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bioRxiv2022

Using a reverse genetics system to generate recombinant SARS-CoV-2 expressing robust levels of reporter genes

Reporter-expressing recombinant virus represents an excellent option and a powerful tool to investigate, among others, viral infection, pathogenicity, and transmission, as well as to identify therapeutic compounds that inhibit viral infection and prophylactic vaccines. To combat the still ongoing coronavirus disease 2019 (COVID-19) pandemic, we have established a robust bacterial artificial chromosome (BAC)-based reverse genetics (RG) system to rapidly generate recombinant severe acute respiratory syndrome coronavirus 2 (rSARS-CoV-2) to study the contribution of viral proteins in viral pathogenesis. In addition, we have also engineered reporter-expressing recombinant viruses in which we place the reporter genes upstream of the viral nucleocapsid (N) gene to promote high levels of reporter gene expression that facilitates the study of SARS-CoV-2 in vitro and in vivo. Although successful, the genetic manipulation of the BAC containing the entire SARS-CoV-2 genome of [~]30,000 nucleotides, is challenging. Herein, we depict the technical details to engineer rSARS-CoV-2 expressing reporter genes using the BAC-based RG approach. We describe i) assembly of the full-length (FL) SARS-CoV-2 genome sequences into the empty pBeloBAC, ii) verification of the pBeloBAC-FL, iii) cloning of a Venus reporter gene into the pBeloBAC-FL, and iv) recovery of the Venus-expressing rSARS-CoV-2. By following this protocol, researchers with basic molecular biology and gene engineering techniques knowledge will be able to generate wild-type and reporter-expressing rSARS-CoV-2.

microbiology↗

bioRxiv2022

BOA: A Partitioned View of Genome Assembly

De novo genome assembly is a fundamental problem in computational molecular biology that aims to reconstruct an unknown genome sequence from a set of short DNA sequences (or reads) obtained from the genome. High throughput sequencers could generate several billions of such short reads in a single run. However, the relative ordering of the reads along the target genome is not known a priori. This lack of information is one of the main contributors to the increased complexity of the assembly process. Typically, state-of-the-art approaches produce an ordering of the reads toward the end of the assembly process, making it rather too late to benefit from the ordering information. In this paper, with the dual objective of improving assembly quality as well as exposing a high degree of parallelism for assemblers, we present a partitioning-based approach. Our framework--which we call BOA (for bucket-order-assemble)--uses a bucketing alongside graph- and hypergraph-based partitioning techniques to produce a partial ordering of the reads. This partial ordering enables us to divide the read set into disjoint blocks that can be independently assembled in parallel using any state-of-the-art serial assembler of choice. We tested the BOA framework on a variety of genomes. Experimental results show that the hypergraph variant of our approach, Hyper-BOA, consistently improves both the overall assembly quality and performance. For the inputs tested, the Hyper-BOA framework consistently improves the N50 values of the popular standalone MEGAHIT assembler by an average of 1.70x and up to 2.13x; while the largest alignment length improves 1.47x on average and up to 1.94x. The time to solution also consistently improves between 3-4x for the system sizes tested.

genomics↗

bioRxiv2022

A hybrid model combining evolutionary probability and machine learning leverages data-driven protein engineering

Protein engineering through directed evolution and (semi-)rational approaches has been applied successfully to optimize protein properties for broad applications in molecular biology, biotechnology, and biomedicine. The potential of protein engineering is not yet fully realized due to the limited screening throughput hampering the efficient exploration of the vast protein sequence space. Data-driven strategies have emerged as a powerful tool to leverage protein engineering by providing a model of the sequence-fitness landscape that can exhaustively be explored in silico and capitalize on the high diversity potential offered by nature However, as both the quality and quantity of the inputted data determine the success of such approaches, the applicability of data-driven strategies is often limited due to sparse data. Here, we present a hybrid model that combines direct coupling analysis and machine learning techniques to enable data-driven protein engineering when only few labeled sequences are available. Our method achieves high performance in predicting a proteins fitness based on its sequence regardless of the number of sequences-fitness pairs in the training dataset. Besides reducing the computational effort compared to state-of-the-art methods, it outperforms them for sparse data situations, i.e., 50 - 250 labeled sequences available for training. In essence, the developed method is auspicious for data-driven protein engineering, especially for protein engineers who have only access to a limited amount of data for sequence-fitness landscape modeling.

synthetic biology↗

bioRxiv2022

TMbed - Transmembrane proteins predicted through Language Model embeddings

BackgroundDespite the immense importance of transmembrane proteins (TMP) for molecular biology and medicine, experimental 3D structures for TMPs remain about 4-5 times underrepresented compared to non-TMPs. Todays top methods such as AlphaFold2 accurately predict 3D structures for many TMPs, but annotating transmembrane regions remains a limiting step for proteome-wide predictions. ResultsHere, we present TMbed, a novel method inputting embeddings from protein Language Models (pLMs, here ProtT5), to predict for each residue one of four classes: transmembrane helix (TMH), transmembrane strand (TMB), signal peptide, or other. TMbed completes predictions for entire proteomes within hours on a single consumer-grade desktop machine at performance levels similar or better than methods, which are using evolutionary information from multiple sequence alignments (MSAs) of protein families. On the per-protein level, TMbed correctly identified 94{+/-}8% of the beta barrel TMPs (53 of 57) and 98{+/-}1% of the alpha helical TMPs (557 of 571) in a non-redundant data set, at false positive rates well below 1% (erred on 30 of 5654 non-membrane proteins). On the per-segment level, TMbed correctly placed, on average, 9 of 10 transmembrane segments within five residues of the experimental observation. Our method can handle sequences of up to 4200 residues on standard graphics cards used in desktop PCs (e.g., NVIDIA GeForce RTX 3060). ConclusionsBased on embeddings from pLMs and two novel filters (Gaussian and Viterbi), TMbed predicts alpha helical and beta barrel TMPs at least as accurately as any other method but at lower false positive rates. Given the few false positives and its outstanding speed, TMbed might be ideal to sieve through millions of 3D structures soon to be predicted, e.g., by AlphaFold2. AvailabilityOur code, method, and data sets are freely available in the GitHub repository, https://github.com/BernhoferM/TMbed.

bioinformatics↗

bioRxiv2022

The differential metabolic signature of breast cancer cellular response to olaparib treatment

Metabolic reprogramming and genomic instability are key hallmarks of cancer, the combined analysis of which has gained recent popularity. Given the emerging evidence indicating the role of oncometabolites in DNA damage repair and its routine use in breast cancer treatment, it is timely to fingerprint the impact of olaparib treatment in cellular metabolism. Here, we report the biomolecular response of breast cancer cell lines with DNA damage repair defects to olaparib exposure. Following evaluation of olaparib sensitivity in breast cancer cell lines, we immunoprobed DNA double strand break foci and evaluated changes in cellular metabolism at various olaparib treatment doses using untargeted mass spectrometry-based metabolomics analysis. Following identification of altered features, we performed pathway enrichment analysis to measure key metabolic changes occurring in response to olaparib treatment. We show a cell-line dependent response to olaparib exposure, and an increased susceptibility to DNA damage foci accumulation in triple-negative breast cancer cell lines. Metabolic changes in response to olaparib treatment were cell-line and dose-dependent, where we predominantly observed metabolic reprogramming of glutamine-derived amino acids and lipids metabolism. Our work demonstrates the effectiveness of combining molecular biology and metabolomics studies for the comprehensive characterisation of cell lines with different genetic profiles. Follow-on studies are needed to map the baseline metabolism of breast cancer cells and their unique response to drug treatment. Fused with genomic and transcriptomics data, such readout can be used to identify key oncometabolites and inform the rationale for the design of novel drugs or chemotherapy combinations.

cancer biology↗

bioRxiv2022

MALDI-TOF : A new tool for the identification of Schistosoma cercariae and detection of hybrids

Schistosomiasis is a neglected water-born parasitic disease caused by Schistosoma affecting more than 200 million people. Introgressive hybridization is common among these parasites and raises issues concerning their zoonotic transmission. Morphological identification of Schistosoma cercariae is difficult and does not permit hybrids detection. Our objective was to assess the performance of MALDI-TOF for the specific identification of cercariae in human and non-human Schistosoma and for the detection of hybridization between S. bovis and S. haematobium Spectra were collected from laboratory reared molluscs infested with strains of S. haematobium, S. mansoni, S. bovis, S. rodhaini and S. bovis x S. haematobium natural (Corsican hybrid) and artificial hybrids. Cluster analysis showed a clear separation between S. haematobium, S. bovis, S. mansoni and S. rodhaini. Corsican hybrids are classified with those of the parental strain of S. haematobium whereas other hybrids formed a distinct cluster. In blind test analysis the developed MALDI-TOF spectral database permits identification of Schistosoma cercariae with high accuracy (94%) and good specificity (S. bovis: 99.59%, S. haematobium 99.56%, S. mansoni and S. rodhaini: 100%). Most misidentifications were between S. haematobium and the Corsican hybrids. The use of machine learning permits to improve the discrimination between these last two taxa, with accuracy, F1 score and Sensitivity/Specificity > 97%. In multivariate analysis the factors associated with obtaining a valid identification score (> 1.7) were absence of ethanol preservation (p < 0.001) and a number of 2-3 cercariae deposited per well (p < 0.001). Also spectra acquired from S. mansoni cercariae are more likely to obtain a valid identification score than those acquired from S. haematobium (p<0.001). MALDI-TOF is a reliable technique for high-throughput identification of Schistosoma cercariae of medical and veterinary importance and could be useful for field survey in endemic areas. Author SummarySchistosomoses are neglected tropical diseases, affecting approximately 200 million people worldwide. They are transmitted during contact with water contaminated with the infesting stage of the parasite (the cercaria stage). Species-level recognition of cercariae present in water has important implications for field campaigns aimed at eradicating schistosomiasis. In addition, Schistosomes are able to hybridize between different species. Identification of Schistosomes cercariae on microscopy is difficult because of their similarity, and it does not allow hybrids to be distinguished. Molecular biology techniques allow a reliable diagnosis but are expensive. MALDI-TOF is a recent technique that permits an inexpensive identification of micro-organisms in a few minutes. In this paper, we evaluate MALDI-TOF identification of Schistosomes cercariae. We have implemented a database of MALDI-TOF cercariae spectra obtained from parental strains and hybrids of species of medical or veterinary interest, allowing reliable identification with an accuracy of 94%. The identification errors mainly come from confusion between the natural Corsican hybrid (S. haematobium x S. bovis) and S. haematobium. The use of machine learning algorithms permits to obtain an accuracy of more than 97% in the recognition of these two parasites. In conclusion, MALDI-TOF is a promising tool for the identification of Schistosome cercariae.

microbiology↗

bioRxiv2022

Laboratory strains of Escherichia coli K-12: not such perfect role models after all.

Escherichia coli K-12 was originally isolated 100 years ago and since then, it has become an invaluable model organism and a cornerstone of molecular biology research. However, despite its apparent pedigree, since its initial isolation, E. coli K-12 has been repeatedly cultured, passaged, and mutagenized, resulting in an organism that carries extensive genetic changes. To understand more about the evolution of this important model organism, we have sequenced the genomes of two ancestral K-12 strains, WG1 and EMG2, considered to be the progenitors of many key laboratory strains. Our analysis confirms that these strains still carry genetic elements such as bacteriophage lambda ({lambda}) and the F plasmid, but also indicates that they have undergone extensive lab-based evolution. Thus, scrutinizing the genomes of ancestral E. coli K-12 strains, leads us to question whether E. coli K-12 is a sufficiently robust model organism for 21st century microbiology. DATA SUMMARYAll supporting data are provided within the article or through supplementary data files. Supplementary Figs. S1 to S14 and Supplementary File S1 are available with the online version of this article. All genome sequence data has been deposited in NCBI GenBank under Bioproject ID PRJNA848777. The assembled and annotated genomes of WG1 and EMG2 have been deposited with the accession numbers, CP099590 and CP099591 (WG1) and CP099588 and CP099589 (EMG2). Impact StatementSince its isolation in 1922, Escherichia coli K-12, has become arguably the premier model organism for contemporary science. The adoption of E. coli K-12 by many microbiologists across the globe, means that it has a complex pedigree, and, although many E. coli K-12 strains have been sequenced, little is known about the early versions of K-12, which still carry the F plasmid and bacteriophage {lambda}. To understand more about the lab-based evolution that has shaped this important model organism, we have sequenced two ancestral K-12 strains, WG1 and EMG2, that are considered to be the progenitors of many of the laboratory strains used today.

genomics↗

bioRxiv2022

Co-transfer of functionally interdependent genes contributes to genome mosaicism in lambdoid phages

Lambdoid (or Lambda-like) phages, are a group of related temperate phages that can infect Escherichia coli and other gut bacteria. A key characteristic of these phages is their mosaic genome structure which served as basis for the "modular genome hypothesis". Accordingly, lambdoid phages evolve by transferring genomic regions, each of which constitutes a functional unit. Nevertheless, it is unknown which genes are preferentially transferred together and what drives such co-transfer events. Here we aim to characterize genome modularity by studying co-transfer of genes among 95 distantly related lambdoid (pro-)phages. Based on gene content, we observed that the genomes cluster into twelve groups, which are characterized by a highly similar gene content within the groups and highly divergent gene content across groups. Highly similar proteins can occur in genomes of different groups, indicating that they have been transferred. About 26% of homologous protein clusters in the four known operons (i.e., the early left, early right, immunity, and late operon) engage in gene transfer, which affects all operons to a similar extent. We identified pairs of genes that are frequently co-transferred and observed that these pairs tend to be in close proximity to one another on the genome. We find that frequently co-transferred genes are involved in related functions and highlight interesting examples involving structural proteins, the CI repressor and Cro regulator, proteins interacting with DNA, and membrane-interacting proteins. We conclude that epistatic effects, where the functioning of one protein depends on the presence of another, plays an important role in the evolution of the modular structure of these genomes. Data summaryThe genomes used in this research are publicly available (Table S1). All supporting data is available in supplementary tables. Source code and documentation to calculate GRR is available under GPLv2 (https://github.com/annecmg/GRRpair). Impact statementTemperate phages, viruses that can integrate their own genetic material into bacterial genomes, are pervasive mobile genetic elements that can influence bacterial fitness in manifold ways. The E. coli phage lambda has been a model phage of molecular biology for decades. Lambdoid phages are highly prevalent in Enterobacteria such as E. coli and Salmonella, have a mosaic-like genome, the same genome architecture as lambda, and can recombine with phage lambda. Nevertheless, these phages can be very distinct, and no lambdoid core genome exits. Although lambdoid phage genomes have been studied for decades, we know relatively little about how they evolve. Early observations led to the modular genome hypothesis, according to which, phages are assemblages of genetic modules. But what determines the structure of these modules and which genes do preferentially occur together in modules? In this study, we provide answers to these questions using a novel computational approach that allows to infer gene transfer events between distantly related phages despite the absence of a core genome. We find that co-transfer of functionally related genes is frequent during the evolution of lambdoid phages. This suggests epistatic interactions among these genes, i.e., the co-transferred genes likely need to function together to ensure a viable phage. A prime example is the co-transfer of structural genes, such as genes encoding for the phage capsid or the phage tail. Additionally, we also find co-transfer of known interacting regulatory genes and co-transfer between functionally related genes that have so far been unknown to interact. Together, our analysis provides novel insights into the evolution of temperate phages. Moreover, our approach, which allows to identify gene transfer in the absence of a core phylogeny might be valuable for studying the evolution of other fast-evolving genomes, including viruses of other hosts.

microbiology↗

bioRxiv2022

Mapping protein-DNA interactions with DiMeLo-seq

We recently developed Directed Methylation with Long-read sequencing (DiMeLo-seq) to map protein-DNA interactions genome wide. DiMeLo-seq is capable of mapping multiple interaction sites on single DNA molecules, profiling protein binding in the context of endogenous DNA methylation, identifying haplotype specific protein-DNA interactions, and mapping protein-DNA interactions in repetitive regions of the genome that are difficult to study with short-read methods. With DiMeLo-seq, adenines in the vicinity of a protein of interest are methylated in situ by tethering the Hia5 methyltransferase to an antibody using protein A. Protein-DNA interactions are then detected by direct readout of adenine methylation with long-read, single-molecule, DNA sequencing platforms such as Nanopore sequencing. Here, we present a detailed protocol and practical guidance for performing DiMeLo-seq. This protocol can be run on nuclei from fresh, lightly fixed, or frozen cells. The protocol requires 1-2 days for performing in situ targeted methylation, 1-5 days for library preparation depending on desired fragment length, and 1-3 days for Nanopore sequencing depending on desired sequencing depth. The protocol requires basic molecular biology skills and equipment, as well as access to a Nanopore sequencer. We also provide a Python package, dimelo, for analysis of DiMeLo-seq data. Key papersAltemose, N., Maslan, A., Smith, O.K., Sundararajan, K., Brown, R.R., Mishra, R., Detweiler, A.M., Neff, N., Miga, K.H., Straight, A.F. and Streets, A., 2022. DiMeLo-seq: a long-read, single-molecule method for mapping protein-DNA interactions genome wide. Nature Methods, pp.1-13. (https://doi.org/10.1038/s41592-022-01475-6)

genomics↗

bioRxiv2022

Cross-phyla protein annotation beyond sequence similarity through structural prediction and alignment

BackgroundAnnotating protein function is a major goal in molecular biology, yet experimentally determined knowledge is often limited to a few model organisms. In non-model species, the sequence-based prediction of gene orthology can be used to infer function, however this approach loses predictive power with longer evolutionary distances. Here we propose a pipeline for the functional annotation of proteins using structural similarity, exploiting the fact that protein structures are directly linked to function and can be more conserved than protein sequences. ResultsWe propose a pipeline of openly available tools for the functional annotation of proteins via structural similarity (MorF: MorphologFinder) and use it to annotate the complete proteome of a sponge. Sponges are highly relevant for inferring the early history of animals, yet their proteomes remain sparsely annotated. MorF accurately predicts the functions of proteins with known homology in >90% cases, and annotates an additional 50% of the proteome beyond standard sequence-based methods. Using this, we uncover new functions for sponge cell types, including extensive FGF, TGF and Ephrin signalling in sponge epithelia, and redox metabolism and control in myopeptidocytes. Notably, we also annotate genes specific to the enigmatic sponge mesocytes, proposing they function to digest cell walls. ConclusionsOur work demonstrates that structural similarity is a powerful approach that complements and extends sequence similarity searches to identify homologous proteins over long evolutionary distances. We anticipate this to be a powerful approach that boosts discovery in numerous -omics datasets, especially for non-model organisms.

evolutionary biology↗

bioRxiv2022

Cardiac electrophysiological remodeling associated with enhanced arrhythmia susceptibility in a canine model of elite exercise

The health benefits of regular physical exercise are well known. Even so, there is increasing evidence that the exercise regimes of elite athletes can evoke cardiac arrhythmias including ventricular fibrillation and even sudden cardiac death (SCD). The mechanism of exercise-induced arrhythmia and SCD is poorly understood. While some studies after endurance training have been performed in small animals these have limited translation value. Here, we show that chronic training in a canine model (12 sedentary and 12 trained dogs) that mimics the regime of elite athletes induces electrophysiological remodeling (measured by ECG, patch-clamp and immunocytochemical techniques) resulting in increases of both the trigger and the substrate for ventricular arrhythmias. Thus, 4 months sustained training lengthened ventricular repolarization (QTc: 213.6{+/-}2.8 ms vs. 237.1{+/-}3.4 ms, n=12; APD90: 370.1{+/-}32.7 ms vs. 472.8{+/-}29.6 ms, n=25 vs. 29), decreased transient outward potassium current (8.8{+/-}0.9 pA/pF vs. 6.4{+/-}0.5 pA/pF at 50 mV, n=42 vs. 54) and increased the short term variability of repolarization (17.5{+/-}4.0 ms vs. 29.5{+/-}3.8 ms, n=18 vs. 27). Left ventricular fibrosis and HCN4 protein expression were also enhanced. These changes were associated with enhanced ectopic activity (number of extrasystoles: 4/hour vs. 366/hour) in vivo and arrhythmia susceptibility (elicited ventricular fibrillation: 3 of 10 sedentary dogs vs. 6 of 10 trained dogs). Our findings provide in vivo, cellular electrophysiological and molecular biological evidence for the enhanced susceptibility to ventricular arrhythmia in an experimental large animal model of endurance training.

physiology↗

bioRxiv2022

TM-Vec: template modeling vectors for fast homology detection and alignment

Exploiting sequence-structure-function relationships in molecular biology and computational modeling relies on detecting proteins with high sequence similarities. However, the most commonly used sequence alignment-based methods, such as BLAST, frequently fail on proteins with low sequence similarity to previously annotated proteins. We developed a deep learning method, TM-Vec, that uses sequence alignments to learn structural features that can then be used to search for structure-structure similarities in large sequence databases. We train TM-Vec to accurately predict TM-scores as a metric of structural similarity for pairs of structures directly from sequence pairs without the need for intermediate computation or solution of structures. For remote homologs (sequence similarity [≤] 10%) that are highly structurally similar (TM-score ? 0.6), we predict TM-scores within 0.026 of their value computed by TM-align. TM-Vec outperforms traditional sequence alignment methods and performs similar to structure-based alignment methods. TM-Vec was trained on the CATH and SwissModel structural databases and it has been tested on carefully curated structure-structure alignment databases that were designed specifically to test very remote homology detection methods. It scales sub-linearly for search against large protein databases and is well suited for discovering remotely homologous proteins.

bioinformatics↗

bioRxiv2022

TraceTrack, an Open-Source Software for Batch Processing, Alignment and Visualization of Sanger Sequencing Chromatograms

BackgroundDespite the advent of Next Generation Sequencing technology and its widespread applications, Sanger sequencing remains instrumental for molecular biology subcloning work in biological and medical research and indispensable for drug discovery campaigns. Although Sanger sequencing technology has been long established, existing software for processing and visualization of trace file chromatograms are limited in terms of functionality, scalability, and availability for commercial use. ResultsTo fill this gap, we developed TraceTrack, an open-source web application tool for batch alignment, analysis and visualization of Sanger trace files. TraceTrack offers high throughput matching of trace files to reference sequences, rapid identification of mutations and an intuitive chromatogram analysis. Comparative analysis between TraceTrack and existing software tools highlights the advantages of TraceTrack with regards to batch processing, visualization and export functionalities. TraceTrack is available at https://github.com/Merck/TraceTrack and also at https://tracetrack.dichlab.org as a web application. ConclusionTraceTrack is a web application for batch processing and visualization of Sanger trace file chromatograms that meets the increasing demand of industrial sequence validation workflows in pharmaceutical settings.

bioinformatics↗

bioRxiv2022

LambdaPP: Fast and accessible protein-specific phenotype predictions

The availability of accurate and fast Artificial Intelligence (AI) solutions predicting aspects of proteins are revolutionizing experimental and computational molecular biology. The webserver LambdaPP aspires to supersede PredictProtein, the first internet server making AI protein predictions available in 1992. Given a protein sequence as input, LambdaPP provides easily accessible visualizations of protein 3D structure, along with predictions at the protein level (GeneOntology, subcellular location), and the residue level (binding to metal ions, small molecules, and nucleotides; conservation; intrinsic disorder; secondary structure; alpha-helical and beta-barrel transmembrane segments; signal-peptides; variant effect) in seconds. The structure prediction provided by LambdaPP - leveraging ColabFold and computed in minutes - is based on MMseqs2 multiple sequence alignments. All other feature prediction methods are based on the pLM ProtT5. Queried by a protein sequence, LambdaPP computes protein and residue predictions almost instantly for various phenotypes, including 3D structure and aspects of protein function. Accessibility StatementLambdaPP is freely available for everyone to use under embed.predictprotein.org, the interactive results for the case study can be found under https://embed.predictprotein.org/o/Q9NZC2. The frontend of LambdaPP can be found on GitHub (github.com/sacdallago/embed.predictprotein.org), and can be freely used and distributed under the academic free use license (AFL-2). For high-throughput applications, all methods can be executed locally via the bio-embeddings (bioembeddings.com) python package, or docker image at ghcr.io/bioembeddings/bio_embeddings, which also includes the backend of LambdaPP. Impact StatementWe introduce LambdaPP, a webserver integrating fast and accurate sequence-only protein feature predictions based on embeddings from protein Language Models (pLMs) available in seconds along with high-quality protein structure predictions. The intuitive interface invites experts and novices to benefit from the latest machine learning tools. LambdaPPs unique combination of predicted features may help in formulating hypotheses for experiments and as input to bioinformatics pipelines.

bioinformatics↗

bioRxiv2022

Race to survival during antibiotic breakdown determines the minimal surviving population size

A common strategy used by bacteria to resist antibiotics is enzymatic degradation or modification. Such a collective mechanism also enhances the survival of nearby cells, an effect that increases with the number of bacteria that are present. Collective resistance is of clinical significance, yet a quantitative understanding at the population level is lacking. Here we develop a general theoretical framework of collective resistance under antibiotic degradation. Our modeling reveals that population survival crucially depends on the ratio of timescales of two processes: the rates of population death and antibiotic removal. However, it is insensitive to molecular, biological and kinetic details of the underlying processes that give rise to these timescales. Another important aspect for this race to survival is the degree of cooperativity, which is related to the permeability of the cell wall for antibiotics and enzymes. These observations motivate a coarse-grained, phenomenological model and simple experimental assay to measure the dose-dependent minimal surviving population size. From this model, two dimensionless parameters can be estimated, representing the populations race to survival and single-cell resistance. Our simple model may serve as reference for more complex situations, such as heterogeneous bacterial communities.

microbiology↗

bioRxiv2022

Statistical analysis of number of genes and chromosome lengths of different microbial species

Genome architecture concerns the organisation of genes on a chromosome, and has important implications to the fidelity in which genes are encoded on the chromosome, and how the information is read by DNA polymerase and RNA polymerase. This facet of genomics did receive attention in the early epoch of genomics, but it has received less attention in contemporary genomics as attention shifts to structural and functional genomics with the goal of annotating the function of each gene in the genome. This work sought to uncover relationships between number of genes and chromosome length in a variety of bacteria and archaea species as a preamble to understanding the prevalence and importance of repetitive sequences in the genome of prokaryotic species. Aggregate results with the ensemble of prokaryotic species profiled revealed a positive linear correlation between number of genes and chromosome length. Upon dissection into the Bacteria and Archaea domains, the linear relationship described above still stands for Bacteria but starts to break down in Archaea. This suggests that repetitive sequences are more important to Archaea species, which generally have a smaller genome (1.8 to 2.8 Mbp) and fewer genes (1500 to 2400) compared to bacterial species. In comparison, the bacterial genome is larger (4 to 5.6 Mbp), and encodes more genes (3600 to 5100). Overall, the results highlight that bacterial genome are efficiently encoded with few repetitive sequences. This, however, is not true for archaeal genome, which provides another line of evidence supporting the notion that archaea are ancestral eukaryotic cells, which like the archaea also houses large repetitive sequences. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=141 SRC="FIGDIR/small/503871v1_ufig1.gif" ALT="Figure 1"> View larger version (15K): org.highwire.dtl.DTLVardef@1632506org.highwire.dtl.DTLVardef@13e91forg.highwire.dtl.DTLVardef@12e1316org.highwire.dtl.DTLVardef@1e7381e_HPS_FORMAT_FIGEXP M_FIG C_FIG Short descriptionStatistical analysis across an ensemble of 59 microbial species revealed a strong linear correlation between number of genes and chromosome length. This suggests that prokaryotic genomes are highly compact with genes, and do not carry significant amounts of repeats unlike the case in eukaryotic organisms. The result holds significant implications for our understanding of genome evolution and compaction in prokaryotic organisms, and what drove their accession as foundational species of many ecosystems. Subject areasgenomics, molecular biology, evolutionary biology, bioinformatics, systems biology,

genomics↗

bioRxiv2022

Alleviating cell lysate-induced inhibition to enable RT-PCR from single cells in picoliter-volume double emulsion droplets

Microfluidic droplet assays enable single-cell PCR and sequencing assays at unprecedented scale, with most methods encapsulating cells within nanoliter sized single emulsion droplets (water-in-oil). Encapsulating cells within picoliter double emulsion (DE) (water-in-oil-in-water) droplets allows sorting on commercially available FACS machines, making it possible to isolate single cells based on phenotypes of interest for downstream sequencing and PCR analyses. However, DE droplets must be within the range of large cells (<20 pl) for sorting on standard cytometers, posing challenges for molecular biology as prior reports suggest that reverse transcription (RT) and PCR amplification cannot proceed efficiently at volumes below 1 nL due to cell lysate-induced inhibition. To overcome this limitation, we used a plate-based RT-PCR assay designed to mimic reactions in picoliter droplets to systematically quantify and ameliorate the inhibition. We find that RT-PCR is blocked by lysate-induced cleavage of nucleic acid probes and primers, but that this cleavage can be efficiently alleviated through heat lysis that simultaneously inactivates inhibitory lysate components. We further show that the magnitude of RT-PCR inhibition depends strongly on cell type, but that RT-PCR can proceed in low-picoscale reaction volumes for most cell lines tested. Finally, we demonstrate one-step RT-PCR from single cells in 20 pL double emulsion droplets with fluorescence detectable via FACS. These results open up exciting new avenues for improving picoscale droplet RT-PCR reactions and expanding microfluidic droplet based single-cell analysis technologies.

bioengineering↗

bioRxiv2022

Genetic depletion of de novo coenzyme A biosynthesis exacerbates puromycin toxicity

Puromycin is an amino nucleoside that inhibits protein synthesis by interrupting elongation of nascent peptide chains. It is a commonly used selection antibiotic in molecular biology research via engineered expression of a puromycin resistance transgene. The enzyme puromycin acetyl transferase (pac) or PuroR inactivates puromycin by N-acetylating its reactive amino group. Puromycin acetylation by pac requires the central metabolite and acetyl group donor acetyl-CoA as a substrate. We found that puromycin treatment exacerbates sensitivity of cancer cells to knockdown of pantothenate kinases, the proteins that catalyze the rate-limiting step of de novo coenzyme A production in cells. Mechanistically, we found that ablation of PANKs together with puromycin depletes acetyl-CoA levels, in a manner modulated by the dose of puromycin. Our findings provide a note of caution and context in the use of puromycin for metabolism research in that interference with the major acyl donor used for inactivating biotransformation may exacerbate toxicity under selection. Broadly, our findings also invite studies to explore how targeting CoA and acetyl-CoA synthesis may be exploited to enhance cytotoxic effects of cancer drugs that undergo acetylation.

cancer biology↗