Phenomics approaches to understand genetic networks and gene function in yeast.

Over the past decade, major efforts have been made to systematically survey the characteristics or phenotypes associated with genetic variation in a variety of model systems. These so-called phenomics projects involve the measurement of 'phenomes', or the set of phenotypic information that describes an organism or cell, in various genetic contexts or states, and in response to external factors, such as environmental signals. Our understanding of the phenome of an organism depends on the availability of reagents that enable systematic evaluation of the spectrum of possible phenotypic variation and the types of measurements that can be taken. Here, we highlight phenomics studies that use the budding yeast, a pioneer model organism for functional genomics research. We focus on genetic perturbation screens designed to explore genetic interactions, using a variety of phenotypic read-outs, from cell growth to subcellular morphology.

Systematic genetics and single-cell imaging reveal widespread morphological pleiotropy and cell-to-cell variability.

Our ability to understand the genotype-to-phenotype relationship is hindered by the lack of detailed understanding of phenotypes at a single-cell level. To systematically assess cell-to-cell phenotypic variability, we combined automated yeast genetics, high-content screening and neural network-based image analysis of single cells, focussing on genes that influence the architecture of four subcellular compartments of the endocytic pathway as a model system. Our unbiased assessment of the morphology of these compartments-endocytic patch, actin patch, late endosome and vacuole-identified 17 distinct mutant phenotypes associated with ~1,600 genes (~30% of all yeast genes). Approximately half of these mutants exhibited multiple phenotypes, highlighting the extent of morphological pleiotropy. Quantitative analysis also revealed that incomplete penetrance was prevalent, with the majority of mutants exhibiting substantial variability in phenotype at the single-cell level. Our single-cell analysis enabled exploration of factors that contribute to incomplete penetrance and cellular heterogeneity, including replicative age, organelle inheritance and response to stress.

Identification of potential microRNA markers related to Crimean-Congo hemorrhagic fever disease.

Crimean-Congo hemorrhagic fever (CCHF) is a tick-borne disease caused by the arbovirus Crimean-Congo hemorrhagic fever virus (CCHFV). The CCHFV has a single-stranded RNA genome of negative sense. MicroRNAs (miRNAs) are key players in virus-host interactions and viral pathogenesis. We investigated the miRNA gene expression profiles in patients with CCHF using microarray for the first time in the world. Microarray analysis was performed using mirBase Ver 21 (Agilent Technologies, Santa Clara, CA). All statistical analyses were performed across the case-control, fatal-control, and fatal-nonfatal case groups using Genespring (Ver 3.0). Fifteen miRNAs were statistical significant in patients with CCHF compared with the controls (5 were upregulated, 10 were downregulated). Seventy-five and sixty-six miRNAs are in fatal compared with control and nonfatal case, respectively (fold change ([FC] ≥50) were statistically significant. In this study, the target genes of important miRNAs were identified and Gene Ontology analyses were performed across all groups. As a result of this study, we propose that the detection of miRNAs in patients with CCHF will allow the determination of therapeutic targets in diseases. CCHF is an important public health problem that can often be fatal. In this study, we investigated miRNA expression in case-control, fatal-control, and fatal-nonfatal case groups. Significant miRNAs associated with fatality were detected in CCHF. This study will serve as a source of data for the development of an antagomir-based therapy against CCHF using miRNAs in the future.

MicroRNA-221/222 expression in atherosclerotic coronary artery plaque versus internal mammarian artery and in peripheral blood samples.

BACKGROUND: Atherosclerosis is a disease of the arterial wall with predilection to some sites on others. MicroRNAs (miRNAs) are a class of the non-coding RNAs regulating the target gene expression at post-transcriptional level. Different miRNAs were found at distinct stages of plaque development and expression of miRNAs' might play an important role in the local behaviour of atherosclerotic plaques. OBJECTIVE: We aimed to investigate and compare mirR-221/222 expression levels in tissues and in circulation in patients with and without overt atherosclerosis. METHODS: RNA was isolated from 40 tissues as 20 tissue samples from coronary artery atherosclerotic plaques (CAAP) and internal mammary arteries (IMA), obtained from same individual) and 80 blood (44 patients with atherosclerosis and 36 healthy subjects) samples. MiR-221/222 expression levels were measured using real time PCR. RESULTS: Expression levels of miR-221 was significantly increased in CAAP compared with completely atherosclerosis-free IMA tissues with a 8.94 times fold-change (p = 0.015). The miR-221 expression in tissue samples was significantly different in patients with hypercholesterolemia (p = 0.010), hypertension (p = 0.018) and family history of CAD (p = 0.033) versus not. Expression of miR-222 was not statistically significant between the two tissue samples overall. CONCLUSIONS: MiR-221 may be a potential biomarker for local atherosclerotic behavior.

Chemogenomics and orthology-based design of antibiotic combination therapies.

Combination antibiotic therapies are being increasingly used in the clinic to enhance potency and counter drug resistance. However, the large search space of candidate drugs and dosage regimes makes the identification of effective combinations highly challenging. Here, we present a computational approach called INDIGO, which uses chemogenomics data to predict antibiotic combinations that interact synergistically or antagonistically in inhibiting bacterial growth. INDIGO quantifies the influence of individual chemical-genetic interactions on synergy and antagonism and significantly outperforms existing approaches based on experimental evaluation of novel predictions in Escherichia coli Our analysis revealed a core set of genes and pathways (e.g. central metabolism) that are predictive of antibiotic interactions. By identifying the interactions that are associated with orthologous genes, we successfully estimated drug-interaction outcomes in the bacterial pathogens Mycobacterium tuberculosis and Staphylococcus aureus, using the E. coli INDIGO model. INDIGO thus enables the discovery of effective combination therapies in less-studied pathogens by leveraging chemogenomics data in model organisms.

Relationship of Toll-Like Receptor 7, 9, and 10 Polymorphisms and the Severity of Coronavirus Disease 2019.

Coronavirus disease 2019 (COVID-19) is a pandemic that is still affecting people and has caused many deaths. Toll-like receptors (TLRs) have an important role in the binding of disease agents to the host cell, disease susceptibility and severity, and host disease resistance. In this study, we investigated the frequencies of TLR7 (C.4-151 A/G), TLR9 (T-1486C and G2848A), and TLR10 (720A/C and 992T/A) single nucleotide polymorphisms in 150 cases with COVID-19 and 171 control samples. We also examined whether TLR7, TLR9, and TLR10 were related to COVID-19 severity. Furthermore, we analyzed the association between COVID-19 and some clinical parameters. Polymerase chain reaction based on restriction fragment length polymorphisms performed for the TLR7, TLR9, and TLR10 single nucleotide polymorphisms. TLR7 C.4-151 A/G G allele and GG genotype; TLR9 T-1486C C allele and TC, CC genotypes; and TLR10 720A/C C allele; TLR10 992T/A A allele and AA genotype frequencies were statistically significant in cases with COVID-19 compared with controls (P < 0.05*). In addition, there was a statistically significant difference in the distribution of TLR7, TLR9, and TLR10 allele and genotype frequencies between the severity groups (P < 0.05*). Our findings suggest that TLR7, TLR9, and TLR10 polymorphisms may be crucial for the clinical course and susceptibility to infection.

Role of lncRNAs in Remodeling of the Coronary Artery Plaques in Patients with Atherosclerosis.

INTRODUCTION: Cardiovascular diseases (CVDs) are the leading cause of death worldwide according to World Health Organization (WHO) data. Atherosclerosis is considered as a chronic inflammatory disease that develops in response to damage to the vascular intima-media layer in most cases. In recent years, epigenetic events have emerged as important players in the development and progression of CVDs. Since noncoding RNA (ncRNAs) are important regulators in the organization of the pathophysiological processes of the cardiovascular system, they have the potential to be used as therapeutic targets, diagnostic and prognostic biomarkers. In this study long noncoding RNA (lncRNA) and mRNA gene expression were compared between coronary atherosclerotic plaques (CAP) and the internal mammary artery (IMA)  which has the same genetic makeup and is exposed to the same environmental stress conditions with CAP in the same individual. METHODS: lncRNA and mRNA gene expressions were determined using the microarray in the samples. Microarray results were validated by RT-qPCR. Differentially expressed genes (DEGs; lncRNAs and mRNAs) were determined by GeneSpring (Ver 3.0) [p values < 0.05 and fold change (FC) > 2]. DAVID bioinformatics program was used for Gene Ontology (GO) annotation and enrichment analyses of statistically significant genes between CAP and IMA tissue. RESULTS AND CONCLUSIONS: In our study, 345 DEGs were found to be statistically significant (p < 0.05; FC > 2) between CAP and IMA. Of these, 65 were lncRNA and 280 were mRNA. Thirty-three lncRNAs were upregulated, while 32 lncRNAs were downregulated. Some of the important mRNAs are SPP1, CYP4B1, CHRDL1, MYOC, and ALKAL2, while some of the lncRNAs are LOC105377123, LINC01857, DIO3OS, LOC101928134, and KCNA3 between CAP and IMA tissue. We also identified genes that correlated with statistically significant lncRNAs. The results of this study are expected to be an important source of data in the development of new genetically based drugs to prevent atherosclerotic plaque. In addition, the data obtained may contribute to the explanation of the epigenetic mechanisms that play a role in the pathological basis of the process that protects the IMA from atherosclerosis.

Reconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig.

The type and genomic context of cancer mutations depend on their causes. These causes have been characterized using signatures that represent mutation types that co-occur in the same tumours. However, it remains unclear how mutation processes change during cancer evolution due to the lack of reliable methods to reconstruct evolutionary trajectories of mutational signature activity. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole-genome sequencing data from 2658 cancers across 38 tumour types, we present TrackSig, a new method that reconstructs these trajectories using optimal, joint segmentation and deconvolution of mutation type and allele frequencies from a single tumour sample. In simulations, we find TrackSig has a 3-5% activity reconstruction error, and 12% false detection rate. It outperforms an aggressive baseline in situations with branching evolution, CNA gain, and neutral mutations. Applied to data from 2658 tumours and 38 cancer types, TrackSig permits pan-cancer insight into evolutionary changes in mutational processes.

Characterizing ABC-Transporter Substrate-Likeness Using a Clean-Slate Genetic Background.

Mutations in ATP Binding Cassette (ABC)-transporter genes can have major effects on the bioavailability and toxicity of the drugs that are ABC-transporter substrates. Consequently, methods to predict if a drug is an ABC-transporter substrate are useful for drug development. Such methods traditionally relied on literature curated collections of ABC-transporter dependent membrane transfer assays. Here, we used a single large-scale dataset of 376 drugs with relative efficacy on an engineered yeast strain with all ABC-transporter genes deleted (ABC-16), to explore the relationship between a drug's chemical structure and ABC-transporter substrate-likeness. We represented a drug's chemical structure by an array of substructure keys and explored several machine learning methods to predict the drug's efficacy in an ABC-16 yeast strain. Gradient-Boosted Random Forest models outperformed all other methods with an AUC of 0.723. We prospectively validated the model using new experimental data and found significant agreement with predictions. Our analysis expands the previously reported chemical substructures associated with ABC-transporter substrates and provides an alternative means to investigate ABC-transporter substrate-likeness.

Regulation of microRNAs in coronary atherosclerotic plaque.

Aim: Identification of microRNAs (miRNAs) associated with atherosclerosis may unravel novel therapeutic targets and biomarkers. We studied miRNAs differentially expressed between coronary atherosclerotic plaques (CAP) and healthy arteries. Materials & methods: Paired CAP and internal mammary arteries (IMA) were collected from 14 coronary artery disease patients. The miRNA profiles between diseased (CAP) and healthy (IMA) tissues were compared using microarrays and quantitative PCR. Results: Thirty-one miRNAs were differentially expressed between CAP and IMA. Among these, miR-486-5p showed a high level of regulation (12-fold), had predicted interactions with atherosclerosis-associated genes and correlated with triglyceride levels and arterial stenosis. Regulation of miR-486-5p was validated by PCR (p = 0.004). Conclusion: The miRNAs are regulated in the atherosclerotic plaque. We highlight miR-486-5p whose role in atherosclerosis requires further investigation.

Machine learning and computer vision approaches for phenotypic profiling.

With recent advances in high-throughput, automated microscopy, there has been an increased demand for effective computational strategies to analyze large-scale, image-based data. To this end, computer vision approaches have been applied to cell segmentation and feature extraction, whereas machine-learning approaches have been developed to aid in phenotypic classification and clustering of data acquired from biological images. Here, we provide an overview of the commonly used computer vision and machine-learning methods for generating and categorizing phenotypic profiles, highlighting the general biological utility of each approach.