title: Robust associations between white matter microstructure and general intelligence

abstract: Early research on the neural correlates of human intelligence was almost exclusively focused on gray matter properties. The advent of diffusion-weighted imaging led to an exponential growth of white matter brain imaging studies. However, this line of research has yielded mixed observations, especially about the relations between general intelligence and white matter microstructure. We used a multi-center approach to identify white matter regions that show replicable structure-function associations, employing data from four independent samples comprising over 2000 healthy participants. We used tract-based spatial statistics to examine associations between g factor scores and white matter microstructure and identified 188 voxels which exhibited positive associations between g factor scores and fractional anisotropy in all four data sets. Replicable voxels formed three clusters: one located around the forceps minor, crossing with extensions of the anterior thalamic radiation, the cingulum-cingulate gyrus, and the inferior fronto-occipital fasciculus in the left hemisphere, one located around the left-hemispheric superior longitudinal fasciculus, and one located around the left-hemispheric cingulum-cingulate gyrus, crossing with extensions of the anterior thalamic radiation and the inferior fronto-occipital fasciculus. Our results indicate that individual differences in general intelligence are robustly associated with white matter organization in specific fiber bundles.


title: Repulsive Sema3E-Plexin-D1 signaling coordinates both axonal extension and steering via activating an autoregulatory factor, Mtss1

abstract: In the developing nervous system, the axons of newly generated neurons extend toward destination targets following an exquisitely designed program. Axon guidance molecules are critical for neuronal pathfinding because they regulate both directionality and growth pace. However, little is known about the molecular mechanism that coordinates proper axonal extension and turning. Here, we show that Metastasis Suppressor 1 (Mtss1), a membrane protrusion protein, was a molecular facilitator ensuring axonal extension while sensitizing axons to Semaphorin 3E (Sema3E)-Plexin-D1 repulsive guidance cue. We demonstrate that Sema3E-Plexin-D1 signaling regulated Mtss1 expression in projecting striatonigral neurons. Mtss1 in turn induced Plexin-D1 localization to the growth cone where it signaled a repulsive cue to Sema3E. Moreover, Mtss1 was important for neurite extension independent of Sema3E. Ablation of Mtss1 expression reduced growth cone collapse and neurite extension in cultured neurons. Mtss1-knockout mice exhibited fewer striatonigral projections and irregular axonal routes, and these defects were recapitulated in Plxnd1-knockout mice. These findings demonstrate that repulsive axon guidance signaling activates an autoregulatory program to coordinate both axonal extension and steering during neuronal pathfinding.

category: developmental biology


title: Extrinsic regulation of interneuron specification and migration

abstract:
The imbalance between excitatory and inhibitory neurons in the human brain might lead to neurodevelopmental and neuropsychiatric disorders including cortical malformations, epilepsy, and autism spectrum disorders. We propose that the extracellular environment regulates interneuron differentiation and migration during development, ultimately affecting the excitatory/inhibitory balance.

Using ventral cerebral organoids and dorso-ventral cerebral assembloids with mutations in the extracellular matrix gene LGALS3BP, we show that the composition of the extracellular environment regulates the molecular differentiation of neurons, resulting in alterations in migratory dynamics. To investigate how the extracellular environment affects neuronal specification and migration, we characterized the protein content of extracellular vesicles from cerebral organoids carrying a mutation in LGALS3BP, previously identified in individuals with cortical malformations and neuropsychiatric disorders. These results revealed differences in protein composition. Interestingly, proteins associated with cell-fate decision, neuronal migration and extracellular matrix composition were altered in mutant extracellular vesicles. Moreover, we show that treatment with extracellular vesicles changes the transcriptomic profile in neural progenitor cells. Our results indicate that neuronal molecular differentiation is regulated by factors released into the extracellular environment.


title: Regulation of translation by lysine acetylation in Escherichia coli

abstract:
N{varepsilon}-lysine acetylation is a common post-translational modification observed in diverse species of bacteria. Aside from a few central metabolic enzymes and transcription factors, little is known about how this post-translational modification regulates protein activity. In this work, we investigated how lysine acetylation affects translation in Escherichia coli. In multiple species of bacteria, ribosomal proteins are highly acetylated at conserved lysine residues, suggesting that this modification may regulate translation. In support of this hypothesis, we found that the addition of the acetyl donors, acetyl phosphate or acetyl-Coenzyme A, inhibits translation but not transcription using an E. coli cell-free system. Further investigations using in vivo assays revealed that acetylation does not appear to alter the rate of translation elongation but rather increases the proportion of dissociated 30S and 50S ribosomes, based on polysome profiles of mutants or growth conditions known to promote lysine acetylation. Furthermore, ribosomal proteins are more acetylated in the disassociated 30S and 50S ribosomal subunit than in the fully assembled 70S complex. The effect of acetylation is also growth rate dependent, with disassociation of the subunits most pronounced during late exponential and early stationary phase growth - the same growth phase where protein acetylation is greatest. Collectively, our data demonstrate that lysine acetylation inhibits translation, most likely by interfering with subunit association. These results have also uncovered a new mechanism for coupling translation to the metabolic state of the cell.

IMPORTANCENumerous cellular processes are regulated in response to the metabolic state of the cell. One such regulatory mechanism involves lysine acetylation, a covalent modification involving the transfer of an acetyl group from the central metabolites acetyl coenzyme A or acetyl phosphate to a lysine residue in a protein. This post-translational modification is known to regulate some central metabolic enzymes and transcription factors in bacteria, though a comprehensive understanding of its effect on cellular physiology is still lacking. In the present study, lysine acetylation was also found to inhibit translation in Escherichia coli by impeding ribosome association, most likely by disrupting salt-bridges along the binding interface of the 30S and 50S ribosomal subunits. These results further our understanding of lysine acetylation by uncovering a new target of regulation, protein synthesis, and aid in the design of bacteria for biotechnology applications where the growth conditions are known to promote lysine acetylation.

category: microbiology


title: Helminth egg derivatives as pro-regenerative immunotherapies

abstract:
The immune system is increasingly recognized as an important regulator of tissue repair. We developed a regenerative immunotherapy from the helminth Schistosoma mansoni soluble egg antigen (SEA) to stimulate production of interleukin (IL)-4 and other type 2-associated cytokines without negative infection-related sequelae. The regenerative SEA (rSEA) applied to a murine muscle injury induced accumulation of IL-4 expressing T helper cells, eosinophils, and regulatory T cells, and decreased expression of IL-17A in gamma delta ({gamma}{delta}) T cells, resulting in improved repair and decreased fibrosis. Encapsulation and controlled release of rSEA in a hydrogel further enhanced type 2 immunity and larger volumes of tissue repair. The broad regenerative capacity of rSEA was validated in articular joint and corneal injury models. These results introduce a new regenerative immunotherapy approach using natural helminth-derivatives.

One-Sentence SummaryHelminth-derived soluble egg antigen regenerative immunotherapies promote tissue repair in multiple injury models.

category: bioengineering


title: A common framework for discriminability and perceived intensity of sensory stimuli

abstract: The perception of sensory attributes is often quantified through measurements of discriminability (an observers ability to detect small changes in stimulus), as well as direct judgements of appearance or intensity. Despite their ubiquity, the relationship between these two measurements is controversial and unresolved. Here, we propose a framework in which they both arise from the properties of a common internal representation. Specifically, we assume that direct measurements of stimulus intensity (e.g., through rating scales) reflect the mean value of an internal representation, whereas measurements of discriminability reflect the ratio of the derivative of mean value to the internal noise amplitude, as captured by the measure of Fisher Information. Combination of the two measurements allows unique identification of internal representation properties. As a central example, we show that Webers Law of perceptual discriminability can co-exist with Stevens observations of power-law scaling of perceptual intensity ratings (for all exponents), if one assumes an internal representation with noise amplitude proportional to the mean. We extend this result by incorporating a more general physiology-inspired model for noise and a discrimination form that extends beyond Webers range, and show that the combination allows accurate prediction of intensity ratings across a variety of sensory modalities and attributes. Our framework unifies two major perceptual measurements, and provides a potential neural interpretation for the underlying representations.

category: neuroscience


title: Identification of key molecules and biological processes in TCF21 treated tumor pericytes

abstract: Colorectal cancer has become a major public health problem in the US. Transcription factor 21 (TCF21) is reported to be silenced in colorectal cancer tissues. However, the mechanism of TCF21 in tumor pericytes is still unclear. In our study, we aim to identify the key biological processes and signaling pathways by analyzing the RNA-seq data. The GSE200064 was produced by the Illumina NovaSeq 6000 (Homo sapiens). The KEGG and GO analyses showed that MAPK signaling pathway and complement/coagulation cascades are the major changed signaling pathways in the progression of tumor pericytes with overexpression of TCF21. Moreover, we identified several interactive molecules including VEGFA, MMP2, CCL2, COL3A1, COL1A2, CXCL12, ELN, PDGFRB, VWF, and APOE. These findings may benefit the study of colorectal cancer treatment.

category: bioinformatics


title: Taxonbridge: an R package to create custom taxonomies based on the NCBI and GBIF taxonomies

abstract:
SummaryBiological taxonomies establish conventions by which researchers can catalogue and systematically compare their work using nomenclature such as species binomial names and reference identifiers. The ideal taxonomy is unambiguous and exhaustive; however, no such single taxonomy exists, partly due to continuous changes and contributions made to existing taxonomies. The degree to which a taxonomy is useful furthermore depends on context provided by such variables as the taxonomic neighbourhood of a species (e.g., selecting arthropod or vertebrate species) or the geological time frame of the study (e.g., selecting extinct versus extant species). Collating the most relevant taxonomic information from multiple taxonomies is hampered by arbitrarily defined identifiers, ambiguity in scientific names, as well as duplicated and erroneous entries. The goal of taxonbridge is to provide tools for merging the Global Biodiversity Information Facility (GBIF) Backbone Taxonomy and the United States National Center for Biotechnology Information (NCBI) Taxonomy in order to create consistent, deduplicated and disambiguated custom taxonomies that reference both extant and extinct species.

AvailabilityTaxonbridge is available as a package in the Comprehensive R Archive Network (CRAN) repository: https://CRAN.R-project.org/package=taxonbridge.

Contactwernerpieter.veldsman@unil.ch


title: PathGPS: Discover shared genetic architecture using biobank data

abstract: The increasing availability and scale of Genome Wide Association Studies (GWAS) bring new horizons for understanding biological mechanisms. PathGPS is an exploratory method that discovers genetic architecture using GWAS summary data. It can separate genetic components from unobserved environmental factors and extract clusters of genes and traits associated with the same biological pathways. When applying to a metabolomics dataset and the UK Biobank, PathGPS confirms several known gene-trait clusters and suggest many new hypotheses for future investigations.


title: H3AGWAS : A portable workflow for Genome Wide Association Studies

abstract:
BackgroundGenome-wide association studies (GWAS) are a powerful method to detect associations between variants and phenotypes. A GWAS requires several complex computations with large datasets, and many steps may need to be repeated with varying parameter. Manual running of these analyses can be tedious, error-prone and hard to reproduce.

ResultsThe H3AGWAS workflow from the Pan-African Bioinformatics Network for H3Africa is a powerful, scalable and portable workflow implementing pre-association analysis, implementation of various association testing methods and postassociation analysis of results.

ConclusionsThe workflow is scalable -- laptop to cluster to cloud (e.g., SLURM, AWS Batch, Azure). All required software is containerised and can run under Docker on Singularity.


title: Computer simulations reveal pathogenicity and inheritance modes of hearing loss-causing germinal variants

abstract: Variants in the gap junction beta-2 (GJB2) gene are the most common cause of hereditary hearing impairment. However, how GJB2 variants lead to local physicochemical and structural changes in the hexameric ion channels of connexin 26 (Cx26), resulting in hearing impairment, remains elusive. In the present study, using molecular dynamics (MD) simulations, we showed that detached inner-wall N-terminal "plugs" aggregated to reduce the channel ion flow in a highly prevalent V37I variant in humans. To examine the predictability of the computational platform, an artificial mutant, V37M, of which the effect was previously unknown in hearing loss, was created. Microsecond simulations showed that homomeric V37M Cx26 hemichannels had an abnormal affinity between the inner edge and N-termini to block the narrower side of the cone-shaped Cx26, while the most stable heteromeric channels did not. Consistent with these predictions, homozygous V37M transgenic mice exhibited apparent hearing loss, but not their heterozygous counterparts, indicating a recessive inheritance mode. Reduced channel conductivity was found in Gjb2V37M/V37M outer sulcus cells and Claudius cells but not in Gjb2WT/WT cells. We view that the current computational platform could serve as an assessment tool for the pathogenesis and inheritance of GJB2-related hearing impairments and other diseases caused by connexin dysfunction.

category: biophysics


title: IDOL deficiency inhibits cholesterol-rich diet-induced atherosclerosis in rabbits

abstract:
BACKGROUNDThe E3 ubiquitin ligase IDOL (Inducible Degrader of the LDL-Receptor) contributes to regulation of cholesterol metabolism through degradation of LDLR, VLDLR and ApoER2. Human genetic studies support the hypothesis that IDOL could serve as a target for the treatment of dyslipidemia. However, species-specific differences in overall lipid metabolism and IDOL regulation require new preclinical models to realize its therapeutic potential. We leveraged the advantages afforded by the rabbit model to address those limitations and generated a novel rabbit IDOL knockout, which we characterized in the context of atherosclerosis.

METHODSIDOL-/- rabbits were generated by CRISPR/Cas9 technology. IDOL-/- and wildtype littermates, on standard (SD) and atherogenic high-cholesterol diets (HCDs) were compared through assessment of lipid and lipoprotein profiles, triglyceride clearance, lipoprotein lipase (LPL) activity, liver pathology, atherosclerosis development, and fecal cholesterol, with bile acid contents assessed by mass spectrometry.

ResultsHepatic IDOL expression was increased in response to hypercholesterolemia and hypertriglyceridemia induced by HCD. On SD, loss of IDOL increased LDLR stability with reduced total cholesterol in plasma. On HCD, IDOL-/- rabbits showed simultaneous and remarkable reduction in hypercholesterolemia and hypertriglyceridemia associated with enhanced lipid clearance and LPL activity as well as increased bile acid excretion in feces. IDOL-/- rabbits presented markedly reduced HCD-induced atherosclerosis in the aorta and left coronary artery, without enhanced liver steatosis.

CONCLUSIONSLoss of IDOL in rabbits recapitulates human genetic findings, thus setting the stage to accelerate preclinical studies towards development of strategies targeting IDOL for the treatment of atherosclerotic cardiovascular disease.

category: physiology


title: A transcriptional complex of FtMYB102 and FtbHLH4 coordinately regulates the accumulation of rutin in Fagopyrum tataricum

abstract: Tartary buckwheat is rich in flavonoids, which not only play an important role in plant-environment interaction, but are also beneficial to human health. Rutin is a therapeutic flavonol which is massively accumulated in Tartary buckwheat. It has been demonstrated that transcription factors control rutin biosynthesis. However, the transcriptional regulatory network of rutin is not fully clear. In this study, through transcriptome and target metabolomics, we validated the role of FtMYB102 and FtbHLH4 TFs at the different developmental stages of Tartary buckwheat. The elevated accumulation of rutin in the sprout appears to be closely associated with the expression of FtMYB102 and FtHLH4. Yeast two-hybrid, transient luciferase activity and co-immunoprecipitation demonstrated that FtMYB102 and FtbHLH4 can interact and form a transcriptional complex. Moreover, yeast one-hybrid showed that both FtMYB102 and FtbHLH4 directly bind to the promoter of chalcone isomerase (CHI), and they can coordinately induce CHI expression as shown by transient luciferase activity assay. Finally, we transferred the FtMYB102 and FtbHLH4 into the hairy roots of Tartary buckwheat and found that they both can promote the accumulation of rutin. Our results indicate that FtMYB102 and FtbHLH4 can form a transcriptional complex by inducing CHI expression to coordinately promote the accumulation of rutin.

category: plant biology


title: Multi-omics analyses of drug repurposing reveal Acebutolol and Amiloride for osteoporosis treatment

abstract: Osteoporosis is a metabolic bone disease that occurs during aging, characterized by low bone mineral density (BMD) and a high risk of trauma fracture. While current pharmacological interventions provide symptomatic benefits, they are unsatisfactory and have major side effects. In this study, we used multi-omics data and drug similarity to construct osteoporosis driver signaling networks (ODSN) and drug functional networks (DFN), respectively. By integrating ODSN and DFN with treatment transcriptional responses, we observed 8 drugs that demonstrated strong targeting effects on ODSN. Mendelian Randomization analysis determines the causal effect on BMD using cis-eQTLs of the drug targets and BMD GWAS data. The findings suggested Acebutolol and Amiloride may increase BMD, while Acenocoumarol, Aminocaproic acid and Armodafinil may enhance bone loss. Zebrafish experiments experimentally showed Acebutolol hydrochloride and Amiloride hydrochloride had significant protective effects on osteoporosis in zebrafish embryos induced by Dexamethasone. Also, Acenocoumarol reduced bone mineralization compared with the control group. The findings suggest that the hypertension drugs Acebutolol and Amiloride warrant further investigation in functional mechanistic experiments to evaluate their effectiveness for osteoporosis treatments.

category: systems biology


title: KINOMO: A non-negative matrix factorization framework for recovering intra- and inter-tumoral heterogeneity from single-cell RNA-seq data

abstract:
Single-cell RNA-sequencing (scRNA-seq) is a powerful technology to uncover cellular heterogeneity in tumor ecosystems. Due to differences in underlying gene load, direct comparison between patient samples is challenging, and this is further complicated by the sparsity of data matrices in scRNA-seq. Here, we present a factorization method called KINOMO (Kernel dIfferentiability correlation-based NOn-negative Matrix factorization algorithm using Kullback-Leibler divergence loss Optimization). This tool uses quadratic approximation approach for error correction and an iterative multiplicative approach, which improves the quality assessment of NMF-identified factorization, while mitigating biases introduced by inter-patient genomic variability. We benchmarked this new approach against nine different methods across 15 scRNA-seq experiments and find that KINOMO outperforms prior methods when evaluated with an adjusted Rand index (ARI), ranging 0.82-0.91 compared to 0.68-0.77. Thus, KINOMO provides an improved approach for determining coherent transcriptional programs (and meta-programs) from scRNA-seq data of cancer tissues, enabling comparison of patients with variable genomic backgrounds.

ClassificationPhysical Sciences (Applied Mathematics; Biophysics and Computational Biology), Biological Sciences (Applied Biological Sciences; Biophysics and Computational Biology; Medical Sciences; Systems Biology.).

Significance StatementIdentification of shared or distinct cell programs in single-cell RNA-seq data of patient cancer cells is challenging due to underlying variability of gene load which determines transcriptional output. We developed an analytical approach to define transcriptional variability more accurately across patients and therefore enable comparison of program expression despite inherent genetic heterogeneity. Thus, this method overcomes challenges not adequately addressed by other methods broadly used for the analysis of single-cell genomics data.


title: Motor and Visual Plasticity Interact in Adult Humans

abstract: Neuroplasticity is maximal during development and declines in adulthood, especially for sensory cortices. On the contrary, the motor cortex retains plasticity throughout the lifespan. This difference has led to a modular view of plasticity in which different brain regions have their own plasticity mechanisms that do not depend or translate on others. Recent evidence indicates that visual and motor plasticity share common neural mechanisms (e.g. GABAergic inhibition), indicating a possible link between these different forms of plasticity, however the interaction between visual and motor plasticity has never been tested directly. Here we show that when visual and motor plasticity are elicited at the same time in adult humans, visual plasticity is impaired, while motor plasticity is spared. This unilateral interaction between visual and motor plasticity demonstrates a clear link between these two forms of plasticity. We conclude that local neuroplasticity in separate systems might be regulated globally, to preserve overall homeostasis in the brain.


title: A neural mechanism for terminating decisions

abstract: The brain makes decisions by accumulating evidence until there is enough to stop and choose. Neural mechanisms of evidence accumulation are well established in association cortex, but the site and mechanism of termination is unknown. Here, we elucidate a mechanism for termination by neurons in the primate superior colliculus. We recorded simultaneously from neurons in lateral intraparietal cortex (LIP) and the superior colliculus (SC) while monkeys made perceptual decisions, reported by eye-movements. Single-trial analyses revealed distinct dynamics: LIP tracked the accumulation of evidence on each decision, and SC generated one burst at the end of the decision, occasionally preceded by smaller bursts. We hypothesized that the bursts manifest a threshold mechanism applied to LIP activity to terminate the decision. Focal inactivation of SC produced behavioral effects diagnostic of an impaired threshold sensor, requiring a stronger LIP signal to terminate a decision. The results reveal the transformation from deliberation to commitment.


title: Independent spatiotemporal effects of spatial attention and background clutter on human object location representations

abstract: 1.Spatial attention helps us to efficiently localize objects in cluttered environments. However, the processing stage at which spatial attention modulates object location representations remains unclear. Here we investigated this question identifying processing stages in time and space in an EEG and fMRI experiment respectively. As both object location representations and attentional effects have been shown to depend on the background on which objects appear, we included object background as an experimental factor. During the experiments, human participants viewed images of objects appearing in different locations on blank or cluttered backgrounds while either performing a task on fixation or on the periphery to direct their covert spatial attention away or towards the objects. We used multivariate classification to assess object location information. Consistent across the EEG and fMRI experiment, we show that spatial attention modulated location representations during late processing stages (>150ms, in middle and high ventral visual stream areas) independent of background condition. Our results clarify the processing stage at which attention modulates object location representations in the ventral visual stream and show that attentional modulation is a cognitive process separate from recurrent processes related to the processing of objects on cluttered backgrounds.


title: The passive representations were consciously formed via the representational transition from the active state

abstract: The passive state serves robust maintenance for the memory representations, which performed a functionally different role from the active state during the mlutistate maintenance in visual working memory. However, it is still unclear how the passive representations are formed. The current study was based on the embedded process model and activity-silent model to explore this question by adopting a sequential presentation paradigm. The temporal modulation focused on the presentation time of subsequent stimuli and the retention interval between the two memory arrays. These results demonstrated that the passive representation was consciously formed via the representational transition from the active state, rather than in a spontaneous manner during memory encoding period; moreover, the representational transition was driven by cognitive demands. The current study presented the insights on the contribution of the passive maintenance to long-term memory.


title: Auditory spatial analysis in reverberant audio-visual multi-talker environments with congruent and incongruent visual room information

abstract: In multi-talker situation, listeners have the challenge to identify a target speech source out of a mixture of interfering background noises. In the current study it was investigate how listeners analyze audio-visual scenes with varying complexity in terms of number of talkers and reverberation. Furthermore, the visual information of the room was either coherent with the acoustic room or incoherent. The listeners task was to locate an ongoing speech source in a mixture of other speech sources. The 3D audio-visual scenarios were presented using a loudspeaker array and virtual reality glasses. It was shown that room reverberation as well as the number of talkers in a scene influence the ability to analyze an auditory scene in terms of accuracy and response time. Incongruent visual information of the room did not affect this ability. When few talkers were presented simultaneously, listeners were able to quickly and accurately detect a target talker even in adverse room acoustical conditions. Reverberation started to affect the response time when four or more talkers were presented. The number of talkers became a significant factor for five or more simultaneous talkers.


title: ISG15 Monomer Promotes IFNα-mediated Antiviral Activity against Pseudorabies Virus by Facilitating phosphorylation of STAT1/STAT2

abstract: Pseudorabies virus (PRV), which presently lacks both an antiviral drug and a viable therapeutic option, is a major viral pathogen that poses a danger to the pig industry worldwide. Interferon-stimulated gene 15 (ISG15) is strongly upregulated during viral infections and has been reported to have proviral or antiviral properties, depending on the virus and host species. Our previous studies demonstrated ISG15 was remarkably upregulated during PRV infection, and the overexpression of ISG15 inhibited PRV replication. Nevertheless, the exact mechanism through which ISG15 influences PRV replication poorly understood unclear. Here, we demonstrate that ISG15 accumulation induced by PRV infection requires viral gene expression and viral growth. Conjugation inhibition assays showed that ISG15 imposes its antiviral effects via unconjugated (free) ISG15 and affects the viral release. In addition, we found ISG15 promoted IFN-mediated antiviral activity against PRV by facilitating the phosphorylation of STAT1 and STAT2, along with an increase of ISGF3-induced ISRE promoter activity. Furthermore, we evaluated the role of ISG15 in host defense to control PRV infection by using ISG15-/- mice model. When challenged with PRV, ISG15-/- mice exhibited increased morbidity and mortality, as well as viral load compared to WT mice. Pathological examination revealed increased lesions, mononuclear cellular infiltration and neuronal death in the brains of ISG15-/- mice, along with the upregulation of the cytokines. Our findings establish the importance of ISG15 in IFN-induced antiviral response and in the control of PRV infection.

category: molecular biology


title: Gene expression analysis of the Xenopus laevis early limb bud proximodistal axis.

abstract: Limb buds develop as bilateral outgrowths of the lateral plate mesoderm and are patterned along three axes. Current models of proximal to distal patterning of early amniote limb buds suggest the presence of two signals, a distal organising signal from the apical epithelial ridge (AER) and an opposing proximal signal, that act early on to form the stylopod and zeugopod. Here, we have used transcriptional analysis of early Xenopus laevis hindlimb buds sectioned along the proximal-distal axis, to show support for this model in anamniotes. The distal limb, which contains AER and distal mesenchyme, is transcriptionally distinct from the rest of the limb. Expression of capn8.3.L, a novel calpain, was located in cells immediately flanking the AER suggesting a novel role in determining the boundary of this critical signalling centre. The Wnt antagonist Dkk1 was AER-specific in Xenopus limbs, which differs from the mesenchymal expression in amniotes. Two transcription factors with no known role in limb development, sall1 and zic5, were expressed in distal mesenchyme. We also describe expression of two proximal genes, gata5.L and tnn.L, not previously associated with limb development. Differentially expressed genes were associated with Fgf, Wnt and retinoic acid (RA) signalling as well as differential cell adhesion and proliferation. We find evidence to support transcriptional gradients of RA-regulated genes across the early hindlimb, suggesting that proximal RA signals regulate proximal to distal patterning.


title: Ability of the Ash dieback pathogen to reproduce and to induce damage on its host are controlled by different environmental parameters

abstract: Ash dieback, induced by an invasive ascomycete, Hymenoscyphus fraxineus, has emerged in the last decade as a severe disease threatening ash populations in Europe. Future prospects for Ash are improved by the existence of individuals with natural genetic resistance to the disease and by limited disease impact in many environmental conditions where ashes are frequent. Nevertheless, it was suggested that even in those conditions, ash trees are infected and enable pathogen transmission. We studied the influence of climate and local environment on the ability of H. fraxineus to infect, be transmitted and cause damage on its host. We showed that healthy carrier, i.e. asymptomatic individuals carrying H. fraxineus, exists and may play a significant role in ash dieback epidemiology. Environment strongly influenced H. fraxineus with different parameters being important depending on the life cycle stage. The ability of H. fraxineus to establish on ash leaves and to reproduce on the leaf debris in the litter (rachises) mainly depended on total precipitations in July-August and was not influenced by local tree cover. By contrast, damages to the host, and in particular shoot mortality was significantly reduced by high summer temperature in July-August and by high autumn average temperature. As a consequence, in many situations ash trees are infected and enable H. fraxineus transmission while showing limited or even no damages. We also observed a decreasing trend of severity (leaf necrosis and shoot mortality likelihood) with the time of disease presence in a plot that could be significant for the future of Ash dieback.

category: ecology


title: Abrupt transitions and its indicators in mutualistic meta-networks: effects of network topology, size of metacommunities and species dispersal

abstract: Gradual changes in the environment could cause dynamical ecological networks to abruptly shift from one state to an alternative state. When this happens ecosystem functions and services provided by ecological networks get disrupted. We, however, know very little about how the topology of such interaction networks can play a role in the transitions of ecological networks at a spatial scale. In the event of such unwanted transitions, little is known how statistical metrics used to inform such impending transitions, measured at the species-level or at the community-level could relate to network architecture and the scale of spatial interactions. Here, using fifty-six empirical plantpollinator networks in a spatial setting, I evaluated the impact of network topology and spatial scale of species interactions on abrupt transitions, and on statistical metrics used as predictors to forecast such abrupt transitions. Using generalized Lotka-Volttera equations in a meta-network framework, I show that species dispersal rate and the size of the metacommunity can impact when an abrupt transition can occur. In addition, forecasting such unwanted abrupt transitions of meta-networks using statistical metrics of instability was also consequently dependent on the topology of the network, species dispersal rate, and the size of the metacommunity. The results indicated that the plantpollinator meta-networks that could exhibit stronger statistical signals before collapse than others were dependent on their network architecture and on the spatial scale of species interactions.


title: An improved species richness estimator using spatial abundance data

abstract: Species richness is an essential biodiversity variable indicative of ecosystem states and mass extinctions both contemporarily and in fossil records. However, limitations to sampling effort and spatial aggregation of organisms mean that surveys often fail to observe some species, making it difficult to estimate true richness and hinder the comparison of communities. Here we present a nonparametric, asymptotic, minimal-bias richness estimator,{Omega} o, by modelling how spatial abundance characteristics affect observation of species richness.{Omega} o consistently outperforms the best-established richness estimators and can detect small differences that other methods cannot. We conducted simulation tests and applied{Omega} o to a seaweed survey dataset.{Omega} o returns more consistent estimates and bootstrapped confidence bounds across years and approaches asymptote with less data than other estimators. The results provide theoretical insights on how biotic and observer variations affect species observation, identify remaining problems, and quantify possible performance gains based on{Omega} o.


title: The role of geology in creating stream climate-change refugia along climate gradients

abstract: Identifying climate-change refugia is a key adaptation strategy for reducing global warming impacts. Knowledge of the effects of underlying geology on thermal regime along climate gradients and the ecological responses to the geology-controlled thermal regime is essential to plan appropriate climate adaptation strategies. The dominance of volcanic rocks in the watershed is used as a landscape-scale surrogate for cold groundwater inputs to clarify the importance of underlying geology. Using statistical models, we explored the relationship between watershed geology and the mean summer water temperature of mountain streams along climate gradients in the Japanese archipelago. Summer water temperature was explained by the interaction between the watershed geology and climate in addition to independent effects. The cooling effect associated with volcanic rocks was more pronounced in streams with less summer precipitation or lower air temperatures. We also examined the function of volcanic streams as cold refugia under contemporary and future climatic conditions. Community composition analyses revealed that volcanic streams hosted distinct stream communities composed of more cold-water species compared with non-volcanic streams. Scenario analyses revealed a geology-related pattern of thermal habitat loss for cold-water species. Non-volcanic streams rapidly declined in thermally suitable habitats for lotic sculpins even under the lowest emission scenario (RCP 2.6). In contrast, most volcanic streams will be sustained below the thermal threshold, especially for low and mid-level emission scenarios (RCP 2.6, 4.5). However, the distinct stream community in volcanic streams and geology-dependent habitat loss for lotic sculpins was not uniform and was more pronounced in areas with less summer precipitation or lower air temperatures. Although further studies are needed to understand underlying mechanisms of the interplay of watershed geology and climate, findings highlight that watershed geology, climate variability, and their interaction should be considered simultaneously for effective management of climate-change refugia in mountain streams.


title: Validation of the Tea Bag Index as a standard approach for assessing organic matter decomposition

abstract: The Tea Bag Index (TBI), a novel approach to assessing organic matter decomposition using commercial tea bags, has been increasingly utilized as a standard method in academic studies worldwide. This approach was designed to obtain an early-stage decomposition constant (k) indicative of early-stage decomposition rates and a litter stabilization factor (S) indicative of long-term carbon stability by using two types of teas--green and rooibos. However, despite the worldwide usage of the method, the accuracy of this approach has never been validated in terrestrial ecosystems. Here, the validity of this approach was tested by examining the two essential premises of the TBI using a laboratory incubation experiment. The first premise of the TBI--namely, that the unstabilized hydrolyzable fraction of green tea is mostly decomposed within 90 days--did not hold in the present study, which caused overestimations of the S of rooibos tea, as well as k. The second premise--namely, that the ratio of stabilized to total hydrolyzable fractions (i.e., S) of rooibos tea is equal to that of green tea--was also rejected, which resulted in substantial underestimations of the S of rooibos tea and k. Overall, the TBI largely underestimated the S of rooibos tea and k (more than 1.5 and 5 times smaller than those determined by time-series data, respectively). The present study suggests that time-series mass loss data of rooibos tea should be obtained to accurately determine k, rather than assuming that the S of rooibos tea is equal to that of green tea.


title: Defining marine bacterioplankton community assembly rules by contrasting the importance of environmental determinants and biotic interactions

abstract:
Bacterioplankton communities play major roles in governing marine productivity and biogeochemical cycling, yet what drives the relative influence of the types of deterministic ecological processes which result in diversity patterns remains unclear. Here we examine how differing deterministic processes (environmental factors and biotic interactions) drive temporal dynamics of bacterioplankton diversity at three different oceanographic time-series locations, spanning 15 degrees of latitude, which are each characterized by different environmental conditions and varying degrees of seasonality. Monthly surface samples, collected over a period of 5.5 years, were analyzed using 16S rRNA amplicon sequencing. The high and mid-latitude sites of Maria Island and Port Hacking were characterized by high and intermediate levels of environmental heterogeneity respectively, with both alpha (local) diversity (72 % and 24 % of total variation) and beta diversity (32 % and 30 %) patterns within bacterioplankton assemblages primarily explained by environmental determinants, including day length, ammonium, and mixed layer depth. In contrast, at North Stradbroke Island, a sub-tropical location where environmental conditions are less seasonally variable, interspecific interactions were of increased importance in structuring bacterioplankton diversity (alpha diversity: 33 %; beta diversity: 26 %) with environment only contributing 11 and 13 % to predicting diversity, respectively. Our results demonstrate that bacterioplankton diversity is the result of both deterministic environmental and biotic processes and that the importance of these different deterministic processes varies, potential in response to environmental heterogeneity.

ImportanceMarine bacterioplankton drives important biological processes, including the cycling of key nutrients or fixing atmospheric carbon. Therefore, to predict future climate scenarios its critical to model these communities accurately. Processes that drive bacterioplankton diversity patterns in the oceans however remain unresolved, with most studies focusing on deterministic environmental drivers, ie temperature or available inorganic nutrients. Biotic deterministic processes including interactions among individuals are also important for structuring diversity patterns, however, this is rarely included to predict bacterioplankton communities. We develop an approach for determining the relative contribution of environmental and potential biotic interactions that structure marine bacterioplankton at three series at different latitudes. Environmental factors best predicted temporal trends in bacterioplankton diversity at the two high latitude time series, while biotic influence was most apparent at the low latitude time series. Our results suggest environmental heterogeneity is an important attribute driving the contribution of varying deterministic influence of bacterioplankton diversity.


title: DNAJC13 influences responses of the extended reward system to conditioned stimuli: a genome-wide association study

abstract: Reward system dysfunction is implicated in the pathogenesis of major psychiatric disorders. We conducted a genome-wide association study (GWAS) to identify genes that influence activation strength of brain regions within the extended reward system in humans. A large homogeneous sample of 214 participants was genotyped and underwent functional magnetic resonance imaging (fMRI). All subjects performed the  desire-reason dilemma (DRD) paradigm allowing systematic investigation of systems-level mechanisms of reward processing in humans. As a main finding, we identified the single nucleotide variant rs113408797 in the DnaJ Heat Shock Protein Family Member C13 gene (DNAJC13, alias RME-8), that strongly influenced the activation of the ventral tegmental area (VTA; p = 2.50E-07) and the nucleus accumbens (NAcc; p = 5.31E-05) in response to conditioned reward stimuli. Moreover, haplotype analysis assessing the information across the entire DNAJC13 locus demonstrated an impact of a five-marker haplotype on VTA activation (p = 3.21E-07), which further corroborates a link between this gene and reward processing. The present findings provide first direct empirical evidence that genetic variation of DNAJC13 influences neural responses within the extended reward system to conditioned stimuli. Further studies are required to investigate the role of this gene in the pathogenesis and pathophysiology of neuropsychiatric disorders.

category: genomics


title: Label-free imaging of large samples: 3D rendering and morphological analysis within histological workflows using serial block face imaging

abstract: Serial block face imaging (SFBI) is a method used to generate 3-dimensional (3D) reconstruction of a sample via serial image acquisition. Several SBFI approaches have been proposed for large samples, differing in the ability to generate contrast as well as in the nature of the detected signal. We propose a new system that detects the endogenous autofluorescence signal of paraffin-embedded samples. The sample preparation is simplified compared to other approaches, and adapted to be integrated into a routine histological preparation. More specifically, it was designed to limit reagent toxicity and to be compatible with downstream histological processing. We show the usefulness of the technique with a wide range of tissues based on the intrinsic autofluorescence signal. Optimization of quality section recovery offers the possibility to develop correlative approaches and multimodal analysis between the 3D dataset with the 2-dimensional (2D) sections. In addition, contrast and resolution of block-face images allow us to successfully perform post processing analysis and morphology quantifications. Overall, our methodology offers a simple, cost effective and rapid approach to obtain quantitative data on a large sample with no specific staining.


title: Extending the Boundaries of Cancer Therapeutic Complexity with Literature Data Mining

abstract: Even though there is high motivation to develop effective monotherapies for cancer, combination drug therapy is still one of the main pillars of oncological treatments. The formation of an effective combinatorial standard of care (SOC) can take many years and its length of development is increasing with complexity of treatment. In this paper, we develop a path to extend the boundaries of complexity in combinatorial cancer treatments using text data mining (TDM). We first used TDM to characterize the current boundaries of cancer treatment complexity. We found that the current complexity limit for clinical trials is 6 drugs per plan and for pre-clinical research is 10. We then developed a TDM based assistive technology, cancer plan builder (CPB), that allows experts to create high complexity combination treatment (HCCT) plans of significantly larger size. We evaluated CPB by comparing it to the standard of care, to fully automated plans and to HCCT plans created by experts without the tool. We found that by average, 86% of elements in the plans generated with CPB are either compatible with standard of care or FDA approved for the correct indication. In addition, CPB assisted plans were superior in every metric to plans generated either automatically or manually. To conclude, we propose a new tool and workflow to handle the vast complexity space of combinatorial cancer treatment facilitate further evaluation of HCCT in experimental cancer research.


title: Multi-task learning uncovers robust translation cis-regulatory features

abstract:
BackgroundMany studies have found that sequence in the 5 untranslated regions (UTRs) impacts the translation rate of an mRNA, but the regulatory grammar that underpins this translation regulation remains elusive. Deep learning methods deployed to analyse massive sequencing datasets offer new solutions to motif discovery. However, existing works focused on extracting sequence motifs in individual datasets, which may not be generalisable to other datasets from the same cell type. We hypothesise that motifs that are genuinely involved in controlling translation rate are the ones that can be extracted from diverse datasets generated by different experimental techniques. In order to reveal more generalised cis-regulatory motifs for RNA translation, we develop a multi-task translation rate predictor, MTtrans, to integrate information from multiple datasets.

ResultsCompared to single-task models, MTtrans reaches a higher prediction accuracy in all the benchmarked datasets generated by various experimental techniques. We show that features learnt in human samples are directly transferable to another dataset in yeast systems, demonstrating its robustness in identifying evolutionarily conserved sequence motifs. Furthermore, our newly generated experimental data corroborated the effect of most of the identified motifs based on MTtrans trained using multiple public datasets, further demonstrating the utility of MTtrans for discovering generalisable motifs.

ConclusionsMTtrans effectively integrates biological insights from diverse experiments and allows robust extraction of translation-associated sequence motifs in 5UTR.


title: GraphPred: An approach to predict multiple DNA motifs from ATAC-seq data using graph neural network and coexisting probability

abstract: Assay for Transposase-Accessible Chromatin sequencing (ATAC-seq) utilizes hyperactive Tn5 transposase to cut open chromatin and reveal chromatin accessibility at a genome-wide level. ATAC-seq can reveal more kinds of transcription factor binding regions than Chromatin immunoprecipitation sequencing (ChIP-seq) and DNase I hypersensitive sites sequencing (DNase-seq). Transcription factor binding sites (TFBSs) prediction is a crucial step to reveal the functions of TFs from the high throughput sequencing data. TFBSs of the same TF tend to be conserved in the sequence level, which is named motif. Several deep learning models based on the convolutional neural networks are used to find motifs from ATAC-seq data. However, these methods didnt take into account that multiple TFs bind to a given sequence and the probability that a fragment of a given sequence is a TFBS. To find binding sites of multiple TFs, we developed a novel GNN model named GraphPred for TFBSs prediction and finding multiple motifs using the coexisting probability of k-mers. In the light of the experiment results, GraphPred can find more and higher quality motifs from 88 ATAC-seq datasets than comparison tools. Meanwhile, GraphPred achieved an area of eight metrics radar (AEMR) score of 2.31.


title: A real data-based simulation procedure to select an imputation strategy for mixed-type trait data

abstract:
Missing observations in trait datasets pose an obstacle for analyses in myriad biological disciplines. Imputation offers an alternative to removing cases with missing values from datasets. Imputation techniques that incorporate phylogenetic information into their estimations have demonstrated improved accuracy over standard techniques. However, previous studies of phylogenetic imputation tools are largely limited to simulations of numerical trait data, with categorical data not evaluated. It also remains to be explored whether the type of genetic data used affects imputation accuracy. We conducted a real data-based simulation study to compare the performance of imputation methods using a mixed-type trait dataset (lizards and amphisbaenians; order: Squamata). Selected methods included mean/mode imputation, k-nearest neighbour, random forests, and multivariate imputation by chained equations (MICE). Known values were removed from a complete-case dataset to simulate different missingness scenarios: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). Each method (with and without phylogenetic information derived from mitochondrial and nuclear gene trees) was used to impute the removed values. The performances of the methods were evaluated for each trait and in each missingness scenario. A random forest method supplemented with a nuclear-derived phylogeny performed best overall, and this method was used to impute missing values in the original squamate dataset. Data with imputed values better reflected the characteristics and distributions of the original data compared to the complete-case data. However, phylogeny did not always improve performance for every trait and in every missingness scenario, and caution should be taken when imputing trait data, particularly in cases of extreme bias. Ultimately, these results support the use of a real data-based simulation procedure to select a suitable imputation strategy for a given mixed-type trait dataset. Moreover, they highlight the potential biases that complete-case usage may introduce into analyses.

Author summaryThe issue of missing data is problematic in trait datasets as observations for rare or threatened species are often missing disproportionately. When only complete cases are used in an analysis, derived results may be biased. Imputation is an alternative to complete-case analysis and entails filling in the missing values using known observations. It has been demonstrated that including phylogenetic information in the imputation process improves accuracy of predicted values. However, most previous evaluations of imputation methods for trait datasets are limited to numerical, simulated data, with categorical traits not considered. Using a reptile dataset comprised of both numerical and categorical trait data, we employed a real data-based simulation strategy to select an optimal imputation method for the dataset. We evaluated the performance of four different imputation methods across different missingness scenarios (e.g. missing completely at random, values missing disproportionately for smaller species. Results indicate that imputed data better reflected the original dataset characteristics compared to complete-case data; however, the optimal imputation strategy for a given scenario was contingent on missingness scenario and trait type. As imputation performance varies depending on the properties of a given dataset, a real data-based simulation strategy can be used to provide guidance on best imputation practices.


title: Probing single cell fermentation flux and intercellular exchange networks via pH-microenvironment sensing and inverse modeling

abstract: The homeostatic control of their environment is an essential task of living cells. It has been hypothesized that when microenvironmental pH inhomogeneities are induced by high cellular metabolic activity, diffusing protons act as signaling molecules, driving the establishment of cross-feeding networks sustained by the cell-to-cell shuttling of overflow products such as lactate. Despite their fundamental role, the extent and dynamics of such networks is largely unknown due to the lack of methods in single cell flux analysis. In this study we provide direct experimental characterization of such exchange networks. We devise a method to quantify single cell fermentation fluxes over time by integrating high-resolution pH microenvironment sensing via ratiometric nanofibers with constraint-based inverse modeling. We apply our method to cell cultures with mixed populations of cancer cells and fibroblasts. We find that the proton trafficking underlying bulk acidification is strongly heterogeneous, with maximal single cell fluxes exceeding typical values by up to 3 orders of magnitude. In addition, a crossover in time from a networked phase sustained by densely connected "hubs" (corresponding to cells with high activity) to a sparse phase dominated by isolated dipolar motifs (i.e. by pair-wise cell-to-cell exchanges) is uncovered, which parallels the time course of bulk acidification. Our method promises to shed light on issues ranging from the homeostatic function of proton exchange to the metabolic coupling of cells with different energetic demands, and paves the way for real-time non-invasive single cell metabolic flux analysis.


title: Digital holography-based 3D particle localisation for single molecule tweezer techniques

abstract:
We present a three-dimensional imaging technique for fast tracking of microscopic objects in a fluid environment. Our technique couples digital holographic microscopy with three-dimensional localisation via parabolic masking. Compared with existing approaches, our method reconstructs 3D volumes from single-plane images, which greatly simplifies image acquisition, reduces the demand on microscope hardware, and facilitates tracking higher densities of microscopic particles while maintaining similar levels of precision. We demonstrate utility of this method in magnetic tweezer experiments, opening their use to multiplexed single-molecule force spectroscopy assays. We propose that our technique will also be useful in other applications that involve the tracking of microscopic objects in three dimensions.

SIGNIFICANCETracking objects in 3D is a common task in biology, but typically requires the acquisition of image stacks, which is limited by speed, the depth of field of microscope objectives and by the presence of other objects that obscure the illumination. Here we develop HoloMiP (Holographic Microscopy with Parabolic masking), which uses digital holography to reconstruct the three-dimensional images from a single plane allowing tracking of light-scattering objects in 3D. HoloMiP outperforms existing methods in precision, speed, simplicity and tolerance to crowding. We show that it is particularly suitable for fast, multiplexed magnetic tweezer experiments, opening new avenues to high-throughput force spectroscopy.


title: Mapping mechanical stress in curved epithelia of designed size and shape

abstract: The function of organs such as lungs, kidneys and mammary glands relies on the three-dimensional geometry of their epithelium. To adopt shapes such as spheres, tubes and ellipsoids, epithelia generate mechanical stresses that are generally unknown. Here we engineered curved epithelial monolayers of controlled size and shape and mapped their state of stress. We designed pressurized epithelia with circular, rectangular and ellipsoidal footprints. We developed a computational method to map the stress tensor in these epithelia. This method establishes a direct correspondence between epithelial shape and mechanical stress without assumptions of material properties. In epithelia with spherical geometry spanning more than one order of magnitude in radius, we show that stress weakly increases with areal strain in a size-independent manner. In epithelia with rectangular and ellipsoidal cross-section we found pronounced stress anisotropies consistent with the asymmetric distribution of tractions measured at the cell-substrate contact line. In these anisotropic profiles, cell shape tended to align with the direction of maximum principal stress but this alignment was non-universal and depended on epithelial geometry. Besides interrogating the fundamental mechanics of epithelia over a broad range of sizes and shapes, our approach will enable a systematic study of how geometry and stress influence epithelial fate and function in three-dimensions.


title: Sensitization to Ionizing Radiation by MEK inhibition is Dependent on SNAI2 in Fusion-negative Rhabdomyosarcoma

abstract: In fusion-negative rhabdomyosarcoma (FN-RMS), a pediatric malignancy with skeletal muscle characteristics, > 90% of high-risk patients have mutations that activate the RAS/MEK signaling pathway. We recently discovered that SNAI2, in addition to blocking myogenic differentiation downstream of MEK signaling in FN-RMS, represses pro-apoptotic BIM expression to protect RMS tumors from ionizing radiation (IR). As clinically relevant concentrations of the MEK inhibitor trametinib elicit poor responses in preclinical xenograft models, we investigated the utility of low-dose trametinib in combination with IR for the treatment of RAS-mutant FN-RMS. We hypothesized that trametinib would sensitize FN-RMS to IR through its downregulation of SNAI2 expression. While we observed little to no difference in myogenic differentiation or cell survival with trametinib treatment alone, robust differentiation and reduced survival were observed after IR. Additionally, IR-induced apoptosis was significantly increased in FN-RMS cells treated concurrently with trametinib, as was increased BIM expression. SNAI2s role in these processes was established using overexpression rescue experiments, where overexpression of SNAI2 prevented IR-induced myogenic differentiation and apoptosis. Moreover, combining MEK inhibitor with IR resulted in complete tumor regression and a 2-4-week delay in event free survival (EFS) in preclinical xenograft and PDX models. Our findings demonstrate that the combination of MEK inhibition and IR results in robust differentiation and apoptosis, due to the reduction of SNAI2, which leads to extended EFS in FN-RMS. SNAI2 thus is a biomarker of IR insensitivity and target for future therapies to sensitize aggressive sarcomas to IR.

category: cancer biology


title: Modeling Reactive Species Metabolism in Colorectal Cancer for Identifying Metabolic Targets and Devising Therapeutics

abstract: Reactive species (RS) are known to play significant roles in cancer development as well as in treating or managing cancer. On the other hand, genome scale metabolic models are being used to understand cell metabolism in disease contexts including cancer, and also in planning strategies to handle diseases. Despite their crucial roles in cancers, the reactive species have not been adequately modeled in the genome scale metabolic models (GSMMs) when probing disease models for their metabolism or detection of drug targets. In this work, we have developed a module of reactive species reactions, which is scalable - it can be integrated with any human metabolic model as it is, or with any metabolic model with fine-tuning. When integrated with a cancer (colorectal cancer in this case) metabolic model, the RS module highlighted the deregulation occurring in important CRC pathways such as fatty acid metabolism, cholesterol metabolism, arachidonic acid and eicosanoid metabolism. We show that the RS module helps in better deciphering crucial metabolic targets for devising better therapeutics such as FDFT1, FADS2 and GUK1 by taking into account the effects mediated by reactive species during colorectal cancer progression. The results from this reactive species integrated CRC metabolic model reinforces ferroptosis as a potential target for colorectal cancer therapy.


title: Excess glutamate release triggers subunit-specific homeostatic receptor scaling

abstract: Ionotropic glutamate receptors (GluRs) are targets for modulation in Hebbian and homeostatic synaptic plasticity and are remodeled by development, experience, and disease. Although much is known about activity-dependent mechanisms that regulate GluR composition and abundance, the role of glutamate itself in these processes is unclear. To determine how glutamate sculpts GluR receptive fields, we have manipulated synaptically released glutamate and generated precise CRISPR mutations in the two postsynaptic GluR subtypes at the Drosophila neuromuscular junction, GluRA and GluRB. We first demonstrate that GluRA and GluRB compete to establish postsynaptic receptive fields, and that proper GluR abundance and localization can be orchestrated in the absence of any synaptic glutamate release. However, excess glutamate release adaptively tunes postsynaptic GluR abundance, echoing GluR receptor scaling observed in mammalian systems. Unexpectedly, when GluRA vs GluRB competition is eliminated, excess glutamate homeostatically regulates GluRA abundance, while GluRB abundance is now insensitive to glutamate modulation. Finally, Ca2+ impermeable GluRA receptors are no longer sensitive to homeostatic regulation by glutamate. Thus, excess glutamate, GluR competition, and Ca2+ signaling collaborate to selectively target GluR subtypes for homeostatic regulation at postsynaptic compartments.


title: Metacognitive domains are not aligned along a dimension of internal-external information source

abstract:
It is still debated whether metacognition, or the ability to monitor our own mental states, relies on mechanisms that are  domain-general (a single mechanism can account for the monitoring of any mental process) or  domain-specific (metacognition is accomplished by a collection of multiple monitoring modules, one for each cognitive domain). It has been speculated that two broad categories of metacognitive mechanisms may exist: those that monitor primarily externally-generated vs. those that monitor primarily internally-generated information.

To test this proposed division, we measured metacognitive performance (using m-ratio, a signal detection theoretical measure) in four tasks that could be ranked along an internal-external axis of the source of information, namely memory, motor, visuomotor and visual tasks. We found correlations between m-ratios in visuomotor and motor tasks only; but no correlations between m-ratios in visual and visuomotor tasks, or between motor and memory tasks.

While we found no correlation in metacognitive ability between visual and memory tasks, and a positive correlation between visuomotor and motor tasks, we found no evidence for a correlation between motor and memory tasks. This pattern of correlations does not support the grouping of domains based on whether the source of information is primarily internal or external. We suggest that other groupings could be more reflective of the nature of metacognition and discuss the need to consider other non-domain task-features when using correlations as a way to test the underlying shared mechanisms between domains.


title: EEG Frequency Tagging Objectively Measures Biological Motion Perception

abstract: Various neuroimaging techniques have been developed to study the brain processes underlying biological motion perception. However, brain activity during movement perception captures a multitude of components. As a result, it is often unclear which components reflect the processing of the movement and which components reflect other, secondary processes that build on movement processing (e.g., inferring the agents mood or identity). To address this issue, we developed and validated a new approach that objectively defines the brain response associated with biological motion perception. Specifically, we showed 30 male and female adults a point-light walker moving at a pace of 2.4 Hz and used EEG frequency tagging to measure the brain response coupled to that pace. The results revealed a reliable response at the walking frequency that was reduced by two manipulations known to disrupt biological motion perception, namely phase scrambling and inversion. Interestingly, we also identified a brain response at half the walking frequency (i.e., 1.2 Hz), corresponding to the rate at which the individual dots repeated their trajectory. In contrast to the 2.4 Hz response, the response at 1.2 Hz was increased for scrambled walkers. These results indicate that frequency tagging can be used to measure the visual processing of biological movements and can dissociate between the global (2.4 Hz) and local (1.2 Hz) processes involved in biological motion perception, at different frequencies of the brain signal.


title: Disentangling object category representations driven by dynamic and static visual input

abstract:
Humans can label and categorize objects in a visual scene with high accuracy and speed--a capacity well-characterized with neuroimaging studies using static images. However, motion is another cue that could be used by the visual system to classify objects. To determine how motion-defined object category information is processed in the brain, we created a novel stimulus set to isolate motion-defined signals from other sources of information. We extracted movement information from videos of 6 object categories and applied the motion to random dot patterns. Using these stimuli, we investigated whether fMRI responses elicited by motion cues could be decoded at the object category level in functionally defined regions of occipitotemporal and parietal cortex. Participants performed a one-back repetition detection task as they viewed motion-defined stimuli or static images from the original videos. Linear classifiers could decode object category for both stimulus formats in all higher order regions of interest. More posterior occipitotemporal and ventral regions showed higher accuracy in the static condition and more anterior occipitotemporal and dorsal regions showed higher accuracy in the dynamic condition. Significantly above chance classification accuracies were also observed in all regions when training and testing the SVM classifier across stimulus formats. These results demonstrate that motion-defined cues can elicit widespread robust category responses on par with those elicited by luminance cues in regions of object-selective visual cortex. The informational content of these responses overlapped with, but also demonstrated interesting distinctions from, those elicited by static cues.

Significance StatementMuch research on visual object recognition has focused on recognizing objects in static images. However, motion cues are a rich source of information that humans might also use to categorize objects. Here, we present the first study to compare neural representations of several animate and inanimate objects when category information is presented in two formats: static cues or isolated dynamic cues. Our study shows that while higher order brain regions differentially process object categories depending on format, they also contain robust, abstract category representations that generalize across format. These results expand our previous understanding of motion-derived animate and inanimate object category processing and provide useful tools for future research on object category processing driven by multiple sources of visual information.


title: Organic Convolution Model of Ventral Visual Path Reproduces the Fine Structure of Shape Tuning in Area V4

abstract: This modeling study investigates whether an orderly convergence of neuronal selectivities from cortical areas V1 and V2 can produce the fine structure of shape selective receptive fields found in area V4 recordings. A model of fast object recognition in the ventral visual pathway is made of spiking neurons having simple convergent functional micro-architectures. The model is based on recent findings about the convergent properties of V2 neurons on V1 afferents and makes a novel proposal for how V4 neurons may create selectivity for local curvature through the orderly convergence of different types of afferent inputs from V2. The model also demonstrates a novel method for simulating spiking neurons using tensor programming and GPU hardware: Assuming that convergent functional micro-architectural patterns repeat in topographically organized visual space, the details of individual unit depolarization and spike time is modeled using convolution operations augmented with a custom tensor model of post-synaptic potentials. The model, described as Organic Convolution, suggests that convergent selectivity patterns equivalent to convolution can be created by developmental mechanisms, laying the foundation for object recognition before an organism learns from experience. This study does not investigate developmental mechanisms directly but, using convergent patterns that are hand crafted to match neurophysiological data, shows that the mechanisms giving rise to object recognition may be very simple. As a result, the model suggests an alternative point of view on how deep neural networks may relate to biology.


title: Hippocampal subfields and their neocortical interactions during autobiographical memory using submillimeter whole-brain fMRI at 7 Tesla

abstract: Recent advances in ultra-high field 7 Tesla magnet resonance imaging (7T MRI) have provided unprecedented opportunities to gain insights into the brain mechanisms supporting human cognition. The hippocampus, a heterogeneous brain structure comprising several smaller subfields has been firmly established to play a central role during vivid re-experiencing of autobiographical memories (AM). However, due to technical limitations inherent to MRI investigations of the hippocampus, how hippocampal subfields differentially support AM and how hippocampal subfields functionally connect with other brain regions typically associated with AM retrieval remains elusive. To tackle this knowledge gap, we leveraged technical advances of parallel imaging and employed a customized Echo Planar Imaging (EPI) sequence at 0.9 mm isotropic voxel size over the whole brain while participants re-experienced vivid, detail-rich AM. In addition, we developed a processing pipeline that allows the examination of differential activation of hippocampal subfields in single subject space as well as hippocampal-neocortical interactions at a group level. We found that all hippocampal subfields were engaged during AM retrieval but that one specific hippocampal subfield, the pre/parasubiculum, contributed over and above the other subfields to AM retrieval. Moreover, functional connectivity analyses revealed that the pre/parasubiculum was the only hippocampal subfield that strongly connected to other brain regions typically associated with AM, such as ventromedial prefrontal cortex (vmPFC) and medial/lateral parietal regions. Our results support recent proposals that the pre/parasubiculum may play an essential role in cognitive tasks that rely heavily on visual imagery, such as the vivid re-experience of personal past events.


title: A hippocampal-anterior thalamic circuit associated with learning rate in ageing

abstract:
Memory normally declines with ageing and these age-related cognitive changes are associated with changes in brain structure. Episodic memory retrieval has been widely studied during ageing, whereas learning has received less attention. Here we examined the neural correlates of learning rate in ageing. Our study sample consisted of 982 cognitively healthy female and male older participants from the Vallecas Project cohort, without a clinical diagnosis of mild cognitive impairment or dementia. The learning rate across three consecutive verbal memory (Free and Cued Selective Reminding Test) recall trials was used as a predictor of grey matter (GM) and white matter (WM) volume using voxel-based morphometry, and WM microstructure using tract-based spatial statistics on fractional anisotropy (FA) and mean diffusivity (MD) measures. Immediate recall improved by 1.4 items per trial on average, and this learning rate was faster in women and negatively associated with age. Structurally, hippocampal and anterior thalamic GM volume correlated positively with learning rate. Learning also correlated with the integrity of WM microstructure (high FA and low MD) in an extensive network of tracts including bilateral anterior thalamic radiation, fornix, and long-range tracts. These results suggest that structural GM and WM characteristics, centred on a hippocampal-anterior thalamic circuit, support learning capacity in ageing. Specifically, reduced volume and microstructure may explain some of the age-related memory deficits that result from impaired learning.

Significance statementA detailed understanding of age-related memory changes is crucial in an increasingly ageing population, in which memory decline is prevalent. Whilst memory performance is usually quantified by the ability to correctly retrieve information, memory impairment could be caused by a reduced ability to learn or encode information. In a cohort of cognitively healthy ageing participants, we found an association between verbal learning rate and grey matter volume and white matter microstructure in the limbic system. These findings establish the neural underpinnings of the often-overlooked learning phase of the memory process and have important implications for our understanding of neurobiological changes in healthy ageing and their cognitive phenotypes.


title: Mixtures of large-scale dynamic functional brain network modes

abstract: Accurate temporal modelling of functional brain networks is essential in the quest for understanding how such networks facilitate cognition. Researchers are beginning to adopt time-varying analyses for electrophysiological data that capture highly dynamic processes on the order of milliseconds. Typically, these approaches, such as clustering of functional connectivity profiles and Hidden Markov Modelling (HMM), assume mutual exclusivity of networks over time. Whilst a powerful constraint, this assumption may be compromising the ability of these approaches to describe the data effectively. Here, we propose a new generative model for functional connectivity as a time-varying linear mixture of spatially distributed statistical "modes". The temporal evolution of this mixture is governed by a recurrent neural network, which enables the model to generate data with a rich temporal structure. We use a Bayesian framework known as amortised variational inference to learn model parameters from observed data. We call the approach DyNeMo (for Dynamic Network Modes), and show using simulations it outperforms the HMM when the assumption of mutual exclusivity is violated. In resting-state MEG, DyNeMo reveals a mixture of modes that activate on fast time scales of 100-150 ms, which is similar to state lifetimes found using an HMM. In task MEG data, DyNeMo finds modes with plausible, task-dependent evoked responses without any knowledge of the task timings. Overall, DyNeMo provides decompositions that are an approximate remapping of the HMMs while showing improvements in overall explanatory power. However, the magnitude of the improvements suggests that the HMMs assumption of mutual exclusivity can be reasonable in practice. Nonetheless, DyNeMo provides a flexible framework for implementing and assessing future modelling developments.


title: State Dependent Coupling of Hippocampal Oscillations

abstract: Oscillations occurring simultaneously in a given area represent a physiological unit of brain states. They allow for temporal segmentation of spikes and support distinct behaviors. To establish how multiple oscillatory components co-varies simultaneously and influence neuronal firing during sleep and wakefulness, we describe a multi-variate analytical framework for constructing the state space of hippocampal oscillations. Examining the co-occurrence patterns of oscillations on the state space, across species, uncovered the presence of network constraints and distinct set of cross-frequency interactions during wakefulness as compared to sleep. We demonstrated how the state space can be used as a canvas to map the neural firing and found that distinct neurons during navigation were tuned to different set of simultaneously occurring oscillations during sleep. This multivariate analytical framework provides a window to move beyond classical bivariate pipelines, for investigating oscillations and neuronal firing, thereby allowing to factor-in the complexity of oscillation-population interactions.


title: Using Transfer Learning for Automated Microbleed Segmentation

abstract:
IntroductionCerebral microbleeds are small perivascular haemorrhages that can occur in both grey and white matter brain regions. Microbleeds are a marker of cerebrovascular pathology, and are associated with an increased risk of cognitive decline and dementia. Microbleeds can be identified and manually segmented by expert radiologists and neurologists, usually from susceptibility-contrast MRI. The latter is hard to harmonize across scanners, while manual segmentation is laborious, time-consuming, and subject to inter- and intra-rater variabiltiy. Automated techniques so far have shown high accuracy at a neighborhood ("patch") level at the expense of a high number of false positives voxel-wise lesions. We aimed to develop an automated, more precise microbleeds segmentation tool able to use standardizable MRI contrasts.

MethodsWe first trained a ResNet50 network on another MRI segmentations task (cerberospinal fluid versus background segmentation) using T1-weighted, T2-weighted, and T2* MRI. We then used transfer learning to train the network for the detection of microbleeds with the same contrasts. As a final step, we employed a combination of morphological operators and rules at the local lesion level to remove false positives. Manual segmentations of microbleeds from 78 participants were used to train and validate the system. We assessed the impact of patch size, freezing weights of the initial layers, mini-batch size, learning rate, as well as data augmentation on the performance of the Microbleed ResNet50 network.

ResultsThe proposed method achieved a high performance, with a patch-level sensitivity, specificity, and accuracy of 99.57%, 99.16%, and 99.93%, respectively. At a per lesion level, sensitivity, precision, and Dice similarity index values were 89.1%, 20.1%, and 0.28 for cortical GM; 100%, 100%, and 1.0 for deep GM; and 91.1%, 44.3%, and 0.58 for WM, respectively.

DiscussionThe proposed microbleed segmentation method is more suitable for the automated detection of microbleeds with high sensitivity.


title: Direct observation of the neural computations underlying a single decision

abstract: Neurobiological investigations of perceptual decision-making have furnished the first glimpse of a flexible cognitive process at the level of single neurons1,2. Neurons in the parietal and prefrontal cortex3-6 are thought to represent the accumulation of noisy evidence, acquired over time, leading to a decision. Neural recordings averaged over many decisions have provided support for the deterministic rise in activity to a termination bound7. Critically, it is the unobserved stochastic component that is thought to confer variability in both choice and decision time8. Here, we elucidate this stochastic, diffusion-like signal on individual decisions by recording simultaneously from hundreds of neurons in the lateral intraparietal cortex (LIP). We show that a small subset of these neurons, previously studied singly, represent a combination of deterministic drift and stochastic diffusion--the integral of noisy evidence--during perceptual decision making, and we provide direct support for the hypothesis that this diffusion signal is the quantity responsible for the variability in choice and reaction times. Neuronal state space and decoding analyses, applied to the whole population, also identify the drift diffusion signal. However, we show that the signal relies on the subset of neurons with response fields that overlap the choice targets. This parsimonious observation would escape detection by these powerful methods, absent a clear hypothesis.


title: Space-time resolved inference-based whole-brain neurophysiological mechanism imaging: application to resting-state alpha rhythm

abstract:
Neural mechanisms are complex and difficult to image. This paper presents a new space-time resolved whole-brain imaging framework, called Neurophysiological Mechanism Imaging (NMI), that identifies neurophysiological mechanisms within cerebral cortex at the macroscopic scale. By fitting neural mass models to electromagnetic source imaging data using a novel nonlinear inference method, population averaged membrane potentials and synaptic connection strengths are efficiently and accurately imaged across the whole brain at a resolution afforded by source imaging. The efficiency of the framework enables return of the augmented source imaging results overnight using high performance computing. This suggests it can be used as a practical and novel imaging tool. To demonstrate the framework, it has been applied to resting-state magnetoencephalographic source estimates. The results suggest that endogenous inputs to cingulate, occipital, and inferior frontal cortex are essential modulators of resting-state alpha power. Moreover, endogenous input and inhibitory and excitatory neural populations play varied roles in mediating alpha power in different resting-state sub-networks. The framework can be applied to arbitrary neural mass models and has broad applicability to image neural mechanisms in different brain states.

HighlightsO_LIThe whole-brain imaging framework can disclose the neurophysiological substrates of complicated brain functions in a spatiotemporal manner.
C_LIO_LIDeveloped a semi-analytical Kalman filter to estimate neurophysiological variables in the nonlinear neural mass model efficiently and accurately from large-scale electromagnetic time-series.
C_LIO_LIThe semi-analytical Kalman filter is 7.5 times faster and 5% more accurate in estimating model parameters than the unscented Kalman filter.
C_LIO_LIProvided several group-level statistical observations based on neurophysiological variables and visualised them in a whole-brain manner to show different perspectives of neurophysiological mechanisms.
C_LIO_LIApplied the framework to study resting-state alpha oscillation and found novel relationships between local neurophysiological variables in specific brain regions and alpha power.
C_LI


title: Dendrify: a new framework for seamless incorporation of dendrites in Spiking Neural Networks

abstract: Computational modeling has been indispensable for understanding how subcellular neuronal features influence circuit processing. However, the role of dendritic computations in network-level operations remains largely unexplored. This is partly because existing tools do not allow the development of realistic and efficient network models that account for dendrites. Current spiking neural networks, although efficient, are usually quite simplistic, overlooking essential dendritic properties. Conversely, circuit models with morphologically detailed neuron models are computationally costly, thus impractical for large-network simulations. To bridge the gap between these two extremes, we introduce Dendrify, an open-source Python package compatible with Brian2, designed to facilitate the development of bioinspired spiking neural networks. Dendrify, through simple commands, automatically generates reduced compartmental neuron models with simplified yet biologically relevant dendritic and synaptic integrative properties. Such models strike a good balance between flexibility, performance, and biological accuracy, allowing us to explore dendritic contributions to network-level functions while paving the way for developing more powerful neuromorphic systems.


title: Learning of new associations invokes a major change in modulations of cortical beta oscillations in human adults

abstract:
Large-scale cortical beta ({beta}) oscillations have been implicated in the learning processes but their exact role is debated. We explored the dynamics of {beta}-oscillations while 25 adult participants learned, through trial and error, novel associations between four auditory pseudowords and movements of four body extremities. We used MEG to evaluate learninginduced changes in beta modulation accompanying cue-triggered movements.

Our findings showed that spatial-temporal characteristics of movement-related {beta}-oscillations underwent a major transition as learning proceeded. Early in learning, suppression of {beta}-power in multiple cortical areas occurred long before movement initiation and sustained throughout the whole behavioral trial. As learning advanced and task performance reached asymptote, {beta}suppression was replaced by a widespread and prolonged rise in {beta}-power. The {beta}-power rise started shortly after the initiation of correct motor response and mainly comprised the prefrontal and medial temporal regions of the left hemisphere. This post-decision {beta}-power predicted trial-by-trial response times (RT) at both stages of learning (before and after the rules become familiar) but in opposite ways. When a subject started to acquire associative rules and gradually improved task performance, a decrease in RT was correlated with the increase in the post-decision {beta}-band power. Repeatedly correct implementation of the learned rules reversed this correlation in the opposite direction with faster (more confident) responses associated with the weaker post-decision {beta}-band synchronization.

Our findings suggest that maximal beta activity is pertinent to a distinct stage of learning and may serve to strengthen the newly learned association in a distributed memory network.


title: Cardiac interoception is enhanced in blind individuals

abstract: Blind individuals have superior abilities to perform perceptual tasks that rely on exteroceptive information, since visual deprivation is associated with massive cross-modal plasticity. However, it is unknown whether neuroplasticity after visual loss also affects interoception, i.e., the sensations arising from ones inner organs that convey information about the physiological state of the body. Herein, we examine the influence of blindness on cardiac interoception, which is an interoceptive submodality that has important links to emotional processing and bodily self-awareness. We tested 36 blind and 36 age-and sex-matched sighted volunteers and examined their cardiac interoceptive ability using a well-established heartbeat counting task. The results showed that blind individuals had significantly higher accuracy in perceiving their heartbeat than did individuals in a matched sighted control group. In contrast, there were no significant differences between the groups in the metacognitive dimensions of cardiac interception or the purely physiological measurement of heart rate, thereby underscoring that the improved accuracy likely reflects a superior perceptual sensitivity to cardiac interoceptive signals in blind individuals. We conclude that visual deprivation leads to enhanced interoception, which has important implications for the study of the extent of massive cross-modal plasticity after visual loss, understanding emotional processing in blind individuals, and learning how bodily self-awareness can develop and be sustained in the absence of visual experience.


title: Characterizing ketamine-induced dissociation using human intracranial neurophysiology: brain dynamics, network activity, and interactions with propofol

abstract: Subanesthetic doses of ketamine produce rapid and sustained anti-depressant effects in patients with treatment-resistant depression. Unfortunately, the usefulness of ketamine as a treatment is limited by its potential for abuse because of psychotropic side effects such as dissociation. Understanding the brain dynamics and the neural circuits involved in ketamines effects could lend insight into improved therapies for depression with fewer adverse effects. It is believed that ketamine acts via NMDA receptor and hyperpolarization-activated cyclic nucleotide-gated 1 (HCN1) channels to produce changes in oscillatory brain dynamics. Here we show, in humans, a detailed description of the principal oscillatory changes in cortical and subcortical structures by administration of a subanesthetic dose of ketamine. Using recordings from intracranial electrodes, we found that ketamine increased gamma oscillations within prefrontal cortical areas and the hippocampus--structures previously implicated in ketamines antidepressant effects. Furthermore, our studies provide direct evidence of a ketamine-induced 3 Hz oscillation in posteromedial cortex that has been proposed as a mechanism for its dissociative effects. By analyzing changes in neural oscillations after the addition of propofol, whose GABAergic activity antagonizes ketamines NMDA-mediated disinhibition alongside a shared HCN1 inhibitory effect, we identified brain dynamics that could be attributed to NMDA-mediated disinhibition versus HCN1 inhibition. Overall, our results imply that ketamine engages different neural circuits in distinct frequency-dependent patterns of activity to produce its antidepressant and dissociative sensory effects. These insights may help guide the development of novel brain dynamic biomarkers and therapeutics for depression.


title: Gestational day 12 moderate prenatal alcohol exposure produces sex-specific social impairments and attenuates prelimbic excitability and amygdala-cortex modulation of adult social behavior.

abstract: Lifelong social impairments are common in individuals with prenatal alcohol exposure (PAE), and preclinical studies have identified gestational day (G)12 as a vulnerable timepoint for producing social deficits following binge-level PAE. While moderate (m)PAE also produces social impairments, the long-term neuroadaptations underlying them are poorly understood. Activity of the projection from the basolateral amygdala to the prelimbic cortex (BLA[->]PL) leads to social avoidance, and the PL alone is implicated in negative social behaviors, making each of these potential candidates for the neuroadaptations underlying mPAE-induced social impairments. To examine this, we first established that G12 mPAE produced sex-specific social impairments lasting into adulthood. We then chemogenetically inhibited the BLA[->]PL using Clozapine N-Oxide (CNO) during adult social testing. This revealed that CNO reduced social investigation in control males, but had no effect on mPAE males or females of either exposure, indicating that mPAE attenuated the role of this projection in regulating male social behavior and highlighting one potential mechanism by which mPAE affects male social behavior more severely. Using whole-cell electrophysiology, we also examined mPAE-induced changes to PL pyramidal cell physiology and determined that mPAE reduced the excitability of these cells, likely due to increased suppression by inhibitory interneurons. Overall, this work identified two mPAE-induced neuroadaptations that last into adulthood and which may underlie the sexspecific vulnerability to mPAE-induced social impairments. Future research is necessary to expand upon how these circuits modulate both normal and pathological social behavior, and to identify sex-specific mechanisms leading to differential vulnerability in males and females.


title: Multifaceted role of RIMBP2 in promoting hearing in murine cochlear hair cells

abstract: In peripheral, the mammalian cochlea is a remarkable sensory apparatus, owning to its outer and inner hair cells (OHCs and IHCs) that amplify and transmit auditory signals to the brain, respectively. Rab3-interacting molecular (RIM) binding protein 2 (RIMBP2) is widely expressed in receptor cells and neurons, specifically in the active zones for exocytosis of synaptic vesicles (SVs), but its exact functions in the cochlea are not very well understood. We therefore generated a Rimbp2 knockout mouse model (Rimbp2-/-), which exhibited severely impaired hearing with not only elevated hearing thresholds but also increased latencies and reduced amplitudes in the Wave I of their auditory brainstem responses (ABRs). Consistent with the threshold elevation, we found significant loss of OHCs, likely through apoptosis, in the Rimbp2-/- cochlea. Consistent with changes observed in the ABR Wave I, we found greatly reduced exocytosis, both spontaneously and upon stimulation, in Rimbp2-/- IHCs. Specifically, our patch-clamp analysis on IHCs and postsynaptic spiral ganglion neurons (SGNs) revealed not only reduced readily releasable pool (RRP) of SVs but also reduced sustained release rate, along with complete blockade of fast endocytosis, in Rimbp2-/- IHCs. Lastly, with immunostaining of whole-mounted cochleae, we found that while the number of ribbon synapses in Rimbp2-/- IHCs was unchanged, their localization moved subtly but significantly towards the basal pole of IHCs. Taken together, we uncovered an unexpected role of RIMBP2 for OHC survival and a more extensive role in promoting IHC exocytosis than previously believed.