Transcription factor binding process is the primary driver of noise in gene expression.

Noise in expression of individual genes gives rise to variations in activity of cellular pathways and generates heterogeneity in cellular phenotypes. Phenotypic heterogeneity has important implications for antibiotic persistence, mutation penetrance, cancer growth and therapy resistance. Specific molecular features such as the presence of the TATA box sequence and the promoter nucleosome occupancy have been associated with noise. However, the relative importance of these features in noise regulation is unclear and how well these features can predict noise has not yet been assessed. Here through an integrated statistical model of gene expression noise in yeast we found that the number of regulating transcription factors (TFs) of a gene was a key predictor of noise, whereas presence of the TATA box and the promoter nucleosome occupancy had poor predictive power. With an increase in the number of regulatory TFs, there was a rise in the number of cooperatively binding TFs. In addition, an increased number of regulatory TFs meant more overlaps in TF binding sites, resulting in competition between TFs for binding to the same region of the promoter. Through modeling of TF binding to promoter and application of stochastic simulations, we demonstrated that competition and cooperation among TFs could increase noise. Thus, our work uncovers a process of noise regulation that arises out of the dynamics of gene regulation and is not dependent on any specific transcription factor or specific promoter sequence.

Yeast adapts to a changing stressful environment by evolving cross-protection and anticipatory gene regulation.

Organisms can protect themselves against future environmental change. An example is cross-protection, where physiological adaptation against a present environmental stressor can protect an organism against a future stressor. Another is anticipation, where an organism uses information about its present environment to trigger gene expression and other physiological changes adaptive in future environments. "Predictive" abilities like this exist in organisms that have been exposed to periodic changes in environments. It is unknown how readily they can evolve. To answer this question, we carried out laboratory evolution experiments in the yeast Saccharomyces cerevisiae. Specifically, we exposed three replicate populations of yeast to environments that varied cyclically between two stressors, salt stress and oxidative stress, every 10 generations, for a total of 300 generations. We evolved six replicate control populations in only one of these stressors for the same amount of time. We analyzed fitness changes and genome-scale expression changes in all these evolved populations. Our populations evolved asymmetric cross protection, where oxidative stress protects against salt stress but not vice versa. Gene expression data also suggest the evolution of anticipation and basal gene expression changes that occur uniquely in cyclic environments. Our study shows that highly complex physiological states that are adaptive in future environments can evolve on very short evolutionary time scales.

Living in a noisy world-origins of gene expression noise and its impact on cellular decision-making.

The expression level of a gene can vary between genetically identical cells under the same environmental condition-a phenomenon referred to as gene expression noise. Several studies have now elucidated a central role of transcription factors in the generation of expression noise. Transcription factors, as the key components of gene regulatory networks, drive many important cellular decisions in response to cellular and environmental signals. Therefore, a very relevant question is how expression noise impacts gene regulation and influences cellular decision-making. In this Review, we summarize the current understanding of the molecular origins of expression noise, highlighting the role of transcription factors in this process, and discuss the ways in which noise can influence cellular decision-making. As advances in single-cell technologies open new avenues for studying expression noise as well as gene regulatory circuits, a better understanding of the influence of noise on cellular decisions will have important implications for many biological processes.

Increased heterogeneity in expression of genes associated with cancer progression and drug resistance.

Fluctuations in the number of regulatory molecules and differences in timings of molecular events can generate variation in gene expression among genetically identical cells in the same environmental condition. This variation, termed as expression noise, can create differences in metabolic state and cellular functions, leading to phenotypic heterogeneity. Expression noise and phenotypic heterogeneity have been recognized as important contributors to intra-tumor heterogeneity, and have been associated with cancer growth, progression, and therapy resistance. However, how expression noise changes with cancer progression in actual cancer patients has remained poorly explored. Such an analysis, through identification of genes with increasing expression noise, can provide valuable insights into generation of intra-tumor heterogeneity, and could have important implications for understanding immune-suppression, drug tolerance and therapy resistance. In this work, we performed a genome-wide identification of changes in gene expression noise with cancer progression using single-cell RNA-seq data of lung adenocarcinoma patients at different stages of cancer. We identified 37 genes in epithelial cells that showed an increasing noise trend with cancer progression, many of which were also associated with cancer growth, EMT and therapy resistance. We found that expression of several of these genes was positively associated with expression of mitochondrial genes, suggesting an important role of mitochondria in generation of heterogeneity. In addition, we uncovered substantial differences in sample-specific noise profiles which could have implications for personalized prognosis and treatment.

A large-scale computational screen identifies strong potential inhibitors for disrupting SARS-CoV-2 S-protein and human ACE2 interaction.

SARS-CoV-2 has infected millions of individuals across the globe and has killed over 2.7 million people. Even though vaccines against this virus have recently been introduced, the antibody generated in the process has been reported to decline quickly. This can reduce the efficacy of vaccines over time and can result in re-infections. Thus, drugs that are effective against COVID-19 can provide a second line of defence and can prevent occurrence of the severe form of the disease. The interaction between SARS-CoV2 S-protein and human ACE2 (hACE2) is essential for the infection of the virus. Thus, drugs that block this interaction could potentially inhibit SARS-CoV-2 infection into the host cells. To identify such drugs, we first analyzed the recently published crystal structure of S-protein-hACE2 complex and identified essential residues of both S-protein and hACE2 for this interaction. We used this knowledge to virtually dock a drug library containing 4115 drug molecules against S-protein for repurposing drugs that could inhibit binding of S-protein to hACE2. We identified several potential inhibitors based on their docking scores, pharmacological effects and ability to block residues of S protein required for interaction with hACE2. The top inhibitors included drugs used for the treatment of hepatitis C (velpatasvir, pibrentasvir) as well as several vitamin D derivatives. Several molecules obtained from our screen already have good experimental support in published literature. Thus, we believe that our results will facilitate the discovery of an effective drug against COVID-19. Communicated by Ramaswamy H. Sarma.

EpICC: A Bayesian neural network model with uncertainty correction for a more accurate classification of cancer.

Accurate classification of cancers into their types and subtypes holds the key for choosing the right treatment strategy and can greatly impact patient well-being. However, existence of large-scale variations in the molecular processes driving even a single type of cancer can make accurate classification a challenging problem. Therefore, improved and robust methods for classification are absolutely critical. Although deep learning-based methods for cancer classification have been proposed earlier, they all provide point estimates for predictions without any measure of confidence and thus, can fall short in real-world applications where key decisions are to be made based on the predictions of the classifier. Here we report a Bayesian neural network-based model for classification of cancer types as well as sub-types from transcriptomic data. This model reported a measure of confidence with each prediction through analysis of epistemic uncertainty. We incorporated an uncertainty correction step with the Bayesian network-based model to greatly enhance prediction accuracy of cancer types (> 97% accuracy) and sub-types (> 80%). Our work suggests that reporting uncertainty measure with each classification can enable more accurate and informed decision-making that can be highly valuable in clinical settings.

Designer bacterial cell factories for improved production of commercially valuable non-ribosomal peptides.

Non-ribosomal peptides have gained significant attention as secondary metabolites of high commercial importance. This group houses a diverse range of bioactive compounds, ranging from biosurfactants to antimicrobial and cytotoxic agents. However, low yield of synthesis by bacteria and excessive losses during purification hinders the industrial-scale production of non-ribosomal peptides, and subsequently limits their widespread applicability. While isolation of efficient producer strains and optimization of bioprocesses have been extensively used to enhance yield, further improvement can be made by optimization of the microbial strain using the tools and techniques of metabolic engineering, synthetic biology, systems biology, and adaptive laboratory evolution. These techniques, which directly target the genome of producer strains, aim to redirect carbon and nitrogen fluxes of the metabolic network towards the desired product, bypass the feedback inhibition and repression mechanisms that limit the maximum productivity of the strain, and even extend the substrate range of the cell for synthesis of the target product. The present review takes a comprehensive look into the biosynthesis of bacterial NRPs, how the same is regulated by the cell, and dives deep into the strategies that have been undertaken for enhancing the yield of NRPs, while also providing a perspective on other potential strategies that can allow for further yield improvement. Furthermore, this review provides the reader with a holistic perspective on the design of cellular factories of NRP production, starting from general techniques performed in the laboratory to the computational techniques that help a biochemical engineer model and subsequently strategize the architectural plan.

A two-step PCR assembly for construction of gene variants across large mutational distances.

Construction of empirical fitness landscapes has transformed our understanding of genotype-phenotype relationships across genes. However, most empirical fitness landscapes have been constrained to the local genotype neighbourhood of a gene primarily due to our limited ability to systematically construct genotypes that differ by a large number of mutations. Although a few methods have been proposed in the literature, these techniques are complex owing to several steps of construction or contain a large number of amplification cycles that increase chances of non-specific mutations. A few other described methods require amplification of the whole vector, thereby increasing the chances of vector backbone mutations that can have unintended consequences for study of fitness landscapes. Thus, this has substantially constrained us from traversing large mutational distances in the genotype network, thereby limiting our understanding of the interactions between multiple mutations and the role these interactions play in evolution of novel phenotypes. In the current work, we present a simple but powerful approach that allows us to systematically and accurately construct gene variants at large mutational distances. Our approach relies on building-up small fragments containing targeted mutations in the first step followed by assembly of these fragments into the complete gene fragment by polymerase chain reaction (PCR). We demonstrate the utility of our approach by constructing variants that differ by up to 11 mutations in a model gene. Our work thus provides an accurate method for construction of multi-mutant variants of genes and therefore will transform the studies of empirical fitness landscapes by enabling exploration of genotypes that are far away from a starting genotype.

Inexpensive and Versatile Paper-Based Platform for 3D Culture of Liver Cells and Related Bioassays.

The present study delineates the fabrication of paper-based devices for culturing liver cells and developing related bioassays. The devices were prepared by conventional lab-based LaserJet printing technology and employed for 3D cell culture. Our results demonstrated that the devices efficiently supported the growth of multiple cell types incuding HepG2, HUVEC, fibroblasts, and MSCs. We further showed that the device specifications (grade of paper or design parameters) greatly impacted the functional phenotype of the HepG2 cells. We also explored the application of the developed devices for routine cell culture, drug screening, coculture, and transwell migration assays. The cellular responses observed on the paper under different culture configurations were similar to those obtained in the case of tissue culture plate (TCP). Moreover, we showed that the paper-based devices were compatible with the immunocytochemistry and ELISA procedures (no indication of nonspecific matrix-antibody interaction). Considering the simplicity, experimental flexibility, cost-effectiveness, and multiplexibility of the paper-based liver models, it is deemed to be ideal for developing cell-based bioassays, especially in resource-limited settings.

Interface of apoptotic protein complexes has distinct properties.

Apoptosis is a programmed mechanism of cell death that is a normal component of development and health of multi-cellular organisms. In this study, we ask if interface properties of apoptotic protein complexes are different from protein complexes in general. We find that although in apoptotic protein complexes the overall distribution of interface size, surface complementarity, hydrogen bonding, hydrophobicity are similar to general interface properties, apoptotic complexes tend to have more fragmented interfaces and different secondary structural preferences. The statistics on the number of interfaces where specific amino acid(s) occur with significantly enhanced frequency suggest that Arg, Met and Asp are most important functional residues. The role of Met is believed to be unique, as evidenced from the existing data on hot spot potential of residues. These findings together provide insight into the possible role of various physico-chemical attributes at the protein interface in regulation of the apoptosis process.

Single cell functional genomics reveals the importance of mitochondria in cell-to-cell phenotypic variation.

Mutations frequently have outcomes that differ across individuals, even when these individuals are genetically identical and share a common environment. Moreover, individual microbial and mammalian cells can vary substantially in their proliferation rates, stress tolerance, and drug resistance, with important implications for the treatment of infections and cancer. To investigate the causes of cell-to-cell variation in proliferation, we used a high-throughput automated microscopy assay to quantify the impact of deleting >1500 genes in yeast. Mutations affecting mitochondria were particularly variable in their outcome. In both mutant and wild-type cells mitochondrial membrane potential - but not amount - varied substantially across individual cells and predicted cell-to-cell variation in proliferation, mutation outcome, stress tolerance, and resistance to a clinically used anti-fungal drug. These results suggest an important role for cell-to-cell variation in the state of an organelle in single cell phenotypic variation.

Author Correction: Changes in gene expression predictably shift and switch genetic interactions.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Changes in gene expression predictably shift and switch genetic interactions.

Non-additive interactions between mutations occur extensively and also change across conditions, making genetic prediction a difficult challenge. To better understand the plasticity of genetic interactions (epistasis), we combine mutations in a single protein performing a single function (a transcriptional repressor inhibiting a target gene). Even in this minimal system, genetic interactions switch from positive (suppressive) to negative (enhancing) as the expression of the gene changes. These seemingly complicated changes can be predicted using a mathematical model that propagates the effects of mutations on protein folding to the cellular phenotype. More generally, changes in gene expression should be expected to alter the effects of mutations and how they interact whenever the relationship between expression and a phenotype is nonlinear, which is the case for most genes. These results have important implications for understanding genotype-phenotype maps and illustrate how changes in genetic interactions can often-but not always-be predicted by hierarchical mechanistic models.

A Concentration-Dependent Liquid Phase Separation Can Cause Toxicity upon Increased Protein Expression.

Multiple human diseases are associated with a liquid-to-solid phase transition resulting in the formation of amyloid fibers or protein aggregates. Here, we present an alternative mechanism for cellular toxicity based on a concentration-dependent liquid-liquid demixing. Analyzing proteins that are toxic when their concentration is increased in yeast reveals that they share physicochemical properties with proteins that participate in physiological liquid-liquid demixing in the cell. Increasing the concentration of one of these proteins indeed results in the formation of cytoplasmic foci with liquid properties. Demixing occurs at the onset of toxicity and titrates proteins and mRNAs from the cytoplasm. Focus formation is reversible, and resumption of growth occurs as the foci dissolve as protein concentration falls. Preventing demixing abolishes the dosage sensitivity of the protein. We propose that triggering inappropriate liquid phase separation may be an important cause of dosage sensitivity and a determinant of human disease.

Slow-growing cells within isogenic populations have increased RNA polymerase error rates and DNA damage.

Isogenic cells show a large degree of variability in growth rate, even when cultured in the same environment. Such cell-to-cell variability in growth can alter sensitivity to antibiotics, chemotherapy and environmental stress. To characterize transcriptional differences associated with this variability, we have developed a method--FitFlow--that enables the sorting of subpopulations by growth rate. The slow-growing subpopulation shows a transcriptional stress response, but, more surprisingly, these cells have reduced RNA polymerase fidelity and exhibit a DNA damage response. As DNA damage is often caused by oxidative stress, we test the addition of an antioxidant, and find that it reduces the size of the slow-growing population. More generally, we find a significantly altered transcriptome in the slow-growing subpopulation that only partially resembles that of cells growing slowly due to environmental and culture conditions. Slow-growing cells upregulate transposons and express more chromosomal, viral and plasmid-borne transcripts, and thus explore a larger genotypic--and so phenotypic--space.

Increased gene dosage plays a predominant role in the initial stages of evolution of duplicate TEM-1 beta lactamase genes.

Gene duplication is important in evolution, because it provides new raw material for evolutionary adaptations. Several existing hypotheses about the causes of duplicate retention and diversification differ in their emphasis on gene dosage, subfunctionalization, and neofunctionalization. Little experimental data exist on the relative importance of gene expression changes and changes in coding regions for the evolution of duplicate genes. Furthermore, we do not know how strongly the environment could affect this importance. To address these questions, we performed evolution experiments with the TEM-1 beta lactamase gene in Escherichia coli to study the initial stages of duplicate gene evolution in the laboratory. We mimicked tandem duplication by inserting two copies of the TEM-1 gene on the same plasmid. We then subjected these copies to repeated cycles of mutagenesis and selection in various environments that contained antibiotics in different combinations and concentrations. Our experiments showed that gene dosage is the most important factor in the initial stages of duplicate gene evolution, and overshadows the importance of point mutations in the coding region.

Analyzing the catalytic mechanism of protein tyrosine phosphatase PtpB from Staphylococcus aureus through site-directed mutagenesis.

Protein tyrosine phosphatase B (PtpB) from Staphylococcus aureus, MRSA 252, is a low molecular weight protein tyrosine phosphatase involved in its pathogenicity. PtpB has been modeled in silico and site-directed mutagenesis performed to ascertain the importance of active site residues Cys8, Arg14, Ser15 and Asp120 in its catalytic mechanism. Kinetic characterization of wild-type and the mutant PtpBs, C8S, R14A, S15T, S15A, D120A, D120E, D120N revealed the reaction mechanism followed by this LMWPTPase. The mutations caused major changes in the local environment resulting in significant decrease of its catalytic activity. Inhibition kinetics for the wild-type enzyme was performed with maleimide and maleimidobutyric acid.