Structure Can Predict Function in the Human Brain: A Graph Neural Network Deep Learning Model of Functional Connectivity and Centrality Based on Structural Connectivity Although functional connectivity and associated graph theory measures (e.g., centrality; how centrally important to the network a region is) are widely used in brain research, the full extent to which these functional measures are related to the underlying structural connectivity is not yet fully understood. Graph neural network deep learning methods have not yet been applied for this purpose, and offer an ideal model architecture for working with connectivity data given their ability to capture and maintain inherent network structure. This model applied here to predict functional connectivity and centrality from structural connectivity accounted for 89% of the variance in mean functional connectivity, 56% of the variance in individual-level functional connectivity, 99% of the variance in mean functional centrality, and 81% of the variance in individual-level functional centrality. This model provides a new benchmark for performance and represents a novel finding that functional centrality can be robustly predicted from structural connectivity. Regions of particular importance to the models performance as determined through lesioning are discussed, whereby regions with higher centrality have a higher impact on model performance. Future research on models of patient, demographic, or behavioural data can also benefit from this graph neural network method as it is ideally-suited for capturing connectivity and centrality in brain networks. These results have set a new benchmark for prediction of functional connectivity from structural connectivity, and models like this may ultimately lead to a way to predict functional connectivity in individuals who are unable to do fMRI tasks (e.g., non-responsive patients).
Replacement of microglia by brain-engrafted macrophages provide long term protection against brain irradiation and concussive injury Brain resident microglia have a distinct origin compared to macrophages in other organs. Under physiological conditions, microglia are maintained by self-renewal from the local pool, independent of hematopoietic progenitors. Pharmacological depletion of microglia during therapeutic whole-brain irradiation prevents synaptic loss and long-term recognition memory deficits but the mechanisms behind these protective effects are unknown. Here we demonstrate that after a combination of therapeutic whole-brain irradiation and microglia depletion, macrophages originating from circulating monocytes engraft into the brain and replace the microglia pool. Comparisons of transcriptomes reveal that brain-engrafted macrophages have an intermediate phenotype that resembles both monocytes and embryonic microglia. Brain-engrafted macrophages display reduced phagocytic activity for synaptic compartments compared to microglia from normal brains in response to a secondary concussive brain injury. In addition to sparing mice from brain radiotherapy-induced long-term cognitive deficits, replacement of microglia by brain-engrafted macrophages can prevent concussive injury-induced memory loss. These results demonstrate the long-term functional role of brain-engrafted macrophages as a possible therapeutic tool against radiation-induced cognitive deficits.
Diet effects on mouse meiotic recombination: a warning for recombination studies Meiotic recombination is a critical process for sexually reproducing organisms. This exchange of genetic information between homologous chromosomes during meiosis is important not only because it generates genetic diversity, but also because it is often required for proper chromosome segregation. Consequently, the frequency and distribution of crossovers are tightly controlled to ensure fertility and offspring viability. However, in many systems it has been shown that environmental factors can alter the frequency of crossover events. Two studies in flies and yeast point to nutritional status affecting the frequency of crossing over. However, this question remains unexplored in mammals. Here we test how crossover frequency varies in response to diet in Mus musculus males. We use immunohistochemistry to estimate crossover frequency in multiple genotypes under two diet treatments. Our results indicate that while crossover frequency was unaffected by diet in some strains, other strains were sensitive even to small composition changes between two common laboratory chows. Therefore, recombination is both resistant and sensitive to certain dietary changes in a strain-dependent manner and, hence, this response is genetically determined. Our study is the first to report a nutrition effect on genome-wide levels of recombination. Moreover, our work highlights the importance of controlling diet in recombination studies and may point to diet as a potential source of variability among studies, which is relevant for reproducibility. Exploitation of the genetic variability in the genotype of Egyptian Wheat by studding Molecular Markers, Molecular Docking and Nano Composites. Regulation of leaf and stem rust This manuscript has been withdrawn by the authors as it was submitted and made public without the full consent of all the authors. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author. SERCA function is impaired in skeletal and cardiac muscles from young DBA/2J mdx mice The DBA/2J (D2) mdx mouse has emerged as a more severe model of Duchenne muscular dystrophy when compared to the traditional C57BL/10 (C57) mdx mouse. Here, we questioned whether sarco(endo)plasmic reticulum Ca2+-ATPase (SERCA) function would differ in muscles from young D2 and C57 mdx mice. In gastrocnemius muscles, both D2- and C57 mdx mice exhibited signs of impaired Ca2+ uptake, however, this was more severe in D2 mdx mice. Maximal SERCA activity was lowered only in D2 mdx gastrocnemius muscles and not C57 mdx muscles. Furthermore, in the left ventricle and diaphragm, Ca2+ uptake was impaired in C57 mdx muscles with lowered rates of Ca2+ uptake compared with C57 WT mice, whereas in muscles from D2 mdx mice, rates of Ca2+ uptake were unattainable due to the severe impairments in their ability to transport Ca2+. Overall, our study demonstrates that SERCA function is drastically impaired in young D2 mdx mice. Intron-mediated induction of phenotypic heterogeneity Introns are universally present in the nuclear genomes of eukaryotes1. The budding yeast, an otherwise intron-poor species, preserves two sets of ribosomal protein (RP) genes differing primarily in their introns2-4. Despite recent findings on the role of RP introns under stress and starvation5-7, understanding the contribution of introns to ribosome regulation remains challenging. Here, combining isogrowth profiling8 with single-cell protein measurements9, we found that introns can mediate inducible phenotypic heterogeneity conferring a clear fitness advantage. Osmotic stress leads to bimodal expression of the small ribosomal subunit protein Rps22B, mediated by an intron in the 5 untranslated region of its transcript. The two resulting yeast subpopulations differ in their ability to cope with starvation. Low Rps22B protein levels resulted in prolonged survival under sustained starvation, while high Rps22B levels enabled cells to grow faster after transient starvation. Further, yeast growing at high sugar concentrations - similar to those in ripe grapes - exhibit bimodal Rps22B expression when approaching stationary phase. Differential intron-mediated regulation of RP genes thus provides a way to diversify the population when starvation looms in natural environments. Our findings reveal a new role for introns in inducing phenotypic heterogeneity in changing environments and suggest that duplicated RP genes in yeast contribute to resolving the evolutionary conflict between precise expression control and environmental responsiveness10. Herbivore life histories are altered by drought stress in their hosts plants Drought associated with climate change can stress plants, altering their interactions with phytophagous arthropods. Dryness not only impacts cultivated plants but also their parasites, which in some cases are favoured by drought. Herbivorous arthropods feeding on drought-stressed plants typically produce bigger offspring and develop faster. However, how much responses to abiotic factors differ among populations of a species remains poorly documented. Here, we evaluate variability among populations of a major agricultural pest, the two spotted spider mite, Tetranychus urticae, in response to drought-stress. We compare key life-history parameters of twelve populations that originate from climates ranging from wet and cool Atlantic locations to medium to dry hot Mediterranean locations. We evaluated how plant drought stress affects four life history traits: development time, fecundity, sex-ratio and emigration rate in an experiment comparing well-watered and drought-stressed bean plants. Mites feeding on drought-stressed plants developed faster and attempted to leave leaves less often, and young females were more fecund. The mites from wet temperate climates exhibited greater plasticity between the two water regimes than mites originating from dryer and hot climates. Thus, climate in the area of origin influences mite response to drought. Medium-throughput zebrafish optogenetic platform identifies deficits in subsequent neural activity following brief early exposure to cannabidiol and Δ-9-tetrahydrocannabinol In the light of legislative changes and the widespread use of cannabis as a recreational and medicinal drug, delayed effects of cannabis upon brief exposure during embryonic development are of high interest as early pregnancies often go undetected. Here, zebrafish embryos were exposed to cannabidiol (CBD) and {Delta}-9-tetrahydrocannabinol l (THC) until the end of gastrulation (1-10 hours post-fertilization) and analyzed later in development (4-5 days post-fertilization). In order to measure neural activity, we implemented CaMPARI (Calcium-Modulated Photoactivatable Ratiometric Integrator) and optimized the protocol for a 96-well format complemented by locomotor analysis. Our results revealed that neural activity was decreased by CBD more than THC. At higher doses, both cannabinoids could dramatically reduce neural activity and locomotor activity. Interestingly, the decrease was more pronounced when CBD and THC were combined. At the receptor level, CBD-mediated reduction of locomotor activity was partially prevented using cannabinoid type 1 and 2 receptor inhibitors. Overall, we report that CBD toxicity occurs via two cannabinoid receptors and is synergistically enhanced by THC exposure to negatively impact neural activity late in larval development. Future studies are warranted to reveal other cannabinoids and receptors involved in this pathway to understand the subsequent health implications of cannabis consumption on fetal development. Maternal microbiota Bifidobacterium promotes placental morphogenesis, nutrient transport and fetal growth in mice The gut microbiota plays a central role in regulating host metabolism. However, while substantial progress has been made in discerning how the microbiota influences host functions post birth and beyond, little is known about how key members of the maternal gut microbiota can influence feto-placental growth. Here, using germ-free and specific-pathogen-free mice, we demonstrate that the bacterium Bifidobacterium breve UCC2003 modulates maternal body adaptations, placental structure and nutrient transporter capacity, with implications for fetal metabolism and growth. The effects of B. breve UCC2003 on feto-placental growth are mediated, in part, by changes in the maternal and placental metabolome (i.e. acetate and carnitine). Histological analysis of the placenta confirmed that Bifidobacterium modifies placental structure via changes in Igf2P0, Dlk1, Mapk1 and Mapk14 expression. Additionally, B. breve UCC2003, acting through Slc2a1 and Fatp1-4 transporters, was shown to restore fetal glycaemia and fetal growth in association with changes in the fetal hepatic transcriptome. This study provides knowledge towards a novel and safe therapeutic strategy for treating pregnancy disorders via modulation of the maternal gut microbiota. HYBRID REACTION-DIFFUSION AND CLOCK-AND-WAVEFRONT MODEL FOR THE ARREST OF OSCILLATIONS IN THE SOMITOGENESIS SEGMENTATION CLOCK AO_SCPLOWBSTRACTC_SCPLOWThe clock and wavefront paradigm is arguably the most widely accepted model for explaining the embryonic process of somitogenesis. According to this model, somitogenesis is based upon the interaction between a genetic oscillator, known as segmentation clock, and a differentiation wavefront, which provides the positional information indicating where each pair of somites is formed. Shortly after the clock and wavefront paradigm was introduced, Meinhardt presented a conceptually different mathematical model for morphogenesis in general, and somitogenesis in particular. Recently, Cotterell et al. rediscovered an equivalent model by systematically enumerating and studying small networks performing segmentation. Cotterell et al. called it a progressive oscillatory reaction-diffusion (PORD) model. In the Meinhardt-PORD model, somitogenesis is driven by short-range interactions and the posterior movement of the front is a local, emergent phenomenon, which is not controlled by global positional information. With this model, it is possible to explain some experimental observations that are incompatible with the clock and wavefront model. However the Meinhardt-PORD model has some important disadvantages of its own. Namely, it is quite sensitive to fluctuations and depends on very specific initial conditions (which are not biologically realistic). In this work, we propose an equivalent Meinhardt-PORD model, and then amend it to couple it with a wavefront consisting of a receding morphogen gradient. By doing so, we get a hybrid model between the Meinhardt-PORD and the clock-and-wavefront ones, which overcomes most of the deficiencies of the two originating models.

Somitogenesis, the process by which somites are formed, is an essential developmental stage in many vertebrates. This process occurs with a strikingly regular periodicity, that is preserved among embryos of a single species. The clock and wavefront paradigm is arguably the most widely accepted model for explaining somitogenesis. However, it is incapable of explaining some experimental facts, like the appearance of somites in the absence of an external wavefront (i.e. a receding morphogen gradient). Shortly after the clock and wavefront paradigm was introduced, Meinhardt presented a conceptually different mathematical model for morphogenesis in general, and somitogenesis in particular. Recently, Cotterell et al. rediscovered an equivalent model by systematically enumerating and studying small networks performing segmentation, and called it a progressive oscillatory reaction-diffusion (PORD) model. The Meinhardt-PORD model tackles some of the deficiencies of the clock and wave-front models, but it has some serious issues of its own. In the present work, we introduce an equivalent Meinhardt-PORD model, and then amend it to couple it with a receding morphogen gradient. By doing so, we get a hybrid model that incorporates characteristics of the Meinhardt-PORD and clock-and-wavefront models. We show that this hybrid model under-goes a bifurcation, from a stable to an unstable limit cycle, as the value of the parameter accounting for a background regulatory input (associated to the receding morphogen gradient) decreases. This bifurcation allows the model to explain why somites can form in the absence of an external wavefront, reassesses the role of the receding morphogen gradient as a conductor for somitogenesis, and makes the model behavior robust to random fluctuations, as well as independent from specific initial conditions (the latter, are two of the weak points of the Meinhardt-PORD model). We argue that this findings provide convincing evidence that reaction-diffusion and positional information (receding morphogen gradient) mechanisms could work together in somitogenesis. The perceptual relevance of the speech envelope-following response in normal hearing and age-related hearing impaired listeners The aim of this work was to investigate the perceptual relevance of the frequency following response to the syllable /da/ for speech intelligibility in noise based on age and hearing deficits. Recordings of the auditory evoked potential from young normal hearing (NH) and older individuals with both normal hearing and high-frequency (HF) hearing loss were analyzed. EFR metrics obtained in quiet and noise condition were calculated and correlated with speech reception. The envelope following responses were analyzed in terms of amplitude, latency and noise robustness. The response was first simulated to form predictions on the effect of cochlear synaptopathy and outer hair cell loss on the EFR. The experimental findings were in line with the computational predictions in the found observation that the EFR was reduced as a consequence of ageing and HF hearing loss. Both the audiogram and the speech EFR magnitude fell short in the individual prediction of SRT in stationary noise, but they accounted well for group performance. We also obtained within-group EFR latency with a cross covariance matrix. Validation of the method confirmed that speech EFR latency was predictive of click ABR Wave V peak latency. Moreover, statistical analysis not only showed that the robustness of the EFR obtained in the noise condition was dependent on the degree of high-frequency hearing loss in the older NH adults, but also dependent on the EFR magnitude in the NH younger adults. These findings provide evidence towards the important role of the EFR in speech-in-noise perception. Catchet-MS identifies IKZF1-targeting Thalidomide analogues as novel HIV-1 latency reversal agents A major pharmacological strategy toward HIV cure aims to reverse latency in infected cells as a first step leading to their elimination. While the unbiased identification of molecular targets physically associated with the latent HIV-1 provirus would be highly valuable to unravel the molecular determinants of HIV-1 transcriptional repression and latency reversal, due to technical limitations, this has not been possible. Here we use a dCas9 targeted chromatin and histone enrichment strategy coupled to mass spectrometry (Catchet-MS) to describe the protein composition of the latent and activated HIV-1 5LTR. Catchet-MS identified known and novel latent 5LTR-associated host factors. Among these, IKZF1 is a novel HIV-1 transcriptional repressor, required for Polycomb Repressive Complex 2 recruitment to the LTR. We find the clinically advanced thalidomide analogue iberdomide, and the FDA approved analogues lenalidomide and pomalidomide, to be novel LRAs that, by targeting IKZF1 for degradation, reverse HIV-1 latency in CD4+T-cells isolated from virally suppressed people living with HIV-1.

One Sentence SummarydCas9 targeted chromatin and histone enrichment for mass spectrometry (Catchet-MS) led to the identification of IKZF1-targeting thalidomide analogues as novel HIV-1 latency reversal agents Diverse stem-chondrichthyan oral structures and evidence for an independently acquired acanthodid dentition The teeth of sharks famously form a series of transversely-organised files with conveyor-belt replacement that are borne directly on the jaw cartilages, in contrast to the dermal plate-borne dentition of bony fishes that undergoes site-specific replacement. A major obstacle in understanding how this system evolved is the poorly understood relationships of the earliest chondrichthyans and the profusion of morphologically and terminologically diverse bones, cartilages, splints and whorls that they possess. Here we use tomographic methods to investigate mandibular structures in several early branching  acanthodian-grade stem-chondrichthyans. We show that the dentigerous jaw bones of disparate genera of ischnacanthids are united by a common construction, being growing bones with non-shedding dentition. Mandibular splints, which support the ventro-lateral edge of the Meckels cartilage in some taxa, are formed from dermal bone and may be an acanthodid synapomorphy. We demonstrate that the teeth of Acanthodopsis are borne directly on the mandibular cartilage and that this taxon is deeply nested within an edentulous radiation, representing an unexpected independent origin of teeth. Many or even all of the range of unusual oral structures may be apomorphic, but they should nonetheless be considered when building hypotheses of tooth and jaw evolution, both in chondrichthyans and more broadly. Balancing sensitivity and specificity in preclinical research The success of scientific discovery in preclinical drug development is based on the different roles of exploration and confirmation. Exploration requires sensitivity while confirmation needs to be specific. Based on published empirical data, we simulated two preclinical research trajectories to examine their effectiveness to detect true effects while keeping animal numbers low. We compare a conventional p-value based approach with a method based on an a priori determined smallest effect size of interest (SESOI). Using a SESOI increases transition rates from exploration to confirmation and results in higher detection rates across the trajectory. Specificity in the SESOI trajectory increases if the prior probability of true effects is low. We conclude that employing a SESOI is superior to a p-value based approach. Based on our findings, we propose a reconsideration of planning and conducting preclinical experiments, especially when the prior probability of true hypotheses is low. Optimized detection of allelic imbalances specific for homologous recombination deficiency improves the prediction of clinical outcomes in cancer Homologous recombination DNA-repair deficiency (HRD) is a common driver of genomic instability and confers a therapeutic vulnerability in human cancer. Using a systematic pan- cancer characterization, we revealed distinct patterns of somatic allelic imbalances (AIs) in high-grade serous ovarian cancer (HGSC). Using machine learning on a multi-omics dataset, we generated an optimized algorithm to detect HRD in HGSC (ovaHRDscar). ovaHRDscar improved the prediction of clinical outcomes in three independent validation cohorts. Characterization of 98 spatiotemporally distinct tumor samples indicated ovary/adnex as the preferred site to assess HRD. Cancer-specific optimization improved the prediction of clinical outcomes also in triple-negative breast cancer (tnbcHRDscar). In conclusion, our systematic approach based on multi-omics data improves the detection of HRD with the premise to improve patient selection for HR-targeted therapies. Inhibitory proteins block substrate access by occupying the active site cleft of Bacillus subtilis intramembrane metalloprotease SpoIVFB Intramembrane proteases function in numerous signaling pathways that impact health, but knowledge about regulation of intramembrane proteolysis is limited. We examined inhibition of intramembrane metalloprotease SpoIVFB by proteins BofA and SpoIVFA. We found that BofA residues in and near a predicted transmembrane segment are required for SpoIVFB inhibition, and cross-linking experiments indicated that this transmembrane segment occupies the SpoIVFB active site cleft. SpoIVFA is also required for SpoIVFB inhibition. The inhibitory proteins block access of the substrate N-terminal Proregion to the membrane-embedded SpoIVFB active site, based on additional cross-linking experiments; however, the inhibitory proteins did not prevent interaction between the substrate C-terminal region and the SpoIVFB soluble domain. A structural model was built of SpoIVFB in complex with BofA and parts of SpoIVFA and substrate, using partial homology and constraints from cross-linking and co-evolutionary analyses. The model predicts that conserved BofA residues interact to stabilize a transmembrane segment and a membrane-embedded C-terminal region. SpoIVFA is predicted to bridge the BofA C-terminal region and SpoIVFB, forming a membrane-embedded inhibition complex. Our results reveal a novel mechanism of intramembrane protease inhibition with clear implications for relief from inhibition in vivo and design of inhibitors as potential therapeutics. Assessing the impact of grassland management on landscape multifunctionality Land-use intensification has contrasting effects on different ecosystem services, often leading to land-use conflicts. While multiple studies have demonstrated how landscape-scale strategies can minimise the trade-off between agricultural production and biodiversity conservation, little is known about which land-use strategies maximise the landscape-level supply of multiple ecosystem services (landscape multifunctionality), a common goal of stakeholder communities.

We combine comprehensive data collected from 150 German grassland sites with a simulation approach to identify landscape compositions, with differing proportions of low-, medium-, and high-intensity grasslands, that minimise trade-offs between the six main grassland ecosystem services prioritised by local stakeholders: biodiversity conservation, aesthetic value, productivity, carbon storage, foraging, and regional identity. Results are made accessible through an online tool that provides information on which compositions best meet any combination of user-defined priorities (https://neyret.shinyapps.io/landscape_composition_for_multifunctionality/).

Results show that an optimal landscape composition can be identified for any pattern of ecosystem service priorities. However, multifunctionality was similar and low for all landscape compositions in cases where there are strong trade-offs between services (e.g. aesthetic value and fodder production), where many services were prioritised, and where drivers other than land use played an important role. We also found that if moderate service levels are deemed acceptable, then strategies in which both high and low intensity grasslands are present can deliver landscape multifunctionality. The tool presented can aid informed decision-making by predicting the impact of future changes in landscape composition, and by allowing for the relative roles of stakeholder priorities and biophysical trade-offs to be understood by scientists and practitioners alike.

HighlightsO_LIAn online tool identifies optimal landscape compositions for desired ecosystem services
C_LIO_LIWhen the desired services are synergic, the optimum is their common best landscape composition
C_LIO_LIWhen the desired services trade-off, a mix of grassland intensity is most multifunctional
C_LIO_LISuch tools could support decision-making processes and aid conflict resolution
C_LI

Graphical abstract

O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/208199v5_ufig1.gif" ALT="Figure 1">
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org.highwire.dtl.DTLVardef@3273f6org.highwire.dtl.DTLVardef@5b3a5aorg.highwire.dtl.DTLVardef@153f526org.highwire.dtl.DTLVardef@103cbea_HPS_FORMAT_FIGEXP  M_FIG C_FIG Fine-scale adaptations to environmental variation and growth strategies drive phyllosphere Methylobacterium diversity. Methylobacterium is a prevalent bacterial genus of the phyllosphere. Despite its ubiquity, little is known about the extent to which its diversity reflects neutral processes like migration and drift, versus environmental filtering of life history strategies and adaptations. In two temperate forests, we investigated how phylogenetic diversity within Methylobacterium was structured by biogeography, seasonality, and growth strategies. Using deep, culture-independent barcoded marker gene sequencing coupled with culture-based approaches, we uncovered a considerable diversity of Methylobacterium in the phyllosphere. We cultured different subsets of Methylobacterium lineages depending upon the temperature of isolation and growth (20 {degrees}C or 30 {degrees}C), suggesting long-term adaptation to temperature. To a lesser extent than temperature adaptation, Methylobacterium diversity was also structured across large (>100km; between forests) and small geographical scales (<1.2km within forests), among host tree species, and was dynamic over seasons. By measuring growth of 79 isolates at different temperature treatments, we observed contrasting growth performances, with strong lineage- and season-dependent variations in growth strategies. Finally, we documented a progressive replacement of lineages with a high-yield growth strategy typical of cooperative, structured communities, in favor of those characterized by rapid growth, resulting in convergence and homogenization of community structure at the end of the growing season. Together our results show how Methylobacterium is phylogenetically structured into lineages with distinct growth strategies, which helps explain their differential abundance across regions, host tree species, and time. This works paves the way for further investigation of adaptive strategies and traits within a ubiquitous phyllosphere genus.

ImportanceMethylobacterium is a bacterial group tied to plants. Despite its ubiquity and importance to their hosts, little is known about the processes driving Methylobacterium community dynamics. By combining traditional culture-dependent and independent (metabarcoding) approaches, we monitored Methylobacterium diversity in two temperate forests over a growing season. On the surface of tree leaves, we discovered remarkably diverse and dynamic Methylobacterium communities over short temporal (from June to October) and spatial scales (within 1.2 km). Because we cultured different subsets of Methylobacterium diversity depending on the temperature of incubation, we suspected that these dynamics partly reflected climatic adaptation. By culturing strains in lab conditions mimicking seasonal variations, we found that diversity and environmental variations were indeed good predictors of Methylobacterium growth performances. Our findings suggest that Methylobacterium community dynamics at the surface of tree leaves results from the succession of strains with contrasted growth strategies in response to environmental variations. Learning and motor inhibitory control in crows and domestic chickens Cognitive abilities allow animals to navigate through complex, fluctuating environments. In the present study, we tested the performance of a captive group of eight crows, Corvus corone, and ten domestic chickens, Gallus gallus domesticus, in the cylinder task, as a test of motor inhibitory control, and reversal learning as a measure of learning ability and behavioural flexibility. Four crows and nine chickens completed the cylinder task, eight crows and six chickens completed the reversal learning experiment. Crows performed better in the cylinder task compared to chickens. In the reversal learning experiment, species did not significantly differ in the number of trials until learning criterion was reached. Performance in the reversal learning experiment did not correlate with performance in the cylinder task in chickens. Our results suggest crows to possess better motor inhibitory control compared to chickens. In contrast, learning performance in a reversal learning task did not differ between the species, indicating similar levels of behavioural flexibility. Interestingly, we describe notable individual differences in performance. We stress the importance not only to compare cognitive performance between species but also between individuals of the same species when investigating the evolution of cognitive skills. Cell type-specific differential expression in spatial transcriptomics Spatial transcriptomics enables spatially resolved gene expression measurements at near single-cell resolution. There is a pressing need for computational tools to enable the detection of genes that are differentially expressed (DE) within specific cell types across tissue context. We show that current approaches cannot learn cell type-specific DE due to changes in cell type composition across space and the fact that measurement units often detect transcripts from more than one cell type. Here, we introduce a statistical method, Cell type-Specific Inference of Differential Expression (C-SIDE), that identifies cell type-specific patterns of differential gene expression while accounting for localization of other cell types. We model spatial transcriptomics gene expression as an additive mixture across cell types of general log-linear cell type-specific expression functions. This approach provides a unified framework for defining and identifying gene expression changes in a wide-range of relevant contexts: changes due to pathology, anatomical regions, physical proximity to specific cell types, and cellular microenvironment. Furthermore, our approach enables statistical inference across multiple samples and replicates when such data is available. We demonstrate, through simulations and validation experiments on Slide-seq and MER-FISH datasets, that our approach accurately identifies cell type-specific differential gene expression and provides valid uncertainty quantification. Lastly, we apply our method to characterize spatially-localized tissue changes in the context of disease. In an Alzheimers mouse model Slide-seq dataset, we identify plaque-dependent patterns of cellular immune activity. We also find a putative interaction between tumor cells and myeloid immune cells in a Slide-seq tumor dataset. We make our C-SIDE method publicly available as part of the open source R package https://github.com/dmcable/spacexr. Robust retrieval of data stored in DNA by de Bruijn graph-based de novo strand assembly DNA data storage is a rapidly developing technology with great potential due to its high density, long-term durability, and low maintenance cost. The major technical challenges include various errors, such as the strand breaks, rearrangements, and indels that frequently arise during DNA synthesis, amplification, sequencing, and preservation. Through a de novo assembly strategy, we developed an algorithm based on the de Bruijn graph and greedy path search (DBGPS) to address these issues. DBGPS shows distinct advantages in handling DNA breaks, rearrangements, and indels. The robustness of DBGPS is demonstrated by accelerated aging, multiple independent data retrievals, deep error-prone PCR, and large data scale simulations. Remarkably, 6.8 MB of data can be retrieved accurately from a seriously corrupted sample that has been treated at 70 {degrees}C for 70 days. With DBGPS, we were able to achieve a logical density of 1.30 bits/cycle and a physical density of 295 PB/g.

One-Sentence SummaryA de Bruijn graph-based de novo assembly algorithm for DNA data storage enables fast and robust data readouts even with DNA samples that have been severely corrupted. Mitofusin 1 and 2 regulation of mitochondrial DNA content is a critical determinant of glucose homeostasis The dynamin-like GTPases Mitofusin 1 and 2 (Mfn1 and Mfn2) are essential for mitochondrial function, which has been principally attributed to their regulation of fission/fusion dynamics. Here, we report that Mfn1 and 2 are critical for glucose-stimulated insulin secretion (GSIS) primarily through control of mitochondrial DNA (mtDNA) content. Whereas Mfn1 and Mfn2 individually were dispensable for glucose homeostasis, combined Mfn1/2 deletion in {beta}-cells reduced mtDNA content, impaired mitochondrial morphology and networking, and decreased respiratory function, ultimately resulting in severe glucose intolerance. Importantly, gene dosage studies unexpectedly revealed that Mfn1/2 control of glucose homeostasis was dependent on maintenance of mtDNA content, rather than mitochondrial structure. Mfn1/2 maintain mtDNA content by regulating the expression of the crucial mitochondrial transcription factor Tfam, as Tfam overexpression ameliorated the reduction in mtDNA content and GSIS in Mfn1/2-deficient {beta}-cells. Thus, the primary physiologic role of Mfn1 and 2 in {beta}-cells is coupled to the preservation of mtDNA content rather than mitochondrial architecture, and Mfn1 and 2 may be promising targets to overcome mitochondrial dysfunction and restore glucose control in diabetes. Elucidation of SARS-CoV-2 budding mechanisms through molecular dynamics simulations of M and E protein complexes SARS-CoV-2 and other coronaviruses pose major threats to global health, yet computational efforts to understand them have largely overlooked the process of budding, a key part of the coronavirus life cycle. When expressed together, coronavirus M and E proteins are sufficient to facilitate budding into the ER-Golgi intermediate compartment (ERGIC). To help elucidate budding, we ran atomistic molecular dynamics (MD) simulations using the Feig laboratorys refined structural models of the SARS-CoV-2 M protein dimer and E protein pentamer. Our MD simulations consisted of M protein dimers and E protein pentamers in patches of membrane. By examining where these proteins induced membrane curvature in silico, we obtained insights around how the budding process may occur. Multiple M protein dimers acted together to induce global membrane curvature through protein-lipid interactions while E protein pentamers kept the membrane planar. These results could eventually help guide development of antiviral therapeutics which inhibit coronavirus budding. Omicron-specific mRNA vaccination alone and as a heterologous booster against SARS-CoV-2 The Omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has high transmissibility and recently swept the globe. Due to the extensive number of mutations, this variant has high level of immune evasion, which drastically reduced the efficacy of existing antibodies and vaccines. Thus, it is important to test an Omicron-specific vaccine, evaluate its immune response against Omicron and other variants, and compare its immunogenicity as boosters with existing vaccine designed against the reference wildtype virus (WT). Here, we generated an Omicron-specific lipid nanoparticle (LNP) mRNA vaccine candidate, and tested its activity in animals, both alone and as a heterologous booster to existing WT mRNA vaccine. Our Omicron-specific LNP-mRNA vaccine elicited strong and specific antibody response in vaccination-naive mice. Mice that received two-dose WT LNP-mRNA, the one mimicking the commonly used Pfizer/Moderna mRNA vaccine, showed a >40-fold reduction in neutralization potency against Omicron variant than that against WT two weeks post second dose, which further reduced to background level >3 months post second dose. As a booster shot for two-dose WT mRNA vaccinated mice, a single dose of either a homologous booster with WT LNP-mRNA or a heterologous booster with Omicron LNP-mRNA restored the waning antibody response against Omicron, with over 40-fold increase at two weeks post injection as compared to right before booster. Interestingly, the heterologous Omicron LNP-mRNA booster elicited neutralizing titers 10-20 fold higher than the homologous WT booster against the Omicron variant, with comparable titers against the Delta variant. All three types of vaccination, including Omicron mRNA alone, WT mRNA homologous booster, and Omicron heterologous booster, elicited broad binding antibody responses against SARS-CoV-2 WA-1, Beta, and Delta variants, as well as other Betacoronavirus species such as SARS-CoV, but not Middle East respiratory syndrome coronavirus (MERS-CoV). These data provided direct proof-of-concept assessments of an Omicron-specific mRNA vaccination in vivo, both alone and as a heterologous booster to the existing widely-used WT mRNA vaccine form. The diverse effects of phenotypic dominance on hybrid fitness When divergent populations interbreed, their alleles are brought together in hybrids. These hybrids may express novel phenotypes, not previously exposed to selection. In the initial F1 cross, most divergent alleles are present as heterozygotes. Therefore, F1 fitness can be influenced by dominance effects that first appear together in the hybrids, and so could not have been selected to function well together. We present a systematic study of these F1 dominance effects by introducing variable phenotypic dominance into Fishers geometric model. We show that dominance often reduces hybrid fitness, which can lead to patterns of optimal outbreeding and a steady decline in F1 fitness at high levels of divergence. We also show that "lucky" beneficial effects sometimes arise by chance, which might be especially important when hybrids can access novel environments. We then explore the interaction of phenotypic dominance with uniparental inheritance, showing that dominance can lead to violations of Haldanes Rule (reduced fitness of the heterogametic sex) while strengthening Darwins Corollary (fitness differences between cross directions). Taken together, our results show that dominance could play an important role in the outcomes of hybridisation after secondary contact, and thus to the maintenance or collapse of isolating barriers. Nevertheless, the telltale signs of dominance are relatively few and subtle. Results also suggest that dominance effects are smaller than the cost of segregation variance, implying that simple additive models may still give good predictions for later-generation recombinant hybrids, even when dominance qualitatively alters outcomes for the F1. Replication of single-cell proteomics data reveals important computational challenges IntroductionMass spectrometry-based proteomics is actively embracing quantitative, single-cell level analyses. Indeed, recent advances in sample preparation and mass spectrometry (MS) have enabled the emergence of quantitative MS-based single-cell proteomics (SCP). While exciting and promising, SCP still has many rough edges. The current analysis workflows are custom and built from scratch. The field is therefore craving for standardized software that promotes principled and reproducible SCP data analyses.

Areas coveredThis special report is the first step toward the formalization and standardization of SCP data analysis. scp, the software that accompanies this work, successfully replicates one of the landmark SCP studies and is applicable to other experiments and designs. We created a repository containing the replicated workflow with comprehensive documentation in order to favor further dissemination and improvements of SCP data analyses.

Expert opinionReplicating SCP data analyses uncovers important challenges in SCP data analysis. We describe two such challenges in detail: batch correction and data missingness. We provide the current state-of-the-art and illustrate the associated limitations. We also highlight the intimate dependence that exists between batch effects and data missingness and offer avenues for dealing with these exciting challenges.

1 Article highlightsO_LISingle-cell proteomics (SCP) is emerging thanks to several recent technological advances, but further progress is still lagging due to the lack of principled and systematic data analysis.
C_LIO_LIThis work offers a standardized solution for the processing of SCP data demonstrated by the replication of a landmark SCP work.
C_LIO_LITwo important challenges remain: batch effects and data missingness. Furthermore, these challenges are not independent and therefore need to be modeled simultaneously.
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