A global genetic interaction network maps a wiring diagram of cellular function

A vast evolutionarily transient translatome contributes to phenotype and fitness

Translation is the process by which ribosomes synthesize proteins. Ribosome profiling recently revealed that many short sequences previously thought to be noncoding are pervasively translated. To identify protein-coding genes in this noncanonical translatome, we combine an integrative framework for extremely sensitive ribosome profiling analysis, iRibo, with high-powered selection inferences tailored for short sequences. We construct a reference translatome for Saccharomyces cerevisiae comprising 5,400 canonical and almost 19,000 noncanonical translated elements. Only 14 noncanonical elements were evolving under detectable purifying selection. A representative subset of translated elements lacking signatures of selection demonstrated involvement in processes including DNA repair, stress response, and post-transcriptional regulation. Our results suggest that most translated elements are not conserved protein-coding genes and contribute to genotype-phenotype relationships through fast-evolving molecular mechanisms.

Genetic interactions under the microscope

Traditional genetic interaction screens profile phenotypes at aggregate level, missing interactions that may influence the distribution of single cells in specific states. Here, Heigwer and colleagues use an imaging approach to generate a large-scale high-resolution genetic interaction map in Drosophila cells and demonstrate its utility for understanding gene function.

Targeting synthetic lethal paralogs in cancer

Synthetic lethal interactions, where mutation of one gene renders cells sensitive to inhibition of another gene, can be exploited for the development of targeted therapeutics in cancer. Pairs of duplicate genes (paralogs) often share common functionality and hence are a potentially rich source of synthetic lethal interactions. Because the majority of human genes have paralogs, exploiting such interactions could be a widely applicable approach for targeting gene loss in cancer. Moreover, existing small-molecule drugs may exploit synthetic lethal interactions by inhibiting multiple paralogs simultaneously. Consequently, the identification of synthetic lethal interactions between paralogs could be extremely informative for drug development. Here we review approaches to identify such interactions and discuss some of the challenges of exploiting them.

Integrative structure determination of histones H3 and H4 using genetic interactions

Integrative structure modeling is increasingly used for determining the architectures of biological assemblies, especially those that are structurally heterogeneous. Recently, we reported on how to convert in vivo genetic interaction measurements into spatial restraints for structural modeling: first, phenotypic profiles are generated for each point mutation and thousands of gene deletions or environmental perturbations. Following, the phenotypic profile similarities are converted into distance restraints on the pairs of mutated residues. We illustrate the approach by determining the structure of the histone H3-H4 complex. The method is implemented in our open-source IMP program, expanding the structural biology toolbox by allowing structural characterization based on in vivo data without the need to purify the target system. We compare genetic interaction measurements to other sources of structural information, such as residue coevolution and deep-learning structure prediction of complex subunits. We also suggest that determining genetic interactions could benefit from new technologies, such as CRISPR-Cas9 approaches to gene editing, especially for mammalian cells. Finally, we highlight the opportunity for using genetic interactions to determine recalcitrant biomolecular structures, such as those of disordered proteins, transient protein assemblies, and host-pathogen protein complexes.

Membrane binding of endocytic myosin-1s is inhibited by a class of ankyrin repeat proteins

Myosin-1s are monomeric actin-based motors that function at membranes. Myo1 is the single myosin-1 isoform in Schizosaccharomyces pombe that works redundantly with Wsp1-Vrp1 to activate the Arp2/3 complex for endocytosis. Here, we identified Ank1 as an uncharacterized cytoplasmic Myo1 binding partner. We found that in ank1Δ cells, Myo1 dramatically redistributed from endocytic patches to decorate the entire plasma membrane and endocytosis was defective. Biochemical analysis and structural predictions suggested that the Ank1 ankyrin repeats bind the Myo1 lever arm and the Ank1 acidic tail binds the Myo1 TH1 domain to prevent TH1-dependent Myo1 membrane binding. Indeed, Ank1 over-expression precluded Myo1 membrane localization and recombinant Ank1 blocked purified Myo1 liposome binding in vitro. Based on biochemical and cell biology analyses, we propose budding yeast Ank1 and human OSTF1 are functional Ank1 orthologs and that cytoplasmic sequestration by small ankyrin repeat proteins is a conserved mechanism regulating myosin-1s in endocytosis.

Summary

Fission yeast long-tailed myosin-1 binds Ank1. Ank1 ankyrin repeats associate with the Myo1 lever arm and Ank1 acidic tail binds the Myo1 TH1 domain to inhibit Myo1 membrane binding. Ank1 orthologs exists in budding yeast (Ank1) and humans (OSTF1).

The proteomic landscape of genome-wide genetic perturbations

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

A global genetic interaction network by single-cell imaging and machine learning

Cellular and organismal phenotypes are controlled by complex gene regulatory networks. However, reference maps of gene function are still scarce across different organisms. Here, we generated synthetic genetic interaction and cell morphology profiles of more than 6,800 genes in cultured Drosophila cells. The resulting map of genetic interactions was used for machine learning-based gene function discovery, assigning functions to genes in 47 modules. Furthermore, we devised Cytoclass as a method to dissect genetic interactions for discrete cell states at the single-cell resolution. This approach identified an interaction of Cdk2 and the Cop9 signalosome complex, triggering senescence-associated secretory phenotypes and immunogenic conversion in hemocytic cells. Together, our data constitute a genome-scale resource of functional gene profiles to uncover the mechanisms underlying genetic interactions and their plasticity at the single-cell level.

Adaptation to high rates of chromosomal instability and aneuploidy through multiple pathways in budding yeast

Both an increased frequency of chromosome missegregation (chromosomal instability, CIN) and the presence of an abnormal complement of chromosomes (aneuploidy) are hallmarks of cancer. To better understand how cells are able to adapt to high levels of chromosomal instability, we previously examined yeast cells that were deleted of the gene BIR1, a member of the chromosomal passenger complex (CPC). We found bir1Δ cells quickly adapted by acquiring specific combinations of beneficial aneuploidies. In this study, we monitored these yeast strains for longer periods of time to determine how cells adapt to high levels of both CIN and aneuploidy in the long term. We identify suppressor mutations that mitigate the chromosome missegregation phenotype. The mutated proteins fall into four main categories: outer kinetochore subunits, the SCFCdc4 ubiquitin ligase complex, the mitotic kinase Mps1, and the CPC itself. The identified suppressor mutations functioned by reducing chromosomal instability rather than alleviating the negative effects of aneuploidy. Following the accumulation of suppressor point mutations, the number of beneficial aneuploidies decreased. These experiments demonstrate a time line of adaptation to high rates of CIN.

Next-generation large-scale binary protein interaction network for Drosophila melanogaster

Generating reference maps of interactome networks illuminates genetic studies by providing a protein-centric approach to finding new components of existing pathways, complexes, and processes. We apply state-of-the-art methods to identify binary protein-protein interactions (PPIs) for Drosophila melanogaster. Four all-by-all yeast two-hybrid (Y2H) screens of > 10,000 Drosophila proteins result in the 'FlyBi' dataset of 8723 PPIs among 2939 proteins. Testing subsets of data from FlyBi and previous PPI studies using an orthogonal assay allows for normalization of data quality; subsequent integration of FlyBi and previous data results in an expanded binary Drosophila reference interaction network, DroRI, comprising 17,232 interactions among 6511 proteins. We use FlyBi data to generate an autophagy network, then validate in vivo using autophagy-related assays. The deformed wings (dwg) gene encodes a protein that is both a regulator and a target of autophagy. Altogether, these resources provide a foundation for building new hypotheses regarding protein networks and function.

Gene-by-environment interactions are pervasive among natural genetic variants

Gene-by-environment (GxE) interactions, in which a genetic variant's phenotypic effect is condition specific, are fundamental for understanding fitness landscapes and evolution but have been difficult to identify at the single-nucleotide level. Although many condition-specific quantitative trait loci (QTLs) have been mapped, these typically contain numerous inconsequential variants in linkage, precluding understanding of the causal GxE variants. Here, we introduce BARcoded Cas9 retron precise parallel editing via homology (CRISPEY-BAR), a high-throughput precision genome editing strategy, and use it to map GxE interactions of naturally occurring genetic polymorphisms impacting yeast growth. We identified hundreds of GxE variants within condition-specific QTLs, revealing unexpected genetic complexity. Moreover, we found that 93.7% of non-neutral natural variants within ergosterol biosynthesis pathway genes showed GxE interactions, including many impacting antifungal drug resistance through diverse molecular mechanisms. In sum, our results suggest an extremely complex, context-dependent fitness landscape characterized by pervasive GxE interactions while also demonstrating massively parallel genome editing as an effective means for investigating this complexity.

Widespread epistasis among beneficial genetic variants revealed by high-throughput genome editing

The phenotypic effect of any genetic variant can be altered by variation at other genomic loci. Known as epistasis, these genetic interactions shape the genotype-phenotype map of every species, yet their origins remain poorly understood. To investigate this, we employed high-throughput genome editing to measure the fitness effects of 1,826 naturally polymorphic variants in four strains of Saccharomyces cerevisiae. About 31% of variants affect fitness, of which 24% have strain-specific fitness effects indicative of epistasis. We found that beneficial variants are more likely to exhibit genetic interactions and that these interactions can be mediated by specific traits such as flocculation ability. This work suggests that adaptive evolution will often involve trade-offs where a variant is only beneficial in some genetic backgrounds, potentially explaining why many beneficial variants remain polymorphic. In sum, we provide a framework to understand the factors influencing epistasis with single-nucleotide resolution, revealing widespread epistasis among beneficial variants.

Whole Exome Sequencing of a Patient with a Milder Phenotype of Xeroderma Pigmentosum Group C

A 17-year-old female Korean patient (XP115KO) was previously diagnosed with Xeroderma pigmentosum group C (XPC) by Direct Sanger sequencing, which revealed a homozygous nonsense mutation in the XPC gene (rs121965088: c.1735C > T, p.Arg579Ter). While rs121965088 is associated with a poor prognosis, our patient presented with a milder phenotype. Hence, we conducted whole-exome sequencing in the patient and her family members to detect coexisting mutations that may have resulted in a milder phenotype of rs121965088 through genetic interaction. Materials and Methods: the whole-exome sequencing analysis of samples obtained from the patient and her family members (father, mother, and brother) was performed. To identify the underlying genetic cause of XPC, the extracted DNA was analyzed using Agilent's SureSelect XT Human All Exon v5. The functional effects of the resultant variants were predicted using the SNPinfo web server, and structural changes in the XPC protein using the 3D protein modeling program SWISS-MODEL. Results: Eight biallelic variants, homozygous in the patient and heterozygous in her parents, were detected. Four were found in the XPC gene: one nonsense variant (rs121965088: c.1735C > T, p.Arg579Ter) and three silent variants (rs2227998: c.2061G > A, p. Arg687Arg; rs2279017: c.2251-6A > C, intron; rs2607775: c.-27G > C, 5'UTR). The remaining four variants were found in non-XP genes, including one frameshift variant [rs72452004 of olfactory receptor family 2 subfamily T member 35 (OR2T35)], three missense variants [rs202089462 of ALF transcription elongation factor 3 (AFF3), rs138027161 of TCR gamma alternate reading frame protein (TARP), and rs3750575 of annexin A7 (ANXA7)]. Conclusions: potential candidates for genetic interactions with rs121965088 were found. The rs2279017 and rs2607775 of XPC involved mutations in the intron region, which affected RNA splicing and protein translation. The genetic variants of AFF3, TARP, and ANXA7 are all frameshift or missense mutations, inevitably disturbing the translation and function of the resultant proteins. Further research on their functions in DNA repair pathways may reveal undiscovered cellular relationships within xeroderma pigmentosum.

An interaction between β'-COP and the ArfGAP, Glo3, maintains post-Golgi cargo recycling

The essential COPI coat mediates retrieval of transmembrane proteins at the Golgi and endosomes following recruitment by the small GTPase, Arf1. ArfGAP proteins regulate COPI coats, but molecular details for COPI recognition by ArfGAPs remain elusive. Biochemical and biophysical data reveal how β'-COP propeller domains directly engage the yeast ArfGAP, Glo3, with a low micromolar binding affinity. Calorimetry data demonstrate that both β'-COP propeller domains are required to bind Glo3. An acidic patch on β'-COP (D437/D450) interacts with Glo3 lysine residues located within the BoCCS (binding of coatomer, cargo, and SNAREs) region. Targeted point mutations in either Glo3 BoCCS or β'-COP abrogate the interaction in vitro, and loss of the β'-COP/Glo3 interaction drives Ste2 missorting to the vacuole and aberrant Golgi morphology in budding yeast. These data suggest that cells require the β'-COP/Glo3 interaction for cargo recycling via endosomes and the TGN, where β'-COP serves as a molecular platform to coordinate binding to multiple proteins, including Glo3, Arf1, and the COPI F-subcomplex.

Multiple phosphorylation of the Cdc48/p97 cofactor protein Shp1/p47 occurs upon cell stress in budding yeast

The homohexameric p97 complex, composed of Cdc48 subunits in yeast, is a crucial component of protein quality control pathways including ER-associated degradation. The complex acts to segregate protein complexes in an ATP-dependent manner, requiring the engagement of cofactor proteins that determine substrate specificity. The function of different Cdc48 cofactors and how they are regulated remains relatively poorly understood. In this study, we assess the phosphorylation of Cdc48 adaptor proteins, revealing a unique and distinctive phosphorylation pattern of Shp1/p47 that changed in response to TORC1 inhibition. Site-directed mutagenesis confirmed that this pattern corresponded to phosphorylation at residues S108 and S315 of Shp1, with the double-phosphorylated form becoming predominant upon TORC1 inhibition, ER-stress, and oxidative stress. Finally, we assessed candidate kinases and phosphatases responsible for Shp1 phosphorylation and identified two regulators. We found that cells lacking the kinase Mpk1/Slt2 show reduced Shp1 phosphorylation, whereas impaired PP1 phosphatase catalytic subunit (Glc7) activity resulted in increased Shp1 phosphorylation. Overall, these findings identify a phosphoregulation of Shp1 at multiple sites by Mpk1 kinase and PP1 phosphatase upon various stresses.

A posttranscriptional pathway regulates cell wall mRNA expression in budding yeast

The fungal cell wall provides protection and structure and is an important target for antifungal compounds. A mitogen-activated protein (MAP) kinase cascade termed the cell wall integrity (CWI) pathway regulates transcriptional responses to cell wall damage. Here, we describe a posttranscriptional pathway that plays an important complementary role. We report that the RNA-binding proteins (RBPs) Mrn1 and Nab6 specifically target the 3' UTRs of a largely overlapping set of cell wall-related mRNAs. These mRNAs are downregulated in the absence of Nab6, indicating a function in target mRNA stabilization. Nab6 acts in parallel to CWI signaling to maintain appropriate expression of cell wall genes during stress. Cells lacking both pathways are hypersensitive to antifungal compounds targeting the cell wall. Deletion of MRN1 partially alleviates growth defects associated with Δnab6, and Mrn1 has an opposing function in mRNA destabilization. Our results uncover a posttranscriptional pathway that mediates cellular resistance to antifungal compounds.

Network Analysis Reveals the Molecular Bases of Statin Pleiotropy That Vary with Genetic Background

Many approved drugs are pleiotropic: for example, statins, whose main cholesterol-lowering activity is complemented by anticancer and prodiabetogenic mechanisms involving poorly characterized genetic interaction networks. We investigated these using the Saccharomyces cerevisiae genetic model, where most genetic interactions known are limited to the statin-sensitive S288C genetic background. We therefore broadened our approach by investigating gene interactions to include two statin-resistant genetic backgrounds: UWOPS87-2421 and Y55. Networks were functionally focused by selection of HMG1 and BTS1 mevalonate pathway genes for detection of genetic interactions. Networks, multilayered by genetic background, were analyzed for key genes using network centrality (degree, betweenness, and closeness), pathway enrichment, functional community modules, and Gene Ontology. Specifically, we found modification genes related to dysregulated endocytosis and autophagic cell death. To translate results to human cells, human orthologues were searched for other drug targets, thus identifying candidates for synergistic anticancer bioactivity. IMPORTANCE Atorvastatin is a highly successful drug prescribed to lower cholesterol and prevent cardiovascular disease in millions of people. Though much of its effect comes from inhibiting a key enzyme in the cholesterol biosynthetic pathway, genes in this pathway interact with genes in other pathways, resulting in 15% of patients suffering painful muscular side effects and 50% having inadequate responses. Such multigenic complexity may be unraveled using gene networks assembled from overlapping pairs of genes that complement each other. We used the unique power of yeast genetics to construct genome-wide networks specific to atorvastatin bioactivity in three genetic backgrounds to represent the genetic variation and varying response to atorvastatin in human individuals. We then used algorithms to identify key genes and their associated FDA-approved drugs in the networks, which resulted in the distinction of drugs that may synergistically enhance the known anticancer activity of atorvastatin.

Dimensionality reduction methods for extracting functional networks from large-scale CRISPR screens

CRISPR-Cas9 screens facilitate the discovery of gene functional relationships and phenotype-specific dependencies. The Cancer Dependency Map (DepMap) is the largest compendium of whole-genome CRISPR screens aimed at identifying cancer-specific genetic dependencies across human cell lines. A mitochondria-associated bias has been previously reported to mask signals for genes involved in other functions, and thus, methods for normalizing this dominant signal to improve co-essentiality networks are of interest. In this study, we explore three unsupervised dimensionality reduction methods - autoencoders, robust, and classical principal component analyses (PCA) - for normalizing the DepMap to improve functional networks extracted from these data. We propose a novel "onion" normalization technique to combine several normalized data layers into a single network. Benchmarking analyses reveal that robust PCA combined with onion normalization outperforms existing methods for normalizing the DepMap. Our work demonstrates the value of removing low-dimensional signals from the DepMap before constructing functional gene networks and provides generalizable dimensionality reduction-based normalization tools.

Point mutations of homologs as an adaptive solution to the gene loss

Gene loss is common and influences genome evolution trajectories. Multiple adaptive strategies to compensate for gene loss have been observed, including copy number gain of paralogous genes and mutations in genes of the same pathway. By using the Ubl-specific protease 2 (ULP2) eviction model, we identify compensatory mutations in the homologous gene ULP1 by laboratory evolution and find that these mutations are capable of rescuing defects caused by the loss of ULP2. Furthermore, bioinformatics analysis of genomes of yeast gene knockout library and natural yeast isolate datasets suggests that point mutations of a homologous gene might be an additional mechanism to compensate gene loss.

A Ubiquitination Cascade Regulating the Integrated Stress Response and Survival in Carcinomas

Systematic identification of signaling pathways required for the fitness of cancer cells will facilitate the development of new cancer therapies. We used gene essentiality measurements in 1,086 cancer cell lines to identify selective coessentiality modules and found that a ubiquitin ligase complex composed of UBA6, BIRC6, KCMF1, and UBR4 is required for the survival of a subset of epithelial tumors that exhibit a high degree of aneuploidy. Suppressing BIRC6 in cell lines that are dependent on this complex led to a substantial reduction in cell fitness in vitro and potent tumor regression in vivo. Mechanistically, BIRC6 suppression resulted in selective activation of the integrated stress response (ISR) by stabilization of the heme-regulated inhibitor, a direct ubiquitination target of the UBA6/BIRC6/KCMF1/UBR4 complex. These observations uncover a novel ubiquitination cascade that regulates ISR and highlight the potential of ISR activation as a new therapeutic strategy.

SIGNIFICANCE

We describe the identification of a heretofore unrecognized ubiquitin ligase complex that prevents the aberrant activation of the ISR in a subset of cancer cells. This provides a novel insight on the regulation of ISR and exposes a therapeutic opportunity to selectively eliminate these cancer cells. See related commentary Leli and Koumenis, p. 535. This article is highlighted in the In This Issue feature, p. 517.

Interaction-integrated linear mixed model reveals 3D-genetic basis underlying Autism

Genetic interactions play critical roles in genotype-phenotype associations. We developed a novel interaction-integrated linear mixed model (ILMM) that integrates a priori knowledge into linear mixed models. ILMM enables statistical integration of genetic interactions upfront and overcomes the problems of searching for combinations. To demonstrate its utility, with 3D genomic interactions (assessed by Hi-C experiments) as a priori, we applied ILMM to whole-genome sequencing data for Autism Spectrum Disorders (ASD) and brain transcriptome data, revealing the 3D-genetic basis of ASD and 3D-expression quantitative loci (3D-eQTLs) for brain tissues. Notably, we reported a potential mechanism involving distal regulation between FOXP2 and DNMT3A, conferring the risk of ASD.

Single-copy locus proteomics of early- and late-firing DNA replication origins identifies a role of Ask1/DASH complex in replication timing control

The chromatin environment at origins of replication is thought to influence DNA replication initiation in eukaryotic genomes. However, it remains unclear how and which chromatin features control the firing of early-efficient (EE) or late-inefficient (LI) origins. Here, we use site-specific recombination and single-locus chromatin isolation to purify EE and LI replication origins in Saccharomyces cerevisiae. Using mass spectrometry, we define the protein composition of native chromatin regions surrounding the EE and LI replication start sites. In addition to known origin interactors, we find the microtubule-binding Ask1/DASH complex as an origin-regulating factor. Strikingly, tethering of Ask1 to individual origin sites advances replication timing (RT) of the targeted chromosomal domain. Targeted degradation of Ask1 globally changes RT of a subset of origins, which can be reproduced by inhibiting microtubule dynamics. Thus, our findings mechanistically connect RT and chromosomal organization via Ask1/DASH with the microtubule cytoskeleton.

PIFiA: Self-supervised Approach for Protein Functional Annotation from Single-Cell Imaging Data

Fluorescence microscopy data describe protein localization patterns at single-cell resolution and have the potential to reveal whole-proteome functional information with remarkable precision. Yet, extracting biologically meaningful representations from cell micrographs remains a major challenge. Existing approaches often fail to learn robust and noise-invariant features or rely on supervised labels for accurate annotations. We developed PIFiA, (Protein Image-based Functional Annotation), a self-supervised approach for protein functional annotation from single-cell imaging data. We imaged the global yeast ORF-GFP collection and applied PIFiA to generate protein feature profiles from single-cell images of fluorescently tagged proteins. We show that PIFiA outperforms existing approaches for molecular representation learning and describe a range of downstream analysis tasks to explore the information content of the feature profiles. Specifically, we cluster extracted features into a hierarchy of functional organization, study cell population heterogeneity, and develop techniques to distinguish multi-localizing proteins and identify functional modules. Finally, we confirm new PIFiA predictions using a colocalization assay, suggesting previously unappreciated biological roles for several proteins. Paired with a fully interactive website (https://thecellvision.org/pifia/), PIFiA is a resource for the quantitative analysis of protein organization within the cell.

Macro-ER-phagy receptors Atg39p and Atg40p confer resistance to aminoglycoside hygromycin B in S. cerevisiae

Receptor-mediated autophagic turnover of portions of the endoplasmic reticulum (ER) is mediated by macro-ER-phagy. We hypothesized macro-ER-phagy promotes proteotoxic stress resistance. We predicted Saccharomyces cerevisiae lacking macro-ER-phagy receptors would exhibit enhanced sensitivity to hygromycin B, which reduces translational fidelity and is expected to globally disrupt protein homeostasis, including at the ER. We observed that loss of either of two yeast macro-ER-phagy receptors (Atg39p or Atg40p) compromised cellular resistance to hygromycin B to a similar extent as loss of ER-associated degradation (ERAD) ubiquitin ligases Hrd1p and Doa10p. Our data are consistent with a model whereby macro-ER-phagy and ERAD collaborate to mediate ER protein quality control. Disruptions of macro-ER-phagy have been linked to neuropathy, dementia, and cancer. A dampened capacity to mediate protein quality control may contribute to these conditions.

What Has Genomics Taught An Evolutionary Biologist?

Genomics, an interdisciplinary field of biology on the structure, function, and evolution of genomes, has revolutionized many subdisciplines of life sciences, including my field of evolutionary biology, by supplying huge data, bringing high-throughput technologies, and offering a new approach to biology. In this review, I describe what I have learned from genomics and highlight the fundamental knowledge and mechanistic insights gained. I focus on three broad topics that are central to evolutionary biology and beyond-variation, interaction, and selection-and use primarily my own research and study subjects as examples. In the next decade or two, I expect that the most important contributions of genomics to evolutionary biology will be to provide genome sequences of nearly all known species on Earth, facilitate high-throughput phenotyping of natural variants and systematically constructed mutants for mapping genotype-phenotype-fitness landscapes, and assist the determination of causality in evolutionary processes using experimental evolution.

A member of the claudin superfamily influences formation of the front domain in pheromone-responding yeast cells

Cell polarization in response to chemical gradients is important in development and homeostasis across eukaryota. Chemosensing cells orient toward or away from gradient sources by polarizing along a front-rear axis. Using the mating response of budding yeast as a model of chemotropic cell polarization, we found that Dcv1, a member of the claudin superfamily, influences front-rear polarity. Although Dcv1 localized uniformly on the plasma membrane (PM) of vegetative cells, it was confined to the rear of cells responding to pheromone, away from the pheromone receptor. dcv1Δ conferred mislocalization of sensory, polarity and trafficking proteins, as well as PM lipids. These phenotypes correlated with defects in pheromone-gradient tracking and cell fusion. We propose that Dcv1 helps demarcate the mating-specific front domain primarily by restricting PM lipid distribution.

Sec61 channel subunit Sbh1/Sec61β promotes ER translocation of proteins with suboptimal targeting sequences and is fine-tuned by phosphorylation

The highly conserved endoplasmic reticulum (ER) protein translocation channel contains one nonessential subunit, Sec61β/Sbh1, whose function is poorly understood so far. Its intrinsically unstructured cytosolic domain makes transient contact with ER-targeting sequences in the cytosolic channel vestibule and contains multiple phosphorylation sites suggesting a potential for regulating ER protein import. In a microscopic screen, we show that 12% of a GFP-tagged secretory protein library depends on Sbh1 for translocation into the ER. Sbh1-dependent proteins had targeting sequences with less pronounced hydrophobicity and often no charge bias or an inverse charge bias which reduces their insertion efficiency into the Sec61 channel. We determined that mutating two N-terminal, proline-flanked phosphorylation sites in the Sbh1 cytosolic domain to alanine phenocopied the temperature-sensitivity of a yeast strain lacking SBH1 and its ortholog SBH2. The phosphorylation site mutations reduced translocation into the ER of a subset of Sbh1-dependent proteins, including enzymes whose concentration in the ER lumen is critical for ER proteostasis. In addition, we found that ER import of these proteins depended on the activity of the phospho-S/T-specific proline isomerase Ess1 (PIN1 in mammals). We conclude that Sbh1 promotes ER translocation of substrates with suboptimal targeting sequences and that its activity can be regulated by a conformational change induced by N-terminal phosphorylation.

Accurately modeling biased random walks on weighted networks using node2vec

MOTIVATION

Accurately representing biological networks in a low-dimensional space, also known as network embedding, is a critical step in network-based machine learning and is carried out widely using node2vec, an unsupervised method based on biased random walks. However, while many networks, including functional gene interaction networks, are dense, weighted graphs, node2vec is fundamentally limited in its ability to use edge weights during the biased random walk generation process, thus under-using all the information in the network.

RESULTS

Here, we present node2vec+, a natural extension of node2vec that accounts for edge weights when calculating walk biases and reduces to node2vec in the cases of unweighted graphs or unbiased walks. Using two synthetic datasets, we empirically show that node2vec+ is more robust to additive noise than node2vec in weighted graphs. Then, using genome-scale functional gene networks to solve a wide range of gene function and disease prediction tasks, we demonstrate the superior performance of node2vec+ over node2vec in the case of weighted graphs. Notably, due to the limited amount of training data in the gene classification tasks, graph neural networks such as GCN and GraphSAGE are outperformed by both node2vec and node2vec+.

AVAILABILITY AND IMPLEMENTATION

The data and code are available on GitHub at https://github.com/krishnanlab/node2vecplus_benchmarks. All additional data underlying this article are available on Zenodo at https://doi.org/10.5281/zenodo.7007164.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Dynamic metabolome profiling uncovers potential TOR signaling genes

Although the genetic code of the yeast Saccharomyces cerevisiae was sequenced 25 years ago, the characterization of the roles of genes within it is far from complete. The lack of a complete mapping of functions to genes hampers systematic understanding of the biology of the cell. The advent of high-throughput metabolomics offers a unique approach to uncovering gene function with an attractive combination of cost, robustness, and breadth of applicability. Here, we used flow-injection time-of-flight mass spectrometry to dynamically profile the metabolome of 164 loss-of-function mutants in TOR and receptor or receptor-like genes under a time course of rapamycin treatment, generating a dataset with >7000 metabolomics measurements. In order to provide a resource to the broader community, those data are made available for browsing through an interactive data visualization app hosted at https://rapamycin-yeast.ethz.ch. We demonstrate that dynamic metabolite responses to rapamycin are more informative than steady-state responses when recovering known regulators of TOR signaling, as well as identifying new ones. Deletion of a subset of the novel genes causes phenotypes and proteome responses to rapamycin that further implicate them in TOR signaling. We found that one of these genes, CFF1, was connected to the regulation of pyrimidine biosynthesis through URA10. These results demonstrate the efficacy of the approach for flagging novel potential TOR signaling-related genes and highlight the utility of dynamic perturbations when using functional metabolomics to deliver biological insight.

Autorepression-Based Conditional Gene Expression System in Yeast for Variation-Suppressed Control of Protein Dosage

Conditional control of gene expression allows an experimenter to investigate many aspects of a gene's function. In the model organism Saccharomyces cerevisiae, a number of methods to control gene expression are widely practiced, including induction by metabolites, small molecules, and even light. However, all current methods suffer from at least one of a set of drawbacks, including need for specialized growth conditions, leaky expression, or requirement of specialized equipment. Here we describe protocols using two transformations to construct strains that carry a new controller in which all these drawbacks are overcome. In these strains, the expression of a controlled gene of interest is repressed by the bacterial repressor TetR and induced by anhydrotetracycline. TetR also regulates its own expression, creating an autorepression loop. This autorepression allows tight control of gene expression and protein dosage with low cell-to-cell variation in expression. A second repressor, TetR-Tup1, prevents any leaky expression. We also present a protocol showing a particular workhorse application of such strains to generate synchronized cell populations. We turn off expression of the cell cycle regulator CDC20 completely, arresting the cell population, and then we turn it back on so that the synchronized cells resume cell cycle progression. This control system can be applied to any endogenous or exogenous gene for precise expression. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Generating a parent WTC846 strain Basic Protocol 2: Generating a WTC846 strain with controlled expression of the targeted gene Alternate Protocol: CRISPR-mediated promoter replacement Basic Protocol 3: Cell cycle synchronization/arrest and release using the WTC846- K3 ::CDC20 strain.

High throughput mutagenesis and screening for yeast engineering

The eukaryotic yeast Saccharomyces cerevisiae is a model host utilized for whole cell biocatalytic conversions, protein evolution, and scientific inquiries into the pathogenesis of human disease. Over the past decade, the scale and pace of such studies has drastically increased alongside the advent of novel tools for both genome-wide studies and targeted genetic mutagenesis. In this review, we will detail past and present (e.g., CRISPR/Cas) genome-scale screening platforms, typically employed in the context of growth-based selections for improved whole cell phenotype or for mechanistic interrogations. We will further highlight recent advances that enable the rapid and often continuous evolution of biomolecules with improved function. Additionally, we will detail the corresponding advances in high throughput selection and screening strategies that are essential for assessing or isolating cellular and protein improvements. Finally, we will describe how future developments can continue to advance yeast high throughput engineering.

UBE3D Regulates mRNA 3'-End Processing and Maintains Adipogenic Potential in 3T3-L1 Cells

We have previously described the role of an essential Saccharomyces cerevisiae gene, important for cleavage and polyadenylation 1 (IPA1), in the regulation of gene expression through its interaction with Ysh1, the endonuclease subunit of the mRNA 3'-end processing complex. Through a similar mechanism, the mammalian homolog ubiquitin protein ligase E3D (UBE3D) promotes the migratory and invasive potential of breast cancer cells, but its role in the regulation of gene expression during normal cellular differentiation has not previously been described. In this study, we show that CRISPR/Cas9-mediated knockout of Ube3d in 3T3-L1 cells blocks their ability to differentiate into mature adipocytes. Consistent with previous studies in other cell types, Ube3d knockout leads to decreased levels of CPSF73 and global changes in cellular mRNAs indicative of a loss of 3'-end processing capacity. Ube3d knockout cells also display decreased expression of known preadipogenic markers. Overexpression of either UBE3D or CPSF73 rescues the differentiation defect and partially restores protein levels of these markers. These results support a model in which UBE3D is necessary for the maintenance of the adipocyte-committed state via its regulation of the mRNA 3'-end processing machinery.

The malaria parasite chaperonin containing TCP-1 (CCT) complex: Data integration with other CCT proteomes

The multi-subunit chaperonin containing TCP-1 (CCT) is an essential molecular chaperone that functions in the folding of key cellular proteins. This paper reviews the interactome of the eukaryotic chaperonin CCT and its primary clients, the ubiquitous cytoskeletal proteins, actin and tubulin. CCT interacts with other nascent proteins, especially the WD40 propeller proteins, and also assists in the assembly of several protein complexes. A new proteomic dataset is presented for CCT purified from the human malarial parasite, P. falciparum (PfCCT). The CCT8 subunit gene was C-terminally FLAG-tagged using Selection Linked Integration (SLI) and CCT complexes were extracted from infected human erythrocyte cultures synchronized for maximum expression levels of CCT at the trophozoite stage of the parasite's asexual life cycle. We analyze the new PfCCT proteome and incorporate it into our existing model of the CCT system, supported by accumulated data from biochemical and cell biological experiments in many eukaryotic species. Together with measurements of CCT mRNA, CCT protein subunit copy number and the post-translational and chemical modifications of the CCT subunits themselves, a cumulative picture is emerging of an essential molecular chaperone system sitting at the heart of eukaryotic cell growth control and cell cycle regulation.

Predicting genetic interactions, cell line dependencies and drug sensitivities with variational graph auto-encoder

Large scale cancer genomics data provide crucial information about the disease and reveal points of intervention. However, systematic data have been collected in specific cell lines and their collection is laborious and costly. Hence, there is a need to develop computational models that can predict such data for any genomic context of interest. Here we develop novel models that build on variational graph auto-encoders and can integrate diverse types of data to provide high quality predictions of genetic interactions, cell line dependencies and drug sensitivities, outperforming previous methods. Our models, data and implementation are available at: https://github.com/aijag/drugGraphNet.

Synthetic lethal gene pairs: Experimental approaches and predictive models

Synthetic lethality (SL) refers to a genetic interaction in which the simultaneous perturbation of two genes leads to cell or organism death, whereas viability is maintained when only one of the pair is altered. The experimental exploration of these pairs and predictive modeling in computational biology contribute to our understanding of cancer biology and the development of cancer therapies. We extensively reviewed experimental technologies, public data sources, and predictive models in the study of synthetic lethal gene pairs and herein detail biological assumptions, experimental data, statistical models, and computational schemes of various predictive models, speculate regarding their influence on individual sample- and population-based synthetic lethal interactions, discuss the pros and cons of existing SL data and models, and highlight potential research directions in SL discovery.

Nab3 nuclear granule accumulation is driven by respiratory capacity

Numerous biological processes involve proteins capable of transiently assembling into subcellular compartments necessary for cellular functions. One process is the RNA polymerase II transcription cycle which involves initiation, elongation, co-transcriptional modification of nascent RNA, and termination. The essential yeast transcription termination factor Nab3 is required for termination of small non-coding RNAs and accumulates into a compact nuclear granule upon glucose removal. Nab3 nuclear granule accumulation varies in penetrance across yeast strains and a higher Nab3 granule accumulation phenotype is associated with petite strains, suggesting a possible ATP-dependent mechanism for granule disassembly. Here, we demonstrate the uncoupling of mitochondrial oxidative phosphorylation by drug treatment or deletions of nuclear-encoded ATP synthase subunit genes were sufficient to increase Nab3 granule accumulation and led to an inability to proliferate during prolonged glucose deprivation, which requires respiration. Additionally, by enriching for respiration competent cells from a petite-prone strain, we generated a low granule-accumulating strain from a relatively high one, providing another link between respiratory competency and Nab3 granules. Consistent with the resulting idea that ATP is involved in granule accumulation, the addition of extracellular ATP to semi-permeabilized cells was sufficient to reduce Nab3 granule accumulation. Deleting the SKY1 gene, which encodes a kinase that phosphorylates nuclear SR repeat-containing proteins and is involved in efficient stress granule disassembly, also resulted in increased granule accumulation. This observation implicates Sky1 in Nab3 granule biogenesis. Taken together, these findings suggest there is normally an equilibrium between termination factor granule assembly and disassembly mediated by ATP-requiring nuclear machinery.

Optimal construction of a functional interaction network from pooled library CRISPR fitness screens

BACKGROUND

Functional interaction networks, where edges connect genes likely to operate in the same biological process or pathway, can be inferred from CRISPR knockout screens in cancer cell lines. Genes with similar knockout fitness profiles across a sufficiently diverse set of cell line screens are likely to be co-functional, and these "coessentiality" networks are increasingly powerful predictors of gene function and biological modularity. While several such networks have been published, most use different algorithms for each step of the network construction process.

RESULTS

In this study, we identify an optimal measure of functional interaction and test all combinations of options at each step-essentiality scoring, sample variance and covariance normalization, and similarity measurement-to identify best practices for generating a functional interaction network from CRISPR knockout data. We show that Bayes Factor and Ceres scores give the best results, that Ceres outperforms the newer Chronos scoring scheme, and that covariance normalization is a critical step in network construction. We further show that Pearson correlation, mathematically identical to ordinary least squares after covariance normalization, can be extended by using partial correlation to detect and amplify signals from "moonlighting" proteins which show context-dependent interaction with different partners.

CONCLUSIONS

We describe a systematic survey of methods for generating coessentiality networks from the Cancer Dependency Map data and provide a partial correlation-based approach for exploring context-dependent interactions.

Genome-Wide Screen for Enhanced Noncanonical Amino Acid Incorporation in Yeast

Numerous applications of noncanonical amino acids (ncAAs) in basic biology and therapeutic development require efficient protein biosynthesis using an expanded genetic code. However, achieving such incorporation at repurposed stop codons in cells is generally inefficient and limited by complex cellular processes that preserve the fidelity of protein synthesis. A more comprehensive understanding of the processes that contribute to ncAA incorporation would aid in the development of genomic engineering strategies for augmenting genetic code manipulation. In this work, we used a series of fluorescent reporters to screen a pooled Saccharomyces cerevisiae molecular barcoded yeast knockout (YKO) collection. Fluorescence-activated cell sorting enabled isolation of strains encoding single-gene deletions exhibiting improved ncAA incorporation efficiency in response to the amber (TAG) stop codon; 55 unique candidate deletions were identified. The deleted genes encoded for proteins that participate in diverse cellular processes, including many genes that have no known connection with protein translation. We then verified that two knockouts, yil014c-aΔ and alo1Δ, exhibited improved ncAA incorporation efficiency starting from independently acquired strains possessing the knockouts. Using additional orthogonal translation systems and ncAAs, we determined that yil014c-aΔ and alo1Δ enhance ncAA incorporation efficiency without loss of fidelity over a wide range of conditions. Our findings highlight opportunities for further modulating gene expression with genetic, genomic, and synthetic biology approaches to improve ncAA incorporation efficiency. In addition, these discoveries have the potential to enhance our fundamental understanding of protein translation. Ultimately, cells that efficiently biosynthesize ncAA-containing proteins will streamline the realization of applications utilizing expanded genetic codes ranging from basic biology to drug discovery.

A comparative analysis of telomere length maintenance circuits in fission and budding yeast

The natural ends of the linear eukaryotic chromosomes are protected by telomeres, which also play an important role in aging and cancer development. Telomere length varies between species, but it is strictly controlled in all organisms. The process of Telomere Length Maintenance (TLM) involves many pathways, protein complexes and interactions that were first discovered in budding and fission yeast model organisms (Saccharomyces cerevisiae, Schizosaccharomyces pombe). In particular, large-scale systematic genetic screens in budding yeast uncovered a network of ≈500 genes that, when mutated, cause telomeres to lengthen or to shorten. In contrast, the TLM network in fission yeast remains largely unknown and systematic data is still lacking. In this work we try to close this gap and develop a unified interpretable machine learning framework for TLM gene discovery and phenotype prediction in both species. We demonstrate the utility of our framework in pinpointing the pathways by which TLM homeostasis is maintained and predicting novel TLM genes in fission yeast. The results of this study could be used for better understanding of telomere biology and serve as a step towards the adaptation of computational methods based on telomeric data for human prognosis.

High-resolution secretory timeline from vesicle formation at the Golgi to fusion at the plasma membrane in S. cerevisiae

Most of the components in the yeast secretory pathway have been studied, yet a high-resolution temporal timeline of their participation is lacking. Here, we define the order of acquisition, lifetime, and release of critical components involved in late secretion from the Golgi to the plasma membrane. Of particular interest is the timing of the many reported effectors of the secretory vesicle Rab protein Sec4, including the myosin-V Myo2, the exocyst complex, the lgl homolog Sro7, and the small yeast-specific protein Mso1. At the trans-Golgi network (TGN) Sec4's GEF, Sec2, is recruited to Ypt31-positive compartments, quickly followed by Sec4 and Myo2 and vesicle formation. While transported to the bud tip, the entire exocyst complex, including Sec3, is assembled on to the vesicle. Before fusion, vesicles tether for 5 s, during which the vesicle retains the exocyst complex and stimulates lateral recruitment of Rho3 on the plasma membrane. Sec2 and Myo2 are rapidly lost, followed by recruitment of cytosolic Sro7, and finally the SM protein Sec1, which appears for just 2 s prior to fusion. Perturbation experiments reveal an ordered and robust series of events during tethering that provide insights into the function of Sec4 and effector exchange.

Genome-wide base editor screen identifies regulators of protein abundance in yeast

Proteins are key molecular players in a cell, and their abundance is extensively regulated not just at the level of gene expression but also post-transcriptionally. Here, we describe a genetic screen in yeast that enables systematic characterization of how protein abundance regulation is encoded in the genome. The screen combines a CRISPR/Cas9 base editor to introduce point mutations with fluorescent tagging of endogenous proteins to facilitate a flow-cytometric readout. We first benchmarked base editor performance in yeast with individual gRNAs as well as in positive and negative selection screens. We then examined the effects of 16,452 genetic perturbations on the abundance of eleven proteins representing a variety of cellular functions. We uncovered hundreds of regulatory relationships, including a novel link between the GAPDH isoenzymes Tdh1/2/3 and the Ras/PKA pathway. Many of the identified regulators are specific to one of the eleven proteins, but we also found genes that, upon perturbation, affected the abundance of most of the tested proteins. While the more specific regulators usually act transcriptionally, broad regulators often have roles in protein translation. Overall, our novel screening approach provides unprecedented insights into the components, scale and connectedness of the protein regulatory network.

Viral protein engagement of GBF1 induces host cell vulnerability through synthetic lethality

Viruses co-opt host proteins to carry out their lifecycle. Repurposed host proteins may thus become functionally compromised; a situation analogous to a loss-of-function mutation. We term such host proteins as viral-induced hypomorphs. Cells bearing cancer driver loss-of-function mutations have successfully been targeted with drugs perturbing proteins encoded by the synthetic lethal (SL) partners of cancer-specific mutations. Similarly, SL interactions of viral-induced hypomorphs can potentially be targeted as host-based antiviral therapeutics. Here, we use GBF1, which supports the infection of many RNA viruses, as a proof-of-concept. GBF1 becomes a hypomorph upon interaction with the poliovirus protein 3A. Screening for SL partners of GBF1 revealed ARF1 as the top hit, disruption of which selectively killed cells that synthesize 3A alone or in the context of a poliovirus replicon. Thus, viral protein interactions can induce hypomorphs that render host cells selectively vulnerable to perturbations that leave uninfected cells otherwise unscathed. Exploiting viral-induced vulnerabilities could lead to broad-spectrum antivirals for many viruses, including SARS-CoV-2.

APC/CCdc20-mediated degradation of Clb4 prompts astral microtubule stabilization at anaphase onset

Key for accurate chromosome partitioning to the offspring is the ability of mitotic spindle microtubules to respond to different molecular signals and remodel their dynamics accordingly. Spindle microtubules are conventionally divided into three classes: kinetochore, interpolar, and astral microtubules (kMTs, iMTs, and aMTs, respectively). Among all, aMT regulation remains elusive. Here, we show that aMT dynamics are tightly regulated. aMTs remain unstable up to metaphase and are stabilized at anaphase onset. This switch in aMT dynamics, important for proper spindle orientation, specifically requires the degradation of the mitotic cyclin Clb4 by the Anaphase Promoting Complex bound to its activator subunit Cdc20 (APC/CCdc20). These data highlight a unique role for mitotic cyclin Clb4 in controlling aMT regulating factors, of which Kip2 is a prime candidate, provide a framework to understand aMT regulation in vertebrates, and uncover mechanistic principles of how the APC/CCdc20 choreographs the timing of late mitotic events by sequentially impacting on the three classes of spindle microtubules.

MFDA: Multiview fusion based on dual-level attention for drug interaction prediction

Drug-drug interaction prediction plays an important role in pharmacology and clinical applications. Most traditional methods predict drug interactions based on drug attributes or network structure. They usually have three limitations: 1) failing to integrate drug features and network structures well, resulting in less informative drug embeddings; 2) being restricted to a single view of drug interaction relationships; 3) ignoring the importance of different neighbors. To tackle these challenges, this paper proposed a multiview fusion based on dual-level attention to predict drug interactions (called MFDA). The MFDA first constructed multiple views for the drug interaction relationship, and then adopted a cross-fusion strategy to deeply fuse drug features with the drug interaction network under each view. To distinguish the importance of different neighbors and views, MFDA adopted a dual-level attention mechanism (node level and view level) to obtain the unified drug embedding for drug interaction prediction. Extensive experiments were conducted on real datasets, and the MFDA demonstrated superior performance compared to state-of-the-art baselines. In the multitask analysis of new drug reactions, MFDA obtained higher scores on multiple metrics. In addition, its prediction results corresponded to specific drug reaction events, which achieved more accurate predictions.

High-throughput approaches to functional characterization of genetic variation in yeast

Expansion of sequencing efforts to include thousands of genomes is providing a fundamental resource for determining the genetic diversity that exists in a population. Now, high-throughput approaches are necessary to begin to understand the role these genotypic changes play in affecting phenotypic variation. Saccharomyces cerevisiae maintains its position as an excellent model system to determine the function of unknown variants with its exceptional genetic diversity, phenotypic diversity, and reliable genetic manipulation tools. Here, we review strategies and techniques developed in yeast that scale classic approaches of assessing variant function. These approaches improve our ability to better map quantitative trait loci at a higher resolution, even for rare variants, and are already providing greater insight into the role that different types of mutations play in phenotypic variation and evolution not just in yeast but across taxa.

BIONIC: biological network integration using convolutions

Biological networks constructed from varied data can be used to map cellular function, but each data type has limitations. Network integration promises to address these limitations by combining and automatically weighting input information to obtain a more accurate and comprehensive representation of the underlying biology. We developed a deep learning-based network integration algorithm that incorporates a graph convolutional network framework. Our method, BIONIC (Biological Network Integration using Convolutions), learns features that contain substantially more functional information compared to existing approaches. BIONIC has unsupervised and semisupervised learning modes, making use of available gene function annotations. BIONIC is scalable in both size and quantity of the input networks, making it feasible to integrate numerous networks on the scale of the human genome. To demonstrate the use of BIONIC in identifying new biology, we predicted and experimentally validated essential gene chemical-genetic interactions from nonessential gene profiles in yeast.

Recent Advances of Utilizing Artificial Intelligence in Lab on a Chip for Diagnosis and Treatment

Nowadays, artificial intelligence (AI) creates numerous promising opportunities in the life sciences. AI methods can be significantly advantageous for analyzing the massive datasets provided by biotechnology systems for biological and biomedical applications. Microfluidics, with the developments in controlled reaction chambers, high-throughput arrays, and positioning systems, generate big data that is not necessarily analyzed successfully. Integrating AI and microfluidics can pave the way for both experimental and analytical throughputs in biotechnology research. Microfluidics enhances the experimental methods and reduces the cost and scale, while AI methods significantly improve the analysis of huge datasets obtained from high-throughput and multiplexed microfluidics. This review briefly presents a survey of the role of AI and microfluidics in biotechnology. Also, the incorporation of AI with microfluidics is comprehensively investigated. Specifically, recent studies that perform flow cytometry cell classification, cell isolation, and a combination of them by gaining from both AI methods and microfluidic techniques are covered. Despite all current challenges, various fields of biotechnology can be remarkably affected by the combination of AI and microfluidic technologies. Some of these fields include point-of-care systems, precision, personalized medicine, regenerative medicine, prognostics, diagnostics, and treatment of oncology and non-oncology-related diseases.

Chaperone requirements for de novo folding of Saccharomyces cerevisiae septins

Polymers of septin protein complexes play cytoskeletal roles in eukaryotic cells. The specific subunit composition within complexes controls functions and higher-order structural properties. All septins have globular GTPase domains. The other eukaryotic cytoskeletal NTPases strictly require assistance from molecular chaperones of the cytosol, particularly the cage-like chaperonins, to fold into oligomerization-competent conformations. We previously identified cytosolic chaperones that bind septins and influence the oligomerization ability of septins carrying mutations linked to human disease, but it was unknown to what extent wild-type septins require chaperone assistance for their native folding. Here we use a combination of in vivo and in vitro approaches to demonstrate chaperone requirements for de novo folding and complex assembly by budding yeast septins. Individually purified septins adopted nonnative conformations and formed nonnative homodimers. In chaperonin- or Hsp70-deficient cells, septins folded slower and were unable to assemble posttranslationally into native complexes. One septin, Cdc12, was so dependent on cotranslational chaperonin assistance that translation failed without it. Our findings point to distinct translation elongation rates for different septins as a possible mechanism to direct a stepwise, cotranslational assembly pathway in which general cytosolic chaperones act as key intermediaries.

Sharing the wealth: The versatility of proteins targeted to peroxisomes and other organelles

Peroxisomes are eukaryotic organelles with critical functions in cellular energy and lipid metabolism. Depending on the organism, cell type, and developmental stage, they are involved in numerous other metabolic and regulatory pathways. Many peroxisomal functions require factors also relevant to other cellular compartments. Here, we review proteins shared by peroxisomes and at least one different site within the cell. We discuss the mechanisms to achieve dual targeting, their regulation, and functional consequences. Characterization of dual targeting is fundamental to understand how peroxisomes are integrated into the metabolic and regulatory circuits of eukaryotic cells.