activities or abundances of Hsp90-dependent TFs varied among strains, resulting in differential strain-specific expression of their target genes, which consequently led to phenotypic diversity. We provide evidence that individual strains can readily display specific Hsp90-dependent gene expression, suggesting that the evolutionary impacts of Hsp90 are widespread in nature.

The proteomic landscape of genome-wide genetic perturbations

Functional genomic strategies have become fundamental for annotating gene function and regulatory networks. Here, we combined functional genomics with proteomics by quantifying protein abundances in a genome-scale knockout library in Saccharomyces cerevisiae, using data-independent acquisition mass spectrometry. We find that global protein expression is driven by a complex interplay of (1) general biological properties, including translation rate, protein turnover, the formation of protein complexes, growth rate, and genome architecture, followed by (2) functional properties, such as the connectivity of a protein in genetic, metabolic, and physical interaction networks. Moreover, we show that functional proteomics complements current gene annotation strategies through the assessment of proteome profile similarity, protein covariation, and reverse proteome profiling. Thus, our study reveals principles that govern protein expression and provides a genome-spanning resource for functional annotation.

A fitness trade-off explains the early fate of yeast aneuploids with chromosome gains

The early development of aneuploidy from an accidental chromosome missegregation shows contrasting effects. On the one hand, it is associated with significant cellular stress and decreased fitness. On the other hand, it often carries a beneficial effect and provides a quick (but typically transient) solution to external stress. These apparently controversial trends emerge in several experimental contexts, particularly in the presence of duplicated chromosomes. However, we lack a mathematical evolutionary modeling framework that comprehensively captures these trends from the mutational dynamics and the trade-offs involved in the early stages of aneuploidy. Here, focusing on chromosome gains, we address this point by introducing a fitness model where a fitness cost of chromosome duplications is contrasted by a fitness advantage from the dosage of specific genes. The model successfully captures the experimentally measured probability of emergence of extra chromosomes in a laboratory evolution setup. Additionally, using phenotypic data collected in rich media, we explored the fitness landscape, finding evidence supporting the existence of a per-gene cost of extra chromosomes. Finally, we show that the substitution dynamics of our model, evaluated in the empirical fitness landscape, explains the relative abundance of duplicated chromosomes observed in yeast population genomics data. These findings lay a firm framework for the understanding of the establishment of newly duplicated chromosomes, providing testable quantitative predictions for future observations.

Systems-level transcriptional regulation of Caenorhabditis elegans metabolism

Metabolism is controlled to ensure organismal development and homeostasis. Several mechanisms regulate metabolism, including allosteric control and transcriptional regulation of metabolic enzymes and transporters. So far, metabolism regulation has mostly been described for individual genes and pathways, and the extent of transcriptional regulation of the entire metabolic network remains largely unknown. Here, we find that three-quarters of all metabolic genes are transcriptionally regulated in the nematode Caenorhabditis elegans. We find that many annotated metabolic pathways are coexpressed, and we use gene expression data and the iCEL1314 metabolic network model to define coregulated subpathways in an unbiased manner. Using a large gene expression compendium, we determine the conditions where subpathways exhibit strong coexpression. Finally, we develop "WormClust," a web application that enables a gene-by-gene query of genes to view their association with metabolic (sub)-pathways. Overall, this study sheds light on the ubiquity of transcriptional regulation of metabolism and provides a blueprint for similar studies in other organisms, including humans.

Proteomics-based trapping with single or multiple inactive mutants reproducibly profiles histone deacetylase 1 substrates

Histone deacetylase 1 (HDAC1) plays a key role in diverse cellular processes. With the aberrant expression of HDAC1 linked to many diseases, including cancers, HDAC inhibitors have been used successfully as therapeutics. HDAC1 has been predominantly associated with histone deacetylation and gene expression. Recently, non-histone substrates have revealed diverse roles of HDAC1 beyond epigenetics. To augment discovery of non-histone substrates, we introduced "substrate trapping" to enrich HDAC1 substrates using an inactive mutant. Herein, we performed a series of proteomics studies to test the robustness of HDAC1 substrate trapping. Based on our recent results documenting that different HDAC1 mutants preferentially bound different substrates, which suggested that multiple mutants could be used for efficient trapping, trapping with three single point mutants simultaneously identified several potential substrates uniquely compared to a single mutant alone. However, a greater number of biologically interesting hits were observed using only a single mutant, which suggests that the C151A HDAC1 mutant is the optimal trap. Importantly, comparing independent trials with a single mutant performed by different experimentalists and HEK293 cell populations, trapping was robust and reproducible. Based on the reproducible trapping data, carnosine N-methyltransferase 1 (CARNMT1) was validated as an HDAC1 substrate. The data document that mutant trapping is an effective method for discovery of unanticipated HDAC substrates. SIGNIFICANCE: Histone deacetylase (HDAC) proteins are well established epigenetic transcriptional regulators that deacetylate histone substrates to control gene expression. More recently, deacetylation of non-histone substrates has linked HDAC activity to functions outside of epigenetics. Given the use of HDAC inhibitor drugs as anti-cancer therapeutics, understanding the full functions of HDAC proteins in cell biology is essential to future drug design. To discover unanticipated non-histone substrates and further characterize HDAC functions, inactive mutants have been used to "trap" putative substrates, which were identified with mass spectrometry-based proteomics analysis. Here multiple trapping studies were performed to test the robustness of using inactive mutants and proteomics for HDAC substrate discovery. The data confirm the value of trapping mutants as effective tools to discover HDAC substrates and link HDAC activity to unexpected biological functions.

LPG-Based Knowledge Graphs: A Survey, a Proposal and Current Trends

A significant part of the current research in the field of Artificial Intelligence is devoted to knowledge bases. New techniques and methodologies are emerging every day for the storage, maintenance and reasoning over knowledge bases. Recently, the most common way of representing knowledge bases is by means of graph structures. More specifically, according to the Semantic Web perspective, many knowledge sources are in the form of a graph adopting the Resource Description Framework model. At the same time, graphs have also started to gain momentum as a model for databases. Graph DBMSs, such as Neo4j, adopt the Labeled Property Graph model. Many works tried to merge these two perspectives. In this paper, we will overview different proposals aimed at combining these two aspects, especially focusing on possibility for them to add reasoning capabilities. In doing this, we will show current trends, issues and possible solutions. In this context, we will describe our proposal and its novelties with respect to the current state of the art, highlighting its current status, potential, the methodology, and our prospect.

Computational drug repurposing by exploiting large-scale gene expression data: Strategy, methods and applications

De novo drug development is an extremely complex, time-consuming and costly task. Urgent needs for therapies of various diseases have greatly accelerated searches for more effective drug development methods. Luckily, drug repurposing provides a new and effective perspective on disease treatment. Rapidly increased large-scale transcriptome data paints a detailed prospect of gene expression during disease onset and thus has received wide attention in the field of computational drug repurposing. However, how to efficiently mine transcriptome data and identify new indications for old drugs remains a critical challenge. This review discussed the irreplaceable role of transcriptome data in computational drug repurposing and summarized some representative databases, tools and strategies. More importantly, it proposed a practical guideline through establishing the correspondence between three gene expression data types and five strategies, which would facilitate researchers to adopt appropriate strategies to deeply mine large-scale transcriptome data and discover more effective therapies.

Roles of the transcriptional regulators Fts1, YlNrg1, YlTup1, and YlSsn6 in the repression of the yeast-to-filament transition in the dimorphic yeast Yarrowia lipolytica

In dimorphic fungi, the yeast-to-filament transition critical for cell survival under nutrient starvation is controlled by both activators and repressors. However, very few filamentation repressors are known. Here we report that, in the dimorphic yeast Yarrowia lipolytica, the conserved transcription factor YlNrg1 plays a minor role whereas Fts1, a newly identified Zn(II)₂ Cys₆ zinc cluster transcription factor, plays a key role in filamentation repression. FTS1 deletion caused hyperfilamentation whereas Fts1 overexpression drastically reduced filamentation. The expression of FTS1 is downregulated substantially during the yeast-to-filament transition. Transcriptome sequencing revealed that Fts1 represses 401 genes, including the filamentation-activating transcription factor genes MHY1, YlAZF1, and YlWOR4 and key cell wall protein genes. Tup1-Ssn6, a general transcriptional corepressor, is involved in the repression of many cellular functions in fungi. We show that both YlTup1 and YlSsn6 strongly repress filamentation in Y. lipolytica. YlTup1 and YlSsn6 together repress 1383 genes, including a large number of transcription factor and cell wall protein genes, which overlap substantially with Fts1-repressed genes. Fts1 interacts with both YlTup1 and YlSsn6, and LexA-Fts1 fusion represses a lexAop-promoter-lacZ reporter in a Tup1-Ssn6-dependent manner. Our findings suggest that Fts1 functions as a transcriptional repressor, directing the repression of target genes through the Tup1-Ssn6 corepressor.

Anticodon sequence determines the impact of mistranslating tRNAAla variants

Transfer RNAs (tRNAs) maintain translation fidelity through accurate charging by their cognate aminoacyl-tRNA synthetase and codon:anticodon base pairing with the mRNA at the ribosome. Mistranslation occurs when an amino acid not specified by the genetic message is incorporated into proteins and has applications in biotechnology, therapeutics and is relevant to disease. Since the alanyl-tRNA synthetase uniquely recognizes a G3:U70 base pair in tRNAAla and the anticodon plays no role in charging, tRNAAla variants with anticodon mutations have the potential to mis-incorporate alanine. Here, we characterize the impact of the 60 non-alanine tRNAAla anticodon variants on the growth of Saccharomyces cerevisiae. Overall, 36 tRNAAla anticodon variants decreased growth in single- or multi-copy. Mass spectrometry analysis of the cellular proteome revealed that 52 of 57 anticodon variants, not decoding alanine or stop codons, induced mistranslation when on single-copy plasmids. Variants with G/C-rich anticodons resulted in larger growth deficits than A/U-rich variants. In most instances, synonymous anticodon variants impact growth differently, with anticodons containing U at base 34 being the least impactful. For anticodons generating the same amino acid substitution, reduced growth generally correlated with the abundance of detected mistranslation events. Differences in decoding specificity, even between synonymous anticodons, resulted in each tRNAAla variant mistranslating unique sets of peptides and proteins. We suggest that these differences in decoding specificity are also important in determining the impact of tRNAAla anticodon variants.

A statistical framework for high-content phenotypic profiling using cellular feature distributions

High-content screening (HCS) uses microscopy images to generate phenotypic profiles of cell morphological data in high-dimensional feature space. While HCS provides detailed cytological information at single-cell resolution, these complex datasets are usually aggregated into summary statistics that do not leverage patterns of biological variability within cell populations. Here we present a broad-spectrum HCS analysis system that measures image-based cell features from 10 cellular compartments across multiple assay panels. We introduce quality control measures and statistical strategies to streamline and harmonize the data analysis workflow, including positional and plate effect detection, biological replicates analysis and feature reduction. We also demonstrate that the Wasserstein distance metric is superior over other measures to detect differences between cell feature distributions. With this workflow, we define per-dose phenotypic fingerprints for 65 mechanistically diverse compounds, provide phenotypic path visualizations for each compound and classify compounds into different activity groups.

Accurate determination of causalities in gene regulatory networks by dissecting downstream target genes

Accurate determination of causalities between genes is a challenge in the inference of gene regulatory networks (GRNs) from the gene expression profile. Although many methods have been developed for the reconstruction of GRNs, most of them are insufficient in determining causalities or regulatory directions. In this work, we present a novel method, namely, DDTG, to improve the accuracy of causality determination in GRN inference by dissecting downstream target genes. In the proposed method, the topology and hierarchy of GRNs are determined by mutual information and conditional mutual information, and the regulatory directions of GRNs are determined by Taylor formula-based regression. In addition, indirect interactions are removed with the sparseness of the network topology to improve the accuracy of network inference. The method is validated on the benchmark GRNs from DREAM3 and DREAM4 challenges. The results demonstrate the superior performance of the DDTG method on causality determination of GRNs compared to some popular GRN inference methods. This work provides a useful tool to infer the causal gene regulatory network.

Navigating Transcriptomic Connectivity Mapping Workflows to Link Chemicals with Bioactivities

Screening new compounds for potential bioactivities against cellular targets is vital for drug discovery and chemical safety. Transcriptomics offers an efficient approach for assessing global gene expression changes, but interpreting chemical mechanisms from these data is often challenging. Connectivity mapping is a potential data-driven avenue for linking chemicals to mechanisms based on the observation that many biological processes are associated with unique gene expression signatures (gene signatures). However, mining the effects of a chemical on gene signatures for biological mechanisms is challenging because transcriptomic data contain thousands of noisy genes. New connectivity mapping approaches seeking to distinguish signal from noise continue to be developed, spurred by the promise of discovering chemical mechanisms, new drugs, and disease targets from burgeoning transcriptomic data. Here, we analyze these approaches in terms of diverse transcriptomic technologies, public databases, gene signatures, pattern-matching algorithms, and statistical evaluation criteria. To navigate the complexity of connectivity mapping, we propose a harmonized scheme to coherently organize and compare published workflows. We first standardize concepts underlying transcriptomic profiles and gene signatures based on various transcriptomic technologies such as microarrays, RNA-Seq, and L1000 and discuss the widely used data sources such as Gene Expression Omnibus, ArrayExpress, and MSigDB. Next, we generalize connectivity mapping as a pattern-matching task for finding similarity between a query (e.g., transcriptomic profile for new chemical) and a reference (e.g., gene signature of known target). Published pattern-matching approaches fall into two main categories: vector-based use metrics like correlation, Jaccard index, etc., and aggregation-based use parametric and nonparametric statistics (e.g., gene set enrichment analysis). The statistical methods for evaluating the performance of different approaches are described, along with comparisons reported in the literature on benchmark transcriptomic data sets. Lastly, we review connectivity mapping applications in toxicology and offer guidance on evaluating chemical-induced toxicity with concentration-response transcriptomic data. In addition to serving as a high-level guide and tutorial for understanding and implementing connectivity mapping workflows, we hope this review will stimulate new algorithms for evaluating chemical safety and drug discovery using transcriptomic data.

In search of a Drosophila core cellular network with single-cell transcriptome data

Along with specialized functions, cells of multicellular organisms also perform essential functions common to most if not all cells. Whether diverse cells do this by using the same set of genes, interacting in a fixed coordinated fashion to execute essential functions, or a subset of genes specific to certain cells, remains a central question in biology. Here, we focus on gene coexpression to search for a core cellular network across a whole organism. Single-cell RNA-sequencing measures gene expression of individual cells, enabling researchers to discover gene expression patterns that contribute to the diversity of cell functions. Current efforts to study cellular functions focus primarily on identifying differentially expressed genes across cells. However, patterns of coexpression between genes are probably more indicative of biological processes than are the expression of individual genes. We constructed cell-type-specific gene coexpression networks using single-cell transcriptome datasets covering diverse cell types from the fruit fly, Drosophila melanogaster. We detected a set of highly coordinated genes preserved across cell types and present this as the best estimate of a core cellular network. This core is very small compared with cell-type-specific gene coexpression networks and shows dense connectivity. Gene members of this core tend to be ancient genes and are enriched for those encoding ribosomal proteins. Overall, we find evidence for a core cellular network in diverse cell types of the fruit fly. The topological, structural, functional, and evolutionary properties of this core indicate that it accounts for only a minority of essential functions.

Cancer and Chaos and the Complex Network Model of a Multicellular Organism

In the search of theoretical models describing cancer, one of promising directions is chaos. It is connected to ideas of "genome chaos" and "life on the edge of chaos", but they profoundly differ in the meaning of the term "chaos". To build any coherent models, notions used by both ideas should be firstly brought closer. The hypothesis "life on the edge of chaos" using deterministic chaos has been radically deepened developed in recent years by the discovery of half-chaos. This new view requires a deeper interpretation within the range of the cell and the organism. It has impacts on understanding "chaos" in the term "genome chaos". This study intends to present such an interpretation on the basis of which such searches will be easier and closer to intuition. We interpret genome chaos as deterministic chaos in a large module of half-chaotic network modeling the cell. We observed such chaotic modules in simulations of evolution controlled by weaker variant of natural selection. We also discuss differences between free and somatic cells in modeling their disturbance using half-chaotic networks.

Analysis of the Plasmid-Based ts Allele of PA0006 Reveals Its Function in Regulation of Cell Morphology and Biosynthesis of Core Lipopolysaccharide in Pseudomonas aeruginosa

Over 300 essential genes are predicted using transposon sequencing in the genome of Pseudomonas aeruginosa. However, methods for reverse genetic analysis of essential genes are scarce. To address this issue, we developed a three-step protocol consisting of integration of deletion plasmid, introduction of temperature-sensitive rescue plasmid, and excision of integrated-deletion plasmid to construct the plasmid-based temperature-sensitive allele of essential genes. Using PA0006 as an example, we showed that PA0006(Ts) exhibited wild-type cell morphology at permissive temperature but filamentous form at restrictive temperatures. We further showed that the glycerol-mannoheptose-bisphosphate phosphatase GmhB in Escherichia coli shared 32.4% identity with that of PA0006p and functionally complemented the defect of PA0006(Ts) at 42°C. SDS-PAGE and Western blotting indicated the presence and absence of the complete core lipopolysaccharide (LPS) and B-band O-antigen in PA0006(Ts) at 30 and 42°C, respectively. An isolated suppressor sup displayed wild-type-like cell morphology but no complete core LPS or O-antigen. Genome resequencing together with comparative transcriptomic profiling identified a candidate suppressor fructose-bisphosphate phosphatase in which the promoter harbored a SNP and the transcription level was not downregulated at 42°C compared to 30°C in sup. It was further validated that fbp overexpression suppressed the lethality of PA0006(Ts) at 42°C. Taken together, our results demonstrate that PA0006 plays a role in regulation of cell morphology and biosynthesis of core LPS. This three-step protocol for construction of conditional lethal allele in P. aeruginosa should be widely applicable for genetic analysis of other essential genes of interest, including analysis of bypass suppressibility. IMPORTANCE Microbial essential genes encode nondispensable function for cell growth and therefore are ideal targets for the development of new drugs. Essential genes are readily identified using transposon-sequencing technology at the genome scale. However, genetic analysis of essential genes of interest was hampered by limited methodologies. To address this issue, we developed a three-step protocol for construction of conditional allele of essential genes in the opportunistic pathogen Pseudomonas aeruginosa. Using PA0006 as an example, we demonstrated that the plasmid-based PA0006(Ts) mutant exhibited defects in regulation of cell morphology, formation of intact core LPS, and attachment of the O-antigen at restrictive temperatures but not at permissive temperatures. A suppressor of PA0006(Ts) was isolated through spontaneous mutations and showed restored cell morphology but not core oligosaccharide or O-antigen. This method should be widely applicable for phenotype and suppressibility analyses of other essential genes of interest in P. aeruginosa.

Genetic background and mistranslation frequency determine the impact of mistranslating tRNASerUGG

Transfer RNA variants increase the frequency of mistranslation, the misincorporation of an amino acid not specified by the “standard” genetic code, to frequencies approaching 10% in yeast and bacteria. Cells cope with these variants by having multiple copies of each tRNA isodecoder and through pathways that deal with proteotoxic stress. In this study, we define the genetic interactions of the gene encoding tRNA^Ser_UGG,G26A, which mistranslates serine at proline codons. Using a collection of yeast temperature-sensitive alleles, we identify negative synthetic genetic interactions between the mistranslating tRNA and 109 alleles representing 91 genes, with nearly half of the genes having roles in RNA processing or protein folding and turnover. By regulating tRNA expression, we then compare the strength of the negative genetic interaction for a subset of identified alleles under differing amounts of mistranslation. The frequency of mistranslation correlated with the impact on cell growth for all strains analyzed; however, there were notable differences in the extent of the synthetic interaction at different frequencies of mistranslation depending on the genetic background. For many of the strains, the extent of the negative interaction with tRNA^Ser_UGG,G26A was proportional to the frequency of mistranslation or only observed at intermediate or high frequencies. For others, the synthetic interaction was approximately equivalent at all frequencies of mistranslation. As humans contain similar mistranslating tRNAs, these results are important when analyzing the impact of tRNA variants on disease, where both the individual’s genetic background and the expression of the mistranslating tRNA variant need to be considered.

SigCom LINCS: data and metadata search engine for a million gene expression signatures

Millions of transcriptome samples were generated by the Library of Integrated Network-based Cellular Signatures (LINCS) program. When these data are processed into searchable signatures along with signatures extracted from Genotype-Tissue Expression (GTEx) and Gene Expression Omnibus (GEO), connections between drugs, genes, pathways and diseases can be illuminated. SigCom LINCS is a webserver that serves over a million gene expression signatures processed, analyzed, and visualized from LINCS, GTEx, and GEO. SigCom LINCS is built with Signature Commons, a cloud-agnostic skeleton Data Commons with a focus on serving searchable signatures. SigCom LINCS provides a rapid signature similarity search for mimickers and reversers given sets of up and down genes, a gene set, a single gene, or any search term. Additionally, users of SigCom LINCS can perform a metadata search to find and analyze subsets of signatures and find information about genes and drugs. SigCom LINCS is findable, accessible, interoperable, and reusable (FAIR) with metadata linked to standard ontologies and vocabularies. In addition, all the data and signatures within SigCom LINCS are available via a well-documented API. In summary, SigCom LINCS, available at https://maayanlab.cloud/sigcom-lincs, is a rich webserver resource for accelerating drug and target discovery in systems pharmacology.

Polyphyllin VII, a novel moesin inhibitor, suppresses cell growth and overcomes bortezomib resistance in multiple myeloma

Multiple myeloma is a plasma cell malignancy, accounting for approximately 1% of new cancer cases. It is the second most common hematological malignancy. Novel clinical agents such as the proteasome inhibitor-bortezomib, have shown improved survival rates in recent decades. However, multiple myeloma remains incurable, as most patients eventually relapse and become refractory to current treatments. Therefore, there is an urgent need for developing new regimens to overcome the bortezomib resistance. Here, we screened a library of 2370 bioactives and found that polyphyllin VII selectively suppressed multiple myeloma cell growth in vitro and in vivo. We identified moesin, one of the critical regulators of the Wnt/β-catenin pathway, as a target of polyphyllin VII by drug affinity responsive target stability assay and cellular thermal shift assay. Polyphyllin VII binds to moesin and induces its degradation via the ubiquitin-proteasome pathway, thereby impairing the Wnt/β-catenin pathway activity and leading to a reduction in the side population cells to overcome bortezomib resistance. Our study identified polyphyllin VII as a promising compound and moesin as a potential diagnostic and therapeutic target for treating multiple myeloma.

Generating weighted and thresholded gene coexpression networks using signed distance correlation

Even within well-studied organisms, many genes lack useful functional annotations. One way to generate such functional information is to infer biological relationships between genes or proteins, using a network of gene coexpression data that includes functional annotations. Signed distance correlation has proved useful for the construction of unweighted gene coexpression networks. However, transforming correlation values into unweighted networks may lead to a loss of important biological information related to the intensity of the correlation. Here we introduce a principled method to construct weighted gene coexpression networks using signed distance correlation. These networks contain weighted edges only between those pairs of genes whose correlation value is higher than a given threshold. We analyse data from different organisms and find that networks generated with our method based on signed distance correlation are more stable and capture more biological information compared to networks obtained from Pearson correlation. Moreover, we show that signed distance correlation networks capture more biological information than unweighted networks based on the same metric. While we use biological data sets to illustrate the method, the approach is general and can be used to construct networks in other domains. Code and data are available on https://github.com/javier-pardodiaz/sdcorGCN.