A Genetic Map of the Response to DNA Damage in Human Cells.

The response to DNA damage is critical for cellular homeostasis, tumor suppression, immunity, and gametogenesis. In order to provide an unbiased and global view of the DNA damage response in human cells, we undertook 31 CRISPR-Cas9 screens against 27 genotoxic agents in the retinal pigment epithelium-1 (RPE1) cell line. These screens identified 890 genes whose loss causes either sensitivity or resistance to DNA-damaging agents. Mining this dataset, we discovered that ERCC6L2 (which is mutated in a bone-marrow failure syndrome) codes for a canonical non-homologous end-joining pathway factor, that the RNA polymerase II component ELOF1 modulates the response to transcription-blocking agents, and that the cytotoxicity of the G-quadruplex ligand pyridostatin involves trapping topoisomerase II on DNA. This map of the DNA damage response provides a rich resource to study this fundamental cellular system and has implications for the development and use of genotoxic agents in cancer therapy.

The ends in sight: Mre11-Rad50-Nbs1 complex structures come into focus.

In this issue of Molecular Cell, Rotheneder et al.1 elucidate the eukaroytic Mre11-Rad50-Nbs1 (MRN) complex quaternary architecture, which together with cryo-EM structures of bacterial Mre11-Rad50-DNA complexes,2 resolves the basis for MRN assembly and its broad nuclease specificity regulating DNA double-strand break repair.

Sen1 architecture: RNA-DNA hybrid resolution, autoregulation, and insights into SETX inactivation in AOA2.

The senataxin (SETX, Sen1 in yeasts) RNA-DNA hybrid resolving helicase regulates multiple nuclear transactions, including DNA replication, transcription, and DNA repair, but the molecular basis for Sen1 activities is ill defined. Here, Sen1 cryoelectron microscopy (cryo-EM) reconstructions reveal an elongated inchworm-like architecture. Sen1 is composed of an amino terminal helical repeat Sen1 N-terminal (Sen1N) regulatory domain that is flexibly linked to its C-terminal SF1B helicase motor core (Sen1Hel) via an intrinsically disordered tether. In an autoinhibited state, the Sen1Sen1N domain regulates substrate engagement by promoting occlusion of the RNA substrate-binding cleft. The X-ray structure of an activated Sen1Hel engaging single-stranded RNA and ADP-SO4 shows that the enzyme encircles RNA and implicates a single-nucleotide power stroke in the Sen1 RNA translocation mechanism. Together, our data unveil dynamic protein-protein and protein-RNA interfaces underpinning helicase regulation and inactivation of human SETX activity by RNA-binding-deficient mutants in ataxia with oculomotor apraxia 2 neurodegenerative disease.

From scientific discovery to cures: bright stars within a galaxy.

We propose that data mining and network analysis utilizing public databases can identify and quantify relationships between scientific discoveries and major advances in medicine (cures). Further development of such approaches could help to increase public understanding and governmental support for life science research and could enhance decision making in the quest for cures.

Endogenous DNA 3' Blocks Are Vulnerabilities for BRCA1 and BRCA2 Deficiency and Are Reversed by the APE2 Nuclease.

The APEX2 gene encodes APE2, a nuclease related to APE1, the apurinic/apyrimidinic endonuclease acting in base excision repair. Loss of APE2 is lethal in cells with mutated BRCA1 or BRCA2, making APE2 a prime target for homologous recombination-defective cancers. However, because the function of APE2 in DNA repair is poorly understood, it is unclear why BRCA-deficient cells require APE2 for viability. Here we present the genetic interaction profiles of APE2, APE1, and TDP1 deficiency coupled to biochemical and structural dissection of APE2. We conclude that the main role of APE2 is to reverse blocked 3' DNA ends, problematic lesions that preclude DNA synthesis. Our work also suggests that TOP1 processing of genomic ribonucleotides is the main source of 3'-blocking lesions relevant to APEX2-BRCA1/2 synthetic lethality. The exquisite sensitivity of BRCA-deficient cells to 3' blocks indicates that they represent a tractable vulnerability in homologous recombination-deficient tumor cells.

Decision trees within a molecular memristor.

Profuse dendritic-synaptic interconnections among neurons in the neocortex embed intricate logic structures enabling sophisticated decision-making that vastly outperforms any artificial electronic analogues1-3. The physical complexity is far beyond existing circuit fabrication technologies: moreover, the network in a brain is dynamically reconfigurable, which provides flexibility and adaptability to changing environments4-6. In contrast, state-of-the-art semiconductor logic circuits are based on threshold switches that are hard-wired to perform predefined logic functions. To advance the performance of logic circuits, we are re-imagining fundamental electronic circuit elements by expressing complex logic in nanometre-scale material properties. Here we use voltage-driven conditional logic interconnectivity among five distinct molecular redox states of a metal-organic complex to embed a 'thicket' of decision trees (composed of multiple if-then-else conditional statements) having 71 nodes within a single memristor. The resultant current-voltage characteristic of this molecular memristor (a 'memory resistor', a globally passive resistive-switch circuit element that axiomatically complements the set of capacitor, inductor and resistor) exhibits eight recurrent and history-dependent non-volatile switching transitions between two conductance levels in a single sweep cycle. The identity of each molecular redox state was determined with in situ Raman spectroscopy and confirmed by quantum chemical calculations, revealing the electron transport mechanism. Using simple circuits of only these elements, we experimentally demonstrate dynamically reconfigurable, commutative and non-commutative stateful logic in multivariable decision trees that execute in a single time step and can, for example, be applied as local intelligence in edge computing7-9.

FEN nucleases: bind, bend, fray, cut.

In this issue, Orans et al. (2011) and Tsutakawa et al. (2011) report exciting insights into the molecular principles governing diverse endo- and exonucleolytic cleavage specificities of members of the RAD2/FEN superfamily of nucleases, which have critical roles in DNA replication and maintenance.

Third-order nanocircuit elements for neuromorphic engineering.

Current hardware approaches to biomimetic or neuromorphic artificial intelligence rely on elaborate transistor circuits to simulate biological functions. However, these can instead be more faithfully emulated by higher-order circuit elements that naturally express neuromorphic nonlinear dynamics1-4. Generating neuromorphic action potentials in a circuit element theoretically requires a minimum of third-order complexity (for example, three dynamical electrophysical processes)5, but there have been few examples of second-order neuromorphic elements, and no previous demonstration of any isolated third-order element6-8. Using both experiments and modelling, here we show how multiple electrophysical processes-including Mott transition dynamics-form a nanoscale third-order circuit element. We demonstrate simple transistorless networks of third-order elements that perform Boolean operations and find analogue solutions to a computationally hard graph-partitioning problem. This work paves a way towards very compact and densely functional neuromorphic computing primitives, and energy-efficient validation of neuroscientific models.

Potential and challenges of computing with molecular materials.

We are at an inflection point in computing where traditional technologies are incapable of keeping up with the demands of exploding data collection and artificial intelligence. This challenge demands a leap to a new platform as transformative as the digital silicon revolution. Over the past 30 years molecular materials for computing have generated great excitement but continually fallen short of performance and reliability requirements. However, recent reports indicate that those historical limitations may have been resolved. Here we assess the current state of computing with molecular-based materials, especially using transition metal complexes of redox active ligands, in the context of neuromorphic computing. We describe two complementary research paths necessary to determine whether molecular materials can be the basis of a new computing technology: continued exploration of the molecular electronic properties that enable computation and, equally important, the process development for on-chip integration of molecular materials.

Structure-specific roles for PolG2-DNA complexes in maintenance and replication of mitochondrial DNA.

The homodimeric PolG2 accessory subunit of the mitochondrial DNA polymerase gamma (Pol γ) enhances DNA binding and processive DNA synthesis by the PolG catalytic subunit. PolG2 also directly binds DNA, although the underlying molecular basis and functional significance are unknown. Here, data from Atomic Force Microscopy (AFM) and X-ray structures of PolG2-DNA complexes define dimeric and hexameric PolG2 DNA binding modes. Targeted disruption of PolG2 DNA-binding interfaces impairs processive DNA synthesis without diminishing Pol γ subunit affinities. In addition, a structure-specific DNA-binding role for PolG2 oligomers is supported by X-ray structures and AFM showing that oligomeric PolG2 localizes to DNA crossings and targets forked DNA structures resembling the mitochondrial D-loop. Overall, data indicate that PolG2 DNA binding has both PolG-dependent and -independent functions in mitochondrial DNA replication and maintenance, which provide new insight into molecular defects associated with PolG2 disruption in mitochondrial disease.

Nbs1 flexibly tethers Ctp1 and Mre11-Rad50 to coordinate DNA double-strand break processing and repair.

The Nijmegen breakage syndrome 1 (Nbs1) subunit of the Mre11-Rad50-Nbs1 (MRN) complex protects genome integrity by coordinating double-strand break (DSB) repair and checkpoint signaling through undefined interactions with ATM, MDC1, and Sae2/Ctp1/CtIP. Here, fission yeast and human Nbs1 structures defined by X-ray crystallography and small angle X-ray scattering (SAXS) reveal Nbs1 cardinal features: fused, extended, FHA-BRCT(1)-BRCT(2) domains flexibly linked to C-terminal Mre11- and ATM-binding motifs. Genetic, biochemical, and structural analyses of an Nbs1-Ctp1 complex show Nbs1 recruits phosphorylated Ctp1 to DSBs via binding of the Nbs1 FHA domain to a Ctp1 pThr-Asp motif. Nbs1 structures further identify an extensive FHA-BRCT interface, a bipartite MDC1-binding scaffold, an extended conformational switch, and the molecular consequences associated with cancer predisposing Nijmegen breakage syndrome mutations. Tethering of Ctp1 to a flexible Nbs1 arm suggests a mechanism for restricting DNA end processing and homologous recombination activities of Sae2/Ctp1/CtIP to the immediate vicinity of DSBs.

Dynamic density functional theory with inertia and background flow.

We present dynamic density functional theory (DDFT) incorporating general inhomogeneous, incompressible, time-dependent background flows and inertia, describing externally driven passive colloidal systems out of equilibrium. We start by considering the underlying nonequilibrium Langevin dynamics, including the effect of the local velocity of the surrounding liquid bath, to obtain the nonlinear, nonlocal partial differential equations governing the evolution of the (coarse-grained) density and velocity fields describing the dynamics of colloids. In addition, we show both with heuristic arguments, and by numerical solution, that our equations and solutions agree with existing DDFTs in the overdamped (high friction) limit. We provide numerical solutions that model the flow of hard spheres, in both unbounded and confined domains, and compare with previously derived DDFTs with and without the background flow.

Alternative metrics for characterizing longer-term clinical outcomes in difficult-to-treat depression: II. Sensitivity to treatment effects.

OBJECTIVE: Characteristics of difficult-to-treat depression (DTD), including infrequent symptom remission and poor durability of benefit, compel reconsideration of the outcome metrics historically used to gauge the effectiveness of antidepressant interventions. METHODS: Self-report and clinician assessments of depression symptom severity were obtained regularly over a 2-year period in a difficult-to-treat depression registry sample receiving treatment as usual (TAU), with or without vagus nerve stimulation (VNS). Alternative outcome metrics for characterizing symptom change were compared in effect size and discriminating power in distinguishing the vagus nerve stimulation + treatment as usual and treatment as usual treatment groups. We expected metrics based on remission status to produce weaker between-group separation than those based on the classifications of partial response or response and metrics that integrate information over time to produce greater separation than those based on single endpoint assessment. RESULTS: Metrics based on remission status had smaller effect size and poorer discrimination in separating the treatment groups than metrics based on partial response or response classifications. Metrics that integrated information over the 2-year observation period had stronger performance characteristics than those based on symptom scores at single endpoint assessment. For both the clinician-rated and self-report depression ratings, the metrics with the strongest performance characteristics were the median percentage change in symptom scores over the observation period and the proportion of the observation period in partial response or better. CONCLUSION: In difficult-to-treat depression, integrative symptom severity-based and time-based measures are sensitive and informative outcomes for assessing between-group treatment effects, while metrics based on remission status are not.

Identifying the nature of episodic memory deficits in Major Depressive Disorder using a Real-World What-Where-When task.

ABSTRACTDeficits in episodic memory have been reported in various psychiatric conditions, including Major Depressive Disorder (MDD). Many widely used episodic memory tests do not have the ability to distinguish between impaired memory of separate components of a real-life event (e.g., what happened, where it happened and when), and impaired binding of such real-life features. To address this issue, a naturalistic, real-world What-Where-When memory task was employed to assess the nature of episodic memory impairments in MDD. A validation study established that the task is sensitive to age-related episodic memory changes, and that intentional encoding does not invalidate the task. The main study then compared the performance of patients with depression and control participants on the intentionally encoded WWW task. Patients with MDD presented an overall episodic memory impairment arising from deficits in object memory and the ability to bind objects to temporal context. Taken together, our study confirms the episodic memory impairment in MDD, by providing evidence of deficient object memory and reduced ability to bind temporal context to objects in patients. Our naturalistic WWW task presents a promising approach for thorough identification of the nature of episodic memory impairments, under a real-world environment, in various conditions, including MDD.

Ethnic differences in early onset multimorbidity and associations with health service use, long-term prescribing, years of life lost, and mortality: A cross-sectional study using clustering in the UK Clinical Practice Research Datalink.

BACKGROUND: The population prevalence of multimorbidity (the existence of at least 2 or more long-term conditions [LTCs] in an individual) is increasing among young adults, particularly in minority ethnic groups and individuals living in socioeconomically deprived areas. In this study, we applied a data-driven approach to identify clusters of individuals who had an early onset multimorbidity in an ethnically and socioeconomically diverse population. We identified associations between clusters and a range of health outcomes. METHODS AND FINDINGS: Using linked primary and secondary care data from the Clinical Practice Research Datalink GOLD (CPRD GOLD), we conducted a cross-sectional study of 837,869 individuals with early onset multimorbidity (aged between 16 and 39 years old when the second LTC was recorded) registered with an English general practice between 2010 and 2020. The study population included 777,906 people of White ethnicity (93%), 33,915 people of South Asian ethnicity (4%), and 26,048 people of Black African/Caribbean ethnicity (3%). A total of 204 LTCs were considered. Latent class analysis stratified by ethnicity identified 4 clusters of multimorbidity in White groups and 3 clusters in South Asian and Black groups. We found that early onset multimorbidity was more common among South Asian (59%, 33,915) and Black (56% 26,048) groups compared to the White population (42%, 777,906). Latent class analysis revealed physical and mental health conditions that were common across all ethnic groups (i.e., hypertension, depression, and painful conditions). However, each ethnic group also presented exclusive LTCs and different sociodemographic profiles: In White groups, the cluster with the highest rates/odds of the outcomes was predominantly male (54%, 44,150) and more socioeconomically deprived than the cluster with the lowest rates/odds of the outcomes. On the other hand, South Asian and Black groups were more socioeconomically deprived than White groups, with a consistent deprivation gradient across all multimorbidity clusters. At the end of the study, 4% (34,922) of the White early onset multimorbidity population had died compared to 2% of the South Asian and Black early onset multimorbidity populations (535 and 570, respectively); however, the latter groups died younger and lost more years of life. The 3 ethnic groups each displayed a cluster of individuals with increased rates of primary care consultations, hospitalisations, long-term prescribing, and odds of mortality. Study limitations include the exclusion of individuals with missing ethnicity information, the age of diagnosis not reflecting the actual age of onset, and the exclusion of people from Mixed, Chinese, and other ethnic groups due to insufficient power to investigate associations between multimorbidity and health-related outcomes in these groups. CONCLUSIONS: These findings emphasise the need to identify, prevent, and manage multimorbidity early in the life course. Our work provides additional insights into the excess burden of early onset multimorbidity in those from socioeconomically deprived and diverse groups who are disproportionately and more severely affected by multimorbidity and highlights the need to ensure healthcare improvements are equitable.

Alternative metrics for characterizing longer-term clinical outcomes in difficult-to-treat depression: I. Association with change in quality of life.

BACKGROUND: In difficult-to-treat depression (DTD) the outcome metrics historically used to evaluate treatment effectiveness may be suboptimal. Metrics based on remission status and on single end-point (SEP) assessment may be problematic given infrequent symptom remission, temporal instability, and poor durability of benefit in DTD. METHODS: Self-report and clinician assessment of depression symptom severity were regularly obtained over a 2-year period in a chronic and highly treatment-resistant registry sample (N = 406) receiving treatment as usual, with or without vagus nerve stimulation. Twenty alternative metrics for characterizing symptomatic improvement were evaluated, contrasting SEP metrics with integrative (INT) metrics that aggregated information over time. Metrics were compared in effect size and discriminating power when contrasting groups that did (N = 153) and did not (N = 253) achieve a threshold level of improvement in end-point quality-of-life (QoL) scores, and in their association with continuous QoL scores. RESULTS: Metrics based on remission status had smaller effect size and poorer discrimination of the binary QoL outcome and weaker associations with the continuous end-point QoL scores than metrics based on partial response or response. The metrics with the strongest performance characteristics were the SEP measure of percentage change in symptom severity and the INT metric quantifying the proportion of the observation period in partial response or better. Both metrics contributed independent variance when predicting end-point QoL scores. CONCLUSIONS: Revision is needed in the metrics used to quantify symptomatic change in DTD with consideration of INT time-based measures as primary or secondary outcomes. Metrics based on remission status may not be useful.

A Delphi-method-based consensus guideline for definition of treatment-resistant depression for clinical trials.

Criteria for treatment-resistant depression (TRD) and partially responsive depression (PRD) as subtypes of major depressive disorder (MDD) are not unequivocally defined. In the present document we used a Delphi-method-based consensus approach to define TRD and PRD and to serve as operational criteria for future clinical studies, especially if conducted for regulatory purposes. We reviewed the literature and brought together a group of international experts (including clinicians, academics, researchers, employees of pharmaceutical companies, regulatory bodies representatives, and one person with lived experience) to evaluate the state-of-the-art and main controversies regarding the current classification. We then provided recommendations on how to design clinical trials, and on how to guide research in unmet needs and knowledge gaps. This report will feed into one of the main objectives of the EUropean Patient-cEntric clinicAl tRial pLatforms, Innovative Medicines Initiative (EU-PEARL, IMI) MDD project, to design a protocol for platform trials of new medications for TRD/PRD.

A charged performance by gp17 in viral packaging.

Packaging of viral genomes into virus capsids requires powerful motors to overcome the repulsive force that builds as the nucleic acids are compressed. Through structural analyses of the T4 bacteriophage packaging motor gp17, Sun et al. (2008) now propose a packaging mechanism in which electrostatic forces cause the motor to alternate between tensed and relaxed conformational states.

Mre11 dimers coordinate DNA end bridging and nuclease processing in double-strand-break repair.

Mre11 forms the core of the multifunctional Mre11-Rad50-Nbs1 (MRN) complex that detects DNA double-strand breaks (DSBs), activates the ATM checkpoint kinase, and initiates homologous recombination (HR) repair of DSBs. To define the roles of Mre11 in both DNA bridging and nucleolytic processing during initiation of DSB repair, we combined small-angle X-ray scattering (SAXS) and crystal structures of Pyrococcus furiosus Mre11 dimers bound to DNA with mutational analyses of fission yeast Mre11. The Mre11 dimer adopts a four-lobed U-shaped structure that is critical for proper MRN complex assembly and for binding and aligning DNA ends. Further, mutations blocking Mre11 endonuclease activity impair cell survival after DSB induction without compromising MRN complex assembly or Mre11-dependant recruitment of Ctp1, an HR factor, to DSBs. These results show how Mre11 dimerization and nuclease activities initiate repair of DSBs and collapsed replication forks, as well as provide a molecular foundation for understanding cancer-causing Mre11 mutations in ataxia telangiectasia-like disorder (ATLD).

Chaotic dynamics in nanoscale NbO2 Mott memristors for analogue computing.

At present, machine learning systems use simplified neuron models that lack the rich nonlinear phenomena observed in biological systems, which display spatio-temporal cooperative dynamics. There is evidence that neurons operate in a regime called the edge of chaos that may be central to complexity, learning efficiency, adaptability and analogue (non-Boolean) computation in brains. Neural networks have exhibited enhanced computational complexity when operated at the edge of chaos, and networks of chaotic elements have been proposed for solving combinatorial or global optimization problems. Thus, a source of controllable chaotic behaviour that can be incorporated into a neural-inspired circuit may be an essential component of future computational systems. Such chaotic elements have been simulated using elaborate transistor circuits that simulate known equations of chaos, but an experimental realization of chaotic dynamics from a single scalable electronic device has been lacking. Here we describe niobium dioxide (NbO2) Mott memristors each less than 100 nanometres across that exhibit both a nonlinear-transport-driven current-controlled negative differential resistance and a Mott-transition-driven temperature-controlled negative differential resistance. Mott materials have a temperature-dependent metal-insulator transition that acts as an electronic switch, which introduces a history-dependent resistance into the device. We incorporate these memristors into a relaxation oscillator and observe a tunable range of periodic and chaotic self-oscillations. We show that the nonlinear current transport coupled with thermal fluctuations at the nanoscale generates chaotic oscillations. Such memristors could be useful in certain types of neural-inspired computation by introducing a pseudo-random signal that prevents global synchronization and could also assist in finding a global minimum during a constrained search. We specifically demonstrate that incorporating such memristors into the hardware of a Hopfield computing network can greatly improve the efficiency and accuracy of converging to a solution for computationally difficult problems.