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Endothelial cells have a crucial role in nervous system function, and mounting evidence points to endothelial impairment as a major contributor to a wide range of neurological diseases. However, tools to genetically interrogate these cells

Broadening gene therapy applications requires manufacturable vectors that efficiently transduce target cells in humans and preclinical models. Conventional selections of adeno-associated virus (AAV) capsid libraries are inefficient at searching the vast sequence space for the small fraction of vectors possessing multiple traits essential for clinical translation. Here, we present Fit4Function, a generalizable machine learning (ML) approach for systematically engineering multi-trait AAV capsids. By leveraging a capsid library that evenly samples the manufacturable sequence space, reproducible screening data are generated to train accurate sequence-to-function models. Combining six models, we designed a multi-trait (liver-targeted, manufacturable) capsid library and validated 89% of library variants on all six predetermined criteria. Furthermore, the models, trained only on mousein vivoand humanin vitroFit4Function data, accurately predicted AAV capsid variant biodistribution in macaque. Top candidates exhibited high production yields, efficient murine liver transduction, up to 1000-fold greater human hepatocyte transduction, and increased enrichment, relative to AAV9, in a screen for liver transduction in macaques. The Fit4Function strategy ultimately makes it possible to predict cross-species traits of peptide-modified AAV capsids and is a critical step toward assembling an ML atlas that predicts AAV capsid performance across dozens of traits.

Viruses have evolved the ability to bind and enter cells through interactions with a wide variety of host cell macromolecules. Here, we screened for AAV capsids that bind two host cell proteins expressed on the mouse blood-brain barrier, LY6A or the related protein LY6C1. Introducing interactions with either protein target generated hundreds of capsids with dramatically enhanced central nervous system (CNS) tropisms. In contrast to the AAV-PHP.B capsid family, which interacts with LY6A and only exhibits its enhanced CNS tropism in a subset of mouse strains, the capsids that engage LY6C1 maintain their CNS tropism in BALB/cJ mice. Compared to conventionalin vivoscreens for CNS cell transducing capsids, a single round of protein target binding screening recovered significantly more capsids with enhanced performance that were validated in subsequentin vivoscreens. Moreover, the initial screening round generated reproducible and quantitative target binding data that enabled the efficient machine learning-guided generation of more diverse targetspecific capsids. This work demonstrates that AAV capsids can be directly targeted to specific proteins to generate potent gene delivery vectors with known mechanisms of action and predictable tropisms.

Biases in data used to train machine learning (ML) models can inflate their prediction performance and confound our understanding of how and what they learn. Although biases are common in biological data, systematic auditing of ML models to identify and eliminate these biases is not a common practice when applying ML in the life sciences. Here we devise a systematic, principled, and general approach to audit ML models in the life sciences. We use this auditing framework to examine biases in three ML applications of therapeutic interest and identify unrecognized biases that hinder the ML process and result in substantially reduced model performance on new datasets. Ultimately, we show that ML models tend to learn primarily from data biases when there is insufficient signal in the data to learn from. We provide detailed protocols, guidelines, and examples of code to enable tailoring of the auditing framework to other biomedical applications., Fatma-Elzahraa Eid et al. illustrate a principled approach for identifying biases that can inflate the performance of biological machine learning models. When applied to three biomedical prediction problems, they identify previously unrecognized biases and ultimately show that models are likely to learn primarily from data biases when there is insufficient learnable signal in the data.

Compared with other SARS-related coronaviruses (SARSr-CoVs), SARS-CoV-2 possesses a unique furin cleavage site (FCS) in its spike. This has stimulated discussion pertaining to the origin of SARS-CoV-2 because the FCS has been observed to be under strong selective pressure in humans and confers the enhanced ability to infect some cell types and induce cell–cell fusion. Furthermore, scientists have demonstrated interest in studying novel cleavage sites by introducing them into SARSr-CoVs. We review what is known about the SARS-CoV-2 FCS in the context of its pathogenesis, origin, and how future wildlife coronavirus sampling may alter the interpretation of existing data.

COVID-19 CG (covidcg.org) is an open resource for tracking SARS-CoV-2 single-nucleotide variations (SNVs), lineages, and clades using the virus genomes on the GISAID database while filtering by location, date, gene, and mutation of interest. COVID-19 CG provides significant time, labor, and cost-saving utility to projects on SARS-CoV-2 transmission, evolution, diagnostics, therapeutics, vaccines, and intervention tracking. Here, we describe case studies in which users can interrogate (1) SNVs in the SARS-CoV-2 spike receptor binding domain (RBD) across different geographical regions to inform the design and testing of therapeutics, (2) SNVs that may impact the sensitivity of commonly used diagnostic primers, and (3) the emergence of a dominant lineage harboring an S477N RBD mutation in Australia in 2020. To accelerate COVID-19 efforts, COVID-19 CG will be upgraded with new features for users to rapidly pinpoint mutations as the virus evolves throughout the pandemic and in response to therapeutic and public health interventions.The discovery of faster spreading variants of the virus that causes coronavirus disease 2019 (COVID-19) has raised alarm. These new variants are the result of changes (called mutations) in the virus’ genetic code. Random mutations can occur each time a virus multiplies. Although most mutations do not introduce any meaningful changes, some can alter the characteristics of the virus, for instance, helping the virus to spread more easily, reinfecting people who have had COVID-19 before, or reducing the sensitivity to treatments or vaccines. Scientists need to know about mutations in the virus that make treatments or vaccines less effective as soon as possible, so they can adjust their pandemic response. As a result, tracking these genetic changes is essential. But individual scientists or public health agencies may not have the staff, time or computer resources to extract usable information from the growing amount of genetic data available. A free online tool created by Chen et al. may help scientists and public health officials to track changes to the virus more easily. The COVID-19 CoV Genetics tool (COVID-19 CG) can quickly provide information on which virus mutations are present in an area during a specific period. It does this by processing data on mutations found in viral genetic material collected worldwide from hundreds of thousands of people with COVID-19, which are hosted in an existing online database. The COVID-19 CG tool presents customizable, interactive visualizations of the data. Thousands of scientists, public health agencies, and COVID-19 vaccine and treatment developers in over 100 countries are already using the COVID-19 CG tool to find the most common mutations in their area and use it for research. They can use this information to develop more effective vaccines or treatments. Chen et al. plan to update and improve the tool as more information becomes available to help advance global efforts to end the COVID-19 pandemic.

Multiple publications have independently described pangolin CoV genomes from the same batch of smuggled pangolins confiscated in Guangdong province in March, 2019. We analyzed the three metagenomic datasets that sampled this batch of pangolins and found that the two complete pangolin CoV genomes, GD_1 by Xiao et al. Nature and MP789 by Liu et al. PLoS Pathogens, were both built primarily using the 2019 dataset first described by Liu et al. Viruses. Other publications, such as Zhang et al. Current Biology and Lam et al. Nature, have also relied on this same dataset by Liu et al. Viruses for their assembly of the Guangdong pangolin CoV sequences and comparisons to SARS-CoV-2. To our knowledge, all of the published pangolin CoV genome sequences that share a highly similar Spike receptor binding domain with SARS-CoV-2 originate from this singular batch of smuggled pangolins. This raises the question of whether pangolins are truly reservoirs or hosts of SARS-CoV-2-related coronaviruses in the wild, or whether the pangolins may have contracted the CoV from another host species during trafficking. Our observations highlight the importance of requiring authors to publish their complete genome assembly pipeline and all contributing raw sequence data, particularly those supporting epidemiological investigations, in order to empower peer review and independent analysis of the sequence data. This is necessary to ensure both the accuracy of the data and the conclusions presented by each publication.

Oxidative stress alters cell viability, from microorganism irradiation sensitivity to human aging and neurodegeneration. Deleterious effects of protein carbonylation by reactive oxygen species (ROS) make understanding molecular properties determining ROS susceptibility essential. The radiation‐resistant bacterium Deinococcus radiodurans accumulates less carbonylation than sensitive organisms, making it a key model for deciphering properties governing oxidative stress resistance. We integrated shotgun redox proteomics, structural systems biology, and machine learning to resolve properties determining protein damage by γ‐irradiation in Escherichia coli and D. radiodurans at multiple scales. Local accessibility, charge, and lysine enrichment accurately predict ROS susceptibility. Lysine, methionine, and cysteine usage also contribute to ROS resistance of the D. radiodurans proteome. Our model predicts proteome maintenance machinery, and proteins protecting against ROS are more resistant in D. radiodurans. Our findings substantiate that protein‐intrinsic protection impacts oxidative stress resistance, identifying causal molecular properties., An integrated approach combining redox proteomics, structural systems biology and machine learning resolves molecular properties underlying superior proteome protection in radiation‐resistant Deinococcus radiodurans.

Oxidative stress alters cell viability, from microorganism irradiation sensitivity to human aging and neurodegeneration. Deleterious effects of protein carbonylation by reactive oxygen species (ROS) make understanding molecular properties determining ROS-susceptibility essential. The radiation-resistant bacterium Deinococcus radiodurans accumulates less carbonylation than sensitive organisms, making it a key model for deciphering properties governing oxidative stress resistance. We integrated shotgun redox proteomics, structural systems biology, and machine learning to resolve properties determining protein damage by γ-irradiation in Escherichia coli and D. radiodurans at multiple scales. Local accessibility, charge, and lysine enrichment accurately predict ROS-susceptibility. Lysine, methionine, and cysteine usage also contribute to ROS-resistance of the D. radiodurans proteome. Our model predicts proteome maintenance machinery and proteins protecting against ROS are more resistant in D. radiodurans. Our findings substantiate that protein-intrinsic protection impacts oxidative stress resistance, identifying causal molecular properties.One Sentence SummaryProteins differ in intrinsic susceptibility to oxidation, a mode of evolutionary adaptation for stress tolerance in bacteria.

Ectopic R-loop accumulation causes DNA replication stress and genome instability. To avoid these outcomes, cells possess a range of anti-R-loop mechanisms, including RNaseH that degrades the RNA moiety in R-loops. To comprehensively identify anti-R-loop mechanisms, we performed a genome-wide trigenic interaction screen in yeast lacking RNH1 and RNH201. We identified >100 genes critical for fitness in the absence of RNaseH, which were enriched for DNA replication fork maintenance factors including the MRE11-RAD50-NBS1 (MRN) complex. While MRN has been shown to promote R-loops at DNA double-strand breaks, we show that it suppresses R-loops and associated DNA damage at transcription–replication conflicts. This occurs through a non-nucleolytic function of MRE11 that is important for R-loop suppression by the Fanconi Anemia pathway. This work establishes a novel role for MRE11-RAD50-NBS1 in directing tolerance mechanisms at transcription–replication conflicts., Accumulations of R-loops can lead to genome instability. Here the authors reveal a role for the MRN complex in suppressing R-loops and associated DNA damage at transcription–replication conflicts.

SUMMARYEctopic R-loop accumulation causes DNA replication stress and genome instability. To avoid these outcomes, cells possess a range of anti-R-loop mechanisms, including RNaseH that degrades the RNA moiety in R-loops. To comprehensively identify anti-R-loop mechanisms, we performed a genome-wide trigenic interaction screen in yeast lacking RNH1 and RNH201. We identified >100 genes critical for fitness in the absence of RNaseH, which were enriched for DNA replication fork maintenance factors such as RAD50. We show in yeast and human cells that R-loops accumulate during RAD50 depletion. In human cancer cell models, we find that RAD50 and its partners in the MRE11-RAD50-NBS1 complex regulate R-loop-associated DNA damage and replication stress. We show that a non-nucleolytic function of MRE11 is important for R-loop suppression via activation of PCNA-ubiquitination by RAD18 and recruiting anti-R-loop helicases in the Fanconi Anemia pathway. This work establishes a novel role for MRE11-RAD50-NBS1 in directing tolerance mechanisms of transcription-replication conflicts.

The ability to rewrite large stretches of genomic DNA enables the creation of new organisms with customized functions. However, few methods currently exist for accumulating such widespread genomic changes in a single organism. In this study, we demonstrate a rapid approach for rewriting bacterial genomes with modified synthetic DNA. We recode 200 kb of the Salmonella typhimurium LT2 genome through a process we term SIRCAS (stepwise integration of rolling circle amplified segments), towards constructing an attenuated and genetically isolated bacterial chassis. The SIRCAS process involves direct iterative recombineering of 10–25 kb synthetic DNA constructs which are assembled in yeast and amplified by rolling circle amplification. Using SIRCAS, we create a Salmonella with 1557 synonymous leucine codon replacements across 176 genes, the largest number of cumulative recoding changes in a single bacterial strain to date. We demonstrate reproducibility over sixteen two-day cycles of integration and parallelization for hierarchical construction of a synthetic genome by conjugation. The resulting recoded strain grows at a similar rate to the wild-type strain and does not exhibit any major growth defects. This work is the first instance of synthetic bacterial recoding beyond the Escherichia coli genome, and reveals that Salmonella is remarkably amenable to genome-scale modification.

Genome recoding will provide a deeper understanding of genetics and transform biotechnology. We bypass the reliance of previous genome recoding methods on site-specific enzymes and demonstrate a rapid recombineering based strategy for writing genomes by Stepwise Integration of Rolling Circle Amplified Segments (SIRCAS). We installed the largest number of codon substitutions in a single organism yet published, creating a strain of Salmonella typhimurium with 1557 leucine codon changes across 200 kb of the genome.

The engineered AAV-PHP.B family of adeno-associated virus efficiently delivers genes throughout the mouse central nervous system. To guide their application across disease models, and to inspire the development of translational gene therapy vectors for targeting neurological diseases in humans, we sought to elucidate the host factors responsible for the CNS tropism of the AAV-PHP.B vectors. Leveraging CNS tropism differences across 13 mouse strains, we systematically determined a set of genetic variants that segregate with the permissivity phenotype, and rapidly identified LY6A as an essential receptor for the AAV-PHP.B vectors. Interfering with LY6A by CRISPR/Cas9-mediated Ly6a disruption or with blocking antibodies reduced transduction of mouse brain endothelial cells by AAV-PHP.eB, while ectopic expression of Ly6a increased AAV-PHP.eB transduction of HEK293T and CHO cells by 30-fold or more. Importantly, we demonstrate that this newly discovered mode of AAV binding and transduction can occur independently of other known AAV receptors. These findings illuminate the previously reported species- and strain-specific tropism characteristics of the AAV-PHP.B vectors and inform ongoing efforts to develop next-generation AAV vehicles for human CNS gene therapy.

DNA:RNA hybrid formation is emerging as a significant cause of genome instability in biological systems ranging from bacteria to mammals. Here we describe the genome-wide distribution of DNA:RNA hybrid prone loci in Saccharomyces cerevisiae by DNA:RNA immunoprecipitation (DRIP) followed by hybridization on tiling microarray. These profiles show that DNA:RNA hybrids preferentially accumulated at rDNA, Ty1 and Ty2 transposons, telomeric repeat regions and a subset of open reading frames (ORFs). The latter are generally highly transcribed and have high GC content. Interestingly, significant DNA:RNA hybrid enrichment was also detected at genes associated with antisense transcripts. The expression of antisense-associated genes was also significantly altered upon overexpression of RNase H, which degrades the RNA in hybrids. Finally, we uncover mutant-specific differences in the DRIP profiles of a Sen1 helicase mutant, RNase H deletion mutant and Hpr1 THO complex mutant compared to wild type, suggesting different roles for these proteins in DNA:RNA hybrid biology. Our profiles of DNA:RNA hybrid prone loci provide a resource for understanding the properties of hybrid-forming regions in vivo, extend our knowledge of hybrid-mitigating enzymes, and contribute to models of antisense-mediated gene regulation. A summary of this paper was presented at the 26th International Conference on Yeast Genetics and Molecular Biology, August 2013., Author Summary RNA processing factors are mutated in human cancers, inherited developmental disorders and neurodegenerative syndromes. Defects in RNA processing have been associated with increased levels of mutations and DNA damage in part via the formation of DNA:RNA hybrids. Although it is likely that specific regions of the genome are more prone to DNA:RNA hybrid formation, a map of hybrid-prone regions is not available. In this study, we describe the genome-wide distribution of DNA:RNA hybrids in both normal and mutant Saccharomyces cerevisiae cells. The resulting profiles contribute to both our understanding of the general properties of hybrid-forming loci and to our knowledge of hybrid-mitigating enzymes. Interestingly, significant DNA:RNA hybrid enrichment was detected at genes associated with antisense transcription. We show that overexpression of RNase H, which degrades the RNA in hybrids, significantly affects the expression of genes associated with antisense transcripts. These findings support a role for DNA:RNA hybrids in regulation of gene expression by antisense transcripts.

The role of normal transcription and RNA processing in maintaining genome integrity is becoming increasingly appreciated in organisms ranging from bacteria to humans. Several mutations in RNA biogenesis factors have been implicated in human cancers, but the mechanisms and potential connections to tumor genome instability are not clear. Here, we discuss how RNA-processing defects could destabilize genomes through mutagenic R-loop structures and by altering expression of genes required for genome stability. A compelling body of evidence now suggests that researchers should be directly testing these mechanisms in models of human cancer.

The delivery of large DNA vectors (>100 000 bp) remains a limiting step in the engineering of mammalian cells and the development of human artificial chromosomes (HACs). Yeast is commonly used to assemble genetic constructs in the megabase size range, and has previously been used to transfer constructs directly into cultured cells. We improved this method to efficiently deliver large (1.1 Mb) synthetic yeast centromeric plasmids (YCps) to cultured cell lines at rates similar to that of 12 kb YCps. Synchronizing cells in mitosis improved the delivery efficiency by 10-fold and a statistical design of experiments approach was employed to boost the vector delivery rate by nearly 300-fold from 1/250 000 to 1/840 cells, and subsequently optimize the delivery process for multiple mammalian, avian, and insect cell lines. We adapted this method to rapidly deliver a 152 kb herpes simplex virus 1 genome cloned in yeast into mammalian cells to produce infectious virus.

SummaryRNA polymerase II (RNAPII) lies at the core of dynamic control of gene expression. Using 53 RNAPII point mutants, we generated a point mutant epistatic miniarray profile (pE-MAP) comprising ∼60,000 quantitative genetic interactions in Saccharomyces cerevisiae. This analysis enabled functional assignment of RNAPII subdomains and uncovered connections between individual regions and other protein complexes. Using splicing microarrays and mutants that alter elongation rates in vitro, we found an inverse relationship between RNAPII speed and in vivo splicing efficiency. Furthermore, the pE-MAP classified fast and slow mutants that favor upstream and downstream start site selection, respectively. The striking coordination of polymerization rate with transcription initiation and splicing suggests that transcription rate is tuned to regulate multiple gene expression steps. The pE-MAP approach provides a powerful strategy to understand other multifunctional machines at amino acid resolution.

Genome instability via RNA:DNA hybrid-mediated R loops has been observed in mutants involved in various aspects of transcription and RNA processing. The prevalence of this mechanism among essential chromosome instability (CIN) genes remains unclear. In a secondary screen for increased Rad52 foci in CIN mutants, representing ∼25% of essential genes, we identified seven essential subunits of the mRNA cleavage and polyadenylation (mCP) machinery. Genome-wide analysis of fragile sites by chromatin immunoprecipitation (ChIP) and microarray (ChIP–chip) of phosphorylated H2A in these mutants supported a transcription-dependent mechanism of DNA damage characteristic of R loops. In parallel, we directly detected increased RNA:DNA hybrid formation in mCP mutants and demonstrated that CIN is suppressed by expression of the R-loop-degrading enzyme RNaseH. To investigate the conservation of CIN in mCP mutants, we focused on FIP1L1, the human ortholog of yeast FIP1, a conserved mCP component that is part of an oncogenic fusion in eosinophilic leukemia. We found that truncation fusions of yeast FIP1 analogous to those in cancer cause loss of function and that siRNA knockdown of FIP1L1 in human cells increases DNA damage and chromosome breakage. Our findings illuminate how mCP maintains genome integrity by suppressing R-loop formation and suggest that this function may be relevant to certain human cancers.