bluuebunny/crossref_metadata_2025 huggingface dataset:
Plasma S-Adenosylmethionine is Associated with Lung Injury in COVID-19
Authors: Kryukov Evgeny , Ivanov Alexander , Karpov Vladimir , Alexandrin Valery , Dygai Alexander , Kruglova Maria , Kostiuchenko Gennady , Kazakov Sergei , Kubatiev Aslan
Published: September 2021

Objective

S-Adenosylmethionine (SAM) and S-adenosylhomocysteine (SAH) are indicators of global transmethylation and may play an important role as markers of severity of COVID-19.

Methods

The levels of plasma SAM and SAH were determined in patients admitted with COVID-19 (n = 56, mean age = 61). Lung injury was identified by computed tomography (CT) in accordance with the CT0-4 classification.

Results

SAM was found to be a potential marker of lung damage risk in COVID-19 patients (SAM > 80 nM; CT3,4 vs. CT 0-2: relative ratio (RR) was 3.0; p = 0.0029). SAM/SAH > 6.0 was also found to be a marker of lung injury (CT2-4 vs. CT0,1: RR = 3.47, p = 0.0004). Interleukin-6 (IL-6) levels were associated with SAM (ρ = 0.44, p = 0.01) and SAH (ρ = 0.534, p = 0.001) levels.

Conclusions

High SAM levels and high methylation index are associated with the risk of lung injury in COVID-19 patients. The association of SAM and SAH with IL-6 indicates an important role of transmethylation in the development of cytokine imbalance in COVID-19 cases.
Low Gut Ruminococcaceae Levels are Associated with Occurrence of Antibiotic-associated Diarrhea
Authors: Gu Xiaoqiong , Sim Jean XY , Lee Wei Lin , Cui Liang , Chan Yvonne FZ , Chang Ega Danu , Teh Yii Ean , Zhang An-Ni , Armas Federica , Chandra Franciscus , Chen Hongjie , Zhao Shijie , Lee Zhanyi , Thompson Janelle R. , Ooi Eng Eong , Low Jenny G. , Alm Eric J. , Kalimuddin Shirin
Published: September 2021

Abstract

Patients receiving antibiotics often suffer from antibiotic-associated diarrhea (AAD). AAD is of clinical significance as it can result in premature antibiotic discontinuation and suboptimal treatment of infection. The drivers of AAD however, remain poorly understood. We sought to understand if differences in the gut microbiome, both at baseline and during antibiotic administration, would influence the development of AAD. We administered a 3-day course of oral amoxicillin-clavulanate to 30 healthy adult volunteers, and performed a detailed interrogation of their stool microbiome at baseline and up to 4-weeks post antibiotic administration, using 16S rRNA gene sequencing. Lower levels of Ruminococcaceae were significantly and consistently observed from baseline till Day 7 in participants who developed AAD. The probability of AAD could be predicted based on qPCR-derived levels of Faecalibacterium prausnitzii, the most dominant species within the Ruminococcaceae family. Overall, participants who developed AAD experienced a greater decrease in microbial diversity during antibiotic dosing. Our findings suggest that a lack of gut Ruminococcaceae at baseline influences development of AAD. In addition, quantification of F. prausnitzii in stool prior to antibiotic administration may help identify patients at risk of AAD, and aid clinicians in devising individualised treatment regimens to minimise such adverse effects.
PANDEMIC POLICY DESIGN VIA FEEDBACK: A MODELLING STUDY
Authors: Van Heusden Klaske , Stewart Greg , Otto Sarah , Dumont Guy
Published: September 2021

The COVID-19 pandemic has had an enormous toll on human health and well-being and led to major social and economic disruptions. Public health interventions in response to burgeoning case numbers and hospitalizations have repeatedly bent down the epidemic curve in many jurisdictions, effectively creating a closed-loop dynamic system. We aim to formalize and illustrate how to incorporate principles of feedback control into pandemic projections and decision making.
Starting with a SEEIQR epidemiological model, we illustrate how feedback control can be incorporated into pandemic management using a simple design (proportional-integral or PI control), which couples recent changes in case numbers or hospital occupancy with explicit policy restrictions. We then analyse a closed-loop system between the SEEIQR model and the designed feedback controller to illustrate the potential benefits of pandemic policy design that incorporates feedback.
We first explored a feedback design that responded to hospital measured infections, demonstrating robust ability to control a pandemic despite simulating large uncertainty in reproduction number R0 (range: 1.04-5.18) and average time to hospital admission (range: 4-28 days). The second design compared responding to hospital occupancy to responding to case counts, showing that shorter delays reduced both the cumulative case count and the average level of interventions. Finally, we show that feedback is robust to changing public compliance to public health directives, and to systemic changes associated with new variants of concern and with the introduction of a vaccination program.
The negative impact of a pandemic on human health and societal disruption can be reduced by coupling models of disease propagation with models of the decision-making process. This creates a closed-loop system that better represents the coupled dynamics of a disease and public health responses. Importantly, we show that feedback control is robust to delays in both measurements and responses, and to uncertainty in model parameters and the efficacy of control measures.
Using one–to–many urine proteome comparisons to provide clues for fever of unknown origin
Authors: Zhao Chenyang , Wei Lilong , Wei Jing , Gao Youhe
Published: September 2021

Objective

To provide diagnostic evidence and clues for patients with fever of unknown origin (FUO) through urine proteomics analysis.

Methods

Urine samples of FUO were one–to–many analysed by using liquid chromatography tandem mass spectrometry(LC–MS/MS) to identify differential proteins and related biological pathways. One–to–many analysis means a comparative analysis of one sample to many controls.

Results

We observed biological pathways related to fever, such as LXR/RXR activation, FXR/RXR activation and acute phase response signaling, etc., which indicates that urine can obviously distinguish disease from health status. In addition, we found that the results of each sample were different, which highlight the necessity of one–to–many analysis.

Conclusions

The combined method of urine proteomics and one–to–many analysis can provide clues for FUO, and might also be applied to the exploration of any unknown disease.
Determining cardiac structure by diffuse reflectance of different wavelengths
Authors: Gemmell Philip , Bishop Martin
Published: September 2021

Catheter ablation in patients suffering from chronic arrhythmias requires detailed knowledge of the underlying cardiac anatomy; such real-time, high resolution mapping is currently unavailable in a clinical setting. We present here preliminary work towards a novel optical strategy based on diffuse optical reflectance to provide quantitative anatomical measurements of the cardiac structure, including tissue thickness and presence of scar. An in-depth literature search is conducted to collate available experimental data regarding optical parameters in cardiac tissue and scar. Computational simulations of photon movement through cardiac tissue using Monte Carlo modelling are performed, with analysis being focussed on the effects on surface emission profiles of (i) optical parameters; (ii) tissue thickness; (iii) presence of scar. Our results demonstrate (i) sensitivity of the approach to changes in optical parameters within tissue, (ii) difference of results depending on light wavelength. These suggest that this can be used to detect cardiac anatomical structure to a depth of ∼ 2 mm, for both thickness of cardiac tissue and presence of scar. This study demonstrates the feasibility of using diffuse optical reflectance to determine cardiac structure, enabling a potential route for high-resolution, real-time structural information to guide catheter ablation and similar surgeries.
AI-driven high-throughput automation of behavioral analysis and dual-channel wireless optogenetics for multiplexing brain dynamics
Authors: Kim Woo Seok , Liu Jianfeng , Li Qinbo , Hong Sungcheol , Qi Kezhuo , Cherukuri Rahul , Yoon Byung-Jun , Moscarello Justin , Choe Yoonsuck , Maren Stephen , Park Sung Il
Published: September 2021

Advances in behavioral optogenetics are limited by the absence of high-throughput pipelines for the automated analysis of behavior in freely behaving animals. Although a variety of AI algorithms has been proposed that enable automation of behavioral analysis, existing methods are generally low-throughput. In addition, optogenetic manipulation of neural circuits typically requires physical tethers to light sources, which limits the number of brain areas that can be targeted and thus constrains behavioral testing. Here, we develop a new wireless platform that combines a battery-free dual-channel optogenetic implant with an AI algorithm for high-throughput behavioral analysis. In our platform, a customized AI algorithm detected and quantified freezing behavior of rats that had undergone fear conditioning. Notably, our platform allows up to four enclosures to be monitored simultaneously. Wireless dual-channel optogenetic devices were implanted in the basolateral amygdala (BLA) to permit independent modulation of BLA principal neurons (red light, AAV-CaMKII-JAWS) or BLA interneurons (blue light, AAV-mDlx-ChR2) in freely behaving animals. In vivo validation with behaving rats demonstrates the utility of the telemetry system for large-scale optogenetic studies.

Significance

AI algorithms can enable automation of behavioral analysis and thereby facilitate the progress on behavioral optogenetics. Successful integration of advanced wireless dual-channel optoelectronic devices with biological systems can also yield new tools and techniques for neuroscience research, particularly in the context of techniques for optogenetics. Here, we propose a new approach that combines an advanced AI algorithm with a low power wireless telemetry system, yielding powerful capabilities in the understanding of brain functions and the evaluation of the behavioral consequences of neural circuit manipulations. In vivo studies using optimized systems demonstrate high-throughput automation of behavioral manipulation and analysis via AI-powered wireless telemetry.
Leveling up: improving power in fMRI by moving beyond cluster-level inference
Authors: Noble Stephanie , Mejia Amanda F. , Zalesky Andrew , Scheinost Dustin
Published: September 2021

Inference in neuroimaging commonly occurs at the level of “clusters” of neighboring voxels or connections, thought to reflect functionally specific brain areas. Yet increasingly large studies reveal effects that are shared throughout the brain, suggesting that reported clusters may only reflect the “tip of the iceberg” of underlying effects. Here, we empirically compare power of traditional levels of inference (edge and cluster) with broader levels of inference (network and whole-brain) by resampling functional connectivity data from the Human Connectome Project (n=40, 80, 120). Only network- and whole brain-level inference attained or surpassed “adequate” power (β=80%) to detect an average effect, with almost double the power for network-compared with cluster-level procedures at more typical sample sizes. Likewise, effects tended to be widespread, and more widespread pooling resulted in stronger magnitude effects. Power also substantially increased when controlling FDR rather than FWER. Importantly, there may be similar implications for task-based activation analyses where effects are also increasingly understood to be widespread. However, increased power with broader levels of inference may diminish the specificity to localize effects, especially for non-task contexts. These findings underscore the benefit of shifting the scale of inference to better capture the underlying signal, which may unlock opportunities for discovery in human neuroimaging.
Salt leverages polyethylene terephthalate hydrolase (PETase) enzymatic activity via the predicted conformational switch
Authors: Shiryaev Sergey A. , Cieplak Piotr , Chernov Andrei V.
Published: September 2021

Plastic pollution spawned a global challenge caused by the environmental accumulation of polyethylene terephthalate (PET) plastics. Ongoing remediation efforts using microbial and engineered PET hydrolyzing enzymes (PETases) are hindered by slow depolymerization activities. Here, we report the optimized reaction conditions that leveraged the PETase hydrolase activity 2 to 3.8-fold in the presence of high NaCl concentrations. Molecular dynamics simulations (MDS) were applied to model salt-dependent conformational changes of the PETase enzyme bound to a 3-unit PET polymer. MDS demonstrated that PETase interaction with flanking polymer units exhibited a striking structural disparity at low and high salt concentrations. At low salt concentrations, flanking polymer units displayed significant bending. In contrast, flanking units extended at high salt concentrations, thus residues D206, H237, and S160 of the catalytic triad positioned in close vicinity of the scissile ester bond of the polymer substrate. The resulting high salt-specific enzyme/substrate geometry can potentially facilitate hydrolysis. We theorized that a salt-dependent conformational switch could attenuate the enzyme to a broad range of natural and artificial polymers consumed as carbon sources. Altogether, new knowledge may advance the engineering of PET hydrolase enzymes and benefit bioconversion programs.
State-dependent representations of mixtures by the olfactory bulb
Authors: Adefuin Aliya Mari , Lindeman Sander , Reinert Janine Kristin , Fukunaga Izumi
Published: September 2021

Sensory systems are often tasked to analyse complex signals from the environment, separating relevant from irrelevant parts. This process of decomposing signals is challenging when a mixture of signals does not equal the sum of its parts, leading to an unpredictable corruption of signal patterns. In olfaction, nonlinear summation is prevalent at various stages of sensory processing. Here, we investigate how the olfactory system deals with binary mixtures of odours under different brain states, using two-photon imaging of olfactory bulb (OB) output neurons. Unlike previous studies using anaesthetised animals, we found that mixture summation is more linear in the early phase of evoked responses in awake, head-fixed mice performing an odour detection task, due to dampened responses. Despite this, and responses being more variable, decoding analyses indicated that the data from behaving mice was well discriminable. Curiously, the time course of decoding accuracy did not correlate strictly with the linearity of summation. Further, a comparison with naïve mice indicated that learning to accurately perform the mixture detection task is not accompanied by more linear mixture summation. Finally, using a simulation, we demonstrate that, while saturating sublinearity tends to degrade the discriminability, the extent of the impairment may depend on other factors, including pattern decorrelation. Altogether, our results demonstrate that the mixture representation in the primary olfactory area is state-dependent, but the analytical perception may not strictly correlate with linearity in summation.
Dr. Sim: Similarity Learning for Transcriptional Phenotypic Drug discovery
Authors: Wei Zhiting , Zhu Sheng , Chen Xiaohan , Zhu Chenyu , Duan Bin , Liu Qi
Published: September 2021

Transcriptional phenotypic drug discovery has achieved great success, and various compound perturbation-based data resources, such as Connectivity Map (CMap) and Library of Integrated Network-Based Cellular Signatures (LINCS), have been presented. Computational strategies fully mining these resources for phenotypic drug discovery have been proposed, and among them, a fundamental issue is to define the proper similarity between the transcriptional profiles to elucidate the drug mechanism of actions and identify new drug indications. Traditionally, this similarity has been defined in an unsupervised way, and due to the high dimensionality and the existence of high noise in those high-throughput data, it lacks robustness with limited performance. In our study, we present Dr. Sim, which is a general learning-based framework that automatically infers similarity measurement rather than being manually designed and can be used to characterize transcriptional phenotypic profiles for drug discovery with generalized good performance. We evaluated Dr. Sim on comprehensively publicly available in vitro and in vivo datasets in drug annotation and repositioning using high-throughput transcriptional perturbation data and indicated that Dr. Sim significantly outperforms the existing methods and is proved to be a conceptual improvement by learning transcriptional similarity to facilitate the broad utility of high-throughput transcriptional perturbation data for phenotypic drug discovery. The source code and usage of Dr. Sim is available at https://github.com/bm2-lab/DrSim/.
NF90 Interacts with Components of RISC and Modulates Association of Ago2 with mRNA
Authors: Grasso Giuseppa , Akkawi Charbel , Franckhauser Celine , Nait-Saidi Rima , Bello Maxime , Barbier Jérôme , Kiernan Rosemary
Published: September 2021

Nuclear Factor 90 (NF90) is a double-stranded RNA-binding protein involved in a multitude of different cellular mechanisms such as transcription, translation, viral infection and mRNA stability. Recent data suggest that NF90 might influence the abundance of target mRNAs in the cytoplasm through miRNA- and Argonaute 2 (Ago2)-dependent activity. Here, we identified the interactome of NF90 in the cytoplasm, which revealed several components of the RNA-induced silencing complex (RISC) and associated factors. Co-immunoprecipitation analysis confirmed the interaction of NF90 with the RISC-associated RNA helicase, Moloney leukemia virus 10 (MOV10), and other proteins involved in RISC-mediated silencing, including Ago2. Furthermore, NF90 association with MOV10 and Ago2 was found to be RNA-dependent. Glycerol gradient sedimentation of NF90 immune complexes indicates that these proteins occur in the same protein complex. At target RNAs predicted to bind both NF90 and MOV10 in their 3’ UTRs, NF90 association was increased upon loss of MOV10 and vice versa, suggesting that the two proteins may compete for the binding of common target mRNAs. Interestingly, loss of NF90 led to an increase in association of Ago2 as well as a decrease in the abundance of the target mRNA. Similarly, during hypoxia, the binding of Ago2 to vascular endothelial growth factor (VEGF) mRNA increased after loss of NF90, while the level of VEGF mRNA decreased. These findings suggest a role for NF90 in the regulation of RISC-mediated silencing which stabilizes target mRNAs, such as VEGF, during cancer-induced hypoxia.
Constitutive depletion of brain serotonin differentially affects rats’ social and cognitive abilities
Authors: Alonso Lucille , Peeva Polina , Stasko Sabrina , Bader Michael , Alenina Natalia , Winter York , Rivalan Marion
Published: September 2021

Background

Central serotonin is an essential neuromodulator for mental disorders. It appears a promising transdiagnostic marker of distinct psychiatric disorders and a common modulator of some of their key behavioral symptoms. We aimed to identify the behavioral markers of serotonergic function in rats and compare them to human deficits.

Methods

We applied a comprehensive profiling approach in adult maleTph2−/−rats constitutively lacking central serotonin. Under classical and ethological testing conditions, we tested each individual’s cognitive, social and non-social abilities and characterized the group organization (i.e. social network, hierarchy). Using unsupervised machine learning, we identified the functions most dependent on central serotonin.

Results

In classical procedures,Tph2−/−rats presented an unexpected normal cognitive profile. Under the complex and experimenter-free conditions of their home-cage, the sameTph2−/−rats presented drastic changes in their daily life. Brain serotonin depletion induced compulsive aggression and sexual behavior, hyperactive and hypervigilant stereotyped behavior, reduced self-care and body weight, and exacerbated corticosterone levels. Group-housedTph2−/−rats showed strong social disorganization with disrupted social networks and hierarchical structure, which may arise from communication deficits and cognitive blunting.

Conclusions

Serotonin depletion induced a profile reminiscent of the symptomatology of impulse control and anxiety disorders. Serotonin was necessary for behavioral adaptation to dynamic social environments. In classical testing conditions, our animal model challenged the concept of an essential role of serotonin in decision-making, flexibility, and impulsivity, although developmental compensations may have occurred. These contrasting findings highlight the need to generalize the evaluation of animal models’ multidimensional functions within the complexity of the social living environment.
Protein sequence profile prediction using ProtAlbert transformer
Authors: Behjati Armin , Zare-Mirakabad Fatemeh , Arab Seyed Shahriar , Nowzari-Dalini Abbas
Published: September 2021

Protein profiles have many applications in bioinformatics. To construct the profile from a protein sequence, the sequence is aligned with database. However, sometimes there are no similar sequences with the query. This paper proposes a method based on pre-trained ProtAlbert transformer to predict the profile for a single protein sequence without alignment. The performance of transformers on natural languages is impressive. Protein sequences can be viewed as a language; therefore, we can benefit from using these models. We analyze the attention heads in different layers of ProtAlbert to show that the transformer can capture five essential protein characteristics of the family from a single protein sequence. These assessments are performed on the CASP13 dataset to find representative heads for each of five protein characteristics. Then, these heads are investigated on one thermophilic and two mesophilic proteins as case studies. The results show the significant attention heads for protein family properties extracted from a single protein sequence. This analysis led us to propose an algorithm called PA_SPP for profile prediction using only a single protein sequence as input. In our algorithm, we apply the masked language modeling method of ProtAlbert. The results display high similarity between the predicted profiles and HSSP profiles.
Genetic behavior analysis for phytochemical traits in coriander: Heterosis, inbreeding depression and genetic effects
Authors: Gholizadeh Amir , Khodadadi Mostafa
Published: September 2021

Increasing fruit yield, fatty acids and essential oils content in coriander are the main objectives. Reaching them need to understand the nature of gene action and quantifying the heterosis and inbreeding depression. Six genetically diverse parents, their 15 F1 one-way hybrids and 15 F2 populations were evaluated under different levels of water treatments. Beside the water treatment and genotype effects, the genetic effects of general (GCA) and specific (SCA) combining ability and their interactions with water treatment were significant for all traits. Water deficit stress decreased all traits in both F1 and F2 generations except for essential oil content which were significantly increased due to water deficit stress. Under water deficit stress, a non-additive gene action nature was predominant in F1 generation while an additive gene action nature was more important in F2 generation for all the traits except fruit yield under severe water deficit stress. There was a positive high heterosis for the traits examined in some hybrids. Also, in F2 generation even after inbreeding depression, some promising populations displayed appropriate mean performance. These show that the parents used for crossing had rich gene pool for studied traits. Therefore, selection between the individuals of relevant F2 populations could be led to develop high yielding hybrids or transgressed lines.
Pathological and metabolic underpinnings of energetic inefficiency in temporal lobe epilepsy
Authors: He Xiaosong , Caciagli Lorenzo , Parkes Linden , Stiso Jennifer , Karrer Teresa M. , Kim Jason Z. , Lu Zhixin , Menara Tommaso , Pasqualetti Fabio , Sperling Michael R. , Tracy Joseph I. , Bassett Dani S.
Published: September 2021

The human brain consumes a disproportionate amount of energy to generate neural dynamics. Yet precisely how energetic processes are altered in neurological disorders remains far from understood. Here, we use network control theory to profile the brain’s energy landscape, describing the rich dynamical repertoire supported by the structural connectome. This approach allows us to estimate the energy required to activate a circuit, and determine which regions most support that activation. Focusing on temporal lobe epilepsy (TLE), we show that patients require more control energy to activate the limbic network than healthy volunteers, especially ipsilateral to the seizure focus. Further, greater energetic costs are largely localized to the ipsilateral temporo-limbic regions. Importantly, the energetic imbalance between ipsilateral and contralateral temporo-limbic regions is tracked by asymmetric metabolic patterns, which in turn are explained by asymmetric gray matter volume loss. In TLE, failure to meet the extra energy demands may lead to suboptimal brain dynamics and inadequate activation. Broadly, our investigation provides a theoretical framework unifying gray matter integrity, local metabolism, and energetic generation of neural dynamics.
On the hybrid origin of the C2Salsola divaricata agg. (Amaranthaceae) from C3 and C4 parental lineages
Authors: Tefarikis Delphine T. , Morales-Briones Diego F. , Yang Ya , Edwards Gerald , Kadereit Gudrun
Published: September 2021

C2 photosynthesis is characterized by recapturing photorespiratory CO2 by RuBisCO in Kranz-like cells and is therefore physiologically intermediate between C3 and C4 photosynthesis. C2 can be interpreted as an evolutionary precursor of C4 and/or as the result of hybridization between a C3 and C4 lineage.

We compared the expression of photosynthetic traits among populations of the Salsola divaricata agg. (C2) from humid subtropical to arid habitats on the coasts of the Canary Islands and Morocco and subjected them to salt and drought treatments. We screened for enhanced C4-like expression of traits related to habitat or treatment. We estimated species trees with a transcriptome dataset of Salsoleae and explored patterns of gene tree discordance. With phylogenetic networks and hybridization analyses we tested for hybrid origin of the Salsola divaricata agg.

We observed distinct independent variation of photosynthetic traits within and among populations and no clear evidence for selection towards C4-like trait expression in more stressful habitats or treatments. We found reticulation and gene tree incongruence in Salsoleae supporting a putative hybrid origin of the Salsola divaricata agg.

C2 photosynthesis in the Salsola divaricata agg. combines traits inherited from its C3 and C4 parental lineages and seems evolutionarily stable, possibly well adapted to a wide climatic amplitude.
A cortico-cortical pathway targets inhibitory interneurons and modulates paw movement during locomotion in mice
Authors: Chang Chia-wei , Zhao Meiling , Grudzien Samantha , Oginsky Max , Yang Yexin , Kwon Sung Eun
Published: September 2021

The primary somatosensory cortex (S1) is important for the control of movement as it encodes sensory input from the body periphery and external environment during ongoing movement. Mouse S1 consists of several distinct sensorimotor subnetworks that receive topographically organized cortico-cortical inputs from distant sensorimotor areas, including the secondary somatosensory cortex (S2) and primary motor cortex (M1). The role of the vibrissal S1 area and associated cortical connections during active sensing is well documented, but whether (and if so, how) non-whisker S1 areas are involved in movement control remains relatively unexplored. Here, we demonstrate that unilateral silencing of the non-whisker S1 area in both male and female mice disrupts hind paw movement during locomotion on a rotarod and a runway. S2 and M1 provide major long-range inputs to this S1 area. Silencing S2 → non-whisker S1 projections alters the hind paw orientation during locomotion while manipulation of the M1 projection has little effect. Using patch-clamp recordings in brain slices from male and female mice, we show that S2 projection preferentially innervates inhibitory interneuron subtypes. We conclude that interneuron-mediated S2–S1 cortico-cortical interactions are critical for efficient locomotion.

Significance

Somatosensory cortex participates in controlling rhythmic movements such as whisking and walking, but the neural circuitry underlying movement control by somatosensory cortex remains relatively unexplored. We uncover a cortico-cortical circuit in primary somatosensory cortex that regulates paw orientation during locomotion in mice. We identify neuronal elements that comprise these cortical pathways using pharmacology, behavioral assays and circuit-mapping methods.
Impedance flow cytometry for viability analysis of Corynebacterium glutamicum
Authors: Hartmann Fabian Stefan Franz , Anastasiou Ioannis , Weiß Tamara , Shen Jing , Seibold Gerd Michael
Published: September 2021

Corynebacterium glutamicum efficiently produces glutamate when growth is inhibited. Analyses of viability in this non-growing state requires time consuming plating and determination of colony forming units. We here establish impedance flow cytometry measurements to assess the viability of non-growing, glutamate producing C. glutamicum cultures within minutes.
The mutation effect reaction norm (Mu-RN) highlights environmentally dependent mutation effects and epistatic interactions
Authors: Ogbunugafor C. Brandon
Published: September 2021

Since the modern synthesis, the fitness effects of mutations and epistasis have been central yet provocative concepts in evolutionary and population genetics. Studies of how the interactions between parcels of genetic information can change as a function of environmental context have added a layer of complexity to these discussions. Here I introduce the “mutation effect reaction norm” (Mu-RN), a new instrument through which one can analyze the phenotypic consequences of mutations and interactions across environmental contexts. It embodies the fusion of measurements of genetic interactions with the reaction norm, a classic depiction of the performance of genotypes across environments. I demonstrate the utility of the Mu-RN through the signature of a “compensatory ratchet” mutation that undermines reverse evolution of antimicrobial resistance. More broadly, I argue that the mutation effect reaction norm may help us resolve the dynamism and unpredictability of evolution, with implications for theoretical biology, genetic modification technology, and public health.
Parallel systems for social and spatial reasoning within the cortical apex
Authors: Deen Ben , Freiwald Winrich A.
Published: September 2021

What is the cognitive and neural architecture for high-level reasoning? We hypothesize that systems for understanding people and places remain separate throughout the brain, but share a parallel organization. We test this hypothesis using deep neuroimaging of individual human brains on diverse tasks involving reasoning and memory about familiar people, places, and objects. We find that thinking about people and places elicits responses in distinct areas of high-level association cortex, spanning the frontal, parietal, and temporal lobes. Person- and place-preferring brain regions are systematically yoked across cortical zones. These areas have strongly domain-specific response profiles across visual, semantic, and episodic tasks, and are specifically functionally connected to other parts of association cortex with like category preference. Social and spatial networks are anatomically separated even at the top of the cortical hierarchy, and include parts of cortex with anatomical connections to the hippocampal formation. These results demonstrate parallel, domain-specific networks within the cortical apex. They suggest that domain-specific systems for reasoning constitute components of a broader cortico-hippocampal system for long-term memory.
Neural network modeling of differential binding between wild-type and mutant CTCF reveals putative binding preferences for zinc fingers 1-2
Authors: Kaplow Irene M. , Banerjee Abhimanyu , Foo Chuan Sheng
Published: September 2021

Background

Many transcription factors (TFs), such as multi zinc-finger (ZF) TFs, have multiple DNA binding domains (DBDs) with multiple components, and deciphering the DNA binding motifs of individual components is a major challenge. One example of such a TF is CCCTC-binding factor (CTCF), a TF with eleven ZFs that plays a variety of roles in transcriptional regulation, most notably anchoring DNA loops. Previous studies found that CTCF zinc fingers (ZFs) 3-7 bind CTCF’s core motif and ZFs 9-11 bind a specific upstream motif, but the motifs of ZFs 1-2 have yet to be identified.

Results

We developed a new approach to identifying the binding motifs of individual DBDs of a TF through analyzing chromatin immunoprecipitation sequencing (ChIP-seq) experiments in which a single DBD is mutated: we train a deep convolutional neural network to predict whether wild-type TF binding sites are preserved in the mutant TF dataset and interpret the model. We applied this approach to mouse CTCF ChIP-seq data and, in addition to identifying the known binding preferences of CTCF ZFs 3-11, we identified a GAG binding motif for ZF1 and a weak ATT binding motif for ZF2. We analyzed other CTCF datasets to provide additional evidence that ZFs 1-2 interact with the motifs we identified, and we found that the presence of the motif for ZF1 is associated with Ctcf peak strength.

Conclusions

Our approach can be applied to any TF for which in vivo binding data from both the wild-type and mutated versions of the TF are available, and our findings provide an unprecedently comprehensive understanding of the binding preferences of CTCF’s DBDs.
Identification of microRNAs in the West Nile virus vector Culex tarsalis
Authors: Asad Sultan , Mehdi Ahmed M. , Pujari Sujit , Rüeckert Claudia , Ebel Gregory D. , Rasgon Jason L.
Published: September 2021

Background

microRNAs (miRNAs) represent a group of small non-coding RNAs that are crucial gene regulators of important biological functions including development and pathogen defense in most living organisms. Presently, there is a lack of availability of information regarding the miRNAs in the mosquito Culex tarsalis, which is one of the most important vectors of West Nile virus (WNV) in the United States. We used small RNA sequencing data and in vitro and in vivo experiments to identify and validate the presence of a repertoire of miRNAs in Cx. tarsalis mosquitoes.

Results

Using bioinformatic approaches we analyzed small RNA sequencing data from the Cx. tarsalis CT embryonic cell line to discover 86 miRNAs. Consistent with other mosquitoes such as Aedes albopictus and Cx. quinquefasciatus, mi-184 was found to be the most abundant miRNA in Cx tarsalis. We also identified an additional 20 novel miRNAs from the recently sequenced Cx. tarsalis genome, for a total of 106 miRNAs identified in this study. The presence of selected miRNAs was biologically validated in both cell line and adult Cx. tarsalis mosquitoes using RT-qPCR and sequencing.

Conclusions

Cx. tarsalis is an important vector of many medically important pathogens including WNV and Western Equine encephalitis. Here we report a detailed insight into the miRNA population in Cx. tarsalis mosquitoes. These results will open new avenues of research deciphering the role of miRNAs in different Cx. tarsalis biological events such as development, metabolism, immunity and pathogen infection.
Meta-analysis of immune induced gene expression changes in diverse Drosophila melanogaster innate immune responses
Authors: Waring Ashley L. , Hill Joshua , Allen Brooke M. , Bretz Nicholas M. , Le Nguyen , Kr Pooja , Fuss Dakota , Mortimer Nathan T.
Published: September 2021

Background

Organisms are commonly infected by a diverse array of pathogen types including bacteria, fungi, viruses, and parasites, and mount functionally distinct responses to each of these varied immune challenges. Host immune responses are characterized by the induction of gene expression in response to infection. However, the extent to which expression changes are shared among responses to distinct pathogens is largely unknown.

Results

We performed meta-analysis of gene expression data collected from Drosophila melanogaster following infection with a wide array of pathogens. We identified 62 genes that are significantly induced by infection. While many of these infection-induced genes encode known immune response factors, we also identified 21 genes that have not been previously associated with host immunity. Examination of the upstream flanking sequences of the infection-induced genes lead to the identification of two conserved enhancer sites. These sites correspond to conserved binding sites for GATA and nuclear factor κB (NFκB) family transcription factors and are associated with higher levels of transcript induction. We further identified 31 genes with predicted functions in metabolism and organismal development that are significantly downregulated following infection by diverse pathogens.

Conclusions

Our study identifies conserved gene expression changes in Drosophila melanogaster following infection with varied pathogens, and transcription factor families that may regulate this immune induction. These findings provide new insight into transcriptional changes that accompany Drosophila immunity. They may suggest possible roles for the differentially regulated genes in innate immune responses to diverse classes of pathogens, and serve to identify candidate genes for further empirical study of these processes.
BindVAE: Dirichlet variational autoencoders for de novo motif discovery from accessible chromatin
Authors: Kshirsagar Meghana , Yuan Han , Ferres Juan Lavista , Leslie Christina
Published: September 2021

We present a novel unsupervised deep learning approach called BindVAE, based on Dirichlet variational autoencoders, for jointly decoding multiple TF binding signals from open chromatin regions. BindVAE can disentangle an input DNA sequence into distinct latent factors that encode cell-type specific in vivo binding signals for individual TFs, composite patterns for TFs involved in cooperative binding, and genomic context surrounding the binding sites. For the task of retrieving motifs of expressed TFs for a given cell type, we find that BindVAE has a higher precision, albeit lower recall, compared to other motif discovery approaches.
Multi-context genetic modeling of transcriptional regulation resolves novel disease loci
Authors: Thompson Mike , Gordon Mary Grace , Lu Andrew , Tandon Anchit , Halperin Eran , Gusev Alexander , Ye Chun Jimmie , Balliu Brunilda , Zaitlen Noah
Published: September 2021

A majority of the variants identified in genome-wide association studies fall in non-coding regions of the genome, indicating their mechanism of impact is mediated via gene expression. Leveraging this hypothesis, transcriptome-wide association studies (TWAS) have assisted in both the interpretation and discovery of additional genes associated with complex traits. However, existing methods for conducting TWAS do not take full advantage of the intra-individual correlation inherently present in multi-context expression studies and do not properly adjust for multiple testing across contexts. We developed CONTENT— a computationally efficient method with proper cross-context false discovery correction that leverages correlation structure across contexts to improve power and generate context-specific and context-shared components of expression. We applied CONTENT to bulk multi-tissue and single-cell RNA-seq data sets and show that CONTENT leads to a 42% (bulk) and 110% (single cell) increase in the number of genetically predicted genes relative to previous approaches. Interestingly, we find the context-specific component of expression comprises 30% of heritability in tissue-level bulk data and 75% in single-cell data, consistent with cell type heterogeneity in bulk tissue. In the context of TWAS, CONTENT increased the number of gene-phenotype associations discovered by over 47% relative to previous methods across 22 complex traits.
JaponicusDB: Rapid deployment of a model organism database for an emerging model species
Authors: Rutherford Kim M. , Harris Midori A. , Oliferenko Snezhana , Wood Valerie
Published: September 2021

The fission yeast Schizosaccharomyces japonicus has recently emerged as a powerful system for studying the evolution of essential cellular processes, drawing on similarities as well as key differences between S. japonicus and the related, well-established model Schizosaccharomyces pombe. We have deployed the open-source, modular code and tools originally developed for PomBase, the S. pombe model organism database (MOD), to create JaponicusDB (www.japonicusdb.org), a new MOD dedicated to S. japonicus. By providing a central resource with ready access to a growing body of experimental data, ontology-based curation, seamless browsing and querying, and the ability to integrate new data with existing knowledge, JaponicusDB supports fission yeast biologists to a far greater extent than any other source of S. japonicus data. JaponicusDB thus enables S. japonicus researchers to realise the full potential of studying a newly emerging model species, and illustrates the widely applicable power and utility of harnessing reusable PomBase code to build a comprehensive, community-maintainable repository of species-relevant knowledge.
PplD is a de-N-acetylase of the cell wall linkage unit of streptococcal rhamnopolysaccharides
Authors: Rush Jeffrey S. , Parajuli Prakash , Ruda Alessandro , Li Jian , Pohane Amol A. , Zamakhaeva Svetlana , Rahman Mohammad M. , Chang Jennifer C. , Gogos Artemis , Kenner Cameron W. , Lambeau Gérard , Federle Michael J. , Korotkov Konstantin V. , Widmalm Göran , Korotkova Natalia
Published: September 2021

The cell wall of the human bacterial pathogen Group A Streptococcus (GAS) consists of peptidoglycan decorated with the Lancefield group A carbohydrate (GAC). GAC is a promising target for the development of GAS vaccines. In this study, employing chemical, compositional, and NMR methods, we show that GAC is attached to peptidoglycan via glucosamine 1-phosphate. This structural feature makes the GAC-peptidoglycan linkage highly sensitive to cleavage by nitrous acid and resistant to mild acid conditions. Using this characteristic of the GAS cell wall, we identify PplD as a protein required for deacetylation of linkage N-acetylglucosamine (GlcNAc). X-ray structural analysis indicates that PplD performs catalysis via a modified acid/base mechanism. Genetic surveys in silico together with functional analysis indicate that PplD homologs deacetylate the polysaccharide linkage in many streptococcal species. We further demonstrate that introduction of positive charges to the cell wall by GlcNAc deacetylation protects GAS against host cationic antimicrobial proteins.
Batch effects removal for microbiome data via conditional quantile regression (ConQuR)
Authors: Ling Wodan , Zhao Ni , Lulla Anju , Plantinga Anna M. , Fu Weijia , Zhang Angela , Liu Hongjiao , Li Zhigang , Chen Jun , Randolph Timothy , Koay Wei Li A. , White James R. , Launer Lenore J. , Fodor Anthony A. , Meyer Katie A. , Wu Michael C.
Published: September 2021

Batch effects in microbiome data arise from differential processing of specimens and can lead to spurious findings and obscure true signals. Most existing strategies for mitigating batch effects rely on approaches designed for genomic analysis, failing to address the zero-inflated and over-dispersed microbiome data. Strategies tailored for microbiome data are restricted to association testing, failing to allow other analytic goals such as visualization. We develop the Conditional Quantile Regression (ConQuR) approach to remove microbiome batch effects using a two-part quantile regression model. It is a fundamental advancement in the field because it is the first comprehensive method that accommodates the complex distributions of microbial read counts, and it generates batch-removed zero-inflated read counts that can be used in and benefit all usual subsequent analyses. We apply ConQuR to real microbiome data sets and demonstrate its state-of-the-art performance in removing batch effects while preserving or even amplifying the signals of interest.
Arterial vasodilation drives convective fluid flow in the brain: a poroelastic model
Authors: Kedarasetti Ravi Teja , Drew Patrick J. , Costanzo Francesco
Published: September 2021

The movement of fluid into, through, and out of the brain plays an important role in clearing metabolic waste. However, there is controversy regarding the mechanisms driving fluid movement, and whether the movement metabolic waste is primarily driven by diffusion or convection. The dilation of penetrating arterioles in the brain in response to increases in neural activity (neurovascular coupling) is an attractive candidate for driving fluid circulation, as it drives deformation of the brain tissue and of the paravascular space around arteries, resulting in fluid movement. We simulated the effects of vasodilation on fluid movement into and out of the brain using a novel poroelastic model of brain tissue. We found that arteriolar dilations could drive convective flow through the brain radially outward from the arteriole, and that this flow is sensitive to the dynamics of the dilation. Simulations of sleep-like conditions, with larger vasodilations and increased extracellular volume in the brain showed enhanced movement of fluid from the paravascular space into the brain. Our simulations suggest that both sensory-evoked and sleep-related arteriolar dilations can drive convective flow of cerebrospinal fluid from the paravascular space into the brain tissue around arterioles.
Mechanics Of Ultrasonic Neuromodulation In A Mouse Subject
Authors: Salahshoor Hossein , Guo Hongsun , Shapiro Mikhail G. , Ortiz Michael
Published: September 2021

Ultrasound neuromodulation (UNM), where a region in the brain is targeted by focused ultrasound (FUS), which, in turn, causes excitation or inhibition of neural activity, has recently received considerable attention as a promising tool for neuroscience. Despite its great potential, several aspects of UNM are still unknown. An important question pertains to the off-target sensory effects of UNM and their dependence on stimulation frequency. To understand these effects, we have developed a finite-element model of a mouse, including elasticity and viscoelasticity, and used it to interrogate the response of mouse models to focused ultrasound (FUS). We find that, while some degree of focusing and magnification of the signal is achieved within the brain, the induced pressure-wave pattern is complex and delocalized. In addition, we find that the brain is largely insulated, or ‘cloaked’, from shear waves by the cranium and that the shear waves are largely carried away from the skull by the vertebral column, which acts as a waveguide. We find that, as expected, this waveguide mechanism is strongly frequency dependent, which may contribute to the frequency dependence of UNM effects. Our calculations further suggest that off-target skin locations experience displacements and stresses at levels that, while greatly attenuated from the source, could nevertheless induce sensory responses in the subject.