Targeting MYC with modular synthetic transcriptional repressors derived from bHLH DNA-binding domains
Not provided.
Biotechnology & Applied Microbiology↗
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Precise transcript targeting by CRISPR-Csm complexes
Robust and precise transcript targeting in mammalian cells remains a difficult challenge using existing approaches due to inefficiency, imprecision and subcellular compartmentalization. Here we show that the clustered regularly interspaced short palindromic repeats (CRISPR)-Csm complex, a multiprotein effector from type III CRISPR immune systems in prokaryotes, provides surgical RNA ablation of both nuclear and cytoplasmic transcripts. As part of the most widely occurring CRISPR adaptive immune pathway, CRISPR-Csm uses a programmable RNA-guided mechanism to find and degrade target RNA molecules without inducing indiscriminate trans-cleavage of cellular RNAs, giving it an important advantage over the CRISPR-Cas13 family of enzymes. Using single-vector delivery of the Streptococcus thermophilus Csm complex, we observe high-efficiency RNA knockdown (90–99%) and minimal off-target effects in human cells, outperforming existing technologies including short hairpin RNA- and Cas13-mediated knockdown. We also find that catalytically inactivated Csm achieves specific and durable RNA binding, a property we harness for live-cell RNA imaging. These results establish the feasibility and efficacy of multiprotein CRISPR-Cas effector complexes as RNA-targeting tools in eukaryotes.
59 BASIC BIOLOGICAL SCIENCES↗
Template-independent enzymatic synthesis of RNA oligonucleotides
Abstract RNA oligonucleotides have emerged as a powerful therapeutic modality to treat disease, yet current manufacturing methods may not be able to deliver on anticipated future demand. Here, we report the development and optimization of an aqueous-based, template-independent enzymatic RNA oligonucleotide synthesis platform as an alternative to traditional chemical methods. The enzymatic synthesis of RNA oligonucleotides is made possible by controlled incorporation of reversible terminator nucleotides with a common 3′-O-allyl ether blocking group using new CID1 poly(U) polymerase mutant variants. We achieved an average coupling efficiency of 95% and demonstrated ten full cycles of liquid phase synthesis to produce natural and therapeutically relevant modified sequences. We then qualitatively assessed the platform on a solid phase, performing enzymatic synthesis of severalN + 5 oligonucleotides on a controlled-pore glass support. Adoption of an aqueous-based process will offer key advantages including the reduction of solvent use and sustainable therapeutic oligonucleotide manufacturing.
Biotechnology & Applied Microbiology↗
Multistate and functional protein design using RoseTTAFold sequence space diffusion
Not provided.
Biotechnology & Applied Microbiology↗
Single-molecule live-cell RNA imaging with CRISPR–Csm
Abstract Understanding the diverse dynamic behaviors of individual RNA molecules in single cells requires visualizing them at high resolution in real time. However, single-molecule live-cell imaging of unmodified endogenous RNA has not yet been achieved in a generalizable manner. Here, we present single-molecule live-cell fluorescence in situ hybridization (smLiveFISH), a robust approach that combines the programmable RNA-guided, RNA-targeting CRISPR–Csm complex with multiplexed guide RNAs for direct and efficient visualization of single RNA molecules in a range of cell types, including primary cells. Using smLiveFISH, we track individual nativeNOTCH2andMAP1Btranscripts in living cells and identify two distinct localization mechanisms including the cotranslational translocation ofNOTCH2mRNA at the endoplasmic reticulum and directional transport ofMAP1BmRNA toward the cell periphery. This method has the potential to unlock principles governing the spatiotemporal organization of native transcripts in health and disease.
Biotechnology & Applied Microbiology↗
Severe, short-term sleep restriction reduces gut microbiota community richness but does not alter intestinal permeability in healthy young men
Sleep restriction alters gut microbiota composition and intestinal barrier function in rodents, but whether similar effects occur in humans is unclear. This study aimed to determine the effects of severe, short-term sleep restriction on gut microbiota composition and intestinal permeability in healthy adults. Fecal microbiota composition, measured by 16S rRNA sequencing, and intestinal permeability were measured in 19 healthy men (mean ± SD; BMI 24.4 ± 2.3 kg/m 2 , 20 ± 2 years) undergoing three consecutive nights of adequate sleep (AS; 7–9 h sleep/night) and restricted sleep (SR; 2 h sleep/night) in random order with controlled diet and physical activity. α-diversity measured by amplicon sequencing variant (ASV) richness was 21% lower during SR compared to AS (P = 0.03), but α-diversity measured by Shannon and Simpson indexes did not differ between conditions. Relative abundance of a single ASV within the family Ruminococcaceae was the only differentially abundant taxon (q = 0.20). No between-condition differences in intestinal permeability or β-diversity were observed. Findings indicated that severe, short-term sleep restriction reduced richness of the gut microbiota but otherwise minimally impacted community composition and did not affect intestinal permeability in healthy young men.
59 BASIC BIOLOGICAL SCIENCES↗
Photosynthetic semiconductor biohybrids for solar-driven biocatalysis
Photosynthetic semiconductor biohybrids integrate the best attributes of biological whole-cell catalysts and semiconducting nanomaterials. Enzymatic machinery enveloped in its native cellular environment offers exquisite product selectivity and low substrate activation barriers while semiconducting nanomaterials harvest light energy stably and efficiently. Here, we illustrate the evolution and advances of photosynthetic semiconductor biohybrids focusing on the conversion of CO 2 to value-added chemicals. We begin by considering the potential of this nascent field to meet global energy challenges while comparing it to alternate approaches. This is followed by a discussion of the advantageous coupling of electrotrophic organisms with light-active electrodes for solar-to-chemical conversion. We detail the dynamic investigation of photosensitized microorganisms creating direct light harvesting within unicellular organisms while describing complementary developments in the understanding of charge transfer mechanisms and cytoprotection. Lastly, we focus on trends and improvements needed in photosynthetic semiconductor biohybrids in order to address future challenges and enhance their widespread adoption for the production of solar chemicals.
59 BASIC BIOLOGICAL SCIENCES↗
n-Butanol production by Rhodopseudomonas palustris TIE-1
Anthropogenic carbon dioxide (CO 2 ) release in the atmosphere from fossil fuel combustion has inspired scientists to study CO 2 to biofuel conversion. Oxygenic phototrophs such as cyanobacteria have been used to produce biofuels using CO 2 . However, oxygen generation during oxygenic photosynthesis adversely affects biofuel production efficiency. To produce n-butanol (biofuel) from CO 2 , here we introduce an n-butanol biosynthesis pathway into an anoxygenic (non-oxygen evolving) photoautotroph, Rhodopseudomonas palustris TIE-1 (TIE-1). Using different carbon, nitrogen, and electron sources, we achieve n-butanol production in wild-type TIE-1 and mutants lacking electron-consuming (nitrogen-fixing) or acetyl-CoA-consuming (polyhydroxybutyrate and glycogen synthesis) pathways. The mutant lacking the nitrogen-fixing pathway produce the highest n-butanol. Coupled with novel hybrid bioelectrochemical platforms, this mutant produces n-butanol using CO 2 , solar panel-generated electricity, and light with high electrical energy conversion efficiency. Overall, this approach showcases TIE-1 as an attractive microbial chassis for carbon-neutral n-butanol bioproduction using sustainable, renewable, and abundant resources.
60 APPLIED LIFE SCIENCES↗
Engineering Pseudomonas putida KT2440 for chain length tailored free fatty acid and oleochemical production
Abstract Despite advances in understanding the metabolism of Pseudomonas putida KT2440, a promising bacterial host for producing valuable chemicals from plant-derived feedstocks, a strain capable of producing free fatty acid-derived chemicals has not been developed. Guided by functional genomics, we engineered P. putida to produce medium- and long-chain free fatty acids (FFAs) to titers of up to 670 mg/L. Additionally, by taking advantage of the varying substrate preferences of paralogous native fatty acyl-CoA ligases, we employed a strategy to control FFA chain length that resulted in a P. putida strain specialized in producing medium-chain FFAs. Finally, we demonstrate the production of oleochemicals in these strains by synthesizing medium-chain fatty acid methyl esters, compounds useful as biodiesel blending agents, in various media including sorghum hydrolysate at titers greater than 300 mg/L. This work paves the road to produce high-value oleochemicals and biofuels from cheap feedstocks, such as plant biomass, using this host.
59 BASIC BIOLOGICAL SCIENCES↗
MethCP: Differentially Methylated Region Detection with Change Point Models
Not provided.
Biochemistry & Molecular Biology↗
Asymmetric Cluster-Based Measures for Comparative Phylogenetics
Not provided.
Biochemistry & Molecular Biology↗
RCSB Protein Data Bank 1D tools and services
Abstract Motivation Interoperability between polymer sequences and structural data is essential for providing a complete picture of protein and gene features and helping to understand biomolecular function. Results Herein, we present two resources designed to improve interoperability between the RCSB Protein Data Bank, the NCBI and the UniProtKB data resources and visualize integrated data therefrom. The underlying tools provide a flexible means of mapping between the different coordinate spaces and an interactive tool allows convenient visualization of the 1-dimensional data over the web. Availabilityand implementation https://1d-coordinates.rcsb.org and https://rcsb.github.io/rcsb-saguaro. Supplementary information Supplementary data are available at Bioinformatics online.
59 BASIC BIOLOGICAL SCIENCES↗
Prediction of histone post-translational modifications using deep learning
Abstract Motivation Histone post-translational modifications (PTMs) are involved in a variety of essential regulatory processes in the cell, including transcription control. Recent studies have shown that histone PTMs can be accurately predicted from the knowledge of transcription factor binding or DNase hypersensitivity data. Similarly, it has been shown that one can predict PTMs from the underlying DNA primary sequence. Results In this study, we introduce a deep learning architecture called DeepPTM for predicting histone PTMs from transcription factor binding data and the primary DNA sequence. Extensive experimental results show that our deep learning model outperforms the prediction accuracy of the model proposed in Benveniste et al. (PNAS 2014) and DeepHistone (BMC Genomics 2019). The competitive advantage of our framework lies in the synergistic use of deep learning combined with an effective pre-processing step. Our classification framework has also enabled the discovery that the knowledge of a small subset of transcription factors (which are histone-PTM and cell-type-specific) can provide almost the same prediction accuracy that can be obtained using all the transcription factors data. Availabilityand implementation https://github.com/dDipankar/DeepPTM. Supplementary information Supplementary data are available at Bioinformatics online.
Baisya, Dipankar Ranjan (ORCID:0000000267847359)↗
GNET2: an R package for constructing gene regulatory networks from transcriptomic data
Abstract Motivation The Gene Network Estimation Tool (GNET) is designed to build gene regulatory networks (GRNs) from transcriptomic gene expression data with a probabilistic graphical model. The data preprocessing, model construction and visualization modules of the original GNET software were developed on different programming platforms, which were inconvenient for users to deploy and use. Results Here, we present GNET2, an improved implementation of GNET as an integrated R package. GNET2 provides more flexibility for parameter initialization and regulatory module construction based on the core iterative modeling process of the original algorithm. The data exchange interface of GNET2 is handled within an R session automatically. Given the growing demand for regulatory network reconstruction from transcriptomic data, GNET2 offers a convenient option for GRN inference on large datasets. Availability and implementation The source code of GNET2 is available at https://github.com/jianlin-cheng/GNET2. Supplementary information Supplementary data are available at Bioinformatics online.
59 BASIC BIOLOGICAL SCIENCES↗
Optimal Bayesian supervised domain adaptation for RNA sequencing data
Abstract Motivation When learning to subtype complex disease based on next-generation sequencing data, the amount of available data is often limited. Recent works have tried to leverage data from other domains to design better predictors in the target domain of interest with varying degrees of success. But they are either limited to the cases requiring the outcome label correspondence across domains or cannot leverage the label information at all. Moreover, the existing methods cannot usually benefit from other information available a priori such as gene interaction networks. Results In this article, we develop a generative optimal Bayesian supervised domain adaptation (OBSDA) model that can integrate RNA sequencing (RNA-Seq) data from different domains along with their labels for improving prediction accuracy in the target domain. Our model can be applied in cases where different domains share the same labels or have different ones. OBSDA is based on a hierarchical Bayesian negative binomial model with parameter factorization, for which the optimal predictor can be derived by marginalization of likelihood over the posterior of the parameters. We first provide an efficient Gibbs sampler for parameter inference in OBSDA. Then, we leverage the gene-gene network prior information and construct an informed and flexible variational family to infer the posterior distributions of model parameters. Comprehensive experiments on real-world RNA-Seq data demonstrate the superior performance of OBSDA, in terms of accuracy in identifying cancer subtypes by utilizing data from different domains. Moreover, we show that by taking advantage of the prior network information we can further improve the performance. Availability and implementation The source code for implementations of OBSDA and SI-OBSDA are available at the following link. https://github.com/SHBLK/BSDA. Supplementary information Supplementary data are available at Bioinformatics online.
Biochemistry & Molecular Biology↗
Quartet-based inference is statistically consistent under the unified duplication-loss-coalescence model
Abstract Motivation The classic multispecies coalescent (MSC) model provides the means for theoretical justification of incomplete lineage sorting-aware species tree inference methods. This has motivated an extensive body of work on phylogenetic methods that are statistically consistent under MSC. One such particularly popular method is ASTRAL, a quartet-based species tree inference method. Novel studies suggest that ASTRAL also performs well when given multi-locus gene trees in simulation studies. Further, Legried et al. recently demonstrated that ASTRAL is statistically consistent under the gene duplication and loss model (GDL). GDL is prevalent in evolutionary histories and is the first core process in the powerful duplication-loss-coalescence evolutionary model (DLCoal) by Rasmussen and Kellis. Results In this work, we prove that ASTRAL is statistically consistent under the general DLCoal model. Therefore, our result supports the empirical evidence from the simulation-based studies. More broadly, we prove that the quartet-based inference approach is statistically consistent under DLCoal. Supplementary information Supplementary data are available at Bioinformatics online.
59 BASIC BIOLOGICAL SCIENCES↗
efam: an e xpanded, metaproteome-supported HMM profile database of viral protein fam ilies
Viruses infect, reprogram and kill microbes, leading to profound ecosystem consequences, from elemental cycling in oceans and soils to microbiome-modulated diseases in plants and animals. Although metagenomic datasets are increasingly available, identifying viruses in them is challenging due to poor representation and annotation of viral sequences in databases. Here, we establish efam, an expanded collection of Hidden Markov Model (HMM) profiles that represent viral protein families conservatively identified from the Global Ocean Virome 2.0 dataset. This resulted in 240 311 HMM profiles, each with at least 2 protein sequences, making efam >7-fold larger than the next largest, pan-ecosystem viral HMM profile database. Adjusting the criteria for viral contig confidence from ‘conservative’ to ‘eXtremely Conservative’ resulted in 37 841 HMM profiles in our efam-XC database. To assess the value of this resource, we integrated efam-XC into VirSorter viral discovery software to discover viruses from less-studied, ecologically distinct oxygen minimum zone (OMZ) marine habitats. This expanded database led to an increase in viruses recovered from every tested OMZ virome by ~24% on average (up to ~42%) and especially improved the recovery of often-missed shorter contigs (<5 kb). Additionally, to help elucidate lesser-known viral protein functions, we annotated the profiles using multiple databases from the DRAM pipeline and virion-associated metaproteomic data, which doubled the number of annotations obtainable by standard, single-database annotation approaches. Together, these marine resources (efam and efam-XC) are provided as searchable, compressed HMM databases that will be updated bi-annually to help maximize viral sequence discovery and study from any ecosystem.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
EpitopeVec: linear epitope prediction using deep protein sequence embeddings
Motivation: B-cell epitopes (BCEs) play a pivotal role in the development of peptide vaccines, immuno-diagnostic reagents and antibody production, and thus in infectious disease prevention and diagnostics in general. Experimental methods used to determine BCEs are costly and time-consuming. Therefore, it is essential to develop computational methods for the rapid identification of BCEs. Although several computational methods have been developed for this task, generalizability is still a major concern, where cross-testing of the classifiers trained and tested on different datasets has revealed accuracies of 51–53%. Results: We describe a new method called EpitopeVec, which uses a combination of residue properties, modified antigenicity scales, and protein language model-based representations (protein vectors) as features of peptides for linear BCE predictions. Extensive benchmarking of EpitopeVec and other state-of-the-art methods for linear BCE prediction on several large and small datasets, as well as cross-testing, demonstrated an improvement in the performance of EpitopeVec over other methods in terms of accuracy and area under the curve. As the predictive performance depended on the species origin of the respective antigens (viral, bacterial and eukaryotic), we also trained our method on a large viral dataset to create a dedicated linear viral BCE predictor with improved cross-testing performance.
59 BASIC BIOLOGICAL SCIENCES↗