title: Variomes: a high recall search engine to support the curation of genomic variants

abstract: Precision oncology relies on the use of treatments targeting specific genetic variants. However, identifying clinically actionable variants as well as relevant information likely to be used to treat a patient with a given cancer is a labor-intensive task, which includes searching the literature for a large set of variants. The lack of universally adopted standard nomenclature for variants requires the development of variant-specific literature search engines. We develop a system to perform triage of publications relevant to support an evidence-based decision. Together with providing a ranked list of articles for a given variant, the system is also able to prioritize variants, as found in a Variant Calling Format, assuming that the clinical actionability of a genetic variant is correlated with the volume of literature published about the variant. Our system searches within three pre-annotated document collections: MEDLINE abstracts, PubMed Central full-text articles and ClinicalTrials.gov clinical trials. A variant synonym generator is used to increase the comprehensiveness of the set of retrieved documents. We then apply different strategies to rank the publications. We assess the search effectiveness of the system using different experimental settings. Experimental setting 1: The literature retrieval task is tuned and evaluated using the TREC Precision Medicine 2018 and 2019 benchmarks consisting respectively in 50 and 40 topics. Almost two thirds (62%) of the publications returned in the top-5 are relevant for clinical decision-support. Experimental setting 2: The evaluation of the variant prioritization task is based on a manually-created benchmark composed of eight patients for a total of 756 variants. For each patient, we used both their complete set of variants and tumor board reports. Our approach enabled identifying 81.8% of clinically actionable variants in the top-3. Experimental setting 3: A comparison of Variomes with LitVar, a well-known search engine for genetic variants is performed. Variomes was able to retrieve on average 90.8% of the content, while LitVar retrieved on average 58.6%. Out of the 9.2% articles, which are "missed" by Variomes, a per error analysis suggests that they are artefacts. To conclude, we are proposing here a competitive system to facilitate the curation of variants for personalized medicine.


title: ppx: Programmatic access to proteomics data repositories

abstract: The volume of proteomics and mass spectrometry data available in public repositories continues to grow at a rapid pace as more researchers embrace open science practices. Open access to the data behind scientific discoveries has become critical to validate published findings and develop new computational tools. Here, we present ppx, a Python package that provides easy, programmatic access to the data stored in ProteomeXchange repositories, such as PRIDE and MassIVE. The ppx package can either be used as a command line tool or a Python package to retrieve the files and metadata associated with a project when provided its identifier. To demonstrate how ppx enhances reproducible research, we used ppx within a Snakemake workflow to reanalyze a published dataset with the open modification search tool ANN-SoLo and compared our reanalysis to the original results. We show that ppx readily integrates into workflows and our reanalysis produced results consistent with the original analysis. We envision that ppx will be a valuable tool for creating reproducible analyses, providing tool developers easy access to data for development, testing, and benchmarking, and enabling the use of mass spectrometry data in data-intensive analyses. The ppx package is freely available and open source under the MIT license at: https://github.com/wfondrie/ppx

category: bioinformatics


title: Federated Multi-Site Normative Modeling using Hierarchical Bayesian Regression

abstract: AO_SCPLOWBSTRACTC_SCPLOWClinical neuroimaging data availability has grown substantially in the last decade, providing the potential for studying heterogeneity in clinical cohorts on a previously unprecedented scale. Normative modeling is an emerging statistical tool for dissecting heterogeneity in complex brain disorders. However, its application remains technically challenging due to medical data privacy issues and difficulties in dealing with nuisance variation, such as the variability in the image acquisition process. Here, we introduce a federated probabilistic framework using hierarchical Bayesian regression (HBR) for multi-site normative modeling. The proposed method completes the life-cycle of normative modeling by providing the possibilities to learn, update, and adapt the model parameters on decentralized neuroimaging data. Our experimental results confirm the superiority of HBR in deriving more accurate normative ranges on large multi-site neuroimaging datasets compared to the current standard methods. In addition, our approach provides the possibility to recalibrate and reuse the learned model on local datasets and even on datasets with very small sample sizes. The proposed federated framework closes the technical loop for applying normative modeling across multiple sites in a decentralized manner. This will facilitate applications of normative modeling as a medical tool for screening the biological deviations in individuals affected by complex illnesses such as mental disorders.


title: GlyNet: A Multi-Task Neural Network for Predicting Protein-Glycan Interactions

abstract: Advances in diagnostics, therapeutics, vaccines, transfusion, and organ transplantation build on a fundamental understanding of glycan-protein interactions. To aid this, we developed GlyNet, a model that accurately predicts interactions (relative binding strengths) between mammalian glycans and 352 glycan-binding proteins, many at multiple concentrations. For each glycan input, our model produces 1257 outputs, each representing the relative interaction strength between the input glycan and a particular protein sample. GlyNet learns these continuous values using relative fluorescence units (RFUs) measured on 599 glycans in the Consortium for Functional Glycomics glycan arrays and extrapolates these to RFUs from additional, untested glycans. GlyNets output of continuous values provides more detailed results than classification models. Such continuous outputs are easily converted by a following classifier, and in this form GlyNet outperforms reported classifiers. GlyNet is the first multi-output regression model for protein-glycan interactions and will serve as an important benchmark, facilitating development of quantitative computational glycobiology.


title: Apical Actin-myosin Network Regulates the Tight Junction of Polarized Madin-Darby Canine Kidney Cells

abstract:
The tight junction outlines the apicolateral border of epithelial cells like a belt, sealing the paracellular space when cells form contacts with each other. The permeability and morphology of tight junction are regulated by actomyosin contractility, which has been conventionally thought from the purse-string-like circumferential actomyosin belt along tight junction. Spatially, the tight junction is close to the apical actin network, which exerts inward contractions orthogonal to the tight junction. To test the contributions from apical actin network, we laser-ablated spots on the apical surface of polarized Madin-Darby Canine Kidney (MDCK) epithelial cells. Laser ablation severed the apical cytoskeleton network, decreased in-plane tension, increased the apical surface area, and rendered the tight junction less tortuous in shape. Consistent with these observations, changes in MDCK cell sheet morphology due to cell proliferation, or perturbation with the ROCK inhibitor Y27632 increased the density of the apical actin network and decreased tight junction tortuosity. The morphological analysis revealed scutoids in flat MDCK cell sheets, contrary to predictions from a previous model that only considered cell-cell interactions as line tension. Additional cell-cell interactions from apical in-plane tension provides probable cause for the occurrence of scutoids on flat geometry. Taken together, our findings identify the importance of the apical actin network exerting in-plane apical tension to regulate tight-junction mechanobiology and epithelial cell shape.

Significance StatementThe tight junction is located at the apicolateral cell border and regulates paracellular diffusion. Adjacent to the tight junction, the actin cytoskeleton forms a dense network beneath the apical surface and an actomyosin belt that circumscribes the lateral surface of the cell. Tight junctions are connected to the actin cytoskeleton which regulates paracellular transport, but the role of tension-mediated regulation of the tight junction by various actin structures is poorly understood. Here, we provide evidence that tension on the tight junction is mediated by the apical actin network. Our results provide a reinterpretion of past reports and broaden our understanding the mechanobiology of tight junctions.

category: biophysics


title: Lateral Preoptic Area Neurons Signal Cocaine Self-Administration Behaviors

abstract: The lateral preoptic area is implicated in numerous aspects of substance use disorder. In particular, the lateral preoptic area is highly sensitive to the pharmacological properties of psychomotor stimulants, and its activity promotes drug-seeking in the face of punishment and reinstatement during abstinence. Despite the lateral preoptic areas complicity in substance use disorder, how precisely lateral preoptic area neurons encode the individual components of drug self-administration has not been ascertained. To bridge this gap, we examined how the firing of single lateral preoptic area neurons correlates with three discrete elements of cocaine self-administration: 1) drug-seeking (pre-response), 2) drug-taking (response), and 3) receipt of the cocaine infusion. A significant subset of lateral preoptic area neurons responded to each component with a mix of increases and decreases in firing-rate. A majority of these neurons encoded the operant response with increases in spiking, though responses during the drug-seeking, taking, and reciept windows were highly correlated.

category: neuroscience


title: Brain structural correlates of autistic traits across the diagnostic divide: A grey matter and white matter microstructure study

abstract: Autism Spectrum Disorders (ASD) are a set of neurodevelopmental conditions characterised by difficulties in social interaction and communication as well as stereotyped and restricted patterns of interest. Autistic traits exist in a continuum across the general population, whilst the extreme end of this distribution is diagnosed as clinical ASD. While many studies have investigated brain structure in autism using a case-control design, few have used a dimensional approach. To add to this growing body of literature, we investigated the structural brain correlates of autistic traits in a mixed sample of adults (N=91) with and without a clinical diagnosis of autism. We examined regional brain volumes (using voxel-based morphometry and surface-based morphometry) and white matter microstructure properties (using Diffusion Tensor Imaging). Our findings show widespread grey matter differences, including in the social brain regions, and some evidence for white matter microstructure differences related to higher autistic traits. These grey matter and white matter microstructure findings from our study are consistent with previous reports and support the brain structural differences in ASD. These findings provide further support for shared aetiology for autistic traits across the diagnostic divide.


title: An Investigation of Neural Mechanisms Underlying Verb Morphology Deficits in Aphasia

abstract: Agrammatic aphasia is an acquired language disorder characterized by slow, non-fluent speech that include primarily content words. It is well-documented that people with agrammatism (PWA) have difficulty with production of verbs and verb morphology, but it is unknown whether these deficits occur at the single word-level, or are the result of a sentence-level impairment. The first aim of this paper is to determine the linguistic level that verb morphology impairments exist at by using magnetoencephalography (MEG) scanning to analyze neural response to two language tasks (one word-level, and one sentence-level). It has also been demonstrated that PWA benefit from a morphosemantic intervention for verb morphology deficits, but it is unknown if this therapy induces neuroplastic changes in the brain. The second aim of this paper is to determine whether or not neuroplastic changes occur after treatment, and explore the neural mechanisms by which this improvement occurs.


title: Effect sizes and test-retest reliability of the fMRI-based Neurologic Pain Signature

abstract:
Identifying biomarkers that predict mental states with large effect sizes and high test-retest reliability is a growing priority for fMRI research. We examined a well-established multivariate brain measure that tracks pain induced by nociceptive input, the Neurologic Pain Signature (NPS). In N = 295 participants across eight studies, NPS responses showed a very large effect size in predicting within-person single-trial pain reports (d = 1.45) and medium effect size in predicting individual differences in pain reports (d = 0.49). The NPS showed excellent shortterm (within-day) test-retest reliability (ICC = 0.84, with average 69.5 trials/person). Reliability scaled with the number of trials within-person, with [≥]60 trials required for excellent test-retest reliability. Reliability was tested in two additional studies across 5-day (N = 29, ICC = 0.74, 30 trials/person) and 1-month (N = 40, ICC = 0.46, 5 trials/person) test-retest intervals. The combination of strong within-person correlations and only modest between-person correlations between the NPS and pain reports indicate that the two measures have different sources of between-person variance. The NPS is not a surrogate for individual differences in pain reports but can serve as a reliable measure of pain-related physiology and mechanistic target for interventions.

Significance statementCurrent efforts towards translating brain biomarkers require identifying brain measures that can strongly and reliably predict outcomes of interest. We systematically examined the performance of a well-established brain activity pattern, the Neurological Pain Signature (NPS), in a large and diverse sample of participants. The NPS showed excellent reliability, and the reliability scaled with the number of trials within-person. The NPS responses showed strong correlations with pain reports at the within-person level but only modest correlations at the between-person level. The findings suggest that the NPS is not a surrogate for individual differences in pain reports but can serve as a reliable measure of a pain-related physiological target.

Author NoteThis project was supported by grants R01MH076136 (T.D.W.), R01DA046064, R01EB026549, and R01DA035484. Elizabeth A. Reynolds Losin was supported by a Mentored Research Scientist Development award from National Institute On Drug Abuse of the National Institutes of Health (K01DA045735). Lauren Y. Atlas was supported in part by funding from the Intramural Research Program of the National Center for Complementary and Integrative Health. Yoni K. Ashar was supported by NCATS Grant # TL1-TR-002386. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Code for all analyses and figures is available at https://github.com/XiaochunHan/NPS_measurement_properties. Data for all analyses and figures is available at https://osf.io/v9px7/.


title: Denoising-based Image Compression for Connectomics

abstract: Connectomic reconstruction of neural circuits relies on nanometer resolution microscopy which produces on the order of a petabyte of imagery for each cubic millimeter of brain tissue. The cost of storing such data is a significant barrier to broadening the use of connectomic approaches and scaling to even larger volumes. We present an image compression approach that uses machine learning-based denoising and standard image codecs to compress raw electron microscopy imagery of neuropil up to 17-fold with negligible loss of 3d reconstruction and synaptic detection accuracy.


title: The role of memory and perspective shifts in systematic biases during object location estimation

abstract: Our previous research highlighted a systematic bias in a spatial memory task, with participants correctly detecting object movements in the same direction as the perspective shift, whilst misjudging the direction of object movements if those were in the opposite direction to the perspective shift. The aim of the current study was to investigate if the introduction of perspective shifts results in systematic biases in object location estimations. To do so, we asked participants to encode the position of an object in a virtual room and to then estimate the objects position following a perspective shift. In addition, by manipulating memory load (perception and memory condition) we investigated if the bias in object position estimates results from systematic distortions introduced in spatial memory. Overall, our results show that participants make systematic errors in estimating object positions in the same direction as the perspective shift. This bias was present in both the memory and the perception condition. We propose that the systematic bias in the same direction as the perspective shift is driven by difficulties in understanding the perspective shifts that may lead participants to use an egocentric representation of object positions as an anchor when estimating the object location following a perspective shift, thereby giving rise to a systematic shift in errors in the same direction as the perspective shift.


title: A method to study honeybee foraging regulatory molecules at different times during foraging

abstract: The foraging of honey bees is one of the most well organized and admirable behaviors that exist among social insects. In behavioral studies, these beautiful insects have been extensively used for understanding time-space learning, landmark use and concept of learning etc. Highly organized behaviors such as social interaction and communication are systematically well organized behavioral components of honeybee foraging. Over the last two decades, understanding the regulatory mechanisms underlying honey bee foraging at the cellular and molecular levels has been increasingly interested to several researchers. Upon the search of regulatory genes of brain and behavior, immediate early (IE) genes are considered as tool to begin the investigation. Our two recent studies, have demonstrated three IE genes namely Egr-1, Hr38 and kakusei having a role in the daily foraging of bees and their association with learning and memory during the foraging. These studies further evidence that IE genes can be used as a tool in finding the specific molecular/cellular players of foraging in honey bees and its behavioral components such as learning, memory, social interaction, social communication etc. In this article we provide the details of the method of sample collection at different times during foraging to investigate the foraging regulatory molecules.


title: Now you see it, now you don't: optimal parameters for interslice stimulation in concurrent TMS-fMRI

abstract: The powerful combination of transcranial magnetic stimulation (TMS) concurrent with functional magnetic resonance imaging (fMRI) provides rare insights into the causal relationships between brain activity and behaviour. Despite a recent resurgence in popularity, TMS-fMRI remains technically challenging. Here we examined the feasibility of applying TMS during short gaps between fMRI slices to avoid incurring artefacts in the fMRI data. We quantified signal dropout and changes in temporal signal-to-noise ratio (tSNR) for TMS pulses presented at timepoints from 100ms before to 100ms after slice onset. Up to 3 pulses were delivered per volume using MagVentures MR-compatible TMS coil. We used a spherical phantom, two 7-channel TMS-dedicated surface coils, and a multiband (MB) sequence (factor=2) with interslice gaps of 100ms and 40ms, on a Siemens 3T Prisma-fit scanner. For comparison we repeated a subset of parameters with a more standard single-channel TxRx (birdcage) coil, and with a human participant and surface coil set up. We found that, even at 100% stimulator output, pulses applied at least - 40ms/+50ms from the onset of slice readout avoid incurring artifacts. This was the case for all three setups. Thus, an interslice protocol can be achieved with a frequency of up to ~10 Hz, using a standard EPI sequence (slice acquisition time: 62.5ms, interslice gap: 40ms). Faster stimulation frequencies would require shorter slice acquisition times, for example using in-plane acceleration. Interslice TMS-fMRI protocols provide a promising avenue for retaining flexible timing of stimulus delivery without incurring TMS artifacts.


title: Distinct behavior of the little finger during the vertical translation of an unsteady thumb platform while grasping

abstract: Object stabilization while grasping is a common topic of research in motor control and robotics. Forces produced by the peripheral fingers (index and little) play a crucial role in sustaining the rotational equilibrium of a handheld object. In this study, we examined the contribution of the peripheral fingers towards object stabilization when the rotational equilibrium is disturbed. For this purpose, the thumb was placed over an unsteady platform and vertically translated. The task was to trace a trapezoid or an inverted trapezoid pattern by moving the thumb platform in the vertical direction. The thumb displacement data served as visual feedback to trace the pattern displayed. Participants were instructed to maintain the handle in static equilibrium at all times. We observed that the change in the normal force of the little finger due to the downward translation of the thumb was significantly greater than the change in the normal force of the index finger due to the upward translation. We speculate that morphological correlations (between thumb and little finger) during the displacement of the thumb might be a reason for such large increases in the little finger forces.


title: Beyond category-supervision: instance-level contrastive learning models predict human visual system responses to objects

abstract: Anterior regions of the ventral visual stream have substantial information about object categories, prompting theories that category-level forces are critical for shaping visual representation. The strong correspondence between category-supervised deep neural networks and ventral stream representation supports this view, but does not provide a viable learning model, as these deepnets rely upon millions of labeled examples. Here we present a fully self-supervised model which instead learns to represent individual images, where views of the same image are embedded nearby in a low-dimensional feature space, distinctly from other recently encountered views. We find category information implicitly emerges in the feature space, and critically that these models achieve parity with category-supervised models in predicting the hierarchical structure of brain responses across the human ventral visual stream. These results provide computational support for learning instance-level representation as a viable goal of the ventral stream, offering an alternative to the category-based framework that has been dominant in visual cognitive neuroscience.


title: Influence of age on functional memory T cell diversity

abstract: Memory T cells exhibit considerable diversity that determines their ability to be protective and their durability. Here, we examined whether changes in T cell heterogeneity contribute to the age-associated failure of immune memory. By screening for age-dependent T cell surface markers, we have identified CD4 and CD8 memory T cell subsets that are unrelated to previously defined subsets of central and effector memory cells. Memory T cells expressing the ecto-5-nucleotidase CD73 constitute a functionally distinct subset of memory T cells that declines with age. They exhibit many features favorable for immune protection, including longevity and polyfunctionality. They have a low turnover, but are poised to display effector functions and to develop into cells resembling tissue-resident memory T cells (TRM). Upstream regulators of differential chromatin accessibility and transcriptomes include transcription factors that are characteristic for conferring these superior memory features as well as facilitating CD73 expression. CD73 is not just a surrogate marker of these regulatory networks but is directly involved in T cell survival and TRM differentiation Interventions preventing the decline of this T cell subset or increasing CD73 expression have the potential to improve immune memory in older adults.

category: immunology


title: Partitioning the temporal changes in abundance-based beta diversity into loss and gain components

abstract:
O_LIEcologists have long recognized that the losses and gains in local species abundances can either decrease or increase spatial beta diversity, phenomena often referred to as biotic homogenization and differentiation, respectively. However, quantifying such dynamic impacts of species abundances on beta diversity has remained a methodological challenge.
C_LIO_LIHere, we develop a numerical method to additively partition the temporal changes in beta diversity into distinct components that reflect the losses and gains in local species abundances. Our method is based on Ru[z]i[c]ka and Bray-Curtis indices and the normalized abundance-based Whittakers beta diversity. The temporal changes in these measures are partitioned into components that represent biotic homogenization and differentiation driven by abundance losses and gains at both species and community levels.
C_LIO_LIApplication of the method to a Swedish fish community dataset revealed decreases in beta diversity between 1990 and 2018. The homogenization of fish communities was explained by gains, but not losses, in species abundances across sites. Species-level partitioning further showed that the homogenization was largely caused by the increased population sizes of a particular species in sites where it was already present.
C_LIO_LIThe results highlight that our partitioning method effectively identifies local population and community processes embedded in regional biodiversity patterns. We believe that explicit analyses of the losses and gains in species abundances should bring deeper insights into the dynamics of beta diversity.
C_LI

category: ecology


title: ILORA: A database of alien vascular flora of India

abstract:
1) Biological invasions pose an unprecedented threat to biodiversity and ecosystems at different spatial scales, especially for a biodiversity-rich developing nation like India. While country-level checklists of alien taxa are important, databases having their biological and ecological attributes are of paramount importance for facilitating research activities and developing policy interventions. Such a comprehensive database for alien flora is lacking in India.

2) We have curated data for 14 variables related to ecology, biogeography, introduction pathway, socio-economy and distribution of 1747 alien vascular plant species from 22 national and global sources to produce the Indian Alien Flora Information (ILORA) version 1.0 database. This paper describes the detailed methodology of curating these data along with the rationale behind selecting these variables.

3) The database, the first of its kind for the Indian alien flora, will provide easy access to high quality data and offer a ready reference to comprehend the existing scenario of alien plant species in the country. The database is dynamic and will be updated regularly. It has a provision to incorporate user submitted data, which will allow increasing the resolution of the database as well as the expansion of its capacity.

4) The database is envisaged to become a nationwide collaborative platform for a wide spectrum of stakeholders. It is freely accessible via an online data repository as well as through a dedicated website (https://ilora2020.wixsite.com/ilora2020).


title: Evaluation of sustainability of agricultural systems of indigenous people in Hidalgo, Mexico

abstract: Agricultural sustainability depends on complex relationships between environmental, economic and social aspects, in particular with the small farm holders from indigenous communities. This work was centered in two municipalities of Hidalgo State in Mexico, Ixmiquilpan (mainly irrigated systems) and El Cardonal (rainfed systems). Our objective was to understand the relationships between the small farm-holders and their agricultural systems. We evaluated the sustainability of their agricultural systems and did some recommendations. We applied the Framework for the Evaluation of Management Systems using Indicators (MESMIS, Spanish acronym), thirty-one indicators were identified, and the quantitative indexes were established to assess sustainability. The results showed that adaptability was a critical factor for irrigated and rainfed systems, the main problem identified was youth migration. Additionally, the access to water and economic resources, as well as environmental resources management, are imperious needs to increase the yield of agriculture crops. Therefore, a holistic approach taking into account the organization of small producers and synergy between indigenous knowledge and modern technologies, are required for the territorial development of the communities.


title: Ocellar spatial vision in Myrmecia ants

abstract: In addition to the compound eyes insects possess simple eyes known as ocelli. Input from the ocelli modulates optomotor responses, flight-time initiation and phototactic responses, behaviours that are predominantly mediated by the compound eyes. In this study, using pattern electroretinography (pERG), we investigated the contribution of the compound eyes to ocellar spatial vision in the diurnal Australian bull ant, Myrmecia tarsata by measuring the contrast sensitivity and spatial resolving power of the ocellar second-order neurons under various occlusion conditions. Furthermore, in four species of Myrmecia ants active at different times of the day and in European honeybee, Apis mellifera, we characterized the ocellar visual properties when both visual systems were available. Among the ants, we found that the time of activity had no significant effect on ocellar spatial vision. Comparing day-active ants and the honeybee we did not find any significant effect of locomotion on ocellar spatial vision. In M. tarsata, when the compound eyes were occluded, the amplitude of the pERG signal from the ocelli reduced by three times compared to conditions when the compound eyes were available. The signals from the compound eyes maintained the maximum contrast sensitivity of the ocelli as 13 (7.7%), and the spatial resolving power as 0.29 cpd. We conclude that ocellar spatial vison improves significantly with input from the compound eyes, with a noticeably larger improvement in contrast sensitivity than in spatial resolving power.

category: evolutionary biology


title: Impact of ploidy and pathogen life cycle on resistance durability

abstract: The breeding of resistant hosts based on the gene-for-gene interaction is crucial to address epidemics of plant pathogens in agroecosystems. Resistant host deployment strategies are developed and studied worldwide to decrease the probability of resistance breakdown and increase the resistance durability in various pathosystems. A major component of deployment strategies is the proportion of resistant hosts in the landscape. However, the impact of this proportion on resistance durability remains unclear for diploid pathogens with complex life cycles. In this study, we modelled pathogen population dynamics and genetic evolution at the virulence locus to assess the impact of the ploidy (haploid or diploid) and the pathogens life cycle (with or without host alternation) on resistance durability. Ploidy has a strong impact on evolutionary trajectories, with much greater stochasticity and delayed times of resistance breakdown for diploids. This result emphasises the importance of genetic drift in this system: as the virulent allele is recessive, positive selection on resistant hosts only applies to homozygous (virulent) individuals, which may lead to population collapse at low frequencies of the virulent allele. We also observed differences in the effect of host deployment depending on the pathogens life cycle. With host alternation, the probability that the pathogen population collapses strongly increases with the proportion of resistant hosts in the landscape. Therefore, resistance breakdown events occurring at high proportions of resistant hosts frequently amount to evolutionary rescue. Last, life cycles correspond to two selection regimes: without host alternation (soft selection) the resistance breakdown is mainly driven by the migration rate. Conversely, host alternation (hard selection) resembles an all-or-nothing game, with stochastic trajectories caused by the recurrent allele redistributions on the alternate host.


title: Dissecting the loci underlying maturation timing in Atlantic salmon using haplotype and multi-SNP based association methods

abstract: Resolving the genetic architecture of fitness-related traits is key to understanding the evolution and maintenance of fitness variation. However, well-characterized genetic architectures of such traits in wild populations remain uncommon. In this study, we used haplotype-based and multi-SNP Bayesian association methods with sequencing data for 313 individuals from wild populations to further characterize known candidate regions for sea age at maturation in Atlantic salmon (Salmo salar). We detected an association at five loci (on chromosomes ssa06, ssa09, ssa21, and ssa25) out of 116 candidates previously identified in an aquaculture strain with maturation timing in wild Atlantic salmon. We found that at each of these five loci, variation explained by the locus was predominantly driven by a single SNP suggesting the genetic architecture of Atlantic salmon maturation includes multiple loci with simple, non-clustered alleles. This highlights the diversity of genetic architectures that can exist for fitness-related traits. Furthermore, this study provides a useful multi-SNP framework for future work using sequencing data to characterize genetic variation underlying phenotypes in wild populations.


title: The haplotype-based analysis of Aegilops tauschii introgression into hard red winter wheat and its impact on productivity traits

abstract: Introgression from wild relatives have a great potential to broaden beneficial allelic diversity available for crop improvement in breeding programs. Here, we assessed the impact of introgression from 21 diverse accessions of Aegilops tauschii, the diploid ancestor of the wheat D genome, into six hard red winter wheat cultivars on yield and yield component traits. We used 5.2 million imputed D genome SNPs identified by whole-genome sequencing of parental lines and the sequence-based genotyping of introgression population including 351 BC1F3:5 lines. Phenotyping data collected from the irrigated and non-irrigated field trials revealed that up to 23% of the introgression lines produce more grain than the parents and check cultivars. Based on sixteen yield stability statistics, the yield of twelve introgression lines (3.4%) was stable across treatments, years and locations; five of these lines were also high yielding, producing 9.8% more grain than the average yield of check cultivars. The most significant SNP-trait and haplotype-trait associations were identified on chromosome arms 2DS and 6DL for spikelet number per spike (SNS), on chromosome arms 2DS, 3DS, 5DS and 7DS for grain length and on chromosome arms 1DL, 2DS, 6DL and 7DS for grain width. Introgression of haplotypes from Ae. tauschii parents was associated with increase in SNS, which positively correlated with heading date, whereas haplotypes from hexaploid wheat parents were associated with increased grain width. We show that haplotypes on 2DS associated with increased spikelet number and heading date are linked with multiple introgressed alleles of Ppd-D1 identified by the whole-genome sequencing of the Ae. tauschii parents. While some introgressed haplotypes exhibited significant pleiotropic effects with the direction of effects on the yield component traits being largely consistent with the previously reported trade-offs, there were haplotype combinations associated with the positive trends in yield. The characterized repertoire of the introgressed haplotypes derived from Ae. tauschii accessions with the combined positive effects on yield and yield components traits in elite germplasm provides a valuable source of alleles for improving the productivity of winter wheat by optimizing the contribution of component traits to yield.

category: genetics


title: Experimental demonstration of tethered gene drive systems for confined population modification or suppression

abstract: Homing gene drives hold great promise for the genetic control of natural populations. However, current homing systems are capable of spreading uncontrollably between populations connected by even marginal levels of migration. This could represent a substantial sociopolitical barrier to the testing or deployment of such drives and may generally be undesirable when the objective is only local population control, such as suppression of an invasive species outside of its native range. Tethered drive systems, in which a locally confined gene drive provides the CRISPR nuclease needed for a homing drive, could provide a solution to this problem, offering the power of a homing drive and confinement of the supporting drive. Here, we demonstrate the engineering of a tethered drive system in Drosophila, using a regionally confined CRISPR Toxin-Antidote Recessive Embryo (TARE) drive to support modification and suppression homing drives. Each drive was able to bias inheritance in its favor, and the TARE drive was shown to spread only when released above a threshold frequency in experimental cage populations. After the TARE drive had established in the population, it facilitated the spread of a subsequently released split homing modification drive (to all individuals in the cage) and of a homing suppression drive (to its equilibrium frequency). Our results show that the tethered drive strategy is a viable and easily engineered option for providing confinement of homing drives to target populations.


title: Robust Characterization of Non-Invasive Post-Traumatic Osteoarthritis Mouse Model

abstract:
ObjectiveBiochemical and molecular changes involved in the pathophysiology of post-traumatic arthritis (PTOA) have not been fully understood. This study used non-invasive mouse models to study biochemical, biomechanical and pain-related behavior changes induced in mice following repetitive mechanical knee loading. Mouse models were used to reflect the effects of the early stages of PTOA in humans.

MethodsForty-eight twelve week old male mice were obtained for three groups: normal control without mechanical loading, trauma (24 hours after loading), and PTOA (early OA) groups. For the non-invasive PTOA mouse model, cyclic comprehensive loading (9 N) was applied on the left knee joint of each mouse. Biochemical and molecular changes induced by mechanical loading were analyzed after loading was completed. Blood and cartilage were collected and further examined using gene expression analysis. Grading of the tissue sections was completed using the osteoarthritis research society international (OARSI) scale. Biomechanical features of mechanically loaded knee joints were determined after 24 hours (Trauma) and three weeks (PTOA) post-mechanical loading sessions to examine the development of PTOA, respectively.

ResultsThe loaded left knee joint showed a greater ROS/RNS signal than the right knee that was not loaded. There was an increase in cartilage damage and MMP activity in the affected knee as the intensity of MabCII680 and MMP750 signal increased in the mechanical loaded joints as compared to unloaded control knee joints. There was also an increase in the difference of viscoelastic energy dissipation ability (tan {delta}) in PTOA. The OA score increased significantly in mechanically loaded knee joints.

ConclusionThis study showed that biomechanical, biochemical, and behavioral characteristics of the murine PTOA groups are significantly different from the control group. These results validate that the current mouse model can be used for translational studies to examine PTOA.

category: animal behavior and cognition


title: "Smart Tanks": An Intelligent High-Throughput Intervention Testing Platform in Daphnia

abstract: We present a novel platform for testing the effect of interventions on life- and health-span of a short-lived semi transparent freshwater organism, sensitive to drugs with complex behavior and physiology - the planktonic crustacean Daphnia magna. Within this platform, dozens of complex behavioural features of both routine motion and response to stimuli are continuously accurately quantified for large homogeneous cohorts via an automated phenotyping pipeline. We build predictive machine learning models calibrated using chronological age and extrapolate onto phenotypic age. We further apply the model to estimate the phenotypic age under pharmacological perturbation. Our platform provides a scalable framework for drug screening and characterization in both life-long and instant assays as illustrated using long term dose response profile of metformin and short term assay of such well-studied substances as caffeine and alcohol.

category: bioengineering


title: Broadening a SARS-CoV-1 neutralizing antibody for potent SARS-CoV-2 neutralization through directed evolution

abstract: The emergence of SARS-CoV-2 underscores the need for strategies to rapidly develop neutralizing monoclonal antibodies that can function as prophylactic and therapeutic agents and to help guide vaccine design. Here, we demonstrate that engineering approaches can be used to refocus an existing neutralizing antibody to a related but resistant virus. Using a rapid affinity maturation strategy, we engineered CR3022, a SARS-CoV-1 neutralizing antibody, to bind SARS-CoV-2 receptor binding domain with >1000-fold improved affinity. The engineered CR3022 neutralized SARS-CoV-2 and provided prophylactic protection from viral challenge in a small animal model of SARS-CoV-2 infection. Deep sequencing throughout the engineering process paired with crystallographic analysis of an enhanced antibody elucidated the molecular mechanisms by which engineered CR3022 can accommodate sequence differences in the epitope between SARS-CoV-1 and SARS-CoV-2. The workflow described provides a blueprint for rapid broadening of neutralization of an antibody from one virus to closely related but resistant viruses.


title: A cost-effective way to design a two-layered cell trapping microfluidic device

abstract: When studying cells population, sometimes is necessary to isolate a specific subset to perform a timeline analysis or determinate the evolution on cell progeny. Microfluidic devices are being used for fulfill this function in multiple designs. However, the design and fabrication of this type of devices involve multiple challenges and high cost. Miniaturization tends to be the primarily challenge where those devices below 10um tends to be so expensive that needs special facilities for fabrication, but using techniques as multilayer fabrication those problems can be overcome. In this work are stated techniques (photolithography, soft-lithography) that helps solving that limitations, from using inexpensive materials, to alignment techniques that does not requires high-tech equipments, and a practical design for microfluidic cell trapping array. Where multiple cell types could be seeded and trapped in small cluster (varying on cell size) to be retrieved later on in an easy and practical way.


title: A Biologically Interpretable Graph Convolutional Network to Link Genetic Risk Pathways and Neuroimaging Phenotypes of Disease

abstract: AO_SCPLOWBSTRACTC_SCPLOWWe propose a novel end-to-end framework for whole-brain and whole-genome imaging-genetics. Our genetics network uses hierarchical graph convolution and pooling operations to embed subject-level data onto a low-dimensional latent space. The hierarchical network implicitly tracks the convergence of genetic risk across well-established biological pathways, while an attention mechanism automatically identifies the salient edges of this network at the subject level. In parallel, our imaging network projects multimodal data onto a set of latent embeddings. For interpretability, we implement a Bayesian feature selection strategy to extract the discriminative imaging biomarkers; these feature weights are optimized alongside the other model parameters. We couple the imaging and genetic embeddings with a predictor network, to ensure that the learned representations are linked to phenotype. We evaluate our framework on a schizophrenia dataset that includes two functional MRI paradigms and gene scores derived from Single Nucleotide Polymorphism data. Using repeated 10-fold cross-validation, we show that our imaging-genetics fusion achieves the better classification performance than state-of-the-art baselines. In an exploratory analysis, we further show that the biomarkers identified by our model are reproducible and closely associated with deficits in schizophrenia.


title: Vulcan: Improved long-read mapping and structural variant calling via dual-mode alignment

abstract:
BackgroundLong-read sequencing has enabled unprecedented surveys of structural variation across the entire human genome. To maximize the potential of long-read sequencing in this context, novel mapping methods have emerged that have primarily focused on either speed or accuracy. Various heuristics and scoring schemas have been implemented in widely used read mappers (minimap2 and NGMLR) to optimize for speed or accuracy, which have variable performance across different genomic regions and for specific structural variants. Our hypothesis is that constraining read mapping to the use of a single gap penalty across distinct mutational hotspots reduces read alignment accuracy and impedes structural variant detection.

FindingsWe tested our hypothesis by implementing a read mapping pipeline called Vulcan that uses two distinct gap penalty modes, which we refer to as dual-mode alignment. The high-level idea is that Vulcan leverages the computed normalized edit distance of the mapped reads via e.g. minimap2 to identify poorly aligned reads and realigns them using the more accurate yet computationally more expensive long read mapper (NGMLR). In support of our hypothesis, we show Vulcan improves the alignments for Oxford Nanopore Technology (ONT) long-reads for both simulated and real datasets. These improvements, in turn, lead to improved accuracy for structural variant calling performance on human genome datasets compared to either of the read mapping methods alone.

ConclusionsVulcan is the first long-read mapping framework that combines two distinct gap penalty modes, resulting in improved structural variant recall and precision. Vulcan is open-source and available under the MIT License at https://gitlab.com/treangenlab/vulcan


title: A Reference-free Approach for Cell Type Classification with scRNA-seq

abstract: The single-cell RNA sequencing (scRNA-seq) has become a revolutionary technology to detect and characterize distinct cell populations under different biological conditions. Unlike bulk RNA-seq, the expression of genes from scRNA-seq is highly sparse due to limited sequencing depth per cell. This is worsened by tossing away a significant portion of reads that cannot be mapped during gene quantification. To overcome data sparsity and fully utilize original sequences, we propose scSimClassify, a reference-free and alignment-free approach to classify cell types with k-mer level features derived from raw reads in a scRNA-seq experiment. The major contribution of scSimClassify is the simhash method compressing k-mers with similar abundance profiles into groups. The compressed k-mer groups (CKGs) serve as the aggregated k-mer level features for cell type classification. We evaluate the performance of CKG features for predicting cell types in four scRNA-seq datasets comparing four state-of-the-art classification methods as well as two scRNA-seq specific algorithms. Our experiments demonstrate that the CKG features lend themselves to better performance than traditional gene expression features in scRNA-seq classification accuracy in the majority of cases. Because CKG features can be efficiently derived from raw reads without a resource-intensive alignment process, scSimClassify offers an efficient alternative to help scientists rapidly classify cell types without relying on reference sequences. The current version of scSimClassify is implemented in python and can be found at https://github.com/digi2002/scSimClassify.


title: Amyloidogenic proteins in the SARS-CoV and SARS-CoV-2 proteomes

abstract:
The phenomenon of protein aggregation is associated with a wide range of human diseases. Our knowledge on the aggregation behaviour of viral proteins, however, is still rather limited. Here, we investigated this behaviour in the the SARS-CoV and SARS-CoV-2 proteomes. An initial analysis using a panel of sequence-based predictors suggested the presence of multiple aggregation-prone regions in these proteomes, and revealed an enhanced aggregation propensity in some SARS-CoV-2 proteins. We then studied the in vitro aggregation of predicted aggregation-prone SARS-CoV-2 proteins, including the signal sequence peptide and fusion peptide 1 of the spike protein, a peptide from the NSP6 protein (NSP6-p), the ORF10 protein, and the NSP11 protein. Our results show that these peptides and proteins form aggregates via a nucleation-dependent mechanism. Moreover, we demonstrated that the aggregates of NSP11 are toxic to mammalian cell cultures. These findings provide evidence about the aggregation of proteins in the SARS-CoV-2 proteome.

SignificanceThe aggregation of proteins is linked with human disease in a variety of ways. In the case of viral infections, one could expect that the aberrant aggregation of viral proteins may damage the host cells, and also that viral particles may trigger the misfolding and aggregation of host proteins, resulting in damage to the host organism. Here we investigate the aggregation propensity of SARS-CoV-2 proteins and show that many of them can form aggregates that are potentially cytotoxic. In perspective, these results suggest that a better understanding of the effects of viruses on the human protein homeostasis system could help future therapeutic efforts.


title: Field Potential Image Classification of Pacemaker Micro-coordination in the Intestine of the Mouse

abstract: The flexible and sophisticated movement of the gastrointestinal (GI) tract implies the involvement of mechanisms other than enteric neural circuits, to coordinate excitation in microregions. We thus performed microimaging of pacemaker dynamics in the small intestine of mice since it contains typical network-forming pacemaker cells. A dialysis membrane-reinforced low-impedance microelectrode array (MEA) enabled field potentials over a wide frequency range to be stably measured in microregions. The pacemaker dynamics were classified into basic patterns despite large variations. In the developmental process, pacemaker activity was categorized as either an  expanding or a  migrating pattern that was initiated in or propagated to the MEA sensing area, respectively. The intercellular current of the volume conductor complicated the waveform of both activities. The existence of  expanding and  migrating patterns was attributable to duplicated pacemaker systems such as intracellular Ca2+ oscillation-activated and voltage-gated mechanisms. Additionally, from the spatio-temporal feature during the period of pacemaker events, the  bumpy/aberrant pattern was defined by aberrant, incoherent propagation, and associated with local impairment of excitability, while the  colliding/converging pattern involved the interaction of multiple activities in the MEA area. Interconversion between the four micro-coordination patterns occurred in the same microregion. 5-Hydroxytryptamine (5-HT) promoted  migrating activity, implying an improvement or restoration of spatial conductivity. These results agree well with the action of 5-HT to change GI movement toward propulsion. In conclusion, our MEA method of microimaging classification enables the quantitative assessment of spatio-temporal electric coordination underlying GI motility, suggesting its application to small model animals.

category: physiology


title: Classification, Using Field Potential Images, of Pacemaker Micro-coordination in the Mouse Intestine


title: Classification of Pacemaker Dynamics in the Mouse Intestine by Field Potential Microimaging


title: FGF21 Response Varies by Sugar Type and is Associated with Body Weight, Dietary Added Sugar, and Neural Signaling in Humans

abstract:
Fibroblast growth factor 21 (FGF21) is a liver-derived hormone that regulates energy homeostasis. In humans, few studies have investigated whether FGF21 may act to suppress sugar intake and influence eating behavior, and the effects of adiposity on post-ingestive FGF21 regulation of appetite are unknown. Here, we demonstrate among two cohorts of healthy, young adults that acute oral fructose and sucrose compared to glucose lead to greater circulating FGF21. Moreover, high compared to low dietary added sugar intake is associated with greater sucrose-stimulated FGF21 among participants with healthy weight but attenuated in people with overweight and obesity. In addition, our study is the first to demonstrate associations between circulating FGF21 and neural signaling following an acute sucrose load among humans with healthy weight. Collectively, our results suggest that these potential compensatory relationships between sucrose-stimulated circulating FGF21, habitual sugar intake, and post-ingestive brain responses may be altered among adults with overweight and obesity.

Significance StatementAnimal models have established FGF21 as an autoregulator of sweet consumption, but few studies have examined post-ingestive FGF21 effects in humans. In this report, we demonstrate a compensatory relationship between sucrose-stimulated FGF21 and high dietary added sugar intake through a potential liver-to-brain negative-feedback cycle among healthy, young adults. Notably, our findings also suggest that humans with overweight and obesity may have altered FGF21 neuroendocrine signaling.


title: NetTIME: improving multitask transcription factor binding site prediction with base-pair resolution

abstract:
MotivationMachine learning models for predicting cell-type-specific transcription factor (TF) binding sites have become increasingly more accurate thanks to the increased availability of next-generation sequencing data and more standardized model evaluation criteria. However, knowledge transfer from data-rich to data-limited TFs and cell types remains crucial for improving TF binding prediction models because available binding labels are highly skewed towards a small collection of TFs and cell types. Transfer prediction of TF binding sites can potentially benefit from a multitask learning approach; however, existing methods typically use shallow single-task models to generate low-resolution predictions. Here we propose NetTIME, a multitask learning framework for predicting cell-type-specific transcription factor binding sites with basepair resolution.

ResultsWe show that the multitask learning strategy for TF binding prediction is more efficient than the single-task approach due to the increased data availability. NetTIME trains high-dimensional embedding vectors to distinguish TF and cell-type identities. We show that this approach is critical for the success of the multitask learning strategy and allows our model to make accurate transfer predictions within and beyond the training panels of TFs and cell types. We additionally train a linear-chain conditional random field (CRF) to classify binding predictions and show that this CRF eliminates the need for setting a probability threshold and reduces classification noise. We compare our methods predictive performance with several state-of-the-art methods, including DeepBind, BindSpace, and Catchitt, and show that our method outperforms previous methods under both supervised and transfer learning settings.

AvailabilityNetTIME is freely available at https://github.com/ryi06/NetTIME

Contactrb133@nyu.edu

category: systems biology


title: Interaction between perineuronal nets and ketamine in antidepressant action

abstract: Depression is highly prevalent, increases suicide risk, and is now the leading cause of disability worldwide. Our ability to treat depression is hampered by the lack of understanding of its biological underpinnings and of the mode of action of effective treatments. We hypothesised that the scaffolding proteins in the medial frontal cortex play a major role in effective antidepressant action. We implanted cannulae into the infralimbic cortex to inject chABC and locally remove perineuronal nets and then tested for antidepressant effects with the forced swim test. We further tested if systemic injections of ketamine had an additive effect. Our preliminary data indicate that neither the removal of these scaffolding proteins nor ketamine were sufficient to decrease depression-like behaviour, but may interact synergistically to decrease immobility time in the forced swim test.


title: Chronic PPARγ Stimulation Shifts Amyloidosis to Higher Fibrillarity but Improves Cognition

abstract:
BackgroundWe undertook longitudinal {beta}-amyloid positron emission tomography (A{beta}-PET) imaging as a translational tool for monitoring of chronic treatment with the peroxisome proliferator-activated receptor gamma (PPAR{gamma}) agonist pioglitazone in A{beta} model mice. We thus tested the hypothesis this treatment would rescue from increases of the A{beta}-PET signal while promoting spatial learning and preservation of synaptic density.

MethodsPS2APP mice (N=23; baseline age: 8 months) and AppNL-G-F mice (N=37; baseline age: 5 months) were investigated longitudinally for five months using A{beta}-PET. Groups of mice were treated with pioglitazone or vehicle during the follow-up interval. We tested spatial memory performance and confirmed terminal PET findings by immunohistochemical and biochemistry analyses.

ResultsSurprisingly, A{beta}-PET and immunohistochemistry revealed a shift towards higher fibrillary composition of A{beta}-plaques during upon chronic pioglitazone treatment. Nonetheless, synaptic density and spatial learning were improved in transgenic mice with pioglitazone treatment, in association with the increased plaque fibrillarity.

ConclusionThese translational data suggest that a shift towards higher plaque fibrillarity protects cognitive function and brain integrity. Increases in the A{beta}-PET signal upon immunomodulatory treatments targeting A{beta} aggregation can thus be protective.


title: Effect of SSRI discontinuation on anxiety-like behaviours in mice

abstract:
BackgroundAbrupt cessation of therapy with a selective serotonin reuptake inhibitor (SSRI) is associated with a discontinuation syndrome, typified by numerous disabling symptoms including anxiety. Surprisingly little is known of the behavioural effect of SSRI discontinuation in animals.

AimHere, the effect of SSRI discontinuation on anxiety-like behaviour was systematically investigated in mice.

MethodsExperiments were based on a 3-arm experimental design comprising saline, continued SSRI and discontinued SSRI. Mice were assessed 2 days after SSRI discontinuation over a 5 day period using the elevated plus maze (EPM) and other anxiety tests.

ResultsAn exploratory experiment found cessation of paroxetine (12 days) was associated with decreased open arm exploration and reduced total distance travelled, in male but not female mice. Follow-up studies confirmed a discontinuation effect on the EPM in male mice after paroxetine (12 days) and also citalopram (12 days). Mice receiving continued paroxetine (but not citalopram) also showed decreased open arm exploration but this was dissociable from effects of discontinuation. The discontinuation response to paroxetine did not strengthen after 28 days treatment but was absent after 7 days treatment. A discontinuation response was not decernable in other anxiety and fear-learning tests applied 3-5 days after treatment cessation. Finally, discontinuation effects on the EPM were typically associated with decreased locomotion on the test. However, separate locomotor testing implicated anxiety-provoked behavioural inhibition rather than a general reduction in motor activity.

ConclusionOverall, the current study provides evidence for a short-lasting behavioural discontinuation response to cessation of SSRI treatment in mice.


title: Subcortical brain segmentation in 5-year-old children: validation of FSL-FIRST and FreeSurfer against manual segmentation

abstract:
Developing accurate subcortical volumetric quantification tools is crucial for neurodevelopmental studies, as they could reduce the need for challenging and time-consuming manual segmentation. In this study the accuracy of two automated segmentation tools, FSL-FIRST (with three different boundary correction settings) and FreeSurfer were compared against manual segmentation of subcortical nuclei, including the hippocampus, amygdala, thalamus, putamen, globus pallidus, caudate and nucleus accumbens, using volumetric and correlation analyses in 80 5-year-olds.

Both FSL-FIRST and FreeSurfer overestimated the volume on all structures except the caudate, and the accuracy varied depending on the structure. Small structures such as the amygdala and nucleus accumbens, which are visually difficult to distinguish, produced significant overestimations and weaker correlations with all automated methods. Larger and more readily distinguishable structures such as the caudate and putamen produced notably lower overestimations and stronger correlations. Overall, the segmentations performed by FSL-FIRSTs Default pipeline were the most accurate, while FreeSurfers results were weaker across the structures.

In line with prior studies, the accuracy of automated segmentation tools was imperfect with respect to manually defined structures. However, apart from amygdala and nucleus accumbens, FSL-FIRSTs agreement could be considered satisfactory (Pearson correlation > 0.74, Intraclass correlation coefficient (ICC) > 0.68 and Dice Score coefficient (DSC) > 0.87) with highest values for the striatal structures (putamen, globus pallidus and caudate) (Pearson correlation > 0.77, ICC > 0.87 and DSC > 0.88, respectively). Overall, automated segmentation tools do not always provide satisfactory results, and careful visual inspection of the automated segmentations is strongly advised.


title: Magnetic Resonance Spectroscopy Frequency and Phase Correction Using Convolutional Neural Networks

abstract: We introduce DeepSPEC, a novel convolutional neural network (CNN) -based approach for frequency-and-phase correction (FPC) of MRS spectra to achieve fast and accurate FPC of single-voxel PRESS MRS and MEGA-PRESS data. In DeepSPEC, two neural networks, including one for frequency correction and one for phase correction were trained and validated using published simulated and in vivo PRESS and MEGA-PRESS MRS dataset with wide-range artificial frequency and phase offsets applied. DeepSPEC was subsequently tested and compared to the current deep learning solution - a "vanilla" neural network approach using multilayer perceptrons (MLP). Furthermore, random noise was added to the original simulated dataset to further investigate the model performance with noise at varied signal-to-noise (SNR) levels (i.e., 6 dB, 3 dB, and 1.5 dB). The testing showed that DeepSPEC is more robust to noise compared to the MLP-based approach due to having a smaller absolute error in both frequency and phase offset prediction. The DeepSPEC framework was capable of correcting frequency offset with 0.01{+/-}0.01 Hz and phase offset with 0.12{+/-}0.09{degrees} absolute errors on average for unseen simulated data at a high SNR (12 dB) and correcting frequency offset with 0.01{+/-}0.02 Hz and phase offset within -0.07{+/-}0.44{degrees} absolute errors on average at very low SNR (1.5 dB). Furthermore, additional frequency and phase offsets (i.e., small, moderate, large) were applied to the in vivo dataset, and DeepSPEC demonstrated better performance for FPC when compared to the MLP-based approach. Results also show DeepSPEC has superior performance than the model-based SR implementation (mSR) in FPC by having higher accuracy in a wider range of additional offsets. These results represent a proof of concept for the use of CNNs for preprocessing MRS data and demonstrate that DeepSPEC accurately predicts frequency and phase offsets at varying noise levels with state-of-the-art performance.


title: The Dual Mechanisms of Cognitive Control dataset: A theoretically-guided within-subject task fMRI battery

abstract: Cognitive control is a critical higher mental function, which is subject to considerable individual variation, and is impaired in a range of mental health disorders. We describe here the initial release of Dual Mechanisms of Cognitive Control (DMCC) project data, the DMCC55B dataset, with 55 healthy unrelated young adult participants. Each participant performed four well-established cognitive control tasks (AX-CPT, Cued Task-Switching, Sternberg Working Memory, and Stroop) while undergoing functional MRI scanning. The dataset includes a range of state and trait self-report questionnaires, as well as behavioural tasks assessing individual differences in cognitive ability. The DMCC project is on-going and features additional components (e.g., related participants, manipulations of cognitive control mode, resting state fMRI, longitudinal testing) that will be publicly released following study completion. This DMCC55B subset is released early with the aim of encouraging wider use and greater benefit to the scientific community. The DMCC55B dataset is suitable for benchmarking and methods exploration, as well as analyses of task performance and individual differences.


title: The Dual Mechanisms of Cognitive Control dataset, a theoretically-guided within-subject task fMRI battery


title: Simultaneous fMRI and fast-scan cyclic voltammetry bridges oxygenation and neurotransmitter dynamics across spatiotemporal scales

abstract: The vascular contributions of neurotransmitters to the hemodynamic response are gaining more attention in neuroimaging studies, as many neurotransmitters are vasomodulatory. To date, well-established electrochemical techniques that detect neurotransmission in high magnetic field environments are limited. Here, we propose an experimental setting enabling simultaneous fast-scan cyclic voltammetry (FSCV) and blood oxygenation-dependent functional magnetic imaging (BOLD fMRI) to measure both local tissue oxygen and dopamine responses, and global BOLD changes, respectively. By using MR-compatible materials and the proposed data acquisition schemes, FSCV detected physiological analyte concentrations with high spatiotemporal resolution inside of a 9.4 T MRI bore. We found that tissue oxygen and BOLD correlate strongly, and brain regions that encode dopamine amplitude differences can be identified via modeling simultaneously acquired dopamine FSCV and BOLD fMRI time-courses. This technique provides complementary neurochemical and hemodynamic information and expands the scope of studying the influence of local neurotransmitter release over the entire brain.


title: Arthropods and the evolution of RNA viruses

abstract: Many viruses of arthropods also infect other organisms including humans, sometimes with devastating consequences. Yet, for the vast diversity of arthropods, their associated viruses remain unexplored. Here, we mined meta-transcriptomes from 711 arthropod species, including insects, arachnids, myriapods, and crustaceans, and uncovered more than 1400 previously unknown RNA viruses, representing 822 novel evolutionary groups at a level between species and genus. These newly found viral groups fill major evolutionary gaps within the five branches of RNA viruses, bridging the evolution of viruses infecting early and later diverging eukaryotes. Additionally, co-phylogenetic analysis implies that RNA viruses of arthropods commonly co-evolved with their hosts. Our analyses indicate that arthropods have played a central role in the macroevolution of RNA viruses by serving as reservoirs in which viruses co-evolved with arthropods while being exchanged with a vast diversity of organisms.

category: microbiology


title: The inner nuclear membrane protein Lem2 coordinates RNA degradation at the nuclear periphery

abstract: Transcriptionally silent chromatin often localizes to the nuclear periphery. However, whether the nuclear envelope (NE) is a site for post-transcriptional gene repression is unknown. Here we demonstrate that S. pombe Lem2, an NE protein, regulates nuclear exosome-mediated RNA degradation. Lem2 deletion causes accumulation of non-coding RNAs and meiotic transcripts. Indeed, an engineered exosome substrate RNA shows Lem2-dependent localization to the nuclear periphery. Lem2 does not directly bind RNA, but instead physically interacts with the exosome-targeting MTREC complex and promotes RNA recruitment. The Lem2-assisted pathway acts independently of nuclear bodies where exosome factors assemble, revealing that multiple spatially distinct degradation pathways exist. The Lem2 pathway is environmentally responsive: nutrient availability modulates Lem2 regulation of meiotic transcripts. Our data indicate that Lem2 recruits exosome co-factors to the nuclear periphery to coordinate RNA surveillance and regulates transcripts during the mitosis-to-meiosis switch.

category: molecular biology


title: Efficient biallelic knock-in in mouse embryonic stem cells by in vivo-linearization of donor and transient inhibition of DNA Polymerase θ/DNA-PK

abstract: CRISPR/Cas9-mediated homology-directed repair (HDR) is used for error-free targeted knock-in of foreign donor DNA. However, the low efficiency of HDR-mediated knock-in hinders establishment of knock-in clones. Double-strand breaks (DSBs) induced by CRISPR/Cas9 are preferentially repaired by non-homologous end joining (NHEJ) or microhomology-mediated end joining (MMEJ) before HDR can occur, thereby preventing HDR-mediated knock-in. NHEJ/MMEJ also cause random integrations, which give rise to false-positive knock-in events, or silently disrupt the genome. In this study, we optimized an HDR-mediated knock-in method for mouse embryonic stem cells (mESCs). We succeeded in improving efficiency of HDR-mediated knock-in of a plasmid donor while almost completely suppressing NHEJ/MMEJ-based integration by combining in vivo-linearization of the donor plasmid, transient knockdown of DNA Polymerase {theta}, and chemical inhibition of DNA-dependent protein kinase (DNA-PK) by M3814. This method also dramatically improved the efficiency of biallelic knock-in; at the Rosa26a locus, 95% of HDR-mediated knock-in clones were biallelic. We designate this method BiPoD (Biallelic knock-in assisted by Pol {theta} and DNA-PK inhibition). BiPoD achieved simultaneous efficient biallelic knock-in into two loci. BiPoD, therefore, enables rapid and easy establishment of biallelic knock-in mESC lines.


title: An insight into the evolutionary history of Indoplanorbis exustus (Bulinidae: Gastropoda)at the scale of population and species

abstract: The history of a lineage is intertwined with the history of the landscape it resides in. Here we showcase how the geo-tectonic and climatic evolution in South Asia and surrounding landmasses have shaped the biogeographic history of Indoplanorbis exustus, a tropical Asian, freshwater, pulmonated snail. We amplified partial COI gene fragment from all over India and combined this with a larger dataset from South and Southeast Asia to carry out phylogenetic reconstruction, species delimitation analysis, and population genetic analyses. Two nuclear genes were also amplified from one individual per putative species to carry out divergence dating and ancestral area reconstruction analyses. The results suggest that Indoplanorbis dispersed out of Africa into India during Eocene. Furthermore, molecular data suggests Indoplanorbis is a species complex consisting of multiple putative species. The primary diversification took place in Northern Indian plains or the Northeast India. The speciation events appear to be primarily allopatric caused by a series of aridification events starting from late Miocene to early Pleistocene. None of the species seemed to have any underlying genetic structure suggestive of high vagility. All the species underwent population fluctuations during the Pleistocene likely driven by the Quaternary climatic fluctuations.


title: Identification of tissue-specific and common methylation quantitative trait loci in healthy individuals using MAGAR

abstract:
BackgroundUnderstanding the influence of genetic variants on DNA methylation is fundamental for the interpretation of epigenomic data in the context of disease. There is a need for systematic approaches not only for determining methylation quantitative trait loci (methQTL) but also for discriminating general from cell-type-specific effects.

ResultsHere, we present a two-step computational framework MAGAR, which fully supports identification of methQTLs from matched genotyping and DNA methylation data, and additionally the identification of quantitative cell-type-specific methQTL effects. In a pilot analysis, we apply MAGAR on data in four tissues (ileum, rectum, T-cells, B-cells) from healthy individuals and demonstrate the discrimination of common from cell-type-specific methQTLs. We experimentally validate both types of methQTLs in an independent dataset comprising additional cell types and tissues. Finally, we validate selected methQTLs (PON1, ZNF155, NRG2) by ultra-deep local sequencing. In line with previous reports, we find cell-type-specific methQTLs to be preferentially located in enhancer elements.

ConclusionsOur analysis demonstrates that a systematic analysis of methQTLs provides important new insights on the influences of genetic variants to cell-type-specific epigenomic variation.


title: Prioritization of autoimmune disease-associated genetic variants that perturb regulatory element activity in T cells

abstract: Genome-wide association studies have uncovered hundreds of autoimmune disease-associated loci; however, the causal genetic variant(s) within each locus are mostly unknown. Here, we perform high-throughput allele-specific reporter assays to prioritize disease-associated variants for five autoimmune diseases. By examining variants that both promote allele-specific reporter expression and are located in accessible chromatin, we identify 60 putatively causal variants that enrich for statistically fine-mapped variants by up to 57.8-fold. We introduced the risk allele of a prioritized variant (rs72928038) into a human T cell line and deleted the orthologous sequence in mice, both resulting in reduced BACH2 expression. Naive CD8 T cells from mice containing the deletion had reduced expression of genes that suppress activation and maintain stemness. Our results represent an example of an effective approach for prioritizing variants and studying their physiologically relevant effects.


title: Computational genomic analysis of the lung tissue microenvironment in COVID-19 patients

abstract:
The coronavirus disease 2019 (COVID-19) pandemic caused by the SARS-CoV-2 virus has affected over 170 million people, and caused over 3.5 million deaths throughout the world as of May 2021. Although over 150 million people around the world have recovered from this disease, the long term effects of the disease are still under study. A year after the start of the pandemic, data from COVID-19 recovered patients shows multiple organs affected with a broad spectrum of manifestations. Long term effects of SARS-CoV-2 infection includes fatigue, chest pain, cellular damage, and robust innate immune response with inflammatory cytokine production. More clinical studies and clinical trials are needed to not only document, but also to understand and determine the factors that predispose certain people to the long term side effects of his infection.

In this manuscript, our goal was to explore the multidimensional landscape of infected lung tissue microenvironment to better understand complex interactions between SARS-CoV-2 viral infection, immune response and the lungs microbiome of COVID-19 patients. Each sample was analyzed with several machine learning tools allowing simultaneous detection and quantification of viral RNA amount at genome and gene level; human gene expression and fractions of major types of immune cells, as well as metagenomic analysis of bacterial and viral abundance. To contrast and compare specific viral response to SARS-COV-2 we have analyzed deep sequencing data from additional cohort of patients infected with NL63 strain of corona virus.

Our correlation analysis of three types of measurements in patients i.e. fraction of viral RNA (at genome and gene level), Human RNA (transcripts and gene level) and bacterial RNA (metagenomic analysis), showed significant correlation between viral load as well as level of specific viral gene expression with the fractions of immune cells present in lung lavage as well as with abundance of major fractions of lung microbiome in COVID-19 patients.

Our exploratory study has provided novel insights into complex regulatory signaling interactions and correlative patterns between the viral infection, inhibition of innate and adaptive immune response as well as microbiome landscape of the lung tissue. These initial findings could provide better understanding of the diverse dynamics of immune response and the side effects of the SARS-CoV-2 infection.

category: genomics


title: Human genome integration of SARS-CoV-2 contradicted by long-read sequencing

abstract: A recent study proposed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) hijacks the LINE-1 (L1) retrotransposition machinery to integrate into the DNA of infected cells. If confirmed, this finding could have significant clinical implications. Here, we applied deep (>50x) long-read Oxford Nanopore Technologies (ONT) sequencing to HEK293T cells infected with SARS-CoV-2, and did not find the virus integrated into the genome. By examining ONT data from separate HEK293T cultivars, we completely resolved 78 L1 insertions arising in vitro in the absence of L1 overexpression systems. ONT sequencing applied to hepatitis B virus (HBV) positive liver cancer tissues located a single HBV insertion. These experiments demonstrate reliable resolution of retrotransposon and exogenous virus insertions via ONT sequencing. That we found no evidence of SARS-CoV-2 integration suggests such events are, at most, extremely rare in vivo, and therefore are unlikely to drive oncogenesis or explain post-recovery detection of the virus.


title: No evidence of human genome integration of SARS-CoV-2 found by long-read DNA sequencing


title: A generative nonparametric Bayesian model for whole genomes

abstract: Generative probabilistic modeling of biological sequences has widespread existing and potential use across biology and biomedicine, particularly given advances in high-throughput sequencing, synthesis and editing. However, we still lack methods with nucleotide resolution that are tractable at the scale of whole genomes and that can achieve high predictive accuracy either in theory or practice. In this article we propose a new generative sequence model, the Bayesian embedded autoregressive (BEAR) model, which uses a parametric autoregressive model to specify a conjugate prior over a nonparametric Bayesian Markov model. We explore, theoretically and empirically, applications of BEAR models to a variety of statistical problems including density estimation, robust parameter estimation, goodness-of-fit tests, and two-sample tests. We prove rigorous asymptotic consistency results including nonparametric posterior concentration rates. We scale inference in BEAR models to datasets containing tens of billions of nucleotides. On genomic, transcriptomic, and metagenomic sequence data we show that BEAR models provide large increases in predictive performance as compared to parametric autoregressive models, among other results. BEAR models offer a flexible and scalable framework, with theoretical guarantees, for building and critiquing generative models at the whole genome scale.


title: A role for ecologically-tuned chunking in the evolution of advanced cognition demonstrated by modelling the cleaner fish market problem

abstract: What makes cognition  advanced is an open and not precisely defined question. One perspective involves increasing the complexity of associative learning, from conditioning to learning sequences of events ( chaining) to representing various cue combinations as  chunks. Here we develop a weighted-graph model to study the conditions for the evolution of chunking ability, based on the ecology of the cleaner fish Labroides dimidiatus. Cleaners must learn to serve visitor clients before resident clients, because a visitor leaves if not attended while a resident waits for service. This challenge has been captured in various versions of the ephemeral-reward task, which has been proven difficult for a range of cognitively capable species. We show that chaining is the minimal requirement for solving the laboratory task, that involves repeated simultaneous exposure to an ephemeral and permanent food source. Adding ephemeral-ephemeral and permanent-permanent combinations, as cleaners face in the wild, requires individuals to have chunking abilities to solve the task. Importantly, chunking parameters need to be calibrated to ecological conditions in order to produce adaptive decisions. Thus, it is the fine tuning of this ability which may be the major target of selection during the evolution of advanced associative learning.


title: Antibody Display of cell surface receptor Tetraspanin12 and SARS-CoV-2 spike protein

abstract: In previous work, Hsieh and Higgins presented a novel structure of antibodies identified from malaria-exposed individuals, in which the extracellular immunoglobulin (Ig)-like domain of leukocyte-associated immunoglobulin-like receptor 1 (LAIR1) is presented on the third complementarity determining regions (CDR3) of the Ig heavy chain. Here we develop an Antibody Display technology based on this LAIR1-containing antibody, by grafting proteins of interest (POI) onto the heavy chain CDR3 while retaining the biological properties of the POI. As a proof of principle, we displayed the second extracellular domain of Tetraspanin12 (Tspan12EC2) and the receptor-binding domain (RBD) of SARS-CoV-2 spike protein on the heavy chain CDR3. Our data revealed that Antibody Display Tspan12EC2 bound to Norrie Disease Protein (Norrin) and Antibody Display SARS-CoV-2 RBD bound to angiotensin-converting enzyme 2 (ACE2) and neutralizing nanobodies. Collectively, Antibody Display technology offers the general strategy of designing novel antibodies by grafting POI onto the CDR3.


title: Biofouling Characteristics of Graphene Oxide Membrane in a Protein-rich Environment

abstract: Membrane biofouling has inhibited permselective separation processes for decades, requiring frequent membrane backwash treatment or replacement to maintain efficacy. However, frequent treatment is not viable for devices with a continuous blood flow such as a wearable or implantable dialyzer. In this study, the biofouling characteristics of a highly hemocompatible graphene oxide (GO) membrane developed through a novel self-assembly process is studied in a protein-rich environment and compared with performance of a state-of-the-art commercial polymer membrane dialyzer. The studies are conducted in phosphate-buffered saline (PBS) environment using human serum albumin (HSA), which represents 60% of the blood protein, at the nominal blood protein concentration of 1 g L-1. Protein aggregation on the membrane surface is evaluated by monitoring the change in the membrane flux and SEM imaging. The GO membrane water flux declined only ~10% over a week-long test whereas the polymer membrane flux declined by 50% during the same period. The SEM images show that HSA primarily aggerates over the graphitic regions of nanoplatelets, away from the charged hydrophilic edges. This phenomenon leaves the open areas of the membrane formed between the nanoplatelets edges, through which the species pass, relatively intact. In contrast, HSA completely plugs the polymer membrane pores resulting in a steady decline in membrane permeability.


title: Landscape of microenvironment in Randall's plaque by single-cell sequencing

abstract: Randalls plaque is significantly associated with the occurrence of nephrolithiasis. However, the microenvironment of Randalls plaque is poorly characterized. To investigate the microenvironment of Randalls plaque, we analyzed single-cell RNA data of 3 Randalls plaque and 3 normal renal papillae tissue and identified 11 different cell types. We screened differentially expressed genes among all cell types between Randalls plaque and normal renal papillae. The microenvironment showed two cell types with multiple stone formation-associated transcriptomic programs. Contrary to previous studies, we did not observe macrophage M1/M2 imbalance. Notably, we detected ossification-associated macrophage is enriched in Randalls plaque and validated GPNMB and ACP5 were potential biomarkers on the ossification-associated macrophage. We also identified an endothelial subset harboring active communication (COL15A1+ PCDH17+ endothelial, DPECs) with other cells. Together with Immunofluorescence, we validated ossification-associated macrophage and DPECs are enriched in Randalls plaque tissue. Finally, cell-to-cell communication revealed that Loop of Henle, DPECs, and osteoblasts-associated macrophages was the main source of SPP1 signaling. Our work will further the understanding of the microenvironment among Randalls plaque tissues and provide deep insight into immune modulation.


title: metapredict: a fast, accurate, and easy-to-use cross-platform predictor of consensus disorder

abstract:
Intrinsically disordered proteins and protein regions make up a substantial fraction of many proteomes where they play a wide variety of essential roles. A critical first step in understanding the role of disordered protein regions in biological function is to identify those disordered regions correctly. Computational methods for disorder prediction have emerged as a core set of tools to guide experiments, interpret results, and develop hypotheses. Given the multiple different predictors available, consensus scores have emerged as a popular approach to mitigate biases or limitations of any single method. Consensus scores integrate the outcome of multiple independent disorder predictors and provide a per-residue value that reflects the number of tools that predict a residue to be disordered. Although consensus scores help mitigate the inherent problems of using any single disorder predictor, they are computationally expensive to generate. They also necessitate the installation of multiple different software tools, which can be prohibitively difficult. To address this challenge, we developed a deep-learning-based predictor of consensus disorder scores. Our predictor, metapredict, utilizes a bidirectional recurrent neural network trained on the consensus disorder scores from 12 proteomes. By benchmarking metapredict using two orthogonal approaches, we found that metapredict is among the most accurate disorder predictors currently available. Metapredict is also remarkably fast, enabling proteome-scale disorder prediction in minutes. Importantly, metapredict is fully open source and is distributed as a Python package, a collection of command-line tools, and a web server, maximizing the potential practical utility of the predictor. We believe metapredict offers a convenient, accessible, accurate, and high-performance predictor for single-proteins and proteomes alike.

Statement of SignificanceIntrinsically disordered regions are found across all kingdoms of life where they play a variety of essential roles. Being able to accurately and quickly identify disordered regions in proteins using just the amino acid sequence is critical for the appropriate design and interpretation of experiments. Despite this, performing large-scale disorder prediction on thousands of sequences is challenging using extant disorder predictors due to various difficulties including general installation and computational requirements. We have developed an accurate, high-performance and easy-to-use predictor of protein disorder and structure. Our predictor, metapredict, was designed for both proteome-scale analysis and individual sequence predictions alike. Metapredict is implemented as a collection of local tools and an online web server, and is appropriate for both seasoned computational biologists and novices alike.


title: metapredict: a fast, accurate, and easy-to-use predictor of consensus disorder and structure


title: BML: a versatile web server for bipartite motif discovery

abstract: Motif discovery and characterization are important for gene regulation analysis. The lack of intuitive and integrative web servers impedes effective use of motifs. Here we describe Bipartite Motifs Learning (BML), a web server that provides a user-friendly portal for online discovery and analysis of sequence motifs, using high-throughput sequencing data as the input. BML utilizes both position weight matrix (PWM) and dinucleotide weight matrix (DWM), the latter of which enables the expression of the interdependencies of neighboring bases. With input parameters concerning the motifs are given, the BML achieves significantly higher accuracy than other available tools for motif finding. When no parameters are given by non-expert users, unlike other tools BML employs a learning method to identify motifs automatically and achieve accuracy comparable to the scenario where the parameters are set. The BML web server is freely available at http://motif.t-ridership.com/.


title: Pseudo-mutant p53 as a targetable phenotype of DNMT3A-mutated pre-leukemia

abstract:
Pre-leukemic clones carrying DNMT3A mutations have a selective advantage and an inherent chemo-resistance, however the basis for this phenotype has not been fully elucidated.

Mutations affecting the gene TP53 occur in pre-leukemic hematopoietic stem/progenitor cells (preL-HSPCs) and lead to chemo-resistance. Many of these mutations cause a conformational change and some of them were shown to enhance self-renewal capacity of preL-HSPCs.

Intriguingly, a misfolded p53 was described in AML blasts that do not harbor mutations in TP53, emphasizing the dynamic equilibrium between a wild-type (WT) and a "pseudomutant" conformations of p53.

By combining single cell analyses and p53 conformation-specific monoclonal antibodies we studied preL-HSPCs from primary human DNMT3A AML samples. We found that while leukemic blasts express mainly the WT conformation, in preL-HSPCs the pseudomutant conformation is the dominant. HSPCs from non-leukemic samples expressed both conformations to a similar extent.

Treatment with a short peptide that can shift the dynamic equilibrium favoring the WT conformation of p53, specifically eliminated preL-HSPCs that had dysfunctional canonical p53 pathway activity as reflected by single cell RNA sequencing.

Our observations shed light upon a possible targetable p53 dysfunction in human preL-HSPCs carrying DNMT3A mutations. This opens new avenues for leukemia prevention.

category: cancer biology


title: High Dimensionality Reduction by Matrix Factorization for Systems Pharmacology

abstract:
The extraction of predictive features from the complex high-dimensional multi-omic data is necessary for decoding and overcoming the therapeutic responses in systems pharmacology. Developing computational methods to reduce high-dimensional space of features in in vitro, in vivo and clinical data is essential to discover the evolution and mechanisms of the drug responses and drug resistance. In this paper, we have utilized the Matrix Factorization (MF) as a modality for high dimensionality reduction in systems pharmacology. In this respect, we have proposed three novel feature selection methods using the mathematical conception of a basis for features. We have applied these techniques as well as three other matrix factorization methods to analyze eight different gene expression datasets to investigate and compare their performance for feature selection. Our results show that these methods are capable of reducing the feature spaces and find predictive features in terms of phenotype determination. The three proposed techniques outperform the other methods used and can extract a 2-gene signature predictive of a Tyrosine Kinase Inhibitor (TKI) treatment response in the Cancer Cell Line Encyclopedia (CCLE).

Key PointsO_LIMatrix Factorization (MF) is a useful framework for high dimensionality reduction in systems pharmacology.
C_LIO_LINovel feature selection methods using the incorporation of the mathematical conception of a basis for features into MF increases the performance of feature selection process.
C_LIO_LIFeature selection based on the basis-concept in MF can provide predictive gene signatures for therapeutic responses in systems pharmacology.
C_LI


title: Distinct regulation of tonic GABAergic inhibition by NMDA receptor subtypes

abstract: Tonic inhibition mediated by extrasynaptic {gamma}-aminobutyric acid type A receptors (GABAARs) play important roles in the regulation of various brain functions. However, the regulatory mechanisms for tonic inhibition remain largely unknown. Here we report distinct actions of GluN2A- and GluN2B-containing subtypes of NMDA receptors (NMDARs) on tonic inhibition in hippocampal neurons. Mechanistically, GluN2A- and GluN2B-containing NMDARs play differential roles in 5-GABAAR internalization. Additionally, GluN2A-, but not GluN2B-, containing receptors are required for the homeostatic potentiation of tonic inhibition. In an acute seizure model induced by kainic acid, tonic inhibition is decreased during acute seizures, while it is increased 24 h later, and these alterations are dependent on the distinct GluN2-containing NMDARs. Collectively, these data reveal a critical link between NMDARs and extrasynaptic GABAARs in both physiological and pathological conditions.


title: Sex and estradiol differences in MAPK and Akt cell signaling across subregions of the hippocampus

abstract:
IntroductionRapid effects of estrogens within the hippocampus of rodents are dependent upon cell signaling cascades, and activation of these cascades by estrogens varies by sex. Whether these pathways are rapidly activated within the dentate gyrus (DG) and CA1 by estrogens and across the anatomical longitudinal axis has been overlooked.

MethodsGonadally-intact female and male rats were given either vehicle or physiological systemic low (1.1{micro}g/kg) or high (37.3{micro}g/kg) doses of 17{beta}-estradiol thirty minutes prior to tissue collection. To control for the effects of circulating estrogens, an additional group of female rats was ovariectomized (OVX) and administered 17{beta}-estradiol. Brains were extracted and tissue punches of the CA1 and DG were taken along the longitudinal hippocampal axis (dorsal and ventral) and analyzed for key MAPK and Akt cascade phosphoproteins.

ResultsIntact females had higher Akt pathway phosphoproteins (pAkt, pGSK-3{beta}, pp70S6K) than males in the DG (dorsal, ventral) and lower pERK1/2 in the dorsal DG. Most effects of 17{beta}-estradiol on cell signalling occurred in OVX animals. In OVX animals, 17{beta}-estradiol increased cell signalling of MAPK and Akt phosphoproteins (pERK1/2, pJNK, pAkt, pGSK-3{beta}) in the CA1 and pERK1/2 and pJNK DG.

Discussion/ConclusionsSystemic 17{beta}-estradiol treatment rapidly alters phosphoprotein levels in the hippocampus dependent on reproductive status and intact females have greater expression of Akt phosphoproteins than intact males across the hippocampus. These findings shed light on underlying mechanisms of sex differences in hippocampal function and response to interventions that affect MAPK or Akt signaling.


title: Sex differences and estradiol effects in MAPK and Akt cell signaling across subregions of the hippocampus


title: Cell-autonomous generation of the wave pattern within the vertebrate segmentation clock

abstract:
Sequential segmentation of the body axis is fundamental to vertebrate embryonic patterning. This relies on the segmentation clock, a multi-cellular oscillating genetic-network, which mainifests as tissue-level kinematic waves of gene expression that arrest at the position of each new segment. How this hallmark wave pattern is generated is an open question. We compare cellular-resolution oscillatory patterns in the embryo to those generated cell-autonomously in culture without extrinsic signals. We find striking similarity, albeit with greater variability in the timing of clock arrest in culture. Our simple physical description of a clock controlled by a noisy cell-intrinsic timer captures these dynamics. We propose the segmentation clock integrates an intrinsic, timer-driven oscillatory program, which underlies the waves and arrest, with extrinsic cues regulating the intrinsic timers duration and precision.

One-sentence SummarySegmentation clock and wavefront activities underlying tissue-level wave patterns are cell-autonomous properties in the PSM.

category: developmental biology