CRISPR-mediated germline mutagenesis for genetic sterilization of Anopheles gambiae males.

Rapid spread of insecticide resistance among anopheline mosquitoes threatens malaria elimination efforts, necessitating development of alternative vector control technologies. Sterile insect technique (SIT) has been successfully implemented in multiple insect pests to suppress field populations by the release of large numbers of sterile males, yet it has proven difficult to adapt to Anopheles vectors. Here we outline adaptation of a CRISPR-based genetic sterilization system to selectively ablate male sperm cells in the malaria mosquito Anopheles gambiae. We achieve robust mosaic biallelic mutagenesis of zero population growth (zpg, a gene essential for differentiation of germ cells) in F1 individuals after intercrossing a germline-expressing Cas9 transgenic line to a line expressing zpg-targeting gRNAs. Approximately 95% of mutagenized males display complete genetic sterilization, and cause similarly high levels of infertility in their female mates. Using a fluorescence reporter that allows detection of the germline leads to a 100% accurate selection of spermless males, improving the system. These males cause a striking reduction in mosquito population size when released at field-like frequencies in competition cages against wild type males. These findings demonstrate that such a genetic system could be adopted for SIT against important malaria vectors.

CRISPR-mediated germline mutagenesis for genetic sterilization of Anopheles gambiae males.

Rapid spread of insecticide resistance among anopheline mosquitoes threatens malaria elimination efforts, necessitating development of alternative vector control technologies. Sterile Insect Technique (SIT) has been successfully implemented in multiple insect pests to suppress field populations by the release of large numbers of sterile males, yet it has proven difficult to adapt to Anopheles vectors. Here we outline adaptation of a CRISPR-based genetic sterilization system to selectively ablate male sperm cells in the malaria mosquito Anopheles gambiae. We achieve robust mosaic biallelic mutagenesis of zero population growth (zpg, a gene essential for differentiation of germ cells) in F1 individuals after intercrossing a germline-expressing Cas9 transgenic line to a line expressing zpg-targeting gRNAs. Approximately 95% of mutagenized males display complete genetic sterilization, and cause similarly high levels of infertility in their female mates. Using a fluorescence reporter that allows detection of the germline leads to a 100% accurate selection of spermless males, improving the system. These males cause a striking reduction in mosquito population size when released at field-like frequencies in competition cages against wild type males. These findings demonstrate that such a genetic system could be adopted for SIT against important malaria vectors.

PyLabRobot: An Open-Source, Hardware Agnostic Interface for Liquid-Handling Robots and Accessories.

Liquid handling robots are often limited by proprietary programming interfaces that are only compatible with a single type of robot and operating system, restricting method sharing and slowing development. Here we present PyLabRobot, an open-source, cross-platform Python interface capable of programming diverse liquid-handling robots, including Hamilton STARs, Tecan EVOs, and Opentron OT-2s. PyLabRobot provides a universal set of commands and representations for deck layout and labware, enabling the control of diverse accessory devices. The interface is extensible and can work with any robot that manipulates liquids within a Cartesian coordinate system. We validated the system through unit tests and several application demonstrations, including a browser-based simulator, a position calibration tool, and a path-teaching tool for complex movements. PyLabRobot provides a flexible, open, and collaborative programming environment for laboratory automation.

Insidious Insights: Implications of viral vector engineering for pathogen enhancement.

Optimizing viral vectors and their properties will be important for improving the effectiveness and safety of clinical gene therapy. However, such research may generate dual-use insights relevant to the enhancement of pandemic pathogens. In particular, reliable and generalizable methods of immune evasion could increase viral fitness sufficient to cause a new pandemic. High potential for misuse is associated with (1) the development of universal genetic elements for immune modulation, (2) specific insights on capsid engineering for antibody evasion applicable to viruses with pandemic potential, and (3) the development of computational methods to inform capsid engineering. These risks may be mitigated by prioritizing non-viral delivery systems, pharmacological immune modulation methods, non-genetic vector surface modifications, and engineering methods specific to AAV and other viruses incapable of unassisted human-to-human transmission. We recommend that computational vector engineering and the publication of associated code and data be limited to AAV until a technical solution for preventing malicious access to viral engineering tools has been established.

Analysis of the first genetic engineering attribution challenge.

The ability to identify the designer of engineered biological sequences-termed genetic engineering attribution (GEA)-would help ensure due credit for biotechnological innovation, while holding designers accountable to the communities they affect. Here, we present the results of the first Genetic Engineering Attribution Challenge, a public data-science competition to advance GEA techniques. Top-scoring teams dramatically outperformed previous models at identifying the true lab-of-origin of engineered plasmid sequences, including an increase in top-1 and top-10 accuracy of 10 percentage points. A simple ensemble of prizewinning models further increased performance. New metrics, designed to assess a model's ability to confidently exclude candidate labs, also showed major improvements, especially for the ensemble. Most winning teams adopted CNN-based machine-learning approaches; however, one team achieved very high accuracy with an extremely fast neural-network-free approach. Future work, including future competitions, should further explore a wide diversity of approaches for bringing GEA technology into practical use.

Direct and indirect impacts of synthetic biology on biodiversity conservation.

The world's biodiversity is in crisis. Synthetic biology has the potential to transform biodiversity conservation, both directly and indirectly, in ways that are negative and positive. However, applying these biotechnology tools to environmental questions is fraught with uncertainty and could harm cultures, rights, livelihoods, and nature. Decisions about whether or not to use synthetic biology for conservation should be understood alongside the reality of ongoing biodiversity loss. In 2022, the 196 Parties to the United Nations Convention on Biological Diversity are negotiating the post-2020 Global Biodiversity Framework that will guide action by governments and other stakeholders for the next decade to conserve the worlds' biodiversity. To date, synthetic biologists, conservationists, and policy makers have operated in isolation. At this critical time, this review brings these diverse perspectives together and emerges out of the need for a balanced and inclusive examination of the potential application of these technologies to biodiversity conservation.

Measuring the tolerance of the genetic code to altered codon size.

Translation using four-base codons occurs in both natural and synthetic systems. What constraints contributed to the universal adoption of a triplet codon, rather than quadruplet codon, genetic code? Here, we investigate the tolerance of the Escherichia coli genetic code to tRNA mutations that increase codon size. We found that tRNAs from all 20 canonical isoacceptor classes can be converted to functional quadruplet tRNAs (qtRNAs). Many of these selectively incorporate a single amino acid in response to a specified four-base codon, as confirmed with mass spectrometry. However, efficient quadruplet codon translation often requires multiple tRNA mutations. Moreover, while tRNAs were largely amenable to quadruplet conversion, only nine of the twenty aminoacyl tRNA synthetases tolerate quadruplet anticodons. These may constitute a functional and mutually orthogonal set, but one that sharply limits the chemical alphabet available to a nascent all-quadruplet code. Our results suggest that the triplet codon code was selected because it is simpler and sufficient, not because a quadruplet codon code is unachievable. These data provide a blueprint for synthetic biologists to deliberately engineer an all-quadruplet expanded genetic code.

Systematic molecular evolution enables robust biomolecule discovery.

Evolution occurs when selective pressures from the environment shape inherited variation over time. Within the laboratory, evolution is commonly used to engineer proteins and RNA, but experimental constraints have limited the ability to reproducibly and reliably explore factors such as population diversity, the timing of environmental changes and chance on outcomes. We developed a robotic system termed phage- and robotics-assisted near-continuous evolution (PRANCE) to comprehensively explore biomolecular evolution by performing phage-assisted continuous evolution in high-throughput. PRANCE implements an automated feedback control system that adjusts the stringency of selection in response to real-time measurements of each molecular activity. In evolving three distinct types of biomolecule, we find that evolution is reproducibly altered by both random chance and the historical pattern of environmental changes. This work improves the reliability of protein engineering and enables the systematic analysis of the historical, environmental and random factors governing biomolecular evolution.

Low-N protein engineering with data-efficient deep learning.

Protein engineering has enormous academic and industrial potential. However, it is limited by the lack of experimental assays that are consistent with the design goal and sufficiently high throughput to find rare, enhanced variants. Here we introduce a machine learning-guided paradigm that can use as few as 24 functionally assayed mutant sequences to build an accurate virtual fitness landscape and screen ten million sequences via in silico directed evolution. As demonstrated in two dissimilar proteins, GFP from Aequorea victoria (avGFP) and E. coli strain TEM-1 ß-lactamase, top candidates from a single round are diverse and as active as engineered mutants obtained from previous high-throughput efforts. By distilling information from natural protein sequence landscapes, our model learns a latent representation of 'unnaturalness', which helps to guide search away from nonfunctional sequence neighborhoods. Subsequent low-N supervision then identifies improvements to the activity of interest. In sum, our approach enables efficient use of resource-intensive high-fidelity assays without sacrificing throughput, and helps to accelerate engineered proteins into the fermenter, field and clinic.

Enabling high-throughput biology with flexible open-source automation.

Our understanding of complex living systems is limited by our capacity to perform experiments in high throughput. While robotic systems have automated many traditional hand-pipetting protocols, software limitations have precluded more advanced maneuvers required to manipulate, maintain, and monitor hundreds of experiments in parallel. Here, we present Pyhamilton, an open-source Python platform that can execute complex pipetting patterns required for custom high-throughput experiments such as the simulation of metapopulation dynamics. With an integrated plate reader, we maintain nearly 500 remotely monitored bacterial cultures in log-phase growth for days without user intervention by taking regular density measurements to adjust the robotic method in real-time. Using these capabilities, we systematically optimize bioreactor protein production by monitoring the fluorescent protein expression and growth rates of a hundred different continuous culture conditions in triplicate to comprehensively sample the carbon, nitrogen, and phosphorus fitness landscape. Our results demonstrate that flexible software can empower existing hardware to enable new types and scales of experiments, empowering areas from biomanufacturing to fundamental biology.

Bidirectional contact tracing could dramatically improve COVID-19 control.

Contact tracing is critical to controlling COVID-19, but most protocols only "forward-trace" to notify people who were recently exposed. Using a stochastic branching-process model, we find that "bidirectional" tracing to identify infector individuals and their other infectees robustly improves outbreak control. In our model, bidirectional tracing more than doubles the reduction in effective reproduction number (Reff) achieved by forward-tracing alone, while dramatically increasing resilience to low case ascertainment and test sensitivity. The greatest gains are realised by expanding the manual tracing window from 2 to 6 days pre-symptom-onset or, alternatively, by implementing high-uptake smartphone-based exposure notification; however, to achieve the performance of the former approach, the latter requires nearly all smartphones to detect exposure events. With or without exposure notification, our results suggest that implementing bidirectional tracing could dramatically improve COVID-19 control.

A machine learning toolkit for genetic engineering attribution to facilitate biosecurity.

The promise of biotechnology is tempered by its potential for accidental or deliberate misuse. Reliably identifying telltale signatures characteristic to different genetic designers, termed 'genetic engineering attribution', would deter misuse, yet is still considered unsolved. Here, we show that recurrent neural networks trained on DNA motifs and basic phenotype data can reach 70% attribution accuracy in distinguishing between over 1,300 labs. To make these models usable in practice, we introduce a framework for weighing predictions against other investigative evidence using calibration, and bring our model to within 1.6% of perfect calibration. Additionally, we demonstrate that simple models can accurately predict both the nation-state-of-origin and ancestor labs, forming the foundation of an integrated attribution toolkit which should promote responsible innovation and international security alike.

The biosecurity benefits of genetic engineering attribution.

Biology can be misused, and the risk of this causing widespread harm increases in step with the rapid march of technological progress. A key security challenge involves attribution: determining, in the wake of a human-caused biological event, who was responsible. Recent scientific developments have demonstrated a capability for detecting whether an organism involved in such an event has been genetically modified and, if modified, to infer from its genetic sequence its likely lab of origin. We believe this technique could be developed into powerful forensic tools to aid the attribution of outbreaks caused by genetically engineered pathogens, and thus protect against the potential misuse of synthetic biology.

Daisy-chain gene drives for the alteration of local populations.

If they are able to spread in wild populations, CRISPR-based gene-drive elements would provide new ways to address ecological problems by altering the traits of wild organisms, but the potential for uncontrolled spread tremendously complicates ethical development and use. Here, we detail a self-exhausting form of CRISPR-based drive system comprising genetic elements arranged in a daisy chain such that each drives the next. "Daisy-drive" systems can locally duplicate any effect achievable by using an equivalent self-propagating drive system, but their capacity to spread is limited by the successive loss of nondriving elements from one end of the chain. Releasing daisy-drive organisms constituting a small fraction of the local wild population can drive a useful genetic element nearly to local fixation for a wide range of fitness parameters without self-propagating spread. We additionally report numerous highly active guide RNA sequences sharing minimal homology that may enable evolutionarily stable daisy drive as well as self-propagating CRISPR-based gene drive. Especially when combined with threshold dependence, daisy drives could simplify decision-making and promote ethical use by enabling local communities to decide whether, when, and how to alter local ecosystems.

Mice Against Ticks: an experimental community-guided effort to prevent tick-borne disease by altering the shared environment.

Mice Against Ticks is a community-guided ecological engineering project that aims to prevent tick-borne disease by using CRISPR-based genome editing to heritably immunize the white-footed mice ( Peromyscus leucopus) responsible for infecting many ticks in eastern North America. Introducing antibody-encoding resistance alleles into the local mouse population is anticipated to disrupt the disease transmission cycle for decades. Technology development is shaped by engagement with community members and visitors to the islands of Nantucket and Martha's Vineyard, including decisions at project inception about which types of disease resistance to pursue. This engagement process has prompted the researchers to use only white-footed mouse DNA if possible, meaning the current project will not involve gene drive. Instead, engineered mice would be released in the spring when the natural population is low, a plan unlikely to increase total numbers above the normal maximum in autumn. Community members are continually asked to share their suggestions and concerns, a process that has already identified potential ecological consequences unanticipated by the research team that will likely affect implementation. As an early example of CRISPR-based ecological engineering, Mice Against Ticks aims to start small and simple by working with island communities whose mouse populations can be lastingly immunized without gene drive. This article is part of a discussion meeting issue 'The ecology and evolution of prokaryotic CRISPR-Cas adaptive immune systems'.

Inoculating science against potential pandemics and information hazards.

The recent de novo assembly of horsepox is an instructive example of an information hazard: published methods enabling poxvirus synthesis led to media coverage spelling out the implications, efficiently disseminating true information that might be used to cause harm. Whether or not the benefits justified the risks, the horsepox saga provides ample reason to upgrade the current system for screening synthesized DNA for hazardous sequences, which does not cover the majority of firms and cannot reliably prevent the assembly of potentially pandemic pathogens. An upgraded system might leverage one-way encryption to confidentially scrutinize virtually all commercial production by a cooperative international network of servers whose integrity can be verified by third parties. Funders could support participating institutions to ease the transition or outright subsidize the market to make clean DNA cheaper, while boycotts by journals, institutions, and funders could ensure compliance and require hardware-level locks on future DNA synthesizers. However, the underlying problem is that security and safety discussions among experts typically follow potentially hazardous events rather than anticipating them. Changing norms and incentives to favor preregistration and advisory peer review of planned experiments could test alternatives to the current closeted research model in select areas of science. Because the fields of synthetic mammalian virology and especially gene drive research involve technologies that could be unilaterally deployed and may self-replicate in the wild, they are compelling candidates for initial trials of early-stage peer review.

Current CRISPR gene drive systems are likely to be highly invasive in wild populations.

Recent reports have suggested that self-propagating CRISPR-based gene drive systems are unlikely to efficiently invade wild populations due to drive-resistant alleles that prevent cutting. Here we develop mathematical models based on existing empirical data to explicitly test this assumption for population alteration drives. Our models show that although resistance prevents spread to fixation in large populations, even the least effective drive systems reported to date are likely to be highly invasive. Releasing a small number of organisms will often cause invasion of the local population, followed by invasion of additional populations connected by very low rates of gene flow. Hence, initiating contained field trials as tentatively endorsed by the National Academies report on gene drive could potentially result in unintended spread to additional populations. Our mathematical results suggest that self-propagating gene drive is best suited to applications such as malaria prevention that seek to affect all wild populations of the target species.

Conservation demands safe gene drive.

Interest in developing gene drive systems to control invasive species is growing, with New Zealand reportedly considering the nascent technology as a way to locally eliminate the mammalian pests that threaten its unique flora and fauna. If gene drives successfully eradicated these invasive populations, many would rejoice, but what are the possible consequences? Here, we explore the risk of accidental spread posed by self-propagating gene drive technologies, highlight new gene drive designs that might achieve better outcomes, and explain why we need open and international discussions concerning a technology that could have global ramifications.

Evolutionary dynamics of CRISPR gene drives.

The alteration of wild populations has been discussed as a solution to a number of humanity's most pressing ecological and public health concerns. Enabled by the recent revolution in genome editing, clustered regularly interspaced short palindromic repeats (CRISPR) gene drives-selfish genetic elements that can spread through populations even if they confer no advantage to their host organism-are rapidly emerging as the most promising approach. However, before real-world applications are considered, it is imperative to develop a clear understanding of the outcomes of drive release in nature. Toward this aim, we mathematically study the evolutionary dynamics of CRISPR gene drives. We demonstrate that the emergence of drive-resistant alleles presents a major challenge to previously reported constructs, and we show that an alternative design that selects against resistant alleles could greatly improve evolutionary stability. We discuss all results in the context of CRISPR technology and provide insights that inform the engineering of practical gene drive systems.