Pre-clinical characterisation of E2814, a high-affinity antibody targeting the microtubule-binding repeat domain of tau for passive immunotherapy in Alzheimer's disease

Tau deposition in the brain is a pathological hallmark of many neurodegenerative disorders, including Alzheimer’s disease (AD). During the course of these tauopathies, tau spreads throughout the brain via synaptically-connected pathways. Such propagation of pathology is thought to be mediated by tau species (“seeds”) containing the microtubule binding region (MTBR) composed of either three repeat (3R) or four repeat (4R) isoforms. The tau MTBR also forms the core of the neuropathological filaments identified in AD brain and other tauopathies. Multiple approaches are being taken to limit tau pathology, including immunotherapy with anti-tau antibodies. Given its key structural role within fibrils, specifically targetting the MTBR with a therapeutic antibody to inhibit tau seeding and aggregation may be a promising strategy to provide disease-modifying treatment for AD and other tauopathies. Therefore, a monoclonal antibody generating campaign was initiated with focus on the MTBR. Herein we describe the pre-clinical generation and characterisation of E2814, a humanised, high affinity, IgG1 antibody recognising the tau MTBR. E2814 and its murine precursor, 7G6, as revealed by epitope mapping, are antibodies bi-epitopic for 4R and mono-epitopic for 3R tau isoforms because they bind to sequence motif HVPGG. Functionally, both antibodies inhibited tau aggregation in vitro. They also immunodepleted a variety of MTBR-containing tau protein species. In an in vivo model of tau seeding and transmission, attenuation of deposition of sarkosyl-insoluble tau in brain could also be observed in response to antibody treatment. In AD brain, E2814 bound different types of tau filaments as shown by immunogold labelling and recognised pathological tau structures by immunohistochemical staining. Tau fragments containing HVPGG epitopes were also found to be elevated in AD brain compared to PSP or control. Taken together, the data reported here have led to E2814 being proposed for clinical developmen

Collective Animal Behavior from Bayesian Estimation and Probability Matching

Animals living in groups make movement decisions that depend, among other factors, on social interactions with other group members. Our present understanding of social rules in animal collectives is based on empirical fits to observations and we lack first-principles approaches that allow their derivation. Here we show that patterns of collective decisions can be derived from the basic ability of animals to make probabilistic estimations in the presence of uncertainty. We build a decision-making model with two stages: Bayesian estimation and probabilistic matching.
In the first stage, each animal makes a Bayesian estimation of which behavior is best to perform taking into account personal information about the environment and social information collected by observing the behaviors of other animals. In the probability matching stage, each animal chooses a behavior with a probability given by the Bayesian estimation that this behavior is the most appropriate one. This model derives very simple rules of interaction in animal collectives that depend only on two types of reliability parameters, one that each animal assigns to the other animals and another given by the quality of the non-social information. We test our model by obtaining theoretically a rich set of observed collective patterns of decisions in three-spined sticklebacks, Gasterosteus aculeatus, a shoaling fish species. The quantitative link shown between probabilistic estimation and collective rules of behavior allows a better contact with other fields such as foraging, mate selection, neurobiology and psychology, and gives predictions for experiments directly testing the relationship between estimation and collective behavior

Context-dependent preferences in starlings: linking ecology, foraging and choice

Foraging animals typically encounter opportunities that they either pursue or skip, but occasionally meet several alternatives simultaneously. Behavioural ecologists predict preferences using absolute properties of each option, while decision theorists focus on relative evaluations at the time of choice. We use European starlings (Sturnus vulgaris) to integrate ecological reasoning with decision models, linking and testing hypotheses for value acquisition and choice mechanism. We hypothesise that options' values depend jointly on absolute attributes, learning context, and subject's state. In simultaneous choices, preference could result either from comparing subjective values using deliberation time, or from processing each alternative independently, without relative comparisons. The combination of the value acquisition hypothesis and independent processing at choice time has been called the Sequential Choice Model. We test this model with options equated in absolute properties to exclude the possibility of preference being built at the time of choice. Starlings learned to obtain food by responding to four stimuli in two contexts. In context [AB], they encountered options A5 or B10 in random alternation; in context [CD], they met C10 or D20. Delay to food is denoted, in seconds, by the suffixes. Observed latency to respond (Li) to each option alone (our measure of value) ranked thus: LA≈LC<LB<<LD, consistently with value being sensitive to both delay and learning context. We then introduced simultaneous presentations of A5 vs. C10 and B10 vs. C10, using latencies in no-choice tests to predict sign and strength of preference in pairings. Starlings preferred A5 over C10 and C10 over B10. There was no detectable evaluation time, and preference magnitude was predictable from latency differentials. This implies that value reflects learning rather than choice context, that preferences are not constructed by relative judgements at the time of choice, and that mechanisms adapted for sequential decisions are effective to predict choice behaviour.This work was supported by Biotechnology and Biological Sciences Research Council (BBSRC) Grant BB/G007144/1 to AK www.bbsrc.ac.uk; TM was supported by a Doctoral Grant from the Portuguese Foundation for Science and Technology (FCT) www.fct.pt/index.phtml.en. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

A Novel Task for the Investigation of Action Acquisition

We present a behavioural task designed for the investigation of how novel instrumental actions are discovered and learnt. The task consists of free movement with a manipulandum, during which the full range of possible movements can be explored by the participant and recorded. A subset of these movements, the ‘target’, is set to trigger a reinforcing signal. The task is to discover what movements of the manipulandum evoke the reinforcement signal. Targets can be defined in spatial, temporal, or kinematic terms, can be a combination of these aspects, or can represent the concatenation of actions into a larger gesture. The task allows the study of how the specific elements of behaviour which cause the reinforcing signal are identified, refined and stored by the participant. The task provides a paradigm where the exploratory motive drives learning and as such we view it as in the tradition of Thorndike [1]. Most importantly it allows for repeated measures, since when a novel action is acquired the criterion for triggering reinforcement can be changed requiring a new action to be discovered. Here, we present data using both humans and rats as subjects, showing that our task is easily scalable in difficulty, adaptable across species, and produces a rich set of behavioural measures offering new and valuable insight into the action learning process

Dissimilar responses of fungal and bacterial communities to soil transplantation simulating abrupt climate changes.

Both fungi and bacteria play essential roles in regulating soil carbon cycling. To predict future carbon stability, it is imperative to understand their responses to environmental changes, which is subject to large uncertainty. As current global warming is causing range shifts toward higher latitudes, we conducted three reciprocal soil transplantation experiments over large transects in 2005 to simulate abrupt climate changes. Six years after soil transplantation, fungal biomass of transplanted soils showed a general pattern of changes from donor sites to destination, which were more obvious in bare fallow soils than in maize cropped soils. Strikingly, fungal community compositions were clustered by sites, demonstrating that fungi of transplanted soils acclimatized to the destination environment. Several fungal taxa displayed sharp changes in relative abundance, including Podospora, Chaetomium, Mortierella and Phialemonium. In contrast, bacterial communities remained largely unchanged. Consistent with the important role of fungi in affecting soil carbon cycling, 8.1%-10.0% of fungal genes encoding carbon-decomposing enzymes were significantly (p &lt; 0.01) increased as compared with those from bacteria (5.7%-8.4%). To explain these observations, we found that fungal occupancy across samples was mainly determined by annual average air temperature and rainfall, whereas bacterial occupancy was more closely related to soil conditions, which remained stable 6 years after soil transplantation. Together, these results demonstrate dissimilar response patterns and resource partitioning between fungi and bacteria, which may have considerable consequences for ecosystem-scale carbon cycling

The Household Water Insecurity Experiences (HWISE) Scale: comparison scores from 27 sites in 22 countries

Abstract Household survey data from 27 sites in 22 countries were collected in 2017–2018 in order to construct and validate a cross-cultural household-level water insecurity scale. The resultant Household Water Insecurity Experiences (HWISE) scale presents a useful tool for monitoring and evaluating water interventions as a complement to traditional metrics used by the development community. It can also help track progress toward achievement of Sustainable Development Goal 6 ‘clean water and sanitation for all’. We present HWISE scale scores from 27 sites as comparative data for future studies using the HWISE scale in low- and middle-income contexts. Site-level mean scores for HWISE-12 (scored 0–36) ranged from 1.64 (SD 4.22) in Pune, India, to 20.90 (7.50) in Cartagena, Colombia, while site-level mean scores for HWISE-4 (scored 0–12) ranged from 0.51 (1.50) in Pune, India, to 8.21 (2.55) in Punjab, Pakistan. Scores tended to be higher in the dry season as expected. Data from this first implementation of the HWISE scale demonstrate the diversity of water insecurity within and across communities and can help to situate findings from future applications of this tool

Food web persistence is enhanced by non-trophic interactions.

The strength of interspecific interactions is often proposed to affect food web stability, with weaker interactions increasing the persistence of species, and food webs as a whole. However, the mechanisms that modify interaction strengths, and their effects on food web persistence are not fully understood. Using food webs containing different combinations of predator, prey, and nonprey species, we investigated how predation risk of susceptible prey is affected by the presence of species not directly trophically linked to either predators or prey. We predicted that indirect alterations to the strength of trophic interactions translate to changes in persistence time of extinction-prone species. We assembled interaction webs of protist consumers and turbellarian predators with eight different combinations of prey, predators and nonprey species, and recorded abundances for over 130 prey generations. Persistence of predation-susceptible species was increased by the presence of nonprey. Furthermore, multiple nonprey species acted synergistically to increase prey persistence, such that persistence was greater than would be predicted from the dynamics of simpler food webs. We also found evidence suggesting increased food web complexity may weaken interspecific competition, increasing persistence of poorer competitors. Our results demonstrate that persistence times in complex food webs cannot be predicted from the dynamics of simplified systems, and that species not directly involved in consumptive interactions likely play key roles in maintaining persistence. Global species diversity is currently declining at an unprecedented rate and our findings reveal that concurrent loss of species that modify trophic interactions may have unpredictable consequences for food web stability