Duplication of a promiscuous transcription factor drives the emergence of a new regulatory network

Fine-Tuning Genetic Circuits via Host Context and RBS Modulation

The choice of organism to host a genetic circuit, the chassis, is often defaulted to model organisms due to their amenability. The chassis-design space has therefore remained underexplored as an engineering variable. In this work, we explored the design space of a genetic toggle switch through variations in nine ribosome binding site compositions and three host contexts, creating 27 circuit variants. Characterization of performance metrics in terms of toggle switch output and host growth dynamics unveils a spectrum of performance profiles from our circuit library. We find that changes in host context cause large shifts in overall performance, while modulating ribosome binding sites leads to more incremental changes. We find that a combined ribosome binding site and host context modulation approach can be used to fine-tune the properties of a toggle switch according to user-defined specifications, such as toward greater signaling strength, inducer sensitivity, or both. Other auxiliary properties, such as inducer tolerance, are also exclusively accessed through changes in the host context. We demonstrate here that exploration of the chassis-design space can offer significant value, reconceptualizing the chassis organism as an important part in the synthetic biologist's toolbox with important implications for the field of synthetic biology.

Specialization Restricts the Evolutionary Paths Available to Yeast Sugar Transporters

Functional innovation at the protein level is a key source of evolutionary novelties. The constraints on functional innovations are likely to be highly specific in different proteins, which are shaped by their unique histories and the extent of global epistasis that arises from their structures and biochemistries. These contextual nuances in the sequence-function relationship have implications both for a basic understanding of the evolutionary process and for engineering proteins with desirable properties. Here, we have investigated the molecular basis of novel function in a model member of an ancient, conserved, and biotechnologically relevant protein family. These Major Facilitator Superfamily sugar porters are a functionally diverse group of proteins that are thought to be highly plastic and evolvable. By dissecting a recent evolutionary innovation in an α-glucoside transporter from the yeast Saccharomyces eubayanus, we show that the ability to transport a novel substrate requires high-order interactions between many protein regions and numerous specific residues proximal to the transport channel. To reconcile the functional diversity of this family with the constrained evolution of this model protein, we generated new, state-of-the-art genome annotations for 332 Saccharomycotina yeast species spanning ∼400 My of evolution. By integrating phylogenetic and phenotypic analyses across these species, we show that the model yeast α-glucoside transporters likely evolved from a multifunctional ancestor and became subfunctionalized. The accumulation of additive and epistatic substitutions likely entrenched this subfunction, which made the simultaneous acquisition of multiple interacting substitutions the only reasonably accessible path to novelty.

The biosynthesis of storage reserves and auxin is coordinated by a hierarchical regulatory network in maize endosperm

Grain filling in maize (Zea mays) is intricately linked to cell development, involving the regulation of genes responsible for the biosynthesis of storage reserves (starch, proteins, and lipids) and phytohormones. However, the regulatory network coordinating these biological functions remains unclear. In this study, we identified 1744 high-confidence target genes co-regulated by the transcription factors (TFs) ZmNAC128 and ZmNAC130 (ZmNAC128/130) through chromatin immunoprecipitation sequencing coupled with RNA-seq analysis in the zmnac128/130 loss-of-function mutants. We further constructed a hierarchical regulatory network using DNA affinity purification sequencing analysis of downstream TFs regulated by ZmNAC128/130. In addition to target genes involved in the biosynthesis of starch and zeins, we discovered novel target genes of ZmNAC128/130 involved in the biosynthesis of lipids and indole-3-acetic acid (IAA). Consistently, the number of oil bodies, as well as the contents of triacylglycerol, and IAA were significantly reduced in zmnac128/130. The hierarchical regulatory network centered by ZmNAC128/130 revealed a significant overlap between the direct target genes of ZmNAC128/130 and their downstream TFs, particularly in regulating the biosynthesis of storage reserves and IAA. Our results indicated that the biosynthesis of storage reserves and IAA is coordinated by a multi-TFs hierarchical regulatory network in maize endosperm.

Specialization restricts the evolutionary paths available to yeast sugar transporters

Functional innovation at the protein level is a key source of evolutionary novelties. The constraints on functional innovations are likely to be highly specific in different proteins, which are shaped by their unique histories and the extent of global epistasis that arises from their structures and biochemistries. These contextual nuances in the sequence-function relationship have implications both for a basic understanding of the evolutionary process and for engineering proteins with desirable properties. Here, we have investigated the molecular basis of novel function in a model member of an ancient, conserved, and biotechnologically relevant protein family. These Major Facilitator Superfamily sugar porters are a functionally diverse group of proteins that are thought to be highly plastic and evolvable. By dissecting a recent evolutionary innovation in an α-glucoside transporter from the yeast Saccharomyces eubayanus, we show that the ability to transport a novel substrate requires high-order interactions between many protein regions and numerous specific residues proximal to the transport channel. To reconcile the functional diversity of this family with the constrained evolution of this model protein, we generated new, state-of-the-art genome annotations for 332 Saccharomycotina yeast species spanning approximately 400 million years of evolution. By integrating phylogenetic and phenotypic analyses across these species, we show that the model yeast α-glucoside transporters likely evolved from a multifunctional ancestor and became subfunctionalized. The accumulation of additive and epistatic substitutions likely entrenched this subfunction, which made the simultaneous acquisition of multiple interacting substitutions the only reasonably accessible path to novelty.

New regulatory role of Znf1 in transcriptional control of pentose phosphate pathway and ATP synthesis for enhanced isobutanol and acid tolerance

To develop a cost-effective microbial cell factory for the production of biofuels and biochemicals, an understanding of tolerant mechanisms is vital for the construction of robust host strains. Here, we characterized a new function of a key metabolic transcription factor named Znf1 and its involvement in stress response in Saccharomyces cerevisiae to enhance tolerance to advanced biofuel, isobutanol. RNA-sequencing analysis of the wild-type versus the znf1Δ deletion strains in glucose revealed a new role for transcription factor Znf1 in the pentose phosphate pathway (PPP) and energy generation. The gene expression analysis confirmed that isobutanol induces an adaptive cell response, resulting in activation of ATP1-3 and COX6 expression. These genes were Znf1 targets that belong to the electron transport chain, important to produce ATPs. Znf1 also activated PPP genes, required for the generation of key amino acids, cellular metabolites, and maintenance of NADP/NADPH redox balance. In glucose, Znf1 also mediated the upregulation of valine biosynthetic genes of the Ehrlich pathway, namely ILV3, ILV5, and ARO10, associated with the generation of key intermediates for isobutanol production. Using S. cerevisiae knockout collection strains, cells with deleted transcriptional regulatory gene ZNF1 or its targets displayed hypersensitivity to isobutanol and acid inhibitors; in contrast, overexpression of ZNF1 enhanced cell survival. Thus, the transcription factor Znf1 functions in the maintenance of energy homeostasis and redox balance at various checkpoints of yeast metabolic pathways. It ensures the rapid unwiring of gene transcription in response to toxic products/by-products generated during biofuel production. Importantly, we provide a new approach to enhance strain tolerance during the conversion of glucose to biofuels.

Evolutionary innovation through transcription factor rewiring in microbes is shaped by levels of transcription factor activity, expression, and existing connectivity

The survival of a population during environmental shifts depends on whether the rate of phenotypic adaptation keeps up with the rate of changing conditions. A common way to achieve this is via change to gene regulatory network (GRN) connections-known as rewiring-that facilitate novel interactions and innovation of transcription factors. To understand the success of rapidly adapting organisms, we therefore need to determine the rules that create and constrain opportunities for GRN rewiring. Here, using an experimental microbial model system with the soil bacterium Pseudomonas fluorescens, we reveal a hierarchy among transcription factors that are rewired to rescue lost function, with alternative rewiring pathways only unmasked after the preferred pathway is eliminated. We identify 3 key properties-high activation, high expression, and preexisting low-level affinity for novel target genes-that facilitate transcription factor innovation. Ease of acquiring these properties is constrained by preexisting GRN architecture, which was overcome in our experimental system by both targeted and global network alterations. This work reveals the key properties that determine transcription factor evolvability, and as such, the evolution of GRNs.

Transcription factor expression levels and environmental signals constrain transcription factor innovation

Evolutionary innovation of transcription factors frequently drives phenotypic diversification and adaptation to environmental change. Transcription factors can gain or lose connections to target genes, resulting in novel regulatory responses and phenotypes. However the frequency of functional adaptation varies between different regulators, even when they are closely related. To identify factors influencing propensity for innovation, we utilise a Pseudomonas fluorescens SBW25 strain rendered incapable of flagellar mediated motility in soft-agar plates via deletion of the flagellar master regulator (fleQ ). This bacterium can evolve to rescue flagellar motility via gene regulatory network rewiring of an alternative transcription factor to rescue activity of FleQ. Previously, we have identified two members (out of 22) of the RpoN-dependent enhancer binding protein (RpoN-EBP) family of transcription factors (NtrC and PFLU1132) that are capable of innovating in this way. These two transcription factors rescue motility repeatably and reliably in a strict hierarchy – with NtrC the only route in a ∆fleQ background, and PFLU1132 the only route in a ∆fleQ ∆ntrC background. However, why other members in the same transcription factor family have not been observed to rescue flagellar activity is unclear. Previous work shows that protein homology cannot explain this pattern within the protein family (RpoN-EBPs), and mutations in strains that rescued motility suggested high levels of transcription factor expression and activation drive innovation. We predict that mutations that increase expression of the transcription factor are vital to unlock evolutionary potential for innovation. Here, we construct titratable expression mutant lines for 11 of the RpoN-EBPs in P. fluorescens . We show that in five additional RpoN-EBPs (FleR, HbcR, GcsR, DctD, AauR and PFLU2209), high expression levels result in different mutations conferring motility rescue, suggesting alternative rewiring pathways. Our results indicate that expression levels (and not protein homology) of RpoN-EBPs are a key constraining factor in determining evolutionary potential for innovation. This suggests that transcription factors that can achieve high expression through few mutational changes, or transcription factors that are active in the selective environment, are more likely to innovate and contribute to adaptive gene regulatory network evolution.

Complementary strategies for directing in vivo transcription factor binding through DNA binding domains and intrinsically disordered regions

DNA binding domains (DBDs) of transcription factors (TFs) recognize DNA sequence motifs that are highly abundant in genomes. Within cells, TFs bind a subset of motif-containing sites as directed by either their DBDs or DBD-external (nonDBD) sequences. To define the relative roles of DBDs and nonDBDs in directing binding preferences, we compared the genome-wide binding of 48 (∼30%) budding yeast TFs with their DBD-only, nonDBD-truncated, and nonDBD-only mutants. With a few exceptions, binding locations differed between DBDs and TFs, resulting from the cumulative action of multiple determinants mapped mostly to disordered nonDBD regions. Furthermore, TFs' preferences for promoters of the fuzzy nucleosome architecture were lost in DBD-only mutants, whose binding spread across promoters, implicating nonDBDs' preferences in this hallmark of budding yeast regulatory design. We conclude that DBDs and nonDBDs employ complementary DNA-targeting strategies, whose balance defines TF binding specificity along genomes.

Deep mutational scanning and machine learning reveal structural and molecular rules governing allosteric hotspots in homologous proteins

A fundamental question in protein science is where allosteric hotspots - residues critical for allosteric signaling - are located, and what properties differentiate them. We carried out deep mutational scanning (DMS) of four homologous bacterial allosteric transcription factors (aTFs) to identify hotspots and built a machine learning model with this data to glean the structural and molecular properties of allosteric hotspots. We found hotspots to be distributed protein-wide rather than being restricted to 'pathways' linking allosteric and active sites as is commonly assumed. Despite structural homology, the location of hotspots was not superimposable across the aTFs. However, common signatures emerged when comparing hotspots coincident with long-range interactions, suggesting that the allosteric mechanism is conserved among the homologs despite differences in molecular details. Machine learning with our large DMS datasets revealed global structural and dynamic properties to be a strong predictor of whether a residue is a hotspot than local and physicochemical properties. Furthermore, a model trained on one protein can predict hotspots in a homolog. In summary, the overall allosteric mechanism is embedded in the structural fold of the aTF family, but the finer, molecular details are sequence-specific.

Mechanisms of protein evolution

How do proteins evolve? How do changes in sequence mediate changes in protein structure, and in turn in function? This question has multiple angles, ranging from biochemistry and biophysics to evolutionary biology. This review provides a brief integrated view of some key mechanistic aspects of protein evolution. First, we explain how protein evolution is primarily driven by randomly acquired genetic mutations and selection for function, and how these mutations can even give rise to completely new folds. Then, we also comment on how phenotypic protein variability, including promiscuity, transcriptional and translational errors, may also accelerate this process, possibly via "plasticity-first" mechanisms. Finally, we highlight open questions in the field of protein evolution, with respect to the emergence of more sophisticated protein systems such as protein complexes, pathways, and the emergence of pre-LUCA enzymes.

Construction of a synthetic Saccharomyces cerevisiae pan-genome neo-chromosome

The Synthetic Yeast Genome Project (Sc2.0) represents the first foray into eukaryotic genome engineering and a framework for designing and building the next generation of industrial microbes. However, the laboratory strain S288c used lacks many of the genes that provide phenotypic diversity to industrial and environmental isolates. To address this shortcoming, we have designed and constructed a neo-chromosome that contains many of these diverse pan-genomic elements and which is compatible with the Sc2.0 design and test framework. The presence of this neo-chromosome provides phenotypic plasticity to the Sc2.0 parent strain, including expanding the range of utilizable carbon sources. We also demonstrate that the induction of programmable structural variation (SCRaMbLE) provides genetic diversity on which further adaptive gains could be selected. The presence of this neo-chromosome within the Sc2.0 backbone may therefore provide the means to adapt synthetic strains to a wider variety of environments, a process which will be vital to transitioning Sc2.0 from the laboratory into industrial applications.

The Sc2.0 consortia is reengineering the yeast genome. To expand the Sc2.0 genetic repertoire, the authors build a neo-chromosome comprising variable loci from diverse yeast isolates, providing phenotypic plasticity for use in synthetic backgrounds.

Evolution of binding preferences among whole-genome duplicated transcription factors

Throughout evolution, new transcription factors (TFs) emerge by gene duplication, promoting growth and rewiring of transcriptional networks. How TF duplicates diverge was studied in a few cases only. To provide a genome-scale view, we considered the set of budding yeast TFs classified as whole-genome duplication (WGD)-retained paralogs (~35% of all specific TFs). Using high-resolution profiling, we find that ~60% of paralogs evolved differential binding preferences. We show that this divergence results primarily from variations outside the DNA-binding domains (DBDs), while DBD preferences remain largely conserved. Analysis of non-WGD orthologs revealed uneven splitting of ancestral preferences between duplicates, and the preferential acquiring of new targets by the least conserved paralog (biased neo/sub-functionalization). Interactions between paralogs were rare, and, when present, occurred through weak competition for DNA-binding or dependency between dimer-forming paralogs. We discuss the implications of our findings for the evolutionary design of transcriptional networks.

GAL Regulon in the Yeast S. cerevisiae is Highly Evolvable via Acquisition in the Coding Regions of the Regulatory Elements of the Network

GAL network in the yeast S. cerevisiae is one of the most well-characterized regulatory network. Expression of GAL genes is contingent on exposure to galactose, and an appropriate combination of the alleles of the regulatory genes GAL3, GAL1, GAL80, and GAL4. The presence of multiple regulators in the GAL network makes it unique, as compared to the many sugar utilization networks studied in bacteria. For example, utilization of lactose is controlled by a single regulator LacI, in E. coli's lac operon. Moreover, recent work has demonstrated that multiple alleles of these regulatory proteins are present in yeast isolated from ecological niches. In this work, we develop a mathematical model, and demonstrate via deterministic and stochastic runs of the model, that behavior/gene expression patterns of the cells (at a population level, and at a single-cell resolution) can be modulated by altering the binding affinities between the regulatory proteins. This adaptability is likely the key to explaining the multiple GAL regulatory alleles discovered in ecological isolates in recent years.

Synthetic multistability in mammalian cells

In multicellular organisms, gene regulatory circuits generate thousands of molecularly distinct, mitotically heritable states through the property of multistability. Designing synthetic multistable circuits would provide insight into natural cell fate control circuit architectures and would allow engineering of multicellular programs that require interactions among distinct cell types. We created MultiFate, a naturally inspired, synthetic circuit that supports long-term, controllable, and expandable multistability in mammalian cells. MultiFate uses engineered zinc finger transcription factors that transcriptionally self-activate as homodimers and mutually inhibit one another through heterodimerization. Using a model-based design, we engineered MultiFate circuits that generate as many as seven states, each stable for at least 18 days. MultiFate permits controlled state switching and modulation of state stability through external inputs and can be expanded with additional transcription factors. These results provide a foundation for engineering multicellular behaviors in mammalian cells.

The relative impact of evolving pleiotropy and mutational correlation on trait divergence

Both pleiotropic connectivity and mutational correlations can restrict the decoupling of traits under divergent selection, but it is unknown which is more important in trait evolution. To address this question, we create a model that permits within-population variation in both pleiotropic connectivity and mutational correlation, and compare their relative importance to trait evolution. Specifically, we developed an individual-based stochastic model where mutations can affect whether a locus affects a trait and the extent of mutational correlations in a population. We find that traits can decouple whether there is evolution in pleiotropic connectivity or mutational correlation, but when both can evolve, then evolution in pleiotropic connectivity is more likely to allow for decoupling to occur. The most common genotype found in this case is characterized by having one locus that maintains connectivity to all traits and another that loses connectivity to the traits under stabilizing selection (subfunctionalization). This genotype is favored because it allows the subfunctionalized locus to accumulate greater effect size alleles, contributing to increasingly divergent trait values in the traits under divergent selection without changing the trait values of the other traits (genetic modularization). These results provide evidence that partial subfunctionalization of pleiotropic loci may be a common mechanism of trait decoupling under regimes of corridor selection.

Yap5 Competes With Hap4 for the Regulation of Iron Homeostasis Genes in the Human Pathogen Candida glabrata

The CCAAT-binding complex (CBC) is a conserved heterotrimeric transcription factor which, in fungi, requires additional regulatory subunits to act on transcription. In the pathogenic yeast Candida glabrata, CBC has a dual role. Together with the Hap4 regulatory subunit, it activates the expression of genes involved in respiration upon growth with non-fermentable carbon sources, while its association with the Yap5 regulatory subunit is required for the activation of iron tolerance genes in response to iron excess. In the present work, we investigated further the interplay between CBC, Hap4 and Yap5. We showed that Yap5 regulation requires a specific Yap Response Element in the promoter of its target gene GRX4 and that the presence of Yap5 considerably strengthens the binding of CBC to the promoters of iron tolerance genes. Chromatin immunoprecipitation (ChIP) and transcriptome experiments showed that Hap4 can also bind these promoters but has no impact on the expression of those genes when Yap5 is present. In the absence of Yap5 however, GRX4 is constitutively regulated by Hap4, similarly to the genes involved in respiration. Our results suggest that the distinction between the two types of CBC targets in C. glabrata is mainly due to the dependency of Yap5 for very specific DNA sequences and to the competition between Hap4 and Yap5 at the promoter of the iron tolerance genes.

A non-invasive, automated diagnosis of Menière's disease using radiomics and machine learning on conventional magnetic resonance imaging: A multicentric, case-controlled feasibility study

Purpose

This study investigated the feasibility of a new image analysis technique (radiomics) on conventional MRI for the computer-aided diagnosis of Menière’s disease.

Materials and methods

A retrospective, multicentric diagnostic case–control study was performed. This study included 120 patients with unilateral or bilateral Menière’s disease and 140 controls from four centers in the Netherlands and Belgium. Multiple radiomic features were extracted from conventional MRI scans and used to train a machine learning-based, multi-layer perceptron classification model to distinguish patients with Menière’s disease from controls. The primary outcomes were accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the classification model.

Results

The classification accuracy of the machine learning model on the test set was 82%, with a sensitivity of 83%, and a specificity of 82%. The positive and negative predictive values were 71%, and 90%, respectively.

Conclusion

The multi-layer perceptron classification model yielded a precise, high-diagnostic performance in identifying patients with Menière’s disease based on radiomic features extracted from conventional T2-weighted MRI scans. In the future, radiomics might serve as a fast and noninvasive decision support system, next to clinical evaluation in the diagnosis of Menière’s disease.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11547-021-01425-w.

Non-canonical LexA proteins regulate the SOS response in the Bacteroidetes

Lesions to DNA compromise chromosome integrity, posing a direct threat to cell survival. The bacterial SOS response is a widespread transcriptional regulatory mechanism to address DNA damage. This response is coordinated by the LexA transcriptional repressor, which controls genes involved in DNA repair, mutagenesis and cell-cycle control. To date, the SOS response has been characterized in most major bacterial groups, with the notable exception of the Bacteroidetes. No LexA homologs had been identified in this large, diverse and ecologically important phylum, suggesting that it lacked an inducible mechanism to address DNA damage. Here, we report the identification of a novel family of transcriptional repressors in the Bacteroidetes that orchestrate a canonical response to DNA damage in this phylum. These proteins belong to the S24 peptidase family, but are structurally different from LexA. Their N-terminal domain is most closely related to CI-type bacteriophage repressors, suggesting that they may have originated from phage lytic phase repressors. Given their role as SOS regulators, however, we propose to designate them as non-canonical LexA proteins. The identification of a new class of repressors orchestrating the SOS response illuminates long-standing questions regarding the origin and plasticity of this transcriptional network.

Were Ancestral Proteins Less Specific?

Some have hypothesized that ancestral proteins were, on average, less specific than their descendants. If true, this would provide a universal axis along which to organize protein evolution and suggests that reconstructed ancestral proteins may be uniquely powerful tools for protein engineering. Ancestral sequence reconstruction studies are one line of evidence used to support this hypothesis. Previously, we performed such a study, investigating the evolution of peptide-binding specificity for the paralogs S100A5 and S100A6. The modern proteins appeared more specific than their last common ancestor (ancA5/A6), as each paralog bound a subset of the peptides bound by ancA5/A6. In this study, we revisit this transition, using quantitative phage display to measure the interactions of 30,533 random peptides with human S100A5, S100A6, and ancA5/A6. This unbiased screen reveals a different picture. While S100A5 and S100A6 do indeed bind to a subset of the peptides recognized by ancA5/A6, they also acquired new peptide partners outside of the set recognized by ancA5/A6. Our previous work showed that ancA5/A6 had lower specificity than its descendants when measured against biological targets; our new work shows that ancA5/A6 has similar specificity to the modern proteins when measured against a random set of peptide targets. This demonstrates that altered biological specificity does not necessarily indicate altered intrinsic specificity, and sounds a cautionary note for using ancestral reconstruction studies with biological targets as a means to infer global evolutionary trends in specificity.

Reprogramming of the Ethanol Stress Response in Saccharomyces cerevisiae by the Transcription Factor Znf1 and Its Effect on the Biosynthesis of Glycerol and Ethanol

High ethanol levels can severely inhibit the growth of yeast cells and fermentation productivity. The ethanologenic yeast Saccharomyces cerevisiae activates several well-defined cellular mechanisms of ethanol stress response (ESR); however, the involved regulatory control remains to be characterized. Here, we report a new transcription factor of ethanol stress adaptation called Znf1. It plays a central role in ESR by activating genes for glycerol and fatty acid production (GUP1, GPP1, GPP2, GPD1, GAT1, and OLE1) to preserve plasma membrane integrity. Importantly, Znf1 also activates genes implicated in cell wall biosynthesis (FKS1, SED1, and SMI1) and in the unfolded protein response (HSP30, HSP104, KAR1, and LHS1) to protect cells from proteotoxic stress. The znf1Δ strain displays increased sensitivity to ethanol, the endoplasmic reticulum (ER) stressor β-mercaptoethanol, and the cell wall-perturbing agent calcofluor white. To compensate for a defective cell wall, the strain lacking ZNF1 or its target SMI1 displays increased glycerol levels of 19.6% and 27.7%, respectively. Znf1 collectively regulates an intricate network of target genes essential for growth, protein refolding, and production of key metabolites. Overexpression of ZNF1 not only confers tolerance to high ethanol levels but also increases ethanol production by 4.6% (8.43 g/liter) or 2.8% (75.78 g/liter) when 2% or 20% (wt/vol) glucose, respectively, is used as a substrate, compared to that of the wild-type strain. The mutually stress-responsive transcription factors Msn2/4, Hsf1, and Yap1 are associated with some promoters of Znf1’s target genes to promote ethanol stress tolerance. In conclusion, this work implicates the novel regulator Znf1 in coordinating expression of ESR genes and illuminates the unifying transcriptional reprogramming during alcoholic fermentation.

IMPORTANCE The yeast S. cerevisiae is a major microbe that is widely used in food and nonfood industries. However, accumulation of ethanol has a negative effect on its growth and limits ethanol production. The Znf1 transcription factor has been implicated as a key regulator of glycolysis and gluconeogenesis in the utilization of different carbon sources, including glucose, the most abundant sugar on earth, and nonfermentable substrates. Here, the role of Znf1 in ethanol stress response is defined. Znf1 actively reprograms expression of genes linked to the unfolded protein response (UPR), heat shock response, glycerol and carbohydrate metabolism, and biosynthesis of cell membrane and cell wall components. A complex interplay among transcription factors of ESR indicates transcriptional fine-tuning as the main mechanism of stress adaptation, and Znf1 plays a major regulatory role in the coordination. Understanding the adaptive ethanol stress mechanism is crucial to engineering robust yeast strains for enhanced stress tolerance or increased ethanol production.

The predictive power of artificial intelligence on mediastinal lymphnode metastasis

OBJECTIVE

The aim of this study was to create the preoperative predictive model on mediastinal lymph-node metastasis based on artificial intelligence in surgically resected lung adenocarcinoma.

METHODS

We enrolled 301 surgical resections of patients with clinical stage N0-1 lung adenocarcinoma, who received positron emission tomography preoperatively between 2015 and 2019. We randomly assigned the patients into two groups: the training (n = 201) and validation groups (n = 100). The training group was used to obtain basic data for learning by artificial intelligence, whereas the validation group was used to verify the constructed algorithm. We used an automatic machine learning platform, to create artificial intelligence model. For comparison, multivariate analysis was performed in the training group, whereas for calculating and verifying the prediction accuracy rate, significant predicting factors were applied to the validation group.

RESULTS

Of the 301 patients, 41 patients were diagnosed as mediastinal lymph node metastasis. In multivariate analysis, the maximum standardized uptake value was an individual predictive factor. The accuracy rate of artificial intelligence model was 84%, and the specificity was 98% which were higher than those of the maximum standardized uptake value (61% and 57%). However, in terms of sensitivity, artificial intelligence model remarked low at 12%.

CONCLUSIONS

An artificial intelligence-based diagnostic algorithm showed remarkable specificity compared with the maximum standardized uptake value. Although this model is not ready to practical use and the result was preliminary because of poor sensitivity, artificial intelligence could be able to complement the shortcomings of existing diagnostic modalities.

The Role of Structural Variation in Adaptation and Evolution of Yeast and Other Fungi

Mutations in DNA can be limited to one or a few nucleotides, or encompass larger deletions, insertions, duplications, inversions and translocations that span long stretches of DNA or even full chromosomes. These so-called structural variations (SVs) can alter the gene copy number, modify open reading frames, change regulatory sequences or chromatin structure and thus result in major phenotypic changes. As some of the best-known examples of SV are linked to severe genetic disorders, this type of mutation has traditionally been regarded as negative and of little importance for adaptive evolution. However, the advent of genomic technologies uncovered the ubiquity of SVs even in healthy organisms. Moreover, experimental evolution studies suggest that SV is an important driver of evolution and adaptation to new environments. Here, we provide an overview of the causes and consequences of SV and their role in adaptation, with specific emphasis on fungi since these have proven to be excellent models to study SV.

Root Adaptation via Common Genetic Factors Conditioning Tolerance to Multiple Stresses for Crops Cultivated on Acidic Tropical Soils

Crop tolerance to multiple abiotic stresses has long been pursued as a Holy Grail in plant breeding efforts that target crop adaptation to tropical soils. On tropical, acidic soils, aluminum (Al) toxicity, low phosphorus (P) availability and drought stress are the major limitations to yield stability. Molecular breeding based on a small suite of pleiotropic genes, particularly those with moderate to major phenotypic effects, could help circumvent the need for complex breeding designs and large population sizes aimed at selecting transgressive progeny accumulating favorable alleles controlling polygenic traits. The underlying question is twofold: do common tolerance mechanisms to Al toxicity, P deficiency and drought exist? And if they do, will they be useful in a plant breeding program that targets stress-prone environments. The selective environments in tropical regions are such that multiple, co-existing regulatory networks may drive the fixation of either distinctly different or a smaller number of pleiotropic abiotic stress tolerance genes. Recent studies suggest that genes contributing to crop adaptation to acidic soils, such as the major Arabidopsis Al tolerance protein, AtALMT1, which encodes an aluminum-activated root malate transporter, may influence both Al tolerance and P acquisition via changes in root system morphology and architecture. However, trans-acting elements such as transcription factors (TFs) may be the best option for pleiotropic control of multiple abiotic stress genes, due to their small and often multiple binding sequences in the genome. One such example is the C2H2-type zinc finger, AtSTOP1, which is a transcriptional regulator of a number of Arabidopsis Al tolerance genes, including AtMATE and AtALMT1, and has been shown to activate AtALMT1, not only in response to Al but also low soil P. The large WRKY family of transcription factors are also known to affect a broad spectrum of phenotypes, some of which are related to acidic soil abiotic stress responses. Hence, we focus here on signaling proteins such as TFs and protein kinases to identify, from the literature, evidence for unifying regulatory networks controlling Al tolerance, P efficiency and, also possibly drought tolerance. Particular emphasis will be given to modification of root system morphology and architecture, which could be an important physiological "hub" leading to crop adaptation to multiple soil-based abiotic stress factors.

Radiomics feature reproducibility under inter-rater variability in segmentations of CT images

Identifying image features that are robust with respect to segmentation variability is a tough challenge in radiomics. So far, this problem has mainly been tackled in test-retest analyses. In this work we analyse radiomics feature reproducibility in two phases: first with manual segmentations provided by four expert readers and second with probabilistic automated segmentations using a recently developed neural network (PHiseg). We test feature reproducibility on three publicly available datasets of lung, kidney and liver lesions. We find consistent results both over manual and automated segmentations in all three datasets and show that there are subsets of radiomic features which are robust against segmentation variability and other radiomic features which are prone to poor reproducibility under differing segmentations. By providing a detailed analysis of robustness of the most common radiomics features across several datasets, we envision that more reliable and reproducible radiomic models can be built in the future based on this work.

[Formula: see text]: deep learning-based radiomics for the time-to-event outcome prediction in lung cancer

Hand-crafted radiomics has been used for developing models in order to predict time-to-event clinical outcomes in patients with lung cancer. Hand-crafted features, however, are pre-defined and extracted without taking the desired target into account. Furthermore, accurate segmentation of the tumor is required for development of a reliable predictive model, which may be objective and a time-consuming task. To address these drawbacks, we propose a deep learning-based radiomics model for the time-to-event outcome prediction, referred to as DRTOP that takes raw images as inputs, and calculates the image-based risk of death or recurrence, for each patient. Our experiments on an in-house dataset of 132 lung cancer patients show that the obtained image-based risks are significant predictors of the time-to-event outcomes. Computed Tomography (CT)-based features are predictors of the overall survival (OS), with the hazard ratio (HR) of 1.35, distant control (DC), with HR of 1.06, and local control (LC), with HR of 2.66. The Positron Emission Tomography (PET)-based features are predictors of OS and recurrence free survival (RFS), with hazard ratios of 1.67 and 1.18, respectively. The concordance indices of [Formula: see text], [Formula: see text], and [Formula: see text] for predicting the OS, DC, and RFS show that the deep learning-based radiomics model is as accurate or better in predicting predefined clinical outcomes compared to hand-crafted radiomics, with concordance indices of [Formula: see text], [Formula: see text], and [Formula: see text], for predicting the OS, DC, and RFS, respectively. Deep learning-based radiomics has the potential to offer complimentary predictive information in the personalized management of lung cancer patients.

The physical basis and practical consequences of biological promiscuity

Proteins interact with metabolites, nucleic acids, and other proteins to orchestrate the myriad catalytic, structural and regulatory functions that support life from the simplest microbes to the most complex multicellular organisms. These molecular interactions are often exquisitely specific, but never perfectly so. Adventitious "promiscuous" interactions are ubiquitous due to the thousands of macromolecules and small molecules crowded together in cells. Such interactions may perturb protein function at the molecular level, but as long as they do not compromise organismal fitness, they will not be removed by natural selection. Although promiscuous interactions are physiologically irrelevant, they are important because they can provide a vast reservoir of potential functions that can provide the starting point for evolution of new functions, both in nature and in the laboratory.

Asymmetrical diversification of the receptor-ligand interaction controlling self-incompatibility in Arabidopsis

How two-component genetic systems accumulate evolutionary novelty and diversify in the course of evolution is a fundamental problem in evolutionary systems biology. In the Brassicaceae, self-incompatibility (SI) is a spectacular example of a diversified allelic series in which numerous highly diverged receptor-ligand combinations are segregating in natural populations. However, the evolutionary mechanisms by which new SI specificities arise have remained elusive. Using in planta ancestral protein reconstruction, we demonstrate that two allelic variants segregating as distinct receptor-ligand combinations diverged through an asymmetrical process whereby one variant has retained the same recognition specificity as their (now extinct) putative ancestor, while the other has functionally diverged and now represents a novel specificity no longer recognized by the ancestor. Examination of the structural determinants of the shift in binding specificity suggests that qualitative rather than quantitative changes of the interaction are an important source of evolutionary novelty in this highly diversified receptor-ligand system.

The causes of evolvability and their evolution

Evolvability is the ability of a biological system to produce phenotypic variation that is both heritable and adaptive. It has long been the subject of anecdotal observations and theoretical work. In recent years, however, the molecular causes of evolvability have been an increasing focus of experimental work. Here, we review recent experimental progress in areas as different as the evolution of drug resistance in cancer cells and the rewiring of transcriptional regulation circuits in vertebrates. This research reveals the importance of three major themes: multiple genetic and non-genetic mechanisms to generate phenotypic diversity, robustness in genetic systems, and adaptive landscape topography. We also discuss the mounting evidence that evolvability can evolve and the question of whether it evolves adaptively.

Transition between fermentation and respiration determines history-dependent behavior in fluctuating carbon sources

Cells constantly adapt to environmental fluctuations. These physiological changes require time and therefore cause a lag phase during which the cells do not function optimally. Interestingly, past exposure to an environmental condition can shorten the time needed to adapt when the condition re-occurs, even in daughter cells that never directly encountered the initial condition. Here, we use the molecular toolbox of Saccharomyces cerevisiae to systematically unravel the molecular mechanism underlying such history-dependent behavior in transitions between glucose and maltose. In contrast to previous hypotheses, the behavior does not depend on persistence of proteins involved in metabolism of a specific sugar. Instead, presence of glucose induces a gradual decline in the cells' ability to activate respiration, which is needed to metabolize alternative carbon sources. These results reveal how trans-generational transitions in central carbon metabolism generate history-dependent behavior in yeast, and provide a mechanistic framework for similar phenomena in other cell types.

Evolutionary potential of transcription factors for gene regulatory rewiring

Gene regulatory networks evolve through rewiring of individual components-that is, through changes in regulatory connections. However, the mechanistic basis of regulatory rewiring is poorly understood. Using a canonical gene regulatory system, we quantify the properties of transcription factors that determine the evolutionary potential for rewiring of regulatory connections: robustness, tunability and evolvability. In vivo repression measurements of two repressors at mutated operator sites reveal their contrasting evolutionary potential: while robustness and evolvability were positively correlated, both were in trade-off with tunability. Epistatic interactions between adjacent operators alleviated this trade-off. A thermodynamic model explains how the differences in robustness, tunability and evolvability arise from biophysical characteristics of repressor-DNA binding. The model also uncovers that the energy matrix, which describes how mutations affect repressor-DNA binding, encodes crucial information about the evolutionary potential of a repressor. The biophysical determinants of evolutionary potential for regulatory rewiring constitute a mechanistic framework for understanding network evolution.

Extracellular maltotriose hydrolysis by Saccharomyces cerevisiae cells lacking the AGT1 permease

In brewing, maltotriose is the least preferred sugar for uptake by Saccharomyces cerevisiae cells. Although the AGT1 permease is required for efficient maltotriose fermentation, we have described a new phenotype in some agt1Δ strains of which the cells do not grow on maltotriose during the first 3-4 days of incubation, but after that, they start to grow on the sugar aerobically. Aiming to characterize this new phenotype, we performed microarray gene expression analysis which indicated upregulation of high-affinity glucose transporters (HXT4, HXT6 and HXT7) and α-glucosidases (MAL12 and IMA5) during this delayed cellular growth. Since these results suggested that this phenotype might be due to extracellular hydrolysis of maltotriose, we attempted to detect glucose in the media during growth. When an hxt-null agt1Δ strain was grown on maltotriose, it also showed the delayed growth on this carbon source, and glucose accumulated in the medium during maltotriose consumption. Considering that the poorly characterized α-glucosidase encoded by IMA5 was among the overexpressed genes, we deleted this gene from an agt1Δ strain that showed delayed growth on maltotriose. The ima5Δ agt1Δ strain showed no maltotriose utilization even after 200 h of incubation, suggesting that IMA5 is likely responsible for the extracellular maltotriose hydrolysis.

SIGNIFICANCE AND IMPACT OF THE STUDY

Maltotriose is the second most abundant sugar present in brewing. However, many yeast strains have difficulties to consume maltotriose, mainly because of its low uptake rate by the yeast cells when compared to glucose and maltose uptake. The AGT1 permease is required for efficient maltotriose fermentation, but some strains deleted in this gene are still able to grow on maltotriose after an extensive lag phase. This manuscript shows that such delayed growth on maltotriose is a consequence of extracellular hydrolysis of the sugar. Our results also indicate that the IMA5-encoded α-glucosidase is likely the enzyme responsible for this phenotype.

Genome Mining of Non-Conventional Yeasts: Search and Analysis of MAL Clusters and Proteins

Genomic clustering of functionally related genes is rare in yeasts and other eukaryotes with only few examples available. Here, we summarize our data on a nontelomeric MAL cluster of a non-conventional methylotrophic yeast Ogataea (Hansenula) polymorpha containing genes for α-glucosidase MAL1, α-glucoside permease MAL2 and two hypothetical transcriptional activators. Using genome mining, we detected MAL clusters of varied number, position and composition in many other maltose-assimilating non-conventional yeasts from different phylogenetic groups. The highest number of MAL clusters was detected in Lipomyces starkeyi while no MAL clusters were found in Schizosaccharomyces pombe and Blastobotrys adeninivorans. Phylograms of α-glucosidases and α-glucoside transporters of yeasts agreed with phylogenesis of the respective yeast species. Substrate specificity of unstudied α-glucosidases was predicted from protein sequence analysis. Specific activities of Scheffersomycesstipitis α-glucosidases MAL7, MAL8, and MAL9 heterologously expressed in Escherichia coli confirmed the correctness of the prediction-these proteins were verified promiscuous maltase-isomaltases. α-Glucosidases of earlier diverged yeasts L. starkeyi, B. adeninivorans and S. pombe showed sequence relatedness with α-glucosidases of filamentous fungi and bacilli.

Trans-regulation and localization of orthologous maltose transporters in the interspecies lager yeast hybrid

In the interspecies lager yeast hybrid there are MAL loci involved in maltose and maltotriose utilization derived from each parent (Saccharomyces cerevisiae and Saccharomyces eubayanus). We show that trans-regulation across hybrid subgenomes occurs for MAL genes. However, gene expression is less efficient with non-native activators (trans-activation) compared to native activators (cis-activation). MAL genes were induced by maltose and repressed by glucose irrespective of host. Despite the strong expression of S. cerevisiae-type genes in the S. eubayanus host, a very low amount of transporter protein was actually observed in cells. This suggests that proper formation and configuration of the S. cerevisiae transporters is not efficient in S. eubayanus. The S. eubayanus-type Malx1 transporter was present in the plasma membrane in high amounts in all hosts (S. cerevisiae, S. eubayanus and Saccharomyces pastorianus) at all times. However, the S. cerevisiae-type transporters appeared sequentially in the plasma membrane; scMalx1 was localized in the plasma membrane during early to late linear growth and subsequently withdrawn to intracellular compartments. In contrast, the scAgt1 transporter was found in the plasma membrane mainly in the stationary phase of growth. Different localization patterns may explain why certain transporter orthologues in natural S. pastorianus strains were lost to mutation.

MAL73, a novel regulator of maltose fermentation, is functionally impaired by single nucleotide polymorphism in sake brewing yeast

For maltose fermentation, budding yeast Saccharomyces cerevisiae operates a mechanism that involves transporters (MALT), maltases (MALS) and regulators (MALR) collectively known as MAL genes. However, functional relevance of MAL genes during sake brewing process remains largely elusive, since sake yeast is cultured under glucose-rich condition achieved by the co-culture partner Aspergillus spp.. Here we isolated an ethyl methane sulfonate (EMS)-mutagenized sake yeast strain exhibiting enhanced maltose fermentation compared to the parental strain. The mutant carried a single nucleotide insertion that leads to the extension of the C-terminal region of a previously uncharacterized MALR gene YPR196W-2, which was renamed as MAL73. Introduction of the mutant allele MAL73L with extended C-terminal region into the parental or other sake yeast strains enhanced the growth rate when fed with maltose as the sole carbon source. In contrast, disruption of endogenous MAL73 in the sake yeasts decreased the maltose fermentation ability of sake yeast, confirming that the original MAL73 functions as a MALR. Importantly, the MAL73L-expressing strain fermented more maltose in practical condition compared to the parental strain during sake brewing process. Our data show that MAL73(L) is a novel MALR gene that regulates maltose fermentation, and has been functionally attenuated in sake yeast by single nucleotide deletion during breeding history. Since the MAL73L-expressing strain showed enhanced ability of maltose fermentation, MAL73L might also be a valuable tool for enhancing maltose fermentation in yeast in general.

Prediction and identification of transcriptional regulatory elements at the lung cancer-specific DKK1 locus

The glycoprotein dickkopf 1 (DKK1) is highly expressed in lung cancer cell lines and tissues. Our previous study demonstrated that DKK1 promoter activity is low in lung cancer cell lines. This may be because it lacks the necessary transcriptional regulatory elements (TREs) required for higher activity levels. However, it is difficult to computationally predict functionally significant TREs, as TREs from different locations can affect large segments of distant DNA. The Encyclopedia of DNA Elements project features multiple integrated technologies and approaches for the discovery and definition of functional elements, including enhancer elements and enhancer-blocking insulators. In the present study, DNase I hypersensitive sites and histone modifications of DKK1 were investigated in the A549 lung cancer cell line using the UCSC Genome Browser. A set of cis-acting enhancer elements were identified by a dual-luciferase reporter gene assay system to increase activity of the DKK1 promoter with lung cancer specificity. To the best of our knowledge, these data provide the first insight into the role of the DKK1 locus in lung cancer, and confirm the contribution of intronic cis-acting elements to the regulation of DKK1 expression, providing a new insight into gene regulation in lung cancer, which could inform the development of targeted therapy.

Comparative Transcriptomics Highlights New Features of the Iron Starvation Response in the Human Pathogen Candida glabrata

In this work, we used comparative transcriptomics to identify regulatory outliers (ROs) in the human pathogen Candida glabrata. ROs are genes that have very different expression patterns compared to their orthologs in other species. From comparative transcriptome analyses of the response of eight yeast species to toxic doses of selenite, a pleiotropic stress inducer, we identified 38 ROs in C. glabrata. Using transcriptome analyses of C. glabrata response to five different stresses, we pointed out five ROs which were more particularly responsive to iron starvation, a process which is very important for C. glabrata virulence. Global chromatin Immunoprecipitation and gene profiling analyses showed that four of these genes are actually new targets of the iron starvation responsive Aft2 transcription factor in C. glabrata. Two of them (HBS1 and DOM34b) are required for C. glabrata optimal growth in iron limited conditions. In S. cerevisiae, the orthologs of these two genes are involved in ribosome rescue by the NO GO decay (NGD) pathway. Hence, our results suggest a specific contribution of NGD co-factors to the C. glabrata adaptation to iron starvation.

Evolution of protein specificity: insights from ancestral protein reconstruction

Specific interactions between proteins and their molecular partners drive most biological processes, so understanding how these interactions evolve is an important question for biochemists and evolutionary biologists alike. It is often thought that ancestral proteins were systematically more promiscuous than modern proteins and that specificity usually evolves after gene duplication by partitioning and refining the activities of multifunctional ancestors. However, recent studies using ancestral protein reconstruction (APR) have found that ligand-specific functions in some modern protein families evolved de novo from ancestors that did not already have those functions. Further, the new specific interactions evolved by simple mechanisms, with just a few mutations changing classically recognized biochemical determinants of specificity, such as steric and electrostatic complementarity. Acquiring new specific interactions during evolution therefore appears to be neither difficult nor rare. Rather, it is likely that proteins continually gain and lose new activities over evolutionary time as mutations cause subtle but consequential changes in the shape and electrostatics of interaction interfaces. Only a few of these activities, however, are incorporated into the biological processes that contribute to fitness before they are lost to the ravages of further mutation.

Evolution of new regulatory functions on biophysically realistic fitness landscapes

Gene expression is controlled by networks of regulatory proteins that interact specifically with external signals and DNA regulatory sequences. These interactions force the network components to co-evolve so as to continually maintain function. Yet, existing models of evolution mostly focus on isolated genetic elements. In contrast, we study the essential process by which regulatory networks grow: the duplication and subsequent specialization of network components. We synthesize a biophysical model of molecular interactions with the evolutionary framework to find the conditions and pathways by which new regulatory functions emerge. We show that specialization of new network components is usually slow, but can be drastically accelerated in the presence of regulatory crosstalk and mutations that promote promiscuous interactions between network components.Gene networks evolve by transcription factor (TF) duplication and divergence of their binding site specificities, but little is known about the global constraints at play. Here, the authors study the coevolution of TFs and binding sites using a biophysical-evolutionary approach, and show that the emerging complex fitness landscapes strongly influence regulatory evolution with a role for crosstalk.

Elimination of sucrose transport and hydrolysis in Saccharomyces cerevisiae: a platform strain for engineering sucrose metabolism

Many relevant options to improve efficacy and kinetics of sucrose metabolism in Saccharomyces cerevisiae and, thereby, the economics of sucrose-based processes remain to be investigated. An essential first step is to identify all native sucrose-hydrolysing enzymes and sucrose transporters in this yeast, including those that can be activated by suppressor mutations in sucrose-negative strains. A strain in which all known sucrose-transporter genes (MAL11, MAL21, MAL31, MPH2, MPH3) were deleted did not grow on sucrose after 2 months of incubation. In contrast, a strain with deletions in genes encoding sucrose-hydrolysing enzymes (SUC2, MAL12, MAL22, MAL32) still grew on sucrose. Its specific growth rate increased from 0.08 to 0.25 h⁻¹ after sequential batch cultivation. This increase was accompanied by a 3-fold increase of in vitro sucrose-hydrolysis and isomaltase activities, as well as by a 3- to 5-fold upregulation of the isomaltase-encoding genes IMA1 and IMA5. One-step Cas9-mediated deletion of all isomaltase-encoding genes (IMA1-5) completely abolished sucrose hydrolysis. Even after 2 months of incubation, the resulting strain did not grow on sucrose. This sucrose-negative strain can be used as a platform to test metabolic engineering strategies and for fundamental studies into sucrose hydrolysis or transport.

Inference and Evolutionary Analysis of Genome-Scale Regulatory Networks in Large Phylogenies

Changes in transcriptional regulatory networks can significantly contribute to species evolution and adaptation. However, identification of genome-scale regulatory networks is an open challenge, especially in non-model organisms. Here, we introduce multi-species regulatory network learning (MRTLE), a computational approach that uses phylogenetic structure, sequence-specific motifs, and transcriptomic data, to infer the regulatory networks in different species. Using simulated data from known networks and transcriptomic data from six divergent yeasts, we demonstrate that MRTLE predicts networks with greater accuracy than existing methods because it incorporates phylogenetic information. We used MRTLE to infer the structure of the transcriptional networks that control the osmotic stress responses of divergent, non-model yeast species and then validated our predictions experimentally. Interrogating these networks reveals that gene duplication promotes network divergence across evolution. Taken together, our approach facilitates study of regulatory network evolutionary dynamics across multiple poorly studied species.

Evolutionary Context Improves Regulatory Network Predictions

A novel algorithm harnesses phylogenetic information and facilitates a better understanding of the evolutionary divergence of gene regulation between species.

Transient Duplication-Dependent Divergence and Horizontal Transfer Underlie the Evolutionary Dynamics of Bacterial Cell-Cell Signaling

Evolutionary expansion of signaling pathway families often underlies the evolution of regulatory complexity. Expansion requires the acquisition of a novel homologous pathway and the diversification of pathway specificity. Acquisition can occur either vertically, by duplication, or through horizontal transfer, while divergence of specificity is thought to occur through a promiscuous protein intermediate. The way by which these mechanisms shape the evolution of rapidly diverging signaling families is unclear. Here, we examine this question using the highly diversified Rap-Phr cell-cell signaling system, which has undergone massive expansion in the genus Bacillus. To this end, genomic sequence analysis of >300 Bacilli genomes was combined with experimental analysis of the interaction of Rap receptors with Phr autoinducers and downstream targets. Rap-Phr expansion is shown to have occurred independently in multiple Bacillus lineages, with >80 different putative rap-phr alleles evolving in the Bacillius subtilis group alone. The specificity of many rap-phr alleles and the rapid gain and loss of Rap targets are experimentally demonstrated. Strikingly, both horizontal and vertical processes were shown to participate in this expansion, each with a distinct role. Horizontal gene transfer governs the acquisition of already diverged rap-phr alleles, while intralocus duplication and divergence of the phr gene create the promiscuous intermediate required for the divergence of Rap-Phr specificity. Our results suggest a novel role for transient gene duplication and divergence during evolutionary shifts in specificity.

Resurrecting ancestral structural dynamics of an antiviral immune receptor: adaptive binding pocket reorganization repeatedly shifts RNA preference

Background

Although resurrecting ancestral proteins is a powerful tool for understanding the molecular-functional evolution of gene families, nearly all studies have examined proteins functioning in relatively stable biological processes. The extent to which more dynamic systems obey the same ‘rules’ governing stable processes is unclear. Here we present the first detailed investigation of the functional evolution of the RIG-like receptors (RLRs), a family of innate immune receptors that detect viral RNA in the cytoplasm.

Results

Using kinetic binding assays and molecular dynamics simulations of ancestral proteins, we demonstrate how a small number of adaptive protein-coding changes repeatedly shifted the RNA preference of RLRs throughout animal evolution by reorganizing the shape and electrostatic distribution across the RNA binding pocket, altering the hydrogen bond network between the RLR and its RNA target. In contrast to observations of proteins involved in metabolism and development, we find that RLR-RNA preference ‘flip flopped’ between two functional states, and shifts in RNA preference were not always coupled to gene duplications or speciation events. We demonstrate at least one reversion of RLR-RNA preference from a derived to an ancestral function through a novel structural mechanism, indicating multiple structural implementations of similar functions.

Conclusions

Our results suggest a model in which frequent shifts in selection pressures imposed by an evolutionary arms race preclude the long-term functional optimization observed in stable biological systems. As a result, the evolutionary dynamics of immune receptors may be less constrained by structural epistasis and historical contingency.

Electronic supplementary material

The online version of this article (doi:10.1186/s12862-016-0818-6) contains supplementary material, which is available to authorized users.

Ongoing resolution of duplicate gene functions shapes the diversification of a metabolic network

The evolutionary mechanisms leading to duplicate gene retention are well understood, but the long-term impacts of paralog differentiation on the regulation of metabolism remain underappreciated. Here we experimentally dissect the functions of two pairs of ancient paralogs of the GALactose sugar utilization network in two yeast species. We show that the Saccharomyces uvarum network is more active, even as over-induction is prevented by a second co-repressor that the model yeast Saccharomyces cerevisiae lacks. Surprisingly, removal of this repression system leads to a strong growth arrest, likely due to overly rapid galactose catabolism and metabolic overload. Alternative sugars, such as fructose, circumvent metabolic control systems and exacerbate this phenotype. We further show that S. cerevisiae experiences homologous metabolic constraints that are subtler due to how the paralogs have diversified. These results show how the functional differentiation of paralogs continues to shape regulatory network architectures and metabolic strategies long after initial preservation.

Maltase protein of Ogataea (Hansenula) polymorpha is a counterpart to the resurrected ancestor protein ancMALS of yeast maltases and isomaltases

Saccharomyces cerevisiae maltases use maltose, maltulose, turanose and maltotriose as substrates, isomaltases use isomaltose, α‐methylglucoside and palatinose and both use sucrose. These enzymes are hypothesized to have evolved from a promiscuous α‐glucosidase ancMALS through duplication and mutation of the genes. We studied substrate specificity of the maltase protein MAL1 from an earlier diverged yeast, Ogataea polymorpha (Op), in the light of this hypothesis. MAL1 has extended substrate specificity and its properties are strikingly similar to those of resurrected ancMALS. Moreover, amino acids considered to determine selective substrate binding are highly conserved between Op MAL1 and ancMALS. Op MAL1 represents an α‐glucosidase in which both maltase and isomaltase activities are well optimized in a single enzyme. Substitution of Thr200 (corresponds to Val216 in S. cerevisiae isomaltase IMA1) with Val in MAL1 drastically reduced the hydrolysis of maltose‐like substrates (α‐1,4‐glucosides), confirming the requirement of Thr at the respective position for this function. Differential scanning fluorimetry (DSF) of the catalytically inactive mutant Asp199Ala of MAL1 in the presence of its substrates and selected monosaccharides suggested that the substrate‐binding pocket of MAL1 has three subsites (–1, +1 and +2) and that binding is strongest at the –1 subsite. The DSF assay results were in good accordance with affinity (K_m) and inhibition (K_i) data of the enzyme for tested substrates, indicating the power of the method to predict substrate binding. Deletion of either the maltase (MAL1) or α‐glucoside permease (MAL2) gene in Op abolished the growth of yeast on MAL1 substrates, confirming the requirement of both proteins for usage of these sugars. © 2016 The Authors. Yeast published by John Wiley & Sons, Ltd.

Gal3 Binds Gal80 Tighter than Gal1 Indicating Adaptive Protein Changes Following Duplication

Derived from the yeast whole-genome duplication, Saccharomyces cerevisiae GAL1 and GAL3 encode the catabolic enzyme galactokinase (Gal1) and its transcriptional coinducer (Gal3), whereas the ancestral, preduplicated GAL1 gene performed both functions. Previous studies indicated that divergence was primarily driven by changes in upstream promoter elements, and changes in GAL3's coding region are assumed to be the result of drift. We show that replacement of GAL3's open-reading-frame with GAL1's results in an extended lag phase upon switching to growth on galactose with up to 2.5-fold differences in the initial cell masses. Accordingly, the binding affinity of Gal3 to Gal80 was found to be greater than 10-folds higher than that of Gal1, with both a higher association rate (ka) and lower dissociation (kd) rate. Thus, while changes in the noncoding, regulatory regions were the initial driving force for GAL3's subfunctionalization as a coinducer, adaptive changes in the protein sequence seem to have followed.

Experimental evolution of the model eukaryote Saccharomyces cerevisiae yields insight into the molecular mechanisms underlying adaptation

Understanding how changes in DNA drive the emergence of new phenotypes and fuel evolution remains a major challenge. One major hurdle is the lack of a fossil record of DNA that allows linking mutations to phenotypic changes. However, the emergence of high-throughput sequencing technologies now allows sequencing genomes of natural and experimentally evolved microbial populations to study how mutations arise and spread through a population, how new phenotypes arise and how this ultimately leads to adaptation. Here, we highlight key studies that have increased our mechanistic understanding of evolution. We specifically focus on the model eukaryote Saccharomyces cerevisiae because its relatively short replication time, much-studied biology and available molecular toolbox have made it a prime model for molecular evolution studies.