bluuebunny/crossref_metadata_2025 huggingface dataset:
RabbitTClust: enabling fast clustering analysis of millions bacteria genomes with MinHash sketches
Authors: Xu Xiaoming , Yin Zekun , Yan Lifeng , Zhang Hao , Xu Borui , Wei Yanjie , Niu Beifang , Schmidt Bertil , Liu Weiguo
Published: October 2022

We present RabbitTClust, a fast and memory-efficient genome clustering tool based on sketch-based distance estimation. Our approach enables efficient processing of large-scale datasets by combining dimensionality reduction techniques with streaming and parallelization on modern multi-core platforms. 113,674 complete bacterial genome sequences (RefSeq: 455 GB in FASTA format) can be clustered within less than 6 minutes and 1,009,738 GenBank assembled bacterial genomes (4.0 TB in FASTA format) within only 34 minutes on a 128-core workstation. Our results further identify 1,269 repetitive genomes (identical nucleotide content) in RefSeq bacterial genomes.
St2cell: Reconstruction of in situ single-cell spatial transcriptomics by integrating high-resolution histological image
Authors: Hou Siyu , Tian Kuan , Yang Sen , Xiang Jinxi , Yang Wei , Zhang Jun , Han Xiao
Published: October 2022

Spatially resolved transcriptomics (SRT) has greatly expanded our understanding of the spatial patterns of gene expression in histological tissue sections. However, most currently available platforms could not provide in situ single-cell spatial transcriptomics, limiting their biological applications. Here, to in silico reconstruct SRT at the single-cell resolution, we propose St2cell which combines deep learning-based frameworks with a novel convex quadratic programming (CQP)-based model. St2cell can thoroughly leverage information in high-resolution (HR) histological images, enabling the accurate segmentation of in situ single cells and identification of their transcriptomics. Applying St2cell on various SRT datasets, we demonstrated the reliability of reconstructed transcriptomics. The single-cell resolution provided by our proposed method greatly promoted the detection of elaborate spatial architectures and further facilitated the integration with single-cell RNA-sequencing data. Moreover, in a breast cancer tissue, St2cell identified general spatial structures and co-occurrence patterns of cell types in the tumor microenvironment. St2cell is also computationally efficient and easily accessible, making it a promising tool for SRT studies.
Accurate detection of shared genetic architecture from GWAS summary statistics in the small-sample context
Authors: Willis Thomas W. , Wallace Chris
Published: October 2022

Assessment of the genetic similarity between two phenotypes can provide insight into a common genetic aetiology and inform the use of pleiotropy-informed, cross-phenotype analytical methods to identify novel genetic associations. The genetic correlation is a well-known means of quantifying and testing for genetic similarity between traits, but its estimates are subject to comparatively large sampling error. This makes it unsuitable for use in a small-sample context. We discuss the use of a nonparametric test of genetic similarity first introduced by Li et al. for application to GWAS summary statistics. We establish that the null distribution of the test statistic is modelled better by an extreme value distribution than a transformation of the standard exponential distribution as originally recommended by Li and colleagues. We show with simulation studies and real data from GWAS of 18 phenotypes from the UK Biobank that the test is to be preferred for use with small sample sizes, particularly when genetic effects are few and large, outperforming the genetic correlation and another nonparametric statistical test of independence. We find the test suitable for the detection of genetic similarity in the rare disease context.

Author summary

The genome-wide association study (GWAS) is a method used to identify genetic variants which contribute to the risk of developing disease. These genetic variants are frequently shared between conditions, such that the study of the genetic basis of one disease can be informed by knowledge of another, similar disease. This approach can be productive where the disease in question is rare such that a GWAS has less power to associate variants with the disease, but there exist larger GWAS of similar diseases. Existing methods do not measure genetic similarity precisely when patients are few. Here we assess a previously published method of testing for genetic similarity between pairs of diseases using GWAS data, the ‘GPS’ test, against three other methods with the use of real and simulated data. We present a new computational procedure for carrying out the test and show that the GPS test is superior to its comparators in identifying genetic similarity when the sample size is small and when the genetic similarity signal is less strong. Use of the test will enable accurate detection of genetic similarity and the study of rarer conditions using data from better-characterised diseases.
Targeted memory reactivation during post-learning sleep does not enhance motor memory consolidation in older adults
Authors: Nicolas Judith , Carrier Julie , Swinnen Stephan P. , Doyon Julien , Albouy Geneviève , King Bradley R.
Published: October 2022

Targeted memory reactivation (TMR) during sleep enhances memory consolidation in young adults by modulating electrophysiological markers of neuroplasticity. Interestingly, older adults exhibit deficits in motor memory consolidation, an impairment that has been linked to age-related degradations in the same sleep features sensitive to TMR. We hypothesized that TMR would enhance consolidation in older adults via the modulation of these markers. Seventeen older participants were trained on a motor task involving two auditory-cued sequences. During a post-learning nap, two auditory cues were played: one associated to a learned (i.e., reactivated) sequence and one control. Performance during two delayed retests did not differ between reactivated and non-reactivated sequences. Moreover, both associated and control sounds modulated brain responses, yet there were no consistent differences between the auditory cue types. Our results collectively demonstrate that older adults do not benefit from specific reactivation of a motor memory trace by an associated auditory cue during post-learning sleep. It is possible, however, that auditory stimulation during post-learning sleep boosts motor memory consolidation in a non-specific manner.
Entropy predicts sensitivity of pseudo-random seeds
Authors: Maier Benjamin Dominik , Sahlin Kristoffer
Published: October 2022

In sequence similarity search applications such as read mapping, it is desired that seeds match between a read and reference in regions with mutations or read errors (seed sensitivity).K-mers are likely the most well-known and used seed construct in bioinformatics, and many studies on,e.g., spacedk-mers aim to improve sensitivity overk-mers. Spacedk-mers are highly sensitive when substitutions largely dominate the mutation rate but quickly deteriorate when indels are present. Recently, we developed a pseudo-random seeding construct, strobemers, which were empirically demonstrated to have high sensitivity also at high indel rates. However, the study lacked a deeper understanding of why. In this study, we demonstrate that a seed’s entropy (randomness) is a good predictor for seed sensitivity. We propose a model to estimate the entropy of a seed and find that seeds with high entropy, according to our model, in most cases have high match sensitivity. We also present three new strobemer seed constructs, mixedstrobes, altstrobes, and multistrobes. We use both simulated and biological data to demonstrate that our new seed constructs improve sequence-matching sensitivity to other strobemers. We implement strobemers into minimap2 and observe slightly faster alignment time and higher accuracy than usingk-mers at various error rates.

Our discovered seed randomness-sensitivity relationship explains why some seeds perform better than others, and the relationship provides a framework for designing even more sensitive seeds. In addition, we show that the three new seed constructs are practically useful. Finally, in cases where our entropy model does not predict the observed sensitivity well, we explain why and how to improve the model in future work.
Inferring transmission routes for foot-and-mouth disease virus within a cattle herd using approximate Bayesian computation
Authors: Ellis John , Brown Emma , Colenutt Claire , Schley David , Gubbins Simon
Published: October 2022

To control an outbreak of an infectious disease it is essential to understand the different routes of transmission and how they contribute to the overall spread of the pathogen. With this information, policy makers can choose the most efficient methods of detection and control during an outbreak. Here we demonstrate a method for assessing the contribution of different routes of transmission using approximate Bayesian computation with sequential Monte Carlo sampling (ABC-SMC). We apply this to infer parameters of an individual based model of within-herd transmission of foot-and-mouth disease virus (FMDV), incorporating transmission through direct contact and via environmental contamination. Additionally, we use ABC-SMC for model selection to assess the plausibility of either transmission route alone being responsible for all infections. We show that direct transmission likely contributes the majority of infections during an outbreak of FMD but there is an accumulation of environmental contamination that can cause infections within a farm and also have the potential to spread between farms via fomites.
Aneuploidy in human embryos is associated with a maternal age-independent increase in mitochondrial DNA content and an enrichment of ultra-rare mitochondrial DNA variants
Authors: Ri Maxim , Ree Natalia , Oreshkov Sergey , Tofilo Maria , Zvereva Irina , Kirillova Anastasia , Gunbin Konstantin , Yurov Valerian , Salumets Andres , Woods Dori C. , Khrapko Konstantin , Fellay Jacques , Tilly Jonathan L. , Mazunin Ilya , Popadin Konstantin
Published: October 2022

Using low-coverage, whole-genome sequences of trophectoderm biopsies from 11,610 human blastocyst-stage embryos, we analyzed the relationship between chromosomal abnormalities and mitochondrial (mt) DNA dynamics. Comparing 6,208 aneuploid and 5,402 euploid embryos in cohort studies, we found that mtDNA content in aneuploid embryos was significantly higher than that in euploid embryos. This outcome was confirmed through intrafamilial analyses of embryos with matched parents andin vitrofertilization cycles, and it occurred independent of maternal age. Additional human population-based studies uncovered a higher abundance of ultra-rare mtDNA variants located in never-altered positions in the human population in aneuploid compared to euploid embryos in both cohort- and family-based analyses. This maternal age-independent association of increased mtDNA content and aneuploidy in human embryos may reflect a novel mechanism of purifying selection against potentially deleterious mtDNA variants, which arise from germline or early developmental mtDNA damaging events, that occurs in human embryos prior to implantation.
TWO DISULFIDE-REDUCING PATHWAYS ARE REQUIRED FOR THE MATURATION OF PLASTIDC-TYPE CYTOCHROMES INCHLAMYDOMONAS REINHARDTII
Authors: Das Ankita , Subrahmanian Nitya , Gabilly Stéphane T. , Andrianova Ekaterina P. , Zhulin Igor B. , Motohashi Ken , Hamel Patrice Paul
Published: October 2022

Abstract

In plastids, conversion of light energy into ATP relies on cytochromef, a key electron carrier with a heme covalently attached to a CXXCH motif. Covalent heme attachment requires reduction of the disulfide bonded CXXCH motif by CCS5 and CCS4, a protein of unknown function. CCS5 receives electrons from the oxido-reductase CCDA at the thylakoid membrane. InChlamydomonas reinhardtii, loss of CCS4 or CCS5 function yields a partial cytochromefassembly defect. Here we report that the Δccs4ccs5double mutant displays a synthetic photosynthetic defect due to a complete loss of holocytochromefassembly, a phenotype that can be chemically corrected by reducing agents. In Δccs4, the CCDA protein accumulation is decreased, indicating that one function of CCS4 is to stabilize CCDA. Dominant suppressor mutations mapping to theCCS4gene were identified in photosynthetic revertants of the Δccs4ccs5mutants. The suppressor mutations correspond to changes in the stroma-facing domain of CCS4 and restore holocytochromefassembly above the residual levels detected in Δccs5. Because disulfide reduction via CCS5 no longer takes place in Δccs5, we hypothesize the suppressor mutations enhance the supply of reducing power independently of CCS5, uncovering the participation of CCS4 in a distinct redox pathway. CCS4-like proteins occur in the green lineage and are related to mitochondrial COX16, a protein involved in a disulfide reducing pathway. We discuss the operation of two pathways controlling the redox status of the heme-binding cysteines of apocytochromefand the possible function of CCS4 as a shared component between the two pathways.

Abstract Figure

Graphical abstract.

The Δccs4ccs5mutant exhibits a photosynthetic growth defect due to a complete loss of cytochromecassembly.

Reduction of apocytochromefin the thylakoid lumen requires the provision of reducing power through two different pathways, pathway 1 and 2. CCDA and CCS5, components of pathway 1, deliver electrons from stroma to apocytochromefvia thiol – disulfide exchange. CCS4 is involved in pathway 1 by stabilizing CCDA, but also functions through a CCS5 – independent pathway (pathway 2). In the absence of CCS5, gain – of – function mutations in the C terminus of CCS4 (indicated by a yellow star) enhance the delivery of reducing power either via CCDA or independently of CCDA to yet-to-be-discovered reductases
Anti-glutamatergic effects of three lignan compounds: arctigenin, matairesinol and trachelogenin - An ex vivo study on rat brain slices
Authors: Koech Peter Kiplangat , Jócsák Gergely , Boldizsár Imre , Moldován Kinga , Borbély Sándor , Világi Ildikó , Dobolyi Arpád , Varró Petra
Published: October 2022

Arctigenin is a bioactive dibenzylbutyrolactone-type lignan exhibiting various pharmacological activities. The neuroprotective effects of arctigenin were demonstrated to be mediated via inhibition of AMPA/KA type glutamate receptors in the somatosensory cortex of the rat brain. The aim of this study was to compare the effects of arctigenin with matairesinol and trachelogenin on synaptic activity inex vivorat brain slices. Arctigenin, matairesinol and trachelogenin were isolated fromArctium lappa, Centaurea scabiosaandCirsium arvense, respectively, and applied on brain slices via perfusion medium at the concentration range of 0.5-40 μM. The effects of the lignans were examined in the CA1 hippocampus and the somatosensory cortex by recording electrically evoked field potentials. Arctigenin and trachelogenin caused a significant dose-dependent decrease in the amplitude of hippocampal population spikes (POPS) and the slope of excitatory postsynaptic potentials (EPSPs), whereas matairesinol (1 μM and 10 μM) decreased EPSP slope but had no effect on POPS amplitude. Trachelogenin effect (0.5 μM, 10 μM, 20 μM) was comparable to arctigenin (1 μM, 20 μM, 40 μM) (p > 0.05). In the neocortex, arctigenin (10 μM, 20 μM) and trachelogenin (10 μM) significantly decreased the amplitude of evoked potential early component, while matairesinol (1 μM and 10 μM) had no significant effect (p>0.05). The results suggest that trachelogenin and arctigenin act via inhibition of AMPA/KA receptors in the brain and trachelogenin has a higher potency than arctigenin. Thus, trachelogenin and arctigenin could serve as lead compounds in the development of alternative neuroprotective drugs.
Human-Sloth Bear (Melursus ursinus) Conflicts in Nawada Forest Division, Nawada, Bihar, India
Authors: Kumar Gourav , Paul Dilip kumar , Kumari Deepa
Published: October 2022

Sloth bears (Melursus ursinus) are endemic to the Indian subcontinent and frequently come into conflict with human. Both in Rajauli Wildlife Sanctuary(RWLS) and Kauwkol Forest Range (KFR), sand mining operations create heavy noise during the summer for stone crushing which could be the cause of disturbance in the sloth bear territory and sloth bear attacks during the summer and winter. During the study period of 2016 to 2018, a total of 16 sloth bear attacks were incidently reported in various ranges of NFD. There was a marked seasonal variation in human casualties by sloth bear in the study area. Out of 16 cases, highest number of casualties were observed in monsoon (n=8, 50%) as compared to casualties in Summer (n=5, 31.25%) and winter (n=3, 18.75%) season. Most of the attacks were reported during 4.01-8.00 hours (n=6, 37.5%), followed by incidences occurred during evening time, 16.01-20.00 hours (n=4, 25%), 8.01-12.00 hrs (n=3, 18.75%) and 12.01-16.00 (n=2, 12.5%). Moreover, males (n=10) were shown to have a 62 percent attack rate compared to females (n=6; 38 percent). The data also shows that the frequency of attacks on victims engaged in various activities were related to their intensity of usage of specific habitats. It is reported that highest incidences of HSBC occurred when the victims were engaged in NTFP collection (37.5%), followed by victims engaged in walking (25%), farming (18.75%), defection (12.5%) and cattle grazing (6.25%) activities. We recommend education programs among the people to reduce human injury through mitigation techniques.
Random noise promotes slow heterogeneous synaptic dynamics important for robust working memory computation
Authors: Rungratsameetaweemana Nuttida , Kim Robert , Chotibut Thiparat , Sejnowski Terrence J.
Published: October 2022

Recurrent neural networks (RNNs) based on model neurons that communicate via continuous signals have been widely used to study how cortical neurons perform cognitive tasks. Training such networks to perform tasks that require information maintenance over a brief period (i.e., working memory tasks) remains a challenge. Critically, the training process becomes difficult when the synaptic decay time constant is not fixed to a large constant number for all the model neurons. Here, we show that introducing random noise to the RNNs not only speeds up the training but also produces stable models that can maintain information longer than the RNNs trained without internal noise. Importantly, this robust working memory performance induced by internal noise during training is attributed to an increase in synaptic decay time constants of a distinct subset of inhibitory units, resulting in slower decay of stimulus-specific activity critical for memory maintenance.
Development and validation of an AI/ML platform for the discovery of splice-switching oligonucleotide targets
Authors: Fronk Alyssa D , Manzanares Miguel A , Zheng Paulina , Geier Adam , Anderson Kendall , Frederick Vanessa , Smith Shaleigh , Gera Sakshi , Munch Robin , Are Mahati , Dhingra Priyanka , Arun Gayatri , Akerman Martin
Published: October 2022

This study demonstrates the value that artificial intelligence/machine learning (AI/ML) provides for the identification of novel and verifiable splice-switching oligonucleotide (SSO) targetsin-silico. SSOs are antisense compounds that act directly on pre-mRNA to modulate alternative splicing (AS). To leverage the potential of AS research for therapeutic development, we created SpliceLearn™, an AI/ML algorithm for the identification of modulatory SSO binding sites on pre-mRNA. SpliceLearn also predicts the identity of specific splicing factors whose binding to pre-mRNA is blocked by SSOs, adding considerable transparency to AI/ML-driven drug discovery and informing biological insights useful in further validation steps. Here we predictedNEDD4Lexon 13 (NEDD4Le13) as a novel target in triple negative breast cancer (TNBC) and computationally designed an SSO to modulateNEDD4Le13. TargetingNEDD4Le13with this SSO decreased the proliferative and migratory behavior of TNBC cells via downregulation of the TGFβ pathway. Overall, this study illustrates the ability of AI/ML to extract actionable insights from RNA-seq data. SpliceLearn is part of the SpliceCore® platform, an AI/ML predictive ensemble for AS-based drug target discovery.
MAP4Ks inhibition promotes retinal neuron regeneration from Müller glia in adult mice
Authors: Zhang Houjian , Guo Yuli , Yang Yaqiong , Zhuang Jingbin , Wang Yuqian , Zhao Jiankai , Zhang Rongrong , Hu Jiaoyue , Li Wei , Wu Jianfeng , Xu Haiwei , Fliesler Steven J. , Liao Yi , Liu Zuguo
Published: October 2022

Mammalian Müller glia (MG) possess limited regenerative capacities. However, the intrinsic capacity of mammalian MG to transdifferentiate to generate mature neurons without transgenic manipulations remains speculative. Here we show that MAP4K4, MAP4K6 and MAP4K7, which are conserved Misshapen subfamily of ste20 kinases homologs, repress YAP activity in mammalian MG and therefore restrict their ability to be reprogrammed. However, by treating with a small molecule inhibitor of MAP4K4/6/7, mouse MG regain their ability to proliferate and enter into a retinal progenitor cell (RPC)-like state after NMDA-induced retinal damage; such plasticity was lost in YAP knockout MG. Moreover, spontaneous trans-differentiation of MG into retinal neurons expressing both amacrine and retinal ganglion cell (RGC) markers occurs after inhibitor withdrawal. Taken together, these findings suggest that MAP4Ks block the reprogramming capacity of MG in a YAP-dependent manner in adult mammals, which provides a novel avenue for the pharmaceutical induction of retinal regenerationin vivo.
Connectome-based predictive modeling of empathy in adolescents with and without the low-prosocial emotion specifier
Authors: Winters Drew E. , Guha Anika , Sakai Joseph T.
Published: October 2022

Empathy impairments are an important part of a broader affective impairments defining the youth antisocial phenotype callous-unemotional (CU) traits and the DSM-5 low prosocial emotion (LPE) specifier. While functional connectivity underlying empathy and CU traits have been well studied, less is known about what functional connections underly differences in empathy amongst adolescents qualifying for the LPE specifier. Such information can provide mechanistic distinctions for this clinically relevant specifier. The present study uses connectome-based predictive modeling that uses whole-brain resting-state functional connectivity data to predict cognitive and affective empathy for those meeting the LPE specifier (n= 29) and those that do not (n= 57). Additionally, we tested if models of empathy generalized between groups as well as density differences for each model of empathy between groups. Results indicate the LPE group had lower cognitive and affective empathy as well as higher CU traits and conduct problems. Negative and positive models were identified for affective empathy for both groups, but only the negative model for the LPE and positive model for the normative group reliably predicted cognitive empathy. Models predicting empathy did not generalize between groups. Density differences within the default mode, salience, executive control, limbic, and cerebellar networks were found as well as between the executive control, salience, and default mode networks. And, importantly, connections between the executive control and default mode networks characterized empathy differences the LPE group such that more positive connections characterized cognitive differences and less negative connections characterized affective differences. These findings indicate neural differences in empathy for those meeting LPE criteria that may explain decrements in empathy amongst these youth. These findings support theoretical accounts of empathy decrements in the LPE clinical specifier and extend them to identify specific circuits accounting for variation in empathy impairments. The identified negative models help understand what connections inhibit empathy whereas the positive models reveal what brain patterns are being used to support empathy in those with the LPE specifier. LPE differences from the normative group and could be an appropriate biomarker for predicting CU trait severity. Replication and validation using other large datasets are important next steps.
An Extensible Vector Toolkit and Parts Library for Advanced Engineering of Plant Genomes
Authors: Chamness James C. , Kumar Jitesh , Cruz Anna J. , Rhuby Elissa , Holum Mason J. , Cody Jon P. , Tibebu Redeat , Gamo Maria Elena , Starker Colby G. , Zhang Feng , Voytas Daniel F.
Published: October 2022

Plant biotechnology is rife with new advances in transformation and genome engineering techniques. A common requirement for delivery and coordinated expression in plant cells, however, places the design and assembly of transformation constructs at a crucial juncture as desired reagent suites grow more complex. Modular cloning principles have simplified some aspects of vector design, yet many important components remain unavailable or poorly adapted for rapid implementation in biotechnology research. Here, we describe a universal Golden Gate cloning toolkit for vector construction. The toolkit chassis is compatible with the widely accepted Phytobrick standard for genetic parts, and supports assembly of arbitrarily complex T-DNAs through improved capacity, positional flexibility, and extensibility in comparison to extant kits. We also provision a substantial library of newly adapted Phytobricks, including regulatory elements for monocot and dicot gene expression, and coding sequences for genes of interest such as reporters, developmental regulators, and site-specific recombinases. Finally, we use a series of dual luciferase assays to measure contributions to expression from promoters, terminators, and from cross-cassette interactions attributable to enhancer elements in certain promoters. Taken together, these publicly available cloning resources can greatly accelerate the testing and deployment of new tools for plant engineering.
A critical analysis of plant science literature reveals ongoing inequities
Authors: Marks Rose A. , Amézquita Erik J. , Percival Sarah , Rougon-Cardoso Alejandra , Chibici-Revneanu Claudia , Tebele Shandry M. , Farrant Jill M. , Chitwood Daniel H. , VanBuren Robert
Published: October 2022

The field of plant science has grown dramatically in the past two decades, but global disparities and systemic inequalities persist. Here, we analyzed ~300,000 papers published over the past two decades to quantify disparities across nations, genders, and taxonomy in the plant science literature. Our analyses reveal striking geographical biases—affluent nations dominate the publishing landscape and vast areas of the globe having virtually no footprint in the literature. Authors in Northern America are cited nearly twice as many times as authors based in Sub-Saharan Africa and Latin America, despite publishing in journals with similar impact factors. Gender imbalances are similarly stark and show remarkably little improvement over time. Some of the most affluent nations have extremely male biased publication records, despite supposed improvements in gender equality. In addition, we find that most studies focus on economically important crop and model species and a wealth of biodiversity is under-represented in the literature. Taken together, our analyses reveal a problematic system of publication, with persistent imbalances that poorly captures the global wealth of scientific knowledge and biological diversity. We conclude by highlighting disparities that can be addressed immediately and offer suggestions for long-term solutions to improve equity in the plant sciences.

SIGNIFICANCE STATEMENT

We analyzed ~300,000 papers published over the past two decades to quantify global, gender, and taxonomic disparities in plant science. Our analyses reveal striking geographical biases that are correlated with national affluence. Gender imbalances were also evident, with far more papers led by authors with masculine names than authors with feminine names. Lastly, we identified substantial taxonomic sampling gaps. The vast majority of surveyed studies focused on major crop and model species and the remaining biodiversity accounted for only a fraction of publications. Taken together, our analyses represent an important addition to the growing conversation about diversifying and decolonizing science.
scMultiSim: simulation of multi-modality single cell data guided by cell-cell interactions and gene regulatory networks
Authors: Li Hechen , Zhang Ziqi , Squires Michael , Chen Xi , Zhang Xiuwei
Published: October 2022

Simulated single-cell data is essential for designing and evaluating computational methods in the absence of experimental ground truth. Existing simulators typically focus on modeling one or two specific biological factors or mechanisms that affect the output data, which limits their capacity to simulate the complexity and multi-modality in real data. Here, we present scMultiSim, anin silicosimulator that generates multi-modal single-cell data, including gene expression, chromatin accessibility, RNA velocity, and spatial cell locations while accounting for the relationships between modalities. scMultiSim jointly models various biological factors that affect the output data, including cell identity, within-cell gene regulatory networks (GRNs), cell-cell interactions (CCIs), and chromatin accessibility, while also incorporating technical noises. Moreover, it allows users to adjust each factor’s effect easily. We validated scMultiSim’s simulated biological effects and demonstrated its applications by benchmarking a wide range of computational tasks, including cell clustering and trajectory inference, multi-modal and multi-batch data integration, RNA velocity estimation, GRN inference and CCI inference using spatially resolved gene expression data. Compared to existing simulators, scMultiSim can benchmark a much broader range of existing computational problems and even new potential tasks.
Predicting FDA approvability of small-molecule drugs
Authors: Huang Chih-Han , Hsu Justine , Yang Li-yen , Chen Tsai-Min , Shih Edward S.C. , Hwang Ming-Jing
Published: October 2022

A high rate of compound attrition makes drug discovery via conventional methods time-consuming and expensive. Here, we showed that machine learning models can be trained to classify compounds into distinctive groups according to their status in the drug development process, which can significantly reduce the compound attrition rate. Using molecular structure fingerprints and physicochemical properties as input, our models accurately predicted which drug compounds would proceed to trial, with an area under the receiver operating curve (AUC) of 0.94 ± 0.01 (mean ± standard deviation). Our models also identified which drugs in clinical trials would be approved by the US Food and Drug Administration (FDA) to go on the market, with an AUC of 0.73 ± 0.02. The predictive power of our models could reduce the attrition rate of preclinical compounds to enter clinical trials from 65%, as with conventional methods, to 12% (with 92% sensitivity) and the clinical trial failure rate from 80–90% to 29% (with 83% sensitivity). The results largely held in additional tests on new clinical trial compounds and new FDA-approved drugs, as well as on drugs uniquely approved for use in Europe and Japan.

SIGNIFICANCE STATEMENT

The odds of developing a drug approved by the US Food and Drug Administration (FDA) are slim, meaning that the vast majority of drug candidates would fail tests for safety and efficacy in the drug discovery process, rendering it highly inefficient and costly. Here, we have developed machine learning models to predict drug compounds worthy of clinical trials with high accuracy, and clinical-trial compounds to receive FDA approval with a much higher success rate than that achieved by the traditional approach. Our computational prediction requires input of only the drug compound’s chemical structure and physicochemical properties. It can help mitigate the long-standing problem of drug discovery.
Comparison of Dimensionality Reduction and Clustering Methods for Single-Cell Transcriptomics Data
Authors: Pandit Vrushali , Swain Asish Kumar , Yadav Pankaj
Published: October 2022

Dimensionality reduction (DR) methods are applied to extract relevant features from inherently high dimensional and noisy single-cell RNA sequencing (scRNA-seq) data. Choice of DR method could influence the performance of clustering algorithm and subsequent analysis outcomes. We performed a benchmarking study of seven popular DR methods and four clustering algorithms widely used for scRNA-seq datasets. For this purpose, we used three publicly available real scRNA-seq datasets. The performance was evaluated using two clustering metrics viz. adjusted random index (ARI) and normalized mutual index (NMI). We also compared our results with a similar study published by Xiang and colleagues. Overall, we observed higher ARI and NMI scores for DR methods when compared with Xiang’s study. We also noticed several differences between our and Xiang’s study. Noteworthy, three methods, namely, Independent Component Analysis (ICA), t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) performed consistently well across three datasets. Linear method ICA was best performer onSegerstolpedataset, while nonlinear methods UMAP and t-SNE best performed onDengandChudatasets, respectively. Neural network-based methods Variational Autoencoder (VAE) and Deep Count Autoencoder (DCA) could not perform well probably due to their sensitivity to hyperparameters and overfitting. Among clustering methods, Gaussian Mixture Models (GMMs) performed consistently well across datasets. This might be because GMMs are the universal approximators of posterior probability densities. We conclude that performance of different DR methods is more dataset dependent and for various scRNA-seq datasets different algorithms are more suited and there is no one-fit-all method.
Fast and Accurate Prediction of Intrinsically Disordered Protein by Protein Language Model
Authors: Xu Shijie , Onoda Akira
Published: October 2022

Motivation

Intrinsically disordered proteins (IDPs) play a vital role in various biological processes and have attracted increasing attention in the last decades. Predicting IDPs from primary structures of proteins provides a very useful tool for protein analysis. However, most of the existing prediction methods heavily rely on multiple sequence alignments (MSAs) of homologous sequences which are formed by evolution over billions of years. Obtaining such information requires searching against the whole protein databases to find similar sequences and since this process becomes increasingly time-consuming, especially in large-scale practical applications, the alternative method is needed.

Results

In this paper, we proposed a novel IDP prediction method named IDP-PLM, based on the protein language model (PLM). The method does not rely on MSAs or MSA-based profiles but leverages only the protein sequences, thereby achieving state-of-the-art performance even compared with predictors using protein profiles. The proposed IDP-PLM is composed of stacked predictors designed for several different protein-related tasks: secondary structure prediction, linker prediction, and binding predictions. In addition, predictors for the single task also achieved the highest accuracy. All these are based on PLMs thus making IDP-PLM not rely on MSA-based profiles. The ablation study reveals that all these stacked predictors contribute positively to the IDP prediction performance of IDP-PLM.

Availability

The method is available athttp://github.com/xu-shi-jie.

Contact

akira.onoda@ees.hokudai.ac.jp

Supplementary information

Supplementary data are available atBioinformaticsonline.
Neural representation of goal direction in the monarch butterfly brain
Authors: Beetz M. Jerome , Kraus Christian , el Jundi Basil
Published: October 2022

Neural processing of a navigational goal requires the continuous comparison between the current heading and the intended goal direction. While the neural basis underlying the current heading is well-studied in insects, the coding of the goal direction is completely unexplored. Here, we identify for the first time neurons that encode goal direction in the brain of a navigating insect, the monarch butterfly. The spatial tuning of these neurons accurately correlates with the animal’s goal direction while being unaffected by compass perturbations. Thus, they specifically encode the goal direction similar to goal neurons described in the mammalian brain. Taken together, a navigation network based on goal-direction and heading-direction neurons generates steering commands that efficiently guides the monarch butterflies to their migratory goal.
Defining Multiple Layers of Intratumor Heterogeneity Based on Variations of Perturbations in Multi-omics Profiling
Authors: Ai Hongjing , Song Dandan , Wang Xiaosheng
Published: October 2022

Intratumor heterogeneity (ITH) is associated with tumor progression, relapse, immunoevasion, and drug resistance. Existing algorithms for measuring ITH are limited to at a single molecular level. We proposed a set of algorithms for measuring ITH at the genome (somatic copy number alterations (CNAs) and mutations), mRNA, microRNA (miRNA), long non-coding RNA (lncRNA), protein, and epigenome level, respectively. These algorithms were designed based on a common concept: information entropy. By analyzing 33 TCGA cancer types, we demonstrated that these ITH measures had the typical properties of ITH, namely their significant correlations with unfavorable prognosis, tumor progression, genomic instability, antitumor immunosuppression, and drug resistance. Furthermore, we showed that the correlations between ITH measures at identical molecular levels were stronger than those at different molecular levels. The mRNA ITH showed stronger correlations with the miRNA, lncRNA, and epigenome ITH than with the genome ITH, supporting the regulatory relationships of miRNA, lncRNA, and DNA methylation towards mRNA. The protein ITH displayed stronger correlations with the transcriptome-level ITH than with the genome-level ITH, supporting the central dogma of molecular biology. Finally, we integrated the seven ITH measures into an ITH measure, which displayed more prominent properties of ITH than the ITH measures at a single molecular level. This analysis of multi-level ITH provides novel insights into tumor biology and potential values in clinical practice for pan-cancer.
Joint Variational Autoencoders for Multimodal Imputation and Embedding
Authors: Kalafut Noah Cohen , Huang Xiang , Wang Daifeng
Published: October 2022

Single-cell multimodal datasets have measured various characteristics of individual cells, enabling a deep understanding of cellular and molecular mechanisms. However, multimodal data generation remains costly and challenging, and missing modalities happen frequently. Recently, machine learning approaches have been developed for data imputation but typically require fully matched multimodalities to learn common latent embeddings that potentially lack modality specificity. To address these issues, we developed an open-source machine learning model, Joint Variational Autoencoders for multimodal Imputation and Embedding (JAMIE). JAMIE takes single-cell multimodal data that can have partially matched samples across modalities. Variational autoencoders learn the latent embeddings of each modality. Then, embeddings from matched samples across modalities are aggregated to identify joint cross-modal latent embeddings before reconstruction. To perform cross-modal imputation, the latent embeddings of one modality can be used with the decoder of the other modality. For interpretability, Shapley values are used to prioritize input features for cross-modal imputation and known sample labels. We applied JAMIE to both simulation data and emerging single-cell multimodal data including gene expression, chromatin accessibility, and electrophysiology in human and mouse brains. JAMIE significantly outperforms existing state-of-the-art methods in general and prioritized multimodal features for imputation, providing potentially novel mechanistic insights at cellular resolution.
Rhythms of Brain Impedance
Authors: Mivalt Filip , Kremen Vaclav , Sladky Vladimir , Cui Jie , Gregg Nicholas , Balzekas Irena , Marks Victoria , Louis Erik K. St , Croarkin Paul E. , Lundstrom Brian Nils , Nelson Noelle , Hermes Dora , Messina Steven , Worrell Samuel , Richner Thomas J. , Brinkmann Benjamin H. , Denison Timothy , Miller Kai J. , Van Gompel Jamie , Stead Matt , Worrell Gregory A.
Published: October 2022

Brain impedance is a fundamental electrical property that depends on tissue extracellular volume. We tracked impedance, behavioral state, and epileptiform activity in humans using an investigational device and identified behavioral state dependent impedance oscillations spanning hours to weeks in amygdala, hippocampus, and thalamus. Impedance reaches a minimum in slow wave sleep, is intermediate in rapid-eye-movement sleep and maximal during wakefulness consistent with previously observed extracellular volume changes in rodent glymphatic system.
Host independent deletion hotspots in the SARS-CoV-2 genome
Authors: Khalid Mohammad
Published: October 2022

SARS-CoV-2 infects a wide range of hosts in varying degrees. The RNA genome of SARS-CoV-2 makes it prone to mutations. Advantageous mutations help the virus to evolve and the virus maintains such mutations across species. Here in this study, all non-human hosts-derived SARS-CoV-2 genomic sequences from the GISAID database were analyzed, and identified several deletion hotspots, which are maintained by the virus, across various host species, indicating their important role in the virus evolution. Several of these deletion hotspots are also found in human-derived SARS-CoV-2 genomic sequences. These deletion hotspots have the potential to affect the pathogenicity and virulence of the virus and have a role in molecular and serological diagnostics. Potentially, they can lead to immune escape, resulting in vaccine failure and drug-resistant variants.
Effects of long-chain omega-3 polyunsaturated fatty acids on reducing anxiety and/or depression in adults; A systematic review and meta-analysis of randomised controlled trials
Authors: Kelaiditis Christos F. , Gibson E. Leigh , Dyall Simon C.
Published: October 2022

First-line treatment for anxiety and depressive disorders comprises pharmacotherapy and psychotherapy; options not safe, effective, or suitable for all. Mounting evidence suggests that the omega-3 polyunsaturated fatty acids (PUFAs) eicosapentaenoic (EPA), docosahexaenoic (DHA) and docosapentaenoic (DPAn-3) acids are promising therapeutic options. However, meta-analyses of randomised controlled trials (RCTs) have produced inconsistent findings. This review assesses for the first time the efficacy of omega-3 PUFAs against the severity of anxiety and depression symptoms, measured by validated scales, with specific consideration of methodological issues encountered in this area. PubMed, CINAHL, PsycINFO, Cochrane Library and Web of Science were searched for eligible RCTs administering omega-3 PUFAs against anxiety and/or depression. This study adopts the PRISMA guidelines. Ten RCTs comprising 1509 participants were included in the quantitative synthesis. EPA-enriched interventions at ≥60% of total EPA+DHA were associated with significant reduction in depression severity, compared to placebo (SMD: -0.32; 95% CI: -0.59, -0.06; p=0.02); however, EPA doses of ≥2000 mg/day were not (SMD: -0.11; 95% CI: -0.43, 0.20; p=0.48). Only 10 RCTs fulfilled the eligibility criteria, and there were some concerns regarding bias and population heterogeneity, highlighting the lack of high-quality RCTs in this area. Overall, these results support previous observations where EPA at proportions ≥60% of total EPA+DHA, up to 2000 mg, reduces depression scores. However, more trials are needed which specifically consider the unique nature of this type of research to elucidate the therapeutic potential of EPA, DHA and DPAn-3.
Microbial Community Interactions on a Chip
Authors: Juang Duane. S. , Wightman Wren E. , Lozano Gabriel L. , Barkal Layla J. , Yu Jiaquan , Garavito Manuel F. , Hurley Amanda , Venturelli Ophelia S. , Handelsman Jo , Beebe David J.
Published: October 2022

Multispecies microbial communities drive most ecosystems on Earth. Chemical and biological interactions within these communities can affect survival of individual members and the entire community. However, the prohibitively high number of possible interactions within a microbial community has made the characterization of factors that influence community development challenging. Here we report a Microbial Community Interaction (μCI) device to advance the systematic study of chemical and biological interactions within a microbial community. The μCI creates a combinatorial landscape made up of an array of triangular wells interconnected with circular wells, which each contains either a different chemical or microbial strain, generating chemical gradients and revealing biological interactions.Bacillus cereusUW85 containing GFP provided the “target” readout in the triangular wells, and antibiotics or microorganisms in adjacent circular wells are designated the “variables”. The μCI device revealed that gentamicin and vancomycin are antagonistic to each other in inhibiting the targetB. cereusUW85, displaying weaker inhibitory activity when used in combination than alone. We identified three-member communities constructed with isolates from the plant rhizosphere that increased or decreased growth ofB. cereus. The μCI device enables both strain-level and community-level insight. The scalable geometric design of the μCI device enables experiments with high combinatorial efficiency, thereby providing a simple, scalable platform for systematic interrogation of three-factor interactions that influence microorganisms in solitary or community life.
The Halo Library, a Tool for Rapid Identification of Ligand Binding Sites on Proteins Using Crystallographic Fragment Screening
Authors: Chopra Ashima , Bauman Joseph D. , Ruiz Francesc X. , Arnold Eddy
Published: October 2022

X-ray crystallographic fragment screening (XCFS) uses fragment-sized molecules (∼60 – 300 Da) to access binding sites on proteins that may be inaccessible to larger drug-like molecules (>300 Da). Previous studies from our lab and others have shown that fragments containing halogen atoms have a higher binding occurrence compared to non-halogenated fragments. Specifically, we showed that 4-halopyrazoles hold potential for predicting the likelihood of success of a XCFS campaign. Here, we designed the Halo Library containing 46 halogenated fragments (including the “universal fragment” 4-bromopyrazole). The basis of fragment selection was presence of (at least) one halogen atom, and binding to or inhibitory activity against (at least) two targets in literature. The library was screened against crystals of HIV-1 reverse transcriptase with drug rilpivirine, yielding an overall hit rate of 26%. Two new binding sites were discovered in addition to previously reported sites, and several hot spots were identified (i.e., sites with multiple fragment hits). This small library may thus provide a convenient tool for assessing feasibility of a target for XCFS, mapping hot spots and cryptic sites, as well as finding fragment binders that can be useful for developing drug leads.