New investigation of encoding secondary metabolites gene by genome mining of a marine bacterium, Pseudoalteromonas viridis BBR56.

Pseudoalteromonas viridis strain BBR56 was isolated from seawater at Dutungan Island, South Sulawesi, Indonesia. Bacterial DNA was isolated using Promega Genomic DNA TM050. DNA purity and quantity were assessed using NanoDrop spectrophotometers and Qubit fluorometers. The DNA library and sequencing were prepared using Oxford Nanopore Technology GridION MinKNOW 20.06.9 with long read, direct, and comprehensive analysis. High accuracy base calling was assessed with Guppy version 4.0.11. Filtlong and NanoPlot were used for filtering and visualizing the FASTQ data. Flye (2.8.1) was used for de novo assembly analysis. Variant calls and consensus sequences were created using Medaka. The annotation of the genome was elaborated by DFAST. The assembled genome and annotation were tested using Busco and CheckM. Herein, we found that the highest similarity of the BBR56 isolate was 98.37% with the 16 S rRNA gene sequence of P. viridis G-1387. The genome size was 5.5 Mb and included chromosome 1 (4.2 Mbp) and chromosome 2 (1.3 Mbp), which encoded 61 pseudogenes, 4 noncoding RNAs, 113 tRNAs, 31 rRNAs, 4,505 coding DNA sequences, 4 clustered regularly interspaced short palindromic repeats, 4,444 coding genes, and a GC content of 49.5%. The sequence of the whole genome of P. viridis BBR56 was uploaded to GenBank under the accession numbers CP072425-CP072426, biosample number SAMN18435505, and bioproject number PRJNA716373. The sequence read archive (SRR14179986) was successfully obtained from NCBI for BBR56 raw sequencing reads. Digital DNA-DNA hybridization results showed that the genome of BBR56 had the potential to be a new species because no other bacterial genomes were similar to the sample. Biosynthetic gene clusters (BGCs) were assessed using BAGEL4 and the antiSMASH bacterial version. The genome harbored diverse BGCs, including genes that encoded polyketide synthase, nonribosomal peptide synthase, RiPP-like, NRP-metallophore, hydrogen cyanide, betalactone, thioamide-NRP, Lant class I, sactipeptide, and prodigiosin. Thus, BBR56 has considerable potential for further exploration regarding the use of its secondary metabolite products in the human and fisheries sectors.

One substrate many enzymes virtual screening uncovers missing genes of carnitine biosynthesis in human and mouse.

The increasing availability of experimental and computational protein structures entices their use for function prediction. Here we develop an automated procedure to identify enzymes involved in metabolic reactions by assessing substrate conformations docked to a library of protein structures. By screening AlphaFold-modeled vitamin B6-dependent enzymes, we find that a metric based on catalytically favorable conformations at the enzyme active site performs best (AUROC Score=0.84) in identifying genes associated with known reactions. Applying this procedure, we identify the mammalian gene encoding hydroxytrimethyllysine aldolase (HTMLA), the second enzyme of carnitine biosynthesis. Upon experimental validation, we find that the top-ranked candidates, serine hydroxymethyl transferase (SHMT) 1 and 2, catalyze the HTMLA reaction. However, a mouse protein absent in humans (threonine aldolase; Tha1) catalyzes the reaction more efficiently. Tha1 did not rank highest based on the AlphaFold model, but its rank improved to second place using the experimental crystal structure we determined at 2.26 Å resolution. Our findings suggest that humans have lost a gene involved in carnitine biosynthesis, with HTMLA activity of SHMT partially compensating for its function.

Comparative Analysis of Single-Cell RNA Sequencing Methods with and without Sample Multiplexing.

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful technique for investigating biological heterogeneity at the single-cell level in human systems and model organisms. Recent advances in scRNA-seq have enabled the pooling of cells from multiple samples into single libraries, thereby increasing sample throughput while reducing technical batch effects, library preparation time, and the overall cost. However, a comparative analysis of scRNA-seq methods with and without sample multiplexing is lacking. In this study, we benchmarked methods from two representative platforms: Parse Biosciences (Parse; with sample multiplexing) and 10x Genomics (10x; without sample multiplexing). By using peripheral blood mononuclear cells (PBMCs) obtained from two healthy individuals, we demonstrate that demultiplexed scRNA-seq data obtained from Parse showed similar cell type frequencies compared to 10x data where samples were not multiplexed. Despite relatively lower cell capture affecting library preparation, Parse can detect rare cell types (e.g., plasmablasts and dendritic cells) which is likely due to its relatively higher sensitivity in gene detection. Moreover, a comparative analysis of transcript quantification between the two platforms revealed platform-specific distributions of gene length and GC content. These results offer guidance for researchers in designing high-throughput scRNA-seq studies.

Automating the Illumina DNA library preparation kit for whole genome sequencing applications on the flowbot ONE liquid handler robot.

Whole-genome sequencing (WGS) is currently making its transition from research tool into routine (clinical) diagnostic practice. The workflow for WGS includes the highly labor-intensive library preparations (LP), one of the most critical steps in the WGS procedure. Here, we describe the automation of the LP on the flowbot ONE robot to minimize the risk of human error and reduce hands-on time (HOT). For this, the robot was equipped, programmed, and optimized to perform the Illumina DNA Prep automatically. Results obtained from 16 LP that were performed both manually and automatically showed comparable library DNA yields (median of 1.5-fold difference), similar assembly quality values, and 100% concordance on the final core genome multilocus sequence typing results. In addition, reproducibility of results was confirmed by re-processing eight of the 16 LPs using the automated workflow. With the automated workflow, the HOT was reduced to 25 min compared to the 125 min needed when performing eight LPs using the manual workflow. The turn-around time was 170 and 200 min for the automated and manual workflow, respectively. In summary, the automated workflow on the flowbot ONE generates consistent results in terms of reliability and reproducibility, while significantly reducing HOT as compared to manual LP.

High-throughput Selection of Human de novo-emerged sORFs with High Folding Potential.

De novo genes emerge from previously noncoding stretches of the genome. Their encoded de novo proteins are generally expected to be similar to random sequences and, accordingly, with no stable tertiary fold and high predicted disorder. However, structural properties of de novo proteins and whether they differ during the stages of emergence and fixation have not been studied in depth and rely heavily on predictions. Here we generated a library of short human putative de novo proteins of varying lengths and ages and sorted the candidates according to their structural compactness and disorder propensity. Using Förster resonance energy transfer combined with Fluorescence-activated cell sorting, we were able to screen the library for most compact protein structures, as well as most elongated and flexible structures. We find that compact de novo proteins are on average slightly shorter and contain lower predicted disorder than less compact ones. The predicted structures for most and least compact de novo proteins correspond to expectations in that they contain more secondary structure content or higher disorder content, respectively. Our experiments indicate that older de novo proteins have higher compactness and structural propensity compared with young ones. We discuss possible evolutionary scenarios and their implications underlying the age-dependencies of compactness and structural content of putative de novo proteins.

Immune checkpoint inhibitors in Cancer patients with rheumatologic preexisting autoimmune diseases: a systematic review and meta-analysis.

BACKGROUND: Patients with rheumatologic preexisting autoimmune disease (PAD) have not been enrolled in clinical trials of immune checkpoint inhibitors (ICIs). Therefore, the risks and benefits of ICI therapy in such patients are unclear. Herein, we investigated the safety and efficacy of ICIs in rheumatologic PAD patients through a meta-analysis. METHODS: The PubMed, Cochrane Library, Embase and Web of Science databases were searched for additional studies. We analyzed the following data through Stata software: incidence of total irAEs (TirAEs), rate of flares, incidence of new on-set irAEs, rate of discontinuation, objective response rate (ORR) and disease control rate (DCR). RESULTS: We identified 23 articles including 643 patients with rheumatologic PAD. The pooled incidences of TirAEs, flares and new-onset irAEs were 64% (95% CI 55%-72%), 41% (95% CI 31%-50%), and 33% (95% CI 28%-38%), respectively. In terms of severity, the incidences were 7% (95% CI 2%-14%) for Grade 3-4 flares and 12% (95% CI 9%-15%) for Grade 3-4 new-onset irAEs. Patients with RA had a greater risk of flares than patients with other rheumatologic PADs did (RR = 1.35, 95% CI 1.03-1.77). The ORR and DCR were 30% and 44%, respectively. Baseline anti-rheumatic treatment was not significantly associated with the frequency of flares (RR = 1.05, 95% CI 0.63-1.77) or the ORR (RR = 0.45, 95% CI 0.12-1.69). CONCLUSIONS: Patients with rheumatologic PAD, particularly those with RA, are susceptible to relapse of their rheumatologic disease following ICI therapy. ICIs are also effective for treating rheumatologic PAD patients. PROSPECTIVE REGISTER OF SYSTEMATIC REVIEWS (PROSPERO): number CRD 42,023,439,702.

TrieDedup: a fast trie-based deduplication algorithm to handle ambiguous bases in high-throughput sequencing.

BACKGROUND: High-throughput sequencing is a powerful tool that is extensively applied in biological studies. However, sequencers may produce low-quality bases, leading to ambiguous bases, 'N's. PCR duplicates introduced in library preparation are conventionally removed in genomics studies, and several deduplication tools have been developed for this purpose. Two identical reads may appear different due to ambiguous bases and the existing tools cannot address 'N's correctly or efficiently. RESULTS: Here we proposed and implemented TrieDedup, which uses the trie (prefix tree) data structure to compare and store sequences. TrieDedup can handle ambiguous base 'N's, and efficiently deduplicate at the level of raw sequences. We also reduced its memory usage by approximately 20% by implementing restrictedDict in Python. We benchmarked the performance of the algorithm and showed that TrieDedup can deduplicate reads up to 270-fold faster than pairwise comparison at a cost of 32-fold higher memory usage. CONCLUSIONS: The TrieDedup algorithm may facilitate PCR deduplication, barcode or UMI assignment, and repertoire diversity analysis of large-scale high-throughput sequencing datasets with its ultra-fast algorithm that can account for ambiguous bases due to sequencing errors.

Pacybara: accurate long-read sequencing for barcoded mutagenized allelic libraries.

MOTIVATION: Long-read sequencing technologies, an attractive solution for many applications, often suffer from higher error rates. Alignment of multiple reads can improve base-calling accuracy, but some applications, e.g. sequencing mutagenized libraries where multiple distinct clones differ by one or few variants, require the use of barcodes or unique molecular identifiers. Unfortunately, sequencing errors can interfere with correct barcode identification, and a given barcode sequence may be linked to multiple independent clones within a given library. RESULTS: Here we focus on the target application of sequencing mutagenized libraries in the context of multiplexed assays of variant effects (MAVEs). MAVEs are increasingly used to create comprehensive genotype-phenotype maps that can aid clinical variant interpretation. Many MAVE methods use long-read sequencing of barcoded mutant libraries for accurate association of barcode with genotype. Existing long-read sequencing pipelines do not account for inaccurate sequencing or nonunique barcodes. Here, we describe Pacybara, which handles these issues by clustering long reads based on the similarities of (error-prone) barcodes while also detecting barcodes that have been associated with multiple genotypes. Pacybara also detects recombinant (chimeric) clones and reduces false positive indel calls. In three example applications, we show that Pacybara identifies and correctly resolves these issues. AVAILABILITY AND IMPLEMENTATION: Pacybara, freely available at https://github.com/rothlab/pacybara, is implemented using R, Python, and bash for Linux. It runs on GNU/Linux HPC clusters via Slurm, PBS, or GridEngine schedulers. A single-machine simplex version is also available.

Medium-Sized Ring Expansion Strategies: Enhancing Small-Molecule Library Development.

The construction of a small molecule library that includes compounds with medium-sized rings is increasingly essential in drug discovery. These compounds are essential for identifying novel therapeutic agents capable of targeting "undruggable" targets through high-throughput and high-content screening, given their structural complexity and diversity. However, synthesizing medium-sized rings presents notable challenges, particularly with direct cyclization methods, due to issues such as transannular strain and reduced degrees of freedom. This review presents an overview of current strategies in synthesizing medium-sized rings, emphasizing innovative approaches like ring-expansion reactions. It highlights the challenges of synthesis and the potential of these compounds to diversify the chemical space for drug discovery, underscoring the importance of medium-sized rings in developing new bioactive compounds.

Dyadic care to improve postnatal outcomes of birthing people and their infants: A scoping review protocol.

INTRODUCTION: Dyadic care, which is the concurrent provision of care for a birthing person and their infant, is an approach that may improve disparities in postnatal health outcomes, but no synthesis of existing dyadic care studies has been conducted. This scoping review seeks to identify and summarize: 1) dyadic care studies globally, in which the birthing person-infant dyad are cared for together, 2) postnatal health outcomes that have been evaluated following dyadic care interventions, and 3) research and practice gaps in the implementation, dissemination, and effectiveness of dyadic care to reduce healthcare disparities. MATERIALS AND METHODS: Eligible studies will (1) include dyadic care instances for the birthing person and infant, and 2) report clinical outcomes for at least one member of the dyad or intervention outcomes. Studies will be excluded if they pertain to routine obstetric care, do not present original data, and/or are not available in English or Spanish. We will search CINAHL, Ovid (both Embase and Medline), Scopus, Cochrane Library, PubMed, Google Scholar, Global Health, Web of Science Core Collection, gray literature, and WHO regional databases. Screening will be conducted via Covidence and data will be extracted to capture the study design, dyad characteristics, clinical outcomes, and implementation outcomes. The risk of bias will be assessed using the Joanna Briggs Institute Critical Appraisal Tool. A narrative synthesis of the study findings will be presented. DISCUSSION: This scoping review will summarize birthing person-infant dyadic care interventions that have been studied and the evidence for their effectiveness. This aggregation of existing data can be used by healthcare systems working to improve healthcare delivery to their patients with the aim of reducing postnatal morbidity and mortality. Areas for future research will also be highlighted. TRAIL REGISTRATION: This review has been registered at Open Science Framework (OSF, https://osf.io/5fs6e/).

Application of an alchemical free energy method for the prediction of thermostable DuraPETase variants.

Non-equilibrium (NEQ) alchemical free energy calculations are an emerging tool for accurately predicting changes in protein folding free energy resulting from amino acid mutations. In this study, this method in combination with the Rosetta ddg monomer tool was applied to predict more thermostable variants of the polyethylene terephthalate (PET) degrading enzyme DuraPETase. The Rosetta ddg monomer tool efficiently enriched promising mutations prior to more accurate prediction by NEQ alchemical free energy calculations. The relative change in folding free energy of 96 single amino acid mutations was calculated by NEQ alchemical free energy calculation. Experimental validation of ten of the highest scoring variants identified two mutations (DuraPETaseS61M and DuraPETaseS223Y) that increased the melting temperature (Tm) of the enzyme by up to 1 °C. The calculated relative change in folding free energy showed an excellent correlation with experimentally determined Tm resulting in a Pearson's correlation coefficient of r = - 0.84. Limitations in the prediction of strongly stabilizing mutations were, however, encountered and are discussed. Despite these challenges, this study demonstrates the practical applicability of NEQ alchemical free energy calculations in prospective enzyme engineering projects. KEY POINTS: • Rosetta ddg monomer enriches stabilizing mutations in a library of DuraPETase variants • NEQ free energy calculations accurately predict changes in Tm of DuraPETase • The DuraPETase variants S223Y, S42M, and S61M have increased Tm.

Identifying the best PCR enzyme for library amplification in NGS.

Background. PCR amplification is a necessary step in many next-generation sequencing (NGS) library preparation methods [1, 2]. Whilst many PCR enzymes are developed to amplify single targets efficiently, accurately and with specificity, few are developed to meet the challenges imposed by NGS PCR, namely unbiased amplification of a wide range of different sizes and GC content. As a result PCR amplification during NGS library prep often results in bias toward GC neutral and smaller fragments. As NGS has matured, optimized NGS library prep kits and polymerase formulations have emerged and in this study we have tested a wide selection of available enzymes for both short-read Illumina library preparation and long fragment amplification ahead of long-read sequencing.We tested over 20 different hi-fidelity PCR enzymes/NGS amplification mixes on a range of Illumina library templates of varying GC content and composition, and find that both yield and genome coverage uniformity characteristics of the commercially available enzymes varied dramatically. Three enzymes Quantabio RepliQa Hifi Toughmix, Watchmaker Library Amplification Hot Start Master Mix (2X) 'Equinox' and Takara Ex Premier were found to give a consistent performance, over all genomes, that mirrored closely that observed for PCR-free datasets. We also test a range of enzymes for long-read sequencing by amplifying size fractionated S. cerevisiae DNA of average size 21.6 and 13.4 kb, respectively.The enzymes of choice for short-read (Illumina) library fragment amplification are Quantabio RepliQa Hifi Toughmix, Watchmaker Library Amplification Hot Start Master Mix (2X) 'Equinox' and Takara Ex Premier, with RepliQa also being the best performing enzyme from the enzymes tested for long fragment amplification prior to long-read sequencing.

Monocarboxylate transporter-1 (MCT-1) inhibitors screened from autodisplayed FV-antibody library.

Monocarboxylate transporter-1 (MCT-1) inhibitors were screened from the Fv-antibody library, which contained complementary determining region 3 with randomized amino acid sequences (11 residues) through site-directed mutagenesis. Fv-antibodies against MCT-1 were screened from the autodisplayed Fv-antibody library. Two clones were screened, and the binding affinity (KD) against MCT-1 was estimated using flow cytometry. The screened Fv-antibodies were expressed as soluble fusion proteins (Fv-1 and Fv-2) and the KD for MCT-1 was estimated using the SPR biosensor. The inhibitory activity of the expressed Fv-antibodies was observed in HEK293T and Jurkat cell lines by measuring intracellular pH and lactate accumulation. The level of cell viability in HEK293T and Jurkat cell lines was decreased by the inhibitory activity of the expressed Fv-antibodies. The binding properties of the Fv-antibodies to MCT-1 were analyzed using molecular docking simulations. Overall, the results showed that the screened Fv-antibodies against MCT-1 from the Fv-antibody library had high binding affinity and inhibitory activity against MCT-1, which could be used as potential therapeutic drug candidates for the MCT-1 inhibitor.

Bayesian inference of relative fitness on high-throughput pooled competition assays.

The tracking of lineage frequencies via DNA barcode sequencing enables the quantification of microbial fitness. However, experimental noise coming from biotic and abiotic sources complicates the computation of a reliable inference. We present a Bayesian pipeline to infer relative microbial fitness from high-throughput lineage tracking assays. Our model accounts for multiple sources of noise and propagates uncertainties throughout all parameters in a systematic way. Furthermore, using modern variational inference methods based on automatic differentiation, we are able to scale the inference to a large number of unique barcodes. We extend this core model to analyze multi-environment assays, replicate experiments, and barcodes linked to genotypes. On simulations, our method recovers known parameters within posterior credible intervals. This work provides a generalizable Bayesian framework to analyze lineage tracking experiments. The accompanying open-source software library enables the adoption of principled statistical methods in experimental evolution.

Cost-Efficient Transcriptomic-Based Drug Screening.

Transcriptomics allows to obtain comprehensive insights into cellular programs and their responses to perturbations. Despite a significant decrease in the costs of library production and sequencing in the last decade, applying these technologies at the scale necessary for drug screening remains prohibitively expensive, obstructing the immense potential of these methods. Our study presents a cost-effective system for transcriptome-based drug screening, combining miniaturized perturbation cultures with mini-bulk transcriptomics. The optimized mini-bulk protocol provides informative biological signals at cost-effective sequencing depth, enabling extensive screening of known drugs and new molecules. Depending on the chosen treatment and incubation time, this protocol will result in sequencing libraries within approximately 2 days. Due to several stopping points within this protocol, the library preparation, as well as the sequencing, can be performed time-independently. Processing simultaneously a high number of samples is possible; measurement of up to 384 samples was tested without loss of data quality. There are also no known limitations to the number of conditions and/or drugs, despite considering variability in optimal drug incubation times.

The Use of Nanopore Sequencing to Analyze the Chloroplast Transcriptome Part I: Library Preparation.

Global understanding of plastid gene expression has always been impaired by its complexity. RNA splicing, editing, and intercistronic processing create multiple transcripts isoforms that can hardly be resolved using traditional molecular biology techniques. During the last decade, the wide adoption of RNA-seq-based techniques has, however, allowed an unprecedented understanding of all the different steps of chloroplast gene expression, from transcription to translation. Current strategies are nonetheless unable to identify and quantify full length transcripts isoforms, a limitation that can now be overcome using Nanopore Sequencing. We here provide a complete protocol to produce, from total leaf RNA, cDNA libraries ready for Nanopore sequencing. While most Nanopore protocols take advantage of the mRNA polyA tail we here first ligate an RNA adapter to the 3' ends of the RNAs and use it to initiate the template switching reverse transcription. The cDNA is then prepared and indexed for use with the regular Oxford Nanopore v14 chemistry. This protocol is of particular interest to researchers willing to simultaneously study the multiple post-transcriptional processes prevalent in the chloroplast.

Machine learning to predict continuous protein properties from binary cell sorting data and map unseen sequence space.

Proteins are a diverse class of biomolecules responsible for wide-ranging cellular functions, from catalyzing reactions to recognizing pathogens. The ability to evolve proteins rapidly and inexpensively toward improved properties is a common objective for protein engineers. Powerful high-throughput methods like fluorescent activated cell sorting and next-generation sequencing have dramatically improved directed evolution experiments. However, it is unclear how to best leverage these data to characterize protein fitness landscapes more completely and identify lead candidates. In this work, we develop a simple yet powerful framework to improve protein optimization by predicting continuous protein properties from simple directed evolution experiments using interpretable, linear machine learning models. Importantly, we find that these models, which use data from simple but imprecise experimental estimates of protein fitness, have predictive capabilities that approach more precise but expensive data. Evaluated across five diverse protein engineering tasks, continuous properties are consistently predicted from readily available deep sequencing data, demonstrating that protein fitness space can be reasonably well modeled by linear relationships among sequence mutations. To prospectively test the utility of this approach, we generated a library of stapled peptides and applied the framework to predict affinity and specificity from simple cell sorting data. We then coupled integer linear programming, a method to optimize protein fitness from linear weights, with mutation scores from machine learning to identify variants in unseen sequence space that have improved and co-optimal properties. This approach represents a versatile tool for improved analysis and identification of protein variants across many domains of protein engineering.

Protocol to dissect and dissociate the mouse brainstem for single-cell RNA-seq applications.

Processing dissociated cells for transcriptomics is challenging when targeting small brain structures, like brainstem nuclei, where cell yield may be low. Here, we present a protocol for dissecting, dissociating, and cryopreserving mouse brainstem that allows asynchronous sample collection and downstream processing of cells obtained from brainstem tissue in neonatal mice. Although we demonstrate this protocol with the isolated preBötzinger complex and downstream SmartSeq3 cDNA library preparation, it could be readily adapted for other brainstem areas and library preparation approaches.

DANCE: a deep learning library and benchmark platform for single-cell analysis.

DANCE is the first standard, generic, and extensible benchmark platform for accessing and evaluating computational methods across the spectrum of benchmark datasets for numerous single-cell analysis tasks. Currently, DANCE supports 3 modules and 8 popular tasks with 32 state-of-art methods on 21 benchmark datasets. People can easily reproduce the results of supported algorithms across major benchmark datasets via minimal efforts, such as using only one command line. In addition, DANCE provides an ecosystem of deep learning architectures and tools for researchers to facilitate their own model development. DANCE is an open-source Python package that welcomes all kinds of contributions.

Recombinant Production and Characterization of VHHs/Nanobodies Targeting Tau to Block Fibrillar Assembly.

Tau protein was extensively studied using nuclear magnetic resonance spectroscopy, providing a powerful way to determine interaction sites between Tau and partner proteins. Here we used this analytical tool to describe the epitopes of Tau-specific VHHs (variable domain of the heavy chain of the heavy chain-only antibodies, aka nanobodies) selected from a synthetic library. An in vitro Tau aggregation assay was subsequently used as a functional screen to check VHH efficacy as aggregation inhibitors. We have observed a correlation between the targeted epitope and the aggregation-inhibition capacity of a series of Tau-specific VHHs.