Emerging and evolving concepts in gene essentiality

The design and engineering of synthetic genomes

Synthetic genomics seeks to design and construct entire genomes to mechanistically dissect fundamental questions of genome function and to engineer organisms for diverse applications, including bioproduction of high-value chemicals and biologics, advanced cell therapies, and stress-tolerant crops. Recent progress has been fuelled by advancements in DNA synthesis, assembly, delivery and editing. Computational innovations, such as the use of artificial intelligence to provide prediction of function, also provide increasing capabilities to guide synthetic genome design and construction. However, translating synthetic genome-scale projects from idea to implementation remains highly complex. Here, we aim to streamline this implementation process by comprehensively reviewing the strategies for design, construction, delivery, debugging and tailoring of synthetic genomes as well as their potential applications.

PRODE recovers essential and context-essential genes through neighborhood-informed scores

Gene context-essentiality assessment supports precision oncology opportunities. The variability of gene effects inference from loss-of-function screenings across models and technologies limits identifying robust hits. We propose a computational framework named PRODE that integrates gene effects with protein-protein interactions to generate neighborhood-informed essential (NIE) and neighborhood-informed context essential (NICE) scores. It outperforms the canonical gene effect approach in recovering missed essential genes in shRNA screens and prioritizing context-essential hits from CRISPR-KO screens, as supported by in vitro validations. Applied to Her2 + breast cancer tumor samples, PRODE identifies oxidative phosphorylation genes as vulnerabilities with prognostic value, highlighting new therapeutic opportunities.

Synthetic lethal strategies for the development of cancer therapeutics

Synthetic lethality is a genetic phenomenon whereby the simultaneous presence of two different genetic alterations impairs cellular viability. Importantly, targeting synthetic lethal interactions offers potential therapeutic strategies for cancers with alterations in pathways that might otherwise be considered undruggable. High-throughput screening methods based on modern CRISPR-Cas9 technologies have emerged and become crucial for identifying novel synthetic lethal interactions with the potential for translation into biologically rational cancer therapeutic strategies as well as associated predictive biomarkers of response capable of guiding patient selection. Spurred by the clinical success of PARP inhibitors in patients with BRCA-mutant cancers, novel agents targeting multiple synthetic lethal interactions within DNA damage response pathways are in clinical development, and rational strategies targeting synthetic lethal interactions spanning alterations in epigenetic, metabolic and proliferative pathways have also emerged and are in late preclinical and/or early clinical testing. In this Review, we provide a comprehensive overview of established and emerging technologies for synthetic lethal drug discovery and development and discuss promising therapeutic strategies targeting such interactions.

Experimental evolution of gene essentiality in bacteria

Essential gene products carry out fundamental cellular activities in interaction with other components. However, the lack of essential gene mutants and appropriate methodologies to link essential gene functions with their partners poses significant challenges. Here, we have generated deletion mutants in 32 genes previously identified as essential, with 23 mutants showing extremely slow growth in the SK36 strain of Streptococcus sanguinis. The 23 genes corresponding to these mutants encode components of diverse pathways, are widely conserved among bacteria, and are essential in many other bacterial species. Whole-genome sequencing of 243 independently evolved populations of these mutants has identified >1000 spontaneous suppressor mutations in experimental evolution. Many of these mutations define new gene and pathway relationships, such as F1Fo-ATPase/V1Vo-ATPase/TrkA1-H1 that were demonstrated across multiple Streptococcus species. Patterns of spontaneous mutations occurring in essential gene mutants differed from those found in wildtype. While gene duplications occurred rarely and appeared most often at later stages of evolution, substitutions, deletions, and insertions were prevalent in evolved populations. These essential gene deletion mutants and spontaneous mutations fixed in the mutant populations during evolution establish a foundation for understanding gene essentiality and the interaction of essential genes in networks.

Transcriptome-scale RNA-targeting CRISPR screens reveal essential lncRNAs in human cells

Mammalian genomes host a diverse array of RNA that includes protein-coding and noncoding transcripts. However, the functional roles of most long noncoding RNAs (lncRNAs) remain elusive. Using RNA-targeting CRISPR-Cas13 screens, we probed how the loss of ∼6,200 lncRNAs impacts cell fitness across five human cell lines and identified 778 lncRNAs with context-specific or broad essentiality. We confirm their essentiality with individual perturbations and find that the majority of essential lncRNAs operate independently of their nearest protein-coding genes. Using transcriptome profiling in single cells, we discover that the loss of essential lncRNAs impairs cell-cycle progression and drives apoptosis. Many essential lncRNAs demonstrate dynamic expression across tissues during development. Using ∼9,000 primary tumors, we pinpoint those lncRNAs whose expression in tumors correlates with survival, yielding new biomarkers and potential therapeutic targets. This transcriptome-wide survey of functional lncRNAs advances our understanding of noncoding transcripts and demonstrates the potential of transcriptome-scale noncoding screens with Cas13.

A Hot-Swappable Genetic Switch: Building an inducible and trackable functional assay for the essential gene MEDIATOR 21

Essential genes, estimated at approximately 20% of the Arabidopsis genome, are broadly expressed and required for reproductive success. They are difficult to study, as interfering with their function leads to premature death. Transcription is one of the essential functions of life, and the multi-protein Mediator complex coordinates the regulation of gene expression at nearly every eukaryotic promoter. In this study, we focused on a core Mediator component called MEDIATOR21 (MED21), which is required for activation of transcription. Our previous work has also shown a role for MED21 in repression of gene expression through its interaction with a corepressor protein. Here, we sought to differentiate the role MED21 plays in activation versus repression using the model plant Arabidopsis. As mutations in MED21 lead to embryo lethal phenotypes, we constructed a set of synthetic switches using PhiC31 serine integrases to create an "on-to-off" inducible loss of function MED21 in a non-essential tissue. Our technology, which we call Integrase Erasers, made it possible for med21 mutant plants to survive into adulthood by ablating protein expression selectively in lateral root primordia, allowing quantification and characterization of med21 mutant phenotypes in a post-embryonic context. In addition, we engineered chemical induction of the Integrase Eraser to ablate MED21 expression in whole seedlings at a user-specified timepoint. Finally, we extended this technology to build a hot swappable Integrase Isoform Switch where expression of the integrase toggled cells from expressing wildtype MED21 to expressing MED21 sequence variants. Our analysis of the entire set of new integrase-based tools demonstrates that this is a highly efficient and robust approach to the study of essential genes.

The identification of essential cellular genes is critical for validating drug targets

Accurately identifying biological targets is crucial for advancing treatment options. Essential genes, vital for cell or organism survival, hold promise as potential drug targets in disease treatment. Although many studies have sought to identify essential genes as therapeutic targets in medicine and bioinformatics, systematic reviews on their relationship with drug targets are relatively rare. This work presents a comprehensive analysis to aid in identifying essential genes as potential targets for drug discovery, encompassing their relevance, identification methods, successful case studies, and challenges. This work will facilitate the identification of essential genes as therapeutic targets, thereby boosting new drug development.

Genome-Wide CRISPR Screen Identifies Genes Involved in Metastasis of Pancreatic Ductal Adenocarcinoma

Background/Objectives: Early and aggressive metastasis is a major feature of pancreatic ductal adenocarcinoma. Understanding the processes underlying metastasis is crucial for making a difference to disease outcome. Towards these ends, we looked in a comprehensive manner for genes that are metastasis-specific. Methods: A genome-wide CRISPR-Cas9 gene knockout screen with 259,900 single guide RNA constructs was performed on pancreatic cancer cell lines with very high or very low metastatic capacity, respectively. Functional aspects of some of the identified genes were analysed in vitro. The injection of tumour cells with or without a gene knockout into mice was used to confirm the effect on metastasis. Results: The knockout of 590 genes-and, with higher analysis stringency, 67 genes-affected the viability of metastatic cells substantially, while these genes were not vital to non-metastasizing cells. Further evaluations identified different molecular processes related to this observation. One of the genes was MYBL2, encoding for a well-known transcription factor involved in the regulation of cell survival, proliferation, and differentiation in cancer tissues. In our metastasis-focussed study, no novel functional activity was detected for MYBL2, however. Instead, a metastasis-specific transformation of its genetic interaction with FOXM1 was observed. The interaction was synergistic in cells of low metastatic capacity, while there was a strong switch to a buffering mode in metastatic cells. In vivo analyses confirmed the strong effect of MYBL2 on metastasis. Conclusions: The genes found to be critical for the viability of metastatic cells form a basis for further investigations of the processes responsible for triggering and driving metastasis. As shown for MYBL2, unexpected processes of regulating metastasis might also be involved.

High-throughput transposon mutagenesis in the family Enterobacteriaceae reveals core essential genes and rapid turnover of essentiality

The Enterobacteriaceae are a scientifically and medically important clade of bacteria, containing the model organism Escherichia coli, as well as major human pathogens including Salmonella enterica and Klebsiella pneumoniae. Essential gene sets have been determined for several members of the Enterobacteriaceae, with the Keio E. coli single-gene deletion library often regarded as a gold standard. However, it remains unclear how gene essentiality varies between related strains and species. To investigate this, we have assembled a collection of 13 sequenced high-density transposon mutant libraries from five genera within the Enterobacteriaceae. We first assess several gene essentiality prediction approaches, investigate the effects of transposon density on essentiality prediction, and identify biases in transposon insertion sequencing data. Based on these investigations, we develop a new classifier for gene essentiality. Using this new classifier, we define a core essential genome in the Enterobacteriaceae of 201 universally essential genes. Despite the presence of a large cohort of variably essential genes, we find an absence of evidence for genus-specific essential genes. A clear example of this sporadic essentiality is given by the set of genes regulating the σE extracytoplasmic stress response, which appears to have independently acquired essentiality multiple times in the Enterobacteriaceae. Finally, we compare our essential gene sets to the natural experiment of gene loss in obligate insect endosymbionts that have emerged from within the Enterobacteriaceae. This isolates a remarkably small set of genes absolutely required for survival and identifies several instances of essential stress responses masked by redundancy in free-living bacteria.IMPORTANCEThe essential genome, that is the set of genes absolutely required to sustain life, is a core concept in genetics. Essential genes in bacteria serve as drug targets, put constraints on the engineering of biological chassis for technological or industrial purposes, and are key to constructing synthetic life. Despite decades of study, relatively little is known about how gene essentiality varies across related bacteria. In this study, we have collected gene essentiality data for 13 bacteria related to the model organism Escherichia coli, including several human pathogens, and investigated the conservation of essentiality. We find that approximately a third of the genes essential in any particular strain are non-essential in another related strain. Surprisingly, we do not find evidence for essential genes unique to specific genera; rather it appears a substantial fraction of the essential genome rapidly gains or loses essentiality during evolution. This suggests that essentiality is not an immutable characteristic but depends crucially on the genomic context. We illustrate this through a comparison of our essential genes in free-living bacteria to genes conserved in 34 insect endosymbionts with naturally reduced genomes, finding several cases where genes generally regarded as being important for specific stress responses appear to have become essential in endosymbionts due to a loss of functional redundancy in the genome.

Systematic, high-throughput characterization of bacteriophage gene essentiality on diverse hosts

Understanding core and conditional gene essentiality is crucial for decoding genotype-phenotype relationships in organisms. We present PhageMaP, a high-throughput method to create genome-scale phage knockout libraries for systematically assessing gene essentiality in bacteriophages. Using PhageMaP, we generate gene essentiality maps across hundreds of genes in the model phage T7 and the non-model phage Bas63, on diverse hosts. These maps provide fundamental insights into genome organization, gene function, and host-specific conditional essentiality. By applying PhageMaP to a collection of anti-phage defense systems, we uncover phage genes that either inhibit or activate eight defenses and offer novel mechanistic hypotheses. Furthermore, we engineer synthetic phages with enhanced infectivity by modular transfer of a PhageMaP-discovered defense inhibitor from Bas63 to T7. PhageMaP is generalizable, as it leverages homologous recombination, a universal cellular process, for locus-specific barcoding. This versatile tool advances bacteriophage functional genomics and accelerates rational phage design for therapy.

HELP: A computational framework for labelling and predicting human common and context-specific essential genes

Machine learning-based approaches are particularly suitable for identifying essential genes as they allow the generation of predictive models trained on features from multi-source data. Gene essentiality is neither binary nor static but determined by the context. The databases for essential gene annotation do not permit the personalisation of the context, and their update can be slower than the publication of new experimental data. We propose HELP (Human Gene Essentiality Labelling & Prediction), a computational framework for labelling and predicting essential genes. Its double scope allows for identifying genes based on dependency or not on experimental data. The effectiveness of the labelling method was demonstrated by comparing it with other approaches in overlapping the reference sets of essential gene annotations, where HELP demonstrated the best compromise between false and true positive rates. The gene attributes, including multi-omics and network embedding features, lead to high-performance prediction of essential genes while confirming the existence of essentiality nuances.

Synthetic lethality and the minimal genome size problem

Gene knockout studies suggest that ~300 genes in a bacterial genome and ~1,100 genes in a yeast genome cannot be deleted without loss of viability. These single-gene knockout experiments do not account for negative genetic interactions, when two or more genes can each be deleted without effect, but their joint deletion is lethal. Thus, large-scale single-gene deletion studies underestimate the size of a minimal gene set compatible with cell survival. In yeast Saccharomyces cerevisiae, the viability of all possible deletions of gene pairs (2-tuples), and of some deletions of gene triplets (3-tuples), has been experimentally tested. To estimate the size of a yeast minimal genome from that data, we first established that finding the size of a minimal gene set is equivalent to finding the minimum vertex cover in the lethality (hyper)graph, where the vertices are genes and (hyper)edges connect k-tuples of genes whose joint deletion is lethal. Using the Lovász-Johnson-Chvatal greedy approximation algorithm, we computed the minimum vertex cover of the synthetic-lethal 2-tuples graph to be 1,723 genes. We next simulated the genetic interactions in 3-tuples, extrapolating from the existing triplet sample, and again estimated minimum vertex covers. The size of a minimal gene set in yeast rapidly approaches the size of the entire genome even when considering only synthetic lethalities in k-tuples with small k. In contrast, several studies reported successful experimental reductions of yeast and bacterial genomes by simultaneous deletions of hundreds of genes, without eliciting synthetic lethality. We discuss possible reasons for this apparent contradiction.IMPORTANCEHow can we estimate the smallest number of genes sufficient for a unicellular organism to survive on a rich medium? One approach is to remove genes one at a time and count how many of such deletion strains are unable to grow. However, the single-gene knockout data are insufficient, because joint gene deletions may result in negative genetic interactions, also known as synthetic lethality. We used a technique from graph theory to estimate the size of minimal yeast genome from partial data on synthetic lethality. The number of potential synthetic lethal interactions grows very fast when multiple genes are deleted, revealing a paradoxical contrast with the experimental reductions of yeast genome by ~100 genes, and of bacterial genomes by several hundreds of genes.

Differentially used codons among essential genes in bacteria identified by machine learning-based analysis

Codon usage bias (CUB), the uneven usage of synonymous codons encoding the same amino acid, differs among genes within and across bacteria genomes. CUB is known to be influenced by gene expression and accordingly, CUB differs between the high-expression and low-expression genes in several bacteria. In this article, we have extended codon usage study considering gene essentiality as a feature. Using machine learning (ML) based approaches, we have analysed Relative Synonymous Codon Usage (RSCU) values between essential and non-essential genes in Escherichia coli and thirty-four other bacterial genomes whose gene essentiality features were available in public databases. We observed significant differences in codon usage patterns between essential and non-essential genes for majority of the bacterial genomes and accordingly, ML based classifiers achieved high area under curve (AUC) scores, with a minimum score of 70.0 across twenty-eight organisms. Further, importance of the codons towards classifying genes found to differ among the codons in each genome. Arg codon CGT and Gly codon GGT were observed to be the most preferred codons among essential genes in Escherichia coli. Interestingly, some of the codons like CGT, ATA, GGT and GGG observed to be contributing consistently towards classifying essential genes across thirty-five bacteria genomes studied. In other hand, codons TGY and CAY encoding amino acids Cys and His respectively were among the least contributing codons towards classification among all these bacteria. This study demonstrates the gene essentiality based differences in synonymous codon usage in bacteria genomes and presents a common codon usage pattern across bacteria.

Gene essentiality in the solventogenic Clostridium acetobutylicum DSM 792

Clostridium acetobutylicum is a solventogenic, anaerobic, gram-positive bacterium that is commonly considered the model organism for studying acetone-butanol-ethanol fermentation. The need to produce these chemicals sustainably and with a minimal impact on the environment has revived the interest in research on this bacterium. The recent development of efficient genetic tools allows to better understand the physiology of this micro-organism, aiming at improving its fermentation capacities. Knowledge about gene essentiality would guide the future genetic editing strategies and support the understanding of crucial cellular functions in this bacterium. In this work, we applied a transposon insertion site sequencing method to generate large mutant libraries containing millions of independent mutants that allowed us to identify a core group of 418 essential genes needed for in vitro development. Future research on this significant biocatalyst will be guided by the data provided in this work, which will serve as a valuable resource for the community.

IMPORTANCE

Clostridium acetobutylicum is a leading candidate to synthesize valuable compounds like three and four carbons alcohols. Its ability to convert carbohydrates into a mixture of acetone, butanol, and ethanol as well as other chemicals of interest upon genetic engineering makes it an advantageous organism for the valorization of lignocellulose-derived sugar mixtures. Since, genetic optimization depends on the fundamental insights supplied by accurate gene function assignment, gene essentiality analysis is of great interest as it can shed light on the function of many genes whose functions are still to be confirmed. The data obtained in this study will be of great value for the research community aiming to develop C. acetobutylicum as a platform organism for the production of chemicals of interest.

Genome-scale exon perturbation screens uncover exons critical for cell fitness

CRISPR-Cas technology has transformed functional genomics, yet understanding of how individual exons differentially shape cellular phenotypes remains limited. Here, we optimized and conducted massively parallel exon deletion and splice-site mutation screens in human cell lines to identify exons that regulate cellular fitness. Fitness-promoting exons are prevalent in essential and highly expressed genes and commonly overlap with protein domains and interaction interfaces. Conversely, fitness-suppressing exons are enriched in nonessential genes, exhibiting lower inclusion levels, and overlap with intrinsically disordered regions and disease-associated mutations. In-depth mechanistic investigation of the screen-hit TAF5 alternative exon-8 revealed that its inclusion is required for assembly of the TFIID general transcription initiation complex, thereby regulating global gene expression output. Collectively, our orthogonal exon perturbation screens established a comprehensive repository of phenotypically important exons and uncovered regulatory mechanisms governing cellular fitness and gene expression.

Combination of computational techniques and RNAi reveal targets in Anopheles gambiae for malaria vector control

Increasing reports of insecticide resistance continue to hamper the gains of vector control strategies in curbing malaria transmission. This makes identifying new insecticide targets or alternative vector control strategies necessary. CLassifier of Essentiality AcRoss EukaRyote (CLEARER), a leave-one-organism-out cross-validation machine learning classifier for essential genes, was used to predict essential genes in Anopheles gambiae and selected predicted genes experimentally validated. The CLEARER algorithm was trained on six model organisms: Caenorhabditis elegans, Drosophila melanogaster, Homo sapiens, Mus musculus, Saccharomyces cerevisiae and Schizosaccharomyces pombe, and employed to identify essential genes in An. gambiae. Of the 10,426 genes in An. gambiae, 1,946 genes (18.7%) were predicted to be Cellular Essential Genes (CEGs), 1716 (16.5%) to be Organism Essential Genes (OEGs), and 852 genes (8.2%) to be essential as both OEGs and CEGs. RNA interference (RNAi) was used to validate the top three highly expressed non-ribosomal predictions as probable vector control targets, by determining the effect of these genes on the survival of An. gambiae G3 mosquitoes. In addition, the effect of knockdown of arginase (AGAP008783) on Plasmodium berghei infection in mosquitoes was evaluated, an enzyme we computationally inferred earlier to be essential based on chokepoint analysis. Arginase and the top three genes, AGAP007406 (Elongation factor 1-alpha, Elf1), AGAP002076 (Heat shock 70kDa protein 1/8, HSP), AGAP009441 (Elongation factor 2, Elf2), had knockdown efficiencies of 91%, 75%, 63%, and 61%, respectively. While knockdown of HSP or Elf2 significantly reduced longevity of the mosquitoes (p<0.0001) compared to control groups, Elf1 or arginase knockdown had no effect on survival. However, arginase knockdown significantly reduced P. berghei oocytes counts in the midgut of mosquitoes when compared to LacZ-injected controls. The study reveals HSP and Elf2 as important contributors to mosquito survival and arginase as important for parasite development, hence placing them as possible targets for vector control.

Genome engineering on size reduction and complexity simplification: A review

BACKGROUND

Genome simplification is an important topic in the field of life sciences that has attracted attention from its conception to the present day. It can help uncover the essential components of the genome and, in turn, shed light on the underlying operating principles of complex biological systems. This has made it a central focus of both basic and applied research in the life sciences. With the recent advancements in related technologies and our increasing knowledge of the genome, now is an opportune time to delve into this topic.

AIM OF REVIEW

Our review investigates the progress of genome simplification from two perspectives: genome size reduction and complexity simplification. In addition, we provide insights into the future development trends of genome simplification.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Reducing genome size requires eliminating non-essential elements as much as possible. This process has been facilitated by advances in genome manipulation and synthesis techniques. However, we still need a better and clearer understanding of living systems to reduce genome complexity. As there is a lack of quantitative and clearly defined standards for this task, we have opted to approach the topic from various perspectives and present our findings accordingly.

Evolutionary analysis of gene ages across TADs associates chromatin topology with whole-genome duplications

Topologically associated domains (TADs) are interaction subnetworks of chromosomal regions in 3D genomes. TAD boundaries frequently coincide with genome breaks while boundary deletion is under negative selection, suggesting that TADs may facilitate genome rearrangements and evolution. We show that genes co-localize by evolutionary age in humans and mice, resulting in TADs having different proportions of younger and older genes. We observe a major transition in the age co-localization patterns between the genes born during vertebrate whole-genome duplications (WGDs) or before and those born afterward. We also find that genes recently duplicated in primates and rodents are more frequently essential when they are located in old-enriched TADs and interact with genes that last duplicated during the WGD. Therefore, the evolutionary relevance of recent genes may increase when located in TADs with established regulatory networks. Our data suggest that TADs could play a role in organizing ancestral functions and evolutionary novelty.

Dissecting the oncogenic properties of essential RNA-modifying enzymes: a focus on NAT10

Chemical modifications of ribonucleotides significantly alter the physicochemical properties and functions of RNA. Initially perceived as static and essential marks in ribosomal RNA (rRNA) and transfer RNA (tRNA), recent discoveries unveiled a dynamic landscape of RNA modifications in messenger RNA (mRNA) and other regulatory RNAs. These findings spurred extensive efforts to map the distribution and function of RNA modifications, aiming to elucidate their distribution and functional significance in normal cellular homeostasis and pathological states. Significant dysregulation of RNA modifications is extensively documented in cancers, accentuating the potential of RNA-modifying enzymes as therapeutic targets. However, the essential role of several RNA-modifying enzymes in normal physiological functions raises concerns about potential side effects. A notable example is N-acetyltransferase 10 (NAT10), which is responsible for acetylating cytidines in RNA. While emerging evidence positions NAT10 as an oncogenic factor and a potential target in various cancer types, its essential role in normal cellular processes complicates the development of targeted therapies. This review aims to comprehensively analyze the essential and oncogenic properties of NAT10. We discuss its crucial role in normal cell biology and aging alongside its contribution to cancer development and progression. We advocate for agnostic approaches to disentangling the intertwined essential and oncogenic functions of RNA-modifying enzymes. Such approaches are crucial for understanding the full spectrum of RNA-modifying enzymes and imperative for designing effective and safe therapeutic strategies.

In pursuit of a minimal CHO genome: Establishment of large-scale genome deletions

Chinese hamster ovary (CHO) cells are the most commonly used mammalian cell line for the production of complex therapeutic glycoproteins. As CHO cells have evolved as part of a multicellular organism, they harbor many cellular functions irrelevant for their application as production hosts in industrial bioprocesses. Consequently, CHO cells have been the target for numerous genetic engineering efforts in the past, but a tailored host cell chassis holistically optimized for its specific task in a bioreactor is still missing. While the concept of genome reduction has already been successfully applied to bacterial production cells, attempts to create higher eukaryotic production hosts exhibiting reduced genomes have not been reported yet. Here, we present the establishment and application of a large-scale genome deletion strategy for targeted excision of large genomic regions in CHO cells. We demonstrate the feasibility of genome reduction in CHO cells using optimized CRISPR/Cas9 based experimental protocols targeting large non-essential genomic regions with high efficiency. Achieved genome deletions of non-essential genetic regions did not introduce negative effects on bioprocess relevant parameters, although we conducted the largest reported genomic excision of 864 kilobase pairs in CHO cells so far. The concept presented serves as a directive to accelerate the development of a significantly genome-reduced CHO host cell chassis paving the way for a next generation of CHO cell factories.

Translation variation across genetic backgrounds reveals a post-transcriptional buffering signature in yeast

Gene expression is known to vary among individuals, and this variability can impact the phenotypic diversity observed in natural populations. While the transcriptome and proteome have been extensively studied, little is known about the translation process itself. Here, we therefore performed ribosome and transcriptomic profiling on a genetically and ecologically diverse set of natural isolates of the Saccharomyces cerevisiae yeast. Interestingly, we found that the Euclidean distances between each profile and the expression fold changes in each pairwise isolate comparison were higher at the transcriptomic level. This observation clearly indicates that the transcriptional variation observed in the different isolates is buffered through a phenomenon known as post-transcriptional buffering at the translation level. Furthermore, this phenomenon seemed to have a specific signature by preferentially affecting essential genes as well as genes involved in complex-forming proteins, and low transcribed genes. We also explored the translation of the S. cerevisiae pangenome and found that the accessory genes related to introgression events displayed similar transcription and translation levels as the core genome. By contrast, genes acquired through horizontal gene transfer events tended to be less efficiently translated. Together, our results highlight both the extent and signature of the post-transcriptional buffering.

Recent advances in the characterization of essential genes and development of a database of essential genes

Over the past few decades, there has been a significant interest in the study of essential genes, which are crucial for the survival of an organism under specific environmental conditions and thus have practical applications in the fields of synthetic biology and medicine. An increasing amount of experimental data on essential genes has been obtained with the continuous development of technological methods. Meanwhile, various computational prediction methods, related databases and web servers have emerged accordingly. To facilitate the study of essential genes, we have established a database of essential genes (DEG), which has become popular with continuous updates to facilitate essential gene feature analysis and prediction, drug and vaccine development, as well as artificial genome design and construction. In this article, we summarized the studies of essential genes, overviewed the relevant databases, and discussed their practical applications. Furthermore, we provided an overview of the main applications of DEG and conducted comprehensive analyses based on its latest version. However, it should be noted that the essential gene is a dynamic concept instead of a binary one, which presents both opportunities and challenges for their future development.

Changing fitness effects of mutations through long-term bacterial evolution

The distribution of fitness effects of new mutations shapes evolution, but it is challenging to observe how it changes as organisms adapt. Using Escherichia coli lineages spanning 50,000 generations of evolution, we quantify the fitness effects of insertion mutations in every gene. Macroscopically, the fraction of deleterious mutations changed little over time whereas the beneficial tail declined sharply, approaching an exponential distribution. Microscopically, changes in individual gene essentiality and deleterious effects often occurred in parallel; altered essentiality is only partly explained by structural variation. The identity and effect sizes of beneficial mutations changed rapidly over time, but many targets of selection remained predictable because of the importance of loss-of-function mutations. Taken together, these results reveal the dynamic-but statistically predictable-nature of mutational fitness effects.

Essential genes identification model based on sequence feature map and graph convolutional neural network

BACKGROUND

Essential genes encode functions that play a vital role in the life activities of organisms, encompassing growth, development, immune system functioning, and cell structure maintenance. Conventional experimental techniques for identifying essential genes are resource-intensive and time-consuming, and the accuracy of current machine learning models needs further enhancement. Therefore, it is crucial to develop a robust computational model to accurately predict essential genes.

RESULTS

In this study, we introduce GCNN-SFM, a computational model for identifying essential genes in organisms, based on graph convolutional neural networks (GCNN). GCNN-SFM integrates a graph convolutional layer, a convolutional layer, and a fully connected layer to model and extract features from gene sequences of essential genes. Initially, the gene sequence is transformed into a feature map using coding techniques. Subsequently, a multi-layer GCN is employed to perform graph convolution operations, effectively capturing both local and global features of the gene sequence. Further feature extraction is performed, followed by integrating convolution and fully-connected layers to generate prediction results for essential genes. The gradient descent algorithm is utilized to iteratively update the cross-entropy loss function, thereby enhancing the accuracy of the prediction results. Meanwhile, model parameters are tuned to determine the optimal parameter combination that yields the best prediction performance during training.

CONCLUSIONS

Experimental evaluation demonstrates that GCNN-SFM surpasses various advanced essential gene prediction models and achieves an average accuracy of 94.53%. This study presents a novel and effective approach for identifying essential genes, which has significant implications for biology and genomics research.

Meta-analysis of dispensable essential genes and their interactions with bypass suppressors

Genes have been historically classified as essential or non-essential based on their requirement for viability. However, genomic mutations can sometimes bypass the requirement for an essential gene, challenging the binary classification of gene essentiality. Such dispensable essential genes represent a valuable model for understanding the incomplete penetrance of loss-of-function mutations often observed in natural populations. Here, we compiled data from multiple studies on essential gene dispensability in Saccharomyces cerevisiae to comprehensively characterize these genes. In analyses spanning different evolutionary timescales, dispensable essential genes exhibited distinct phylogenetic properties compared with other essential and non-essential genes. Integration of interactions with suppressor genes that can bypass the gene essentiality revealed the high functional modularity of the bypass suppression network. Furthermore, dispensable essential and bypass suppressor gene pairs reflected simultaneous changes in the mutational landscape of S. cerevisiae strains. Importantly, species in which dispensable essential genes were non-essential tended to carry bypass suppressor mutations in their genomes. Overall, our study offers a comprehensive view of dispensable essential genes and illustrates how their interactions with bypass suppressors reflect evolutionary outcomes.

Enabling pathway design by multiplex experimentation and machine learning

The remarkable metabolic diversity observed in nature has provided a foundation for sustainable production of a wide array of valuable molecules. However, transferring the biosynthetic pathway to the desired host often runs into inherent failures that arise from intermediate accumulation and reduced flux resulting from competing pathways within the host cell. Moreover, the conventional trial and error methods utilized in pathway optimization struggle to fully grasp the intricacies of installed pathways, leading to time-consuming and labor-intensive experiments, ultimately resulting in suboptimal yields. Considering these obstacles, there is a pressing need to explore the enzyme expression landscape and identify the optimal pathway configuration for enhanced production of molecules. This review delves into recent advancements in pathway engineering, with a focus on multiplex experimentation and machine learning techniques. These approaches play a pivotal role in overcoming the limitations of traditional methods, enabling exploration of a broader design space and increasing the likelihood of discovering optimal pathway configurations for enhanced production of molecules. We discuss several tools and strategies for pathway design, construction, and optimization for sustainable and cost-effective microbial production of molecules ranging from bulk to fine chemicals. We also highlight major successes in academia and industry through compelling case studies.

A Machine Learning Approach for Predicting Essentiality of Metabolic Genes

The identification of essential genes is a key challenge in systems and synthetic biology, particularly for engineering metabolic pathways that convert feedstocks into valuable products. Assessment of gene essentiality at a genome scale requires large and costly growth assays of knockout strains. Here we describe a strategy to predict the essentiality of metabolic genes using binary classification algorithms. The approach combines elements from genome-scale metabolic models, directed graphs, and machine learning into a predictive model that can be trained on small knockout data. We demonstrate the efficacy of this approach using the most complete metabolic model of Escherichia coli and various machine learning algorithms for binary classification.

Untangling the Context-Specificity of Essential Genes by Means of Machine Learning: A Constructive Experience

Gene essentiality is a genetic concept crucial for a comprehensive understanding of life and evolution. In the last decade, many essential genes (EGs) have been determined using different experimental and computational approaches, and this information has been used to reduce the genomes of model organisms. A growing amount of evidence highlights that essentiality is a property that depends on the context. Because of their importance in vital biological processes, recognising context-specific EGs (csEGs) could help for identifying new potential pharmacological targets and to improve precision therapeutics. Since most of the computational procedures proposed to identify and predict EGs neglect their context-specificity, we focused on this aspect, providing a theoretical and experimental overview of the literature, data and computational methods dedicated to recognising csEGs. To this end, we adapted existing computational methods to exploit a specific context (the kidney tissue) and experimented with four different prediction methods using the labels provided by four different identification approaches. The considerations derived from the analysis of the obtained results, confirmed and validated also by further experiments for a different tissue context, provide the reader with guidance on exploiting existing tools for achieving csEGs identification and prediction.

Unrealized targets in the discovery of antibiotics for Gram-negative bacterial infections

Advances in areas that include genomics, systems biology, protein structure determination and artificial intelligence provide new opportunities for target-based antibacterial drug discovery. The selection of a 'good' new target for direct-acting antibacterial compounds is the first decision, for which multiple criteria must be explored, integrated and re-evaluated as drug discovery programmes progress. Criteria include essentiality of the target for bacterial survival, its conservation across different strains of the same species, bacterial species and growth conditions (which determines the spectrum of activity of a potential antibiotic) and the level of homology with human genes (which influences the potential for selective inhibition). Additionally, a bacterial target should have the potential to bind to drug-like molecules, and its subcellular location will govern the need for inhibitors to penetrate one or two bacterial membranes, which is a key challenge in targeting Gram-negative bacteria. The risk of the emergence of target-based drug resistance for drugs with single targets also requires consideration. This Review describes promising but as-yet-unrealized targets for antibacterial drugs against Gram-negative bacteria and examples of cognate inhibitors, and highlights lessons learned from past drug discovery programmes.

'Bingo'-a large language model- and graph neural network-based workflow for the prediction of essential genes from protein data

The identification and characterization of essential genes are central to our understanding of the core biological functions in eukaryotic organisms, and has important implications for the treatment of diseases caused by, for example, cancers and pathogens. Given the major constraints in testing the functions of genes of many organisms in the laboratory, due to the absence of in vitro cultures and/or gene perturbation assays for most metazoan species, there has been a need to develop in silico tools for the accurate prediction or inference of essential genes to underpin systems biological investigations. Major advances in machine learning approaches provide unprecedented opportunities to overcome these limitations and accelerate the discovery of essential genes on a genome-wide scale. Here, we developed and evaluated a large language model- and graph neural network (LLM-GNN)-based approach, called 'Bingo', to predict essential protein-coding genes in the metazoan model organisms Caenorhabditis elegans and Drosophila melanogaster as well as in Mus musculus and Homo sapiens (a HepG2 cell line) by integrating LLM and GNNs with adversarial training. Bingo predicts essential genes under two 'zero-shot' scenarios with transfer learning, showing promise to compensate for a lack of high-quality genomic and proteomic data for non-model organisms. In addition, the attention mechanisms and GNNExplainer were employed to manifest the functional sites and structural domain with most contribution to essentiality. In conclusion, Bingo provides the prospect of being able to accurately infer the essential genes of little- or under-studied organisms of interest, and provides a biological explanation for gene essentiality.

Essential gene complement of Planctopirus limnophila from the bacterial phylum Planctomycetes

Planctopirus limnophila belongs to the bacterial phylum Planctomycetes, a relatively understudied lineage with remarkable cell biology features. Here, we report a genome-wide analysis of essential gene content in P. limnophila. We show that certain genes involved in peptidoglycan synthesis or cell division, which are essential in most other studied bacteria, are not essential for growth under laboratory conditions in this species. We identify essential genes likely involved in lipopolysaccharide biosynthesis, consistent with the view of Planctomycetes as diderm bacteria, and highlight other essential genes of unknown functions. Furthermore, we explore potential stages of evolution of the essential gene repertoire in Planctomycetes and the related phyla Verrucomicrobia and Chlamydiae. Our results provide insights into the divergent molecular and cellular biology of Planctomycetes.

TimeTalk uses single-cell RNA-seq datasets to decipher cell-cell communication during early embryo development

Early embryonic development is a dynamic process that relies on proper cell-cell communication to form a correctly patterned embryo. Early embryo development-related ligand-receptor pairs (eLRs) have been shown to guide cell fate decisions and morphogenesis. However, the scope of eLRs and their influence on early embryo development remain elusive. Here, we developed a computational framework named TimeTalk from integrated public time-course mouse scRNA-seq datasets to decipher the secret of eLRs. Extensive validations and analyses were performed to ensure the involvement of identified eLRs in early embryo development. Process analysis identified that eLRs could be divided into six temporal windows corresponding to sequential events in the early embryo development process. With the interpolation strategy, TimeTalk is powerful in revealing paracrine settings and studying cell-cell communication during early embryo development. Furthermore, by using TimeTalk in the blastocyst and blastoid models, we found that the blastoid models share the core communication pathways with the epiblast and primitive endoderm lineages in the blastocysts. This result suggests that TimeTalk has transferability to other bio-dynamic processes. We also curated eLRs recognized by TimeTalk, which may provide valuable clues for understanding early embryo development and relevant disorders.

Essential genes: a cross-species perspective

Protein coding genes exhibit different degrees of intolerance to loss-of-function variation. The most intolerant genes, whose function is essential for cell or/and organism survival, inform on fundamental biological processes related to cell proliferation and organism development and provide a window on the molecular mechanisms of human disease. Here we present a brief overview of the resources and knowledge gathered around gene essentiality, from cancer cell lines to model organisms to human development. We outline the implications of using different sources of evidence and definitions to determine which genes are essential and highlight how information on the essentiality status of a gene can inform novel disease gene discovery and therapeutic target identification.

High-throughput functional genomics: A (myco)bacterial perspective

Advances in sequencing technologies have enabled unprecedented insights into bacterial genome composition and dynamics. However, the disconnect between the rapid acquisition of genomic data and the (much slower) confirmation of inferred genetic function threatens to widen unless techniques for fast, high-throughput functional validation can be applied at scale. This applies equally to Mycobacterium tuberculosis, the leading infectious cause of death globally and a pathogen whose genome, despite being among the first to be sequenced two decades ago, still contains many genes of unknown function. Here, we summarize the evolution of bacterial high-throughput functional genomics, focusing primarily on transposon (Tn)-based mutagenesis and the construction of arrayed mutant libraries in diverse bacterial systems. We also consider the contributions of CRISPR interference as a transformative technique for probing bacterial gene function at scale. Throughout, we situate our analysis within the context of functional genomics of mycobacteria, focusing specifically on the potential to yield insights into M. tuberculosis pathogenicity and vulnerabilities for new drug and regimen development. Finally, we offer suggestions for future approaches that might be usefully applied in elucidating the complex cellular biology of this major human pathogen.

Genome-wide fitness analysis identifies genes required for in vitro growth and macrophage infection by African and global epidemic pathovariants of Salmonella enterica Enteritidis

Salmonella enterica Enteritidis is the second most common serovar associated with invasive non-typhoidal Salmonella (iNTS) disease in sub-Saharan Africa. Previously, genomic and phylogenetic characterization of S . enterica Enteritidis isolates from the human bloodstream led to the discovery of the Central/Eastern African clade (CEAC) and West African clade, which were distinct from the gastroenteritis-associated global epidemic clade (GEC). The African S . enterica Enteritidis clades have unique genetic signatures that include genomic degradation, novel prophage repertoires and multi-drug resistance, but the molecular basis for the enhanced propensity of African S . enterica Enteritidis to cause bloodstream infection is poorly understood. We used transposon insertion sequencing (TIS) to identify the genetic determinants of the GEC representative strain P125109 and the CEAC representative strain D7795 for growth in three in vitro conditions (LB or minimal NonSPI2 and InSPI2 growth media), and for survival and replication in RAW 264.7 murine macrophages. We identified 207 in vitro -required genes that were common to both S . enterica Enteritidis strains and also required by S . enterica Typhimurium, S . enterica Typhi and Escherichia coli , and 63 genes that were only required by individual S . enterica Enteritidis strains. Similar types of genes were required by both P125109 and D7795 for optimal growth in particular media. Screening the transposon libraries during macrophage infection identified 177 P125109 and 201 D7795 genes that contribute to bacterial survival and replication in mammalian cells. The majority of these genes have proven roles in Salmonella virulence. Our analysis uncovered candidate strain-specific macrophage fitness genes that could encode novel Salmonella virulence factors.

ARID1A in cancer: Friend or foe?

ARID1A belongs to a class of chromatin regulatory proteins that function by maintaining accessibility at most promoters and enhancers, thereby regulating gene expression. The high frequency of ARID1A alterations in human cancers has highlighted its significance in tumorigenesis. The precise role of ARID1A in cancer is highly variable since ARID1A alterations can have a tumor suppressive or oncogenic role, depending on the tumor type and context. ARID1A is mutated in about 10% of all tumor types including endometrial, bladder, gastric, liver, biliopancreatic cancer, some ovarian cancer subtypes, and the extremely aggressive cancers of unknown primary. Its loss is generally associated with disease progression more often than onset. In some cancers, ARID1A loss is associated with worse prognostic features, thus supporting a major tumor suppressive role. However, some exceptions have been reported. Thus, the association of ARID1A genetic alterations with patient prognosis is controversial. However, ARID1A loss of function is considered conducive for the use of inhibitory drugs which are based on synthetic lethality mechanisms. In this review we summarize the current knowledge on the role of ARID1A as tumor suppressor or oncogene in different tumor types and discuss the strategies for treating ARID1A mutated cancers.

Gene-by-gene screen of the unknown proteins encoded on Plasmodium falciparum chromosome 3

Taxon-specific proteins are key determinants defining the biology of all organisms and represent prime drug targets in pathogens. However, lacking comparability with proteins in other lineages makes them particularly difficult to study. In malaria parasites, this is exacerbated by technical limitations. Here, we analyzed the cellular location, essentiality, function, and, in selected cases, interactome of all unknown non-secretory proteins encoded on an entire P. falciparum chromosome. The nucleus was the most common localization, indicating that it is a hotspot of parasite-specific biology. More in-depth functional studies with four proteins revealed essential roles in DNA replication and mitosis. The mitosis proteins defined a possible orphan complex and a highly diverged complex needed for spindle-kinetochore connection. Structure-function comparisons indicated that the taxon-specific proteins evolved by different mechanisms. This work demonstrates the feasibility of gene-by-gene screens to elucidate the biology of malaria parasites and reveal critical parasite-specific processes of interest as drug targets.

Complexity and self-organization in the evolution of cell polarization

Cellular life exhibits order and complexity, which typically increase over the course of evolution. Cell polarization is a well-studied example of an ordering process that breaks the internal symmetry of a cell by establishing a preferential axis. Like many cellular processes, polarization is driven by self-organization, meaning that the macroscopic pattern emerges as a consequence of microscopic molecular interactions at the biophysical level. However, the role of self-organization in the evolution of complex protein networks remains obscure. In this Review, we provide an overview of the evolution of polarization as a self-organizing process, focusing on the model species Saccharomyces cerevisiae and its fungal relatives. Moreover, we use this model system to discuss how self-organization might relate to evolutionary change, offering a shift in perspective on evolution at the microscopic scale.

DeepCellEss: cell line-specific essential protein prediction with attention-based interpretable deep learning

MOTIVATION

Protein essentiality is usually accepted to be a conditional trait and strongly affected by cellular environments. However, existing computational methods often do not take such characteristics into account, preferring to incorporate all available data and train a general model for all cell lines. In addition, the lack of model interpretability limits further exploration and analysis of essential protein predictions.

RESULTS

In this study, we proposed DeepCellEss, a sequence-based interpretable deep learning framework for cell line-specific essential protein predictions. DeepCellEss utilizes a convolutional neural network and bidirectional long short-term memory to learn short- and long-range latent information from protein sequences. Further, a multi-head self-attention mechanism is used to provide residue-level model interpretability. For model construction, we collected extremely large-scale benchmark datasets across 323 cell lines. Extensive computational experiments demonstrate that DeepCellEss yields effective prediction performance for different cell lines and outperforms existing sequence-based methods as well as network-based centrality measures. Finally, we conducted some case studies to illustrate the necessity of considering specific cell lines and the superiority of DeepCellEss. We believe that DeepCellEss can serve as a useful tool for predicting essential proteins across different cell lines.

AVAILABILITY AND IMPLEMENTATION

The DeepCellEss web server is available at http://csuligroup.com:8000/DeepCellEss. The source code and data underlying this study can be obtained from https://github.com/CSUBioGroup/DeepCellEss.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Integration of text mining and biological network analysis: Identification of essential genes in sulfate-reducing bacteria

The growth and survival of an organism in a particular environment is highly depends on the certain indispensable genes, termed as essential genes. Sulfate-reducing bacteria (SRB) are obligate anaerobes which thrives on sulfate reduction for its energy requirements. The present study used Oleidesulfovibrio alaskensis G20 (OA G20) as a model SRB to categorize the essential genes based on their key metabolic pathways. Herein, we reported a feedback loop framework for gene of interest discovery, from bio-problem to gene set of interest, leveraging expert annotation with computational prediction. Defined bio-problem was applied to retrieve the genes of SRB from literature databases (PubMed, and PubMed Central) and annotated them to the genome of OA G20. Retrieved gene list was further used to enrich protein-protein interaction and was corroborated to the pangenome analysis, to categorize the enriched gene sets and the respective pathways under essential and non-essential. Interestingly, the sat gene (dde_2265) from the sulfur metabolism was the bridging gene between all the enriched pathways. Gene clusters involved in essential pathways were linked with the genes from seleno-compound metabolism, amino acid metabolism, secondary metabolite synthesis, and cofactor biosynthesis. Furthermore, pangenome analysis demonstrated the gene distribution, where 69.83% of the 116 enriched genes were mapped under "persistent," inferring the essentiality of these genes. Likewise, 21.55% of the enriched genes, which involves specially the formate dehydrogenases and metallic hydrogenases, appeared under "shell." Our methodology suggested that semi-automated text mining and network analysis may play a crucial role in deciphering the previously unexplored genes and key mechanisms which can help to generate a baseline prior to perform any experimental studies.

CRISPRi-Mediated Gene Suppression Reveals Putative Reverse Transcriptase Gene PA0715 to Be a Global Regulator of Pseudomonas aeruginosa

Purpose

Pseudomonas aeruginosa is a common pathogen of infection in burn and trauma patients, and multi-drug resistant P. aeruginosa has become an increasingly important pathogen. Essential genes are key to the development of novel antibiotics. The PA0715 gene is a novel unidentified essential gene that has attracted our interest as a potential antibiotic target. Our study aims to determine the exact role of PA0715 in cell physiology and bacterial pathogenicity, providing important clues for antibiotic development.

Patients and Methods

The shuttle vector pHERD20T containing an arabinose inducible promoter was used to construct the CRISPRi system. Alterations in cellular physiology and bacterial pathogenicity of P. aeruginosa PAO1 after PA0715 inhibition were characterized. High-throughput RNA-seq was performed to gain more insight into the mechanisms by which PA0715 regulates the vital activity of P. aeruginosa.

Results

We found that down-regulation of PA0715 significantly reduced PAO1 growth rate, motility and chemotaxis, antibiotic resistance, pyocyanin and biofilm production. In addition, PA0715 inhibition reduced the pathogenicity of PAO1 to the greater galleria mellonella larvae. Transcriptional profiling identified 1757 genes including those related to amino acid, carbohydrate, ketone body and organic salt metabolism, whose expression was directly or indirectly controlled by PA0715. Unexpectedly, genes involved in oxidative phosphorylation also varied with PA0715 levels, and these findings support a hitherto unrecognized critical role for PA0715 in oxidative respiration in P. aeruginosa.

Conclusion

We identified PA0715 as a global regulator of the metabolic network that is indispensable for the survival and reproduction of P. aeruginosa. Our results provide a basis for future studies of potential antibiotic targets for P. aeruginosa and offer new ideas for P. aeruginosa infection control.

Gene transferability and sociality do not correlate with gene connectivity

The connectivity of a gene, defined as the number of interactions a gene's product has with other genes' products, is a key characteristic of a gene. In prokaryotes, the complexity hypothesis predicts that genes which undergo more frequent horizontal transfer will be less connected than genes which are only very rarely transferred. We tested the role of horizontal gene transfer, and other potentially important factors, by examining the connectivity of chromosomal and plasmid genes, across 134 diverse prokaryotic species. We found that (i) genes on plasmids were less connected than genes on chromosomes; (ii) connectivity of plasmid genes was not correlated with plasmid mobility; and (iii) the sociality of genes (cooperative or private) was not correlated with gene connectivity.

Accurate Prediction of Human Essential Proteins Using Ensemble Deep Learning

Essential proteins are considered the foundation of life as they are indispensable for the survival of living organisms. Computational methods for essential protein discovery provide a fast way to identify essential proteins. But most of them heavily rely on various biological information, especially protein-protein interaction networks, which limits their practical applications. With the rapid development of high-throughput sequencing technology, sequencing data has become the most accessible biological data. However, using only protein sequence information to predict essential proteins has limited accuracy. In this paper, we propose EP-EDL, an ensemble deep learning model using only protein sequence information to predict human essential proteins. EP-EDL integrates multiple classifiers to alleviate the class imbalance problem and to improve prediction accuracy and robustness. In each base classifier, we employ multi-scale text convolutional neural networks to extract useful features from protein sequence feature matrices with evolutionary information. Our computational results show that EP-EDL outperforms the state-of-the-art sequence-based methods. Furthermore, EP-EDL provides a more practical and flexible way for biologists to accurately predict essential proteins. The source code and datasets can be downloaded from https://github.com/CSUBioGroup/EP-EDL.

A bacterial pan-genome makes gene essentiality strain-dependent and evolvable

Many bacterial species are represented by a pan-genome, whose genetic repertoire far outstrips that of any single bacterial genome. Here we investigate how a bacterial pan-genome might influence gene essentiality and whether essential genes that are initially critical for the survival of an organism can evolve to become non-essential. By using Transposon insertion sequencing (Tn-seq), whole-genome sequencing and RNA-seq on a set of 36 clinical Streptococcus pneumoniae strains representative of >68% of the species' pan-genome, we identify a species-wide 'essentialome' that can be subdivided into universal, core strain-specific and accessory essential genes. By employing 'forced-evolution experiments', we show that specific genetic changes allow bacteria to bypass essentiality. Moreover, by untangling several genetic mechanisms, we show that gene essentiality can be highly influenced by and/or be dependent on: (1) the composition of the accessory genome, (2) the accumulation of toxic intermediates, (3) functional redundancy, (4) efficient recycling of critical metabolites and (5) pathway rewiring. While this functional characterization underscores the evolvability potential of many essential genes, we also show that genes with differential essentiality remain important antimicrobial drug target candidates, as their inactivation almost always has a severe fitness cost in vivo.

Individual Genetic Heterogeneity

Genetic variation has been widely covered in literature, however, not from the perspective of an individual in any species. Here, a synthesis of genetic concepts and variations relevant for individual genetic constitution is provided. All the different levels of genetic information and variation are covered, ranging from whether an organism is unmixed or hybrid, has variations in genome, chromosomes, and more locally in DNA regions, to epigenetic variants or alterations in selfish genetic elements. Genetic constitution and heterogeneity of microbiota are highly relevant for health and wellbeing of an individual. Mutation rates vary widely for variation types, e.g., due to the sequence context. Genetic information guides numerous aspects in organisms. Types of inheritance, whether Mendelian or non-Mendelian, zygosity, sexual reproduction, and sex determination are covered. Functions of DNA and functional effects of variations are introduced, along with mechanism that reduce and modulate functional effects, including TARAR countermeasures and intraindividual genetic conflict. TARAR countermeasures for tolerance, avoidance, repair, attenuation, and resistance are essential for life, integrity of genetic information, and gene expression. The genetic composition, effects of variations, and their expression are considered also in diseases and personalized medicine. The text synthesizes knowledge and insight on individual genetic heterogeneity and organizes and systematizes the central concepts.

Data Incompleteness May form a Hard-to-Overcome Barrier to Decoding Life's Mechanism

In this brief review, we attempt to demonstrate that the incompleteness of data, as well as the intrinsic heterogeneity of biological systems, may form very strong and possibly insurmountable barriers for researchers trying to decipher the mechanisms of the functioning of live systems. We illustrate this challenge using the two most studied organisms: E. coli, with 34.6% genes lacking experimental evidence of function, and C. elegans, with identified proteins for approximately 50% of its genes. Another striking example is an artificial unicellular entity named JCVI-syn3.0, with a minimal set of genes. A total of 31.5% of the genes of JCVI-syn3.0 cannot be ascribed a specific biological function. The human interactome mapping project identified only 5-10% of all protein interactions in humans. In addition, most of the available data are static snapshots, and it is barely possible to generate realistic models of the dynamic processes within cells. Moreover, the existing interactomes reflect the de facto interaction but not its functional result, which is an unpredictable emerging property. Perhaps the completeness of molecular data on any living organism is beyond our reach and represents an unsolvable problem in biology.

Evidence that conserved essential genes are enriched for pro-longevity factors

At the cellular level, many aspects of aging are conserved across species. This has been demonstrated by numerous studies in simple model organisms like Saccharomyces cerevisiae, Caenorhabdits elegans, and Drosophila melanogaster. Because most genetic screens examine loss of function mutations or decreased expression of genes through reverse genetics, essential genes have often been overlooked as potential modulators of the aging process. By taking the approach of increasing the expression level of a subset of conserved essential genes, we found that 21% of these genes resulted in increased replicative lifespan in S. cerevisiae. This is greater than the ~ 3.5% of genes found to affect lifespan upon deletion, suggesting that activation of essential genes may have a relatively disproportionate effect on increasing lifespan. The results of our experiments demonstrate that essential gene overexpression is a rich, relatively unexplored means of increasing eukaryotic lifespan.

A Simple Evolutionary Model of Genetic Robustness After Gene Duplication

When a dispensable gene is duplicated (referred to the ancestral dispensability denoted by O⁺), genetic buffering and duplicate compensation together maintain the duplicate redundancy, whereas duplicate compensation is the only mechanism when an essential gene is duplicated (referred to the ancestral essentiality denoted by O^-). To investigate these evolutionary scenarios of genetic robustness, I formulated a simple mixture model for analyzing duplicate pairs with one of the following states: double dispensable (DD), semi-dispensable (one dispensable one essential, DE), or double essential (EE). This model was applied to the yeast duplicate pairs from a whole-genome duplication (WGD) occurred about 100 million years ago (mya), and the mouse duplicate pairs from a WGD occurred about more than 500 mya. Both case studies revealed that the proportion of essentiality for those duplicates with ancestral essentiality [P_E(O^-)] was much higher than that for those with ancestral dispensability [P_E(O⁺)]. While it was negligible in the yeast duplicate pairs, P_E(O⁺) (about 20%) was shown statistically significant in the mouse duplicate pairs. These findings, together, support the hypothesis that both sub-functionalization and neo-functionalization may play some roles after gene duplication, though the former may be much faster than the later.