bluuebunny/crossref_metadata_2025 huggingface dataset:
Simplified fecal community transplant restores Clostridioides difficile colonization resistance to antibiotic perturbed murine communities
Authors: Lesniak Nicholas A. , Tomkovich Sarah , Henry Andrew , Taylor Ana , Colovas Joanna , Bishop Lucas , McBride Kathryn , Schloss Patrick D.
Published: May 2022

Fecal communities transplanted into individuals can eliminate recurrent Clostridioides difficile infection (CDI) with high efficacy. However, this treatment is only used once CDI becomes resistant to antibiotics or has recurred multiple times. We sought to investigate whether a fecal community transplant (FCT) pre-treatment could be used to prevent CDI altogether. We treated male C57BL/6 mice with either clindamycin, cefoperazone, or streptomycin, and then inoculated them with the microbial community from untreated mice before challenging with C. difficile. We measured colonization and sequenced the V4 region of the 16S rRNA gene to understand the dynamics of the murine fecal community in response to the FCT and C. difficile challenge. Clindamycin-treated mice became colonized with C. difficile but cleared it naturally and did not benefit from the FCT. Cefoperazone-treated mice became colonized by C. difficile, but the FCT enabled clearance of C. difficile. In streptomycin-treated mice, the FCT was able to prevent C. difficile from colonizing. Then we diluted the FCT and repeated the experiments. Cefoperazone-treated mice no longer cleared C. difficile. However, streptomycin-treated mice colonized with 1:102 dilutions resisted C. difficile colonization. Streptomycin-treated mice that received a FCT diluted 1:103, C. difficile colonized but later was cleared. In streptomycin-treated mice, inhibition of C. difficile was associated with increased relative abundance of a group of bacteria related to Porphyromonadaceae and Lachnospiraceae. These data demonstrate that C. difficile colonization resistance can be restored to a susceptible community with a FCT as long as it complements the missing populations.

Importance

Antibiotic use, ubiquitous with the healthcare environment, is a major risk factor for Clostridioides difficile infection (CDI), the most common nosocomial infection. When C. difficile becomes resistant to antibiotics, a fecal microbiota transplant from a healthy individual can effectively restore the gut bacterial community and eliminate the infection. While this relationship between the gut bacteria and CDI is well established, there are no therapies to treat a perturbed gut community to prevent CDI. This study explored the potential of restoring colonization resistance to antibiotic-induced susceptible gut communities. We described the effect gut bacteria community variation has on the effectiveness of a fecal community transplant for inhibiting CDI. These data demonstrated that communities susceptible to CDI can be supplemented with fecal communities but the effectiveness depended on the structure of the community following the perturbation. Thus, a simplified bacterial community may be able to recover colonization resistance to patients treated with antibiotics.
Structure and mechanism of LARGE1 matriglycan polymerase
Authors: Joseph Soumya , Schnicker Nicholas J. , Xu Zhen , Yang Tiandi , Hopkins Jesse , Watkins Maxwell , Chakravarthy Srinivas , Davulcu Omar , Anderson Mary E. , Venzke David , Campbell Kevin P.
Published: May 2022

Matriglycan is a linear polysaccharide of alternating xylose and glucuronate that binds extracellular matrix proteins and acts as a receptor for Lassa fever virus. LARGE1 synthesizes matriglycan on dystroglycan and mutations in LARGE1 cause muscular dystrophy with abnormal brain development. However, the mechanism of matriglycan polymerization by LARGE1 is unknown. Here, we report the cryo-EM structure of LARGE1. We show that LARGE1 functions as a dimer to polymerize matriglycan by alternating activities between the xylose transferase domain on one protomer and the glucuronate transferase domain on the other protomer. Biochemical analyses using a recombinant Golgi form of dystroglycan reveal that LARGE1 polymerizes matriglycan processively. Our results provide mechanistic insights into LARGE1 function and may facilitate novel therapeutic strategies for treating neuromuscular disorders or arenaviral infections.

One-Sentence Summary

Dimeric LARGE1 processively polymerizes matriglycan on dystroglycan using orthogonal active sites on alternate protomers.
Neuro-immune Crosstalk in the Enteric Nervous System from Early Postnatal Development to Adulthood
Authors: Francesca Viola Maria , Marta Chavero-Pieres , Elodie Modave , Nathalie Stakenborg , Marcello Delfini , Fabre Naomi , Appeltans Iris , Martens Tobie , Vandereyken Katy , Van Herck Jens , Petry Philippe , Verheijden Simon , De Schepper Sebastiaan , Sifrim Alejandro , Kierdorf Katrin , Prinz Marco , Berghe Pieter Vanden , Voet Thierry , Boeckxstaens Guy
Published: May 2022

Correct development and maturation of the enteric nervous system (ENS) is critical for survival. Early in life, the ENS requires significant refinement in order to adapt to the evolving needs of the tissue, changing from milk to solid food at the time of weaning. Here, we demonstrate that resident macrophages of the muscularis externa, MMϕ, refine the ENS early in life by pruning synapses and phagocytosing abundant enteric neurons. After weaning, MMϕ continue to closely interact with the ENS, acquire a microglia-like phenotype and are crucial for the survival of enteric neurons. Of note, this microglia-like phenotype is instructed by TGFβ produced by the ENS, introducing a novel reciprocal cell-cell communication responsible for the maintenance of the neuron-associated MMФ niche in the gut. These findings elucidate a novel role of intestinal macrophages in ENS refinement early in life, and open new opportunities to treat intestinal neurodegenerative disorders by manipulating the ENS-macrophage niche.
A Multimodal Neuroimaging Dataset to Study Spatiotemporal Dynamics of Visual Processing in Humans
Authors: Ebrahiminia Fatemeh , Mahdiani Morteza , Khaligh-Razavi Seyed-Mahdi
Published: May 2022

We describe structural and multimodal functional neuroimaging data collected from 21 healthy volunteers. Functional magnetic resonance images (fMRI) and electroencephalography (EEG) signals were acquired in separate sessions from the same individuals while they were performing a visual one-back repetition task. During functional sessions, participants were presented with images from five categories, including animals, chairs, faces, fruits, and vehicles. The stimulus set and experimental parameters were chosen to be similar to that of an available electrocorticography (ECoG) dataset, therefore creating a unique opportunity to study vision in humans with multiple complementary neuroimaging modalities. Individual-specific head models can be constructed for each participant using T1-weighted MPRAGE images and the recorded positions of the EEG electrodes. By combining the three functional modalities and the structural data, this dataset provides a unique setting to explore spatiotemporal dynamics of invariant object recognition in humans. This multimodal data can also be used to develop new methods for combining fMRI and electrophysiological modalities to come up with more accurate spatiotemporally resolved maps of brain function, which is inaccessible by any of the modalities alone.
Uncovering the mechanism for aggregation in repeat expanded RNA reveals a reentrant transition
Authors: Kimchi Ofer , King Ella M. , Brenner Michael P.
Published: May 2022

Repeat expanded RNA molecules aggregate under certain conditions bothin vitroandin vivo. Understanding the mechanism for aggregation—including how aggregation properties change with sequence and environmental conditions—would explain and predict the behavior of RNA-based biomolecular condensates, and enable the rational design of RNA-based materials. Here, we introduce an analytical framework to predict aggregation for any repeat RNA sequence, accounting for both intra- and inter-molecular bonding. By enumerating the equilibrium landscape of multimers, we reveal the driving force for aggregation: the increased configurational entropy associated with the multiplicity of ways to form bonds in the aggregate. Our model uncovers rich phase behavior, including a sequence-dependent reentrant phase transition, and repeat parity-dependent aggregation. We validate our results by comparison to a complete computational enumeration of the landscape, and to previously published molecular dynamics simulations. Our work unifies and extends published results, and enables the design of programmable RNA condensates.
Complexities of recapitulating polygenic effects in natural populations: replication of genetic effects on wing shape in artificially selected and wild caught populations ofDrosophila melanogaster
Authors: Pelletier Katie , Pitchers William R. , Mammel Anna , Northrop-Albrecht Emmalee , Márquez Eladio J. , Moscarella Rosa A. , Houle David , Dworkin Ian
Published: May 2022

Identifying the genetic architecture of complex traits is important to many geneticists, including those interested in human disease, plant and animal breeding, and evolutionary genetics. Advances in sequencing technology and statistical methods for genome-wide association studies (GWAS) have allowed for the identification of more variants with smaller effect sizes, however, many of these identified polymorphisms fail to be replicated in subsequent studies. In addition to sampling variation, this failure to replicate reflects the complexities introduced by factors including environmental variation, genetic background, and differences in allele frequencies among populations. UsingDrosophila melanogasterwing shape, we ask if we can replicate allelic effects of polymorphisms first identified in a GWAS (Pitchers et al. 2019) in three genes:dachsous (ds),extra-macrochaete (emc)andneuralized (neur),using artificial selection in the lab, and bulk segregant mapping in natural populations. We demonstrate that multivariate wing shape changes associated with these genes are aligned with major axes of phenotypic and genetic variation in natural populations. Following seven generations of artificial selection along thedsshape change vector, we observe genetic differentiation of variants indsand genomic regions containing other genes in the hippo signaling pathway. This suggests a shared direction of effects within a developmental network. We also performed artificial selection with theemcshape change vector, which is not a part of the hippo signaling network, but showed a largely shared direction of effects. The response to selection along theemcvector was similar to that ofds, suggesting that the available genetic diversity of a population, summarized by the genetic (co)variance matrix (G), influenced alleles captured by selection. Despite the success with artificial selection, bulk segregant analysis using natural populations did not detect these same variants, likely due to the contribution of environmental variation and low minor allele frequencies, coupled with small effect sizes of the contributing variants.
Astrocytic Gi-GPCR activation enhances stimulus-evoked extracellular glutamate
Authors: Vaidyanathan Trisha V. , Tse Vincent , Lim Esther M. , Poskanzer Kira E.
Published: May 2022

Astrocytes perform critical functions in the nervous system, many of which are dependent on neurotransmitter-sensing through G protein-coupled receptors (GPCRs). However, whether specific astrocytic outputs follow specific GPCR activity remains unclear, and exploring this question is critical for understanding how astrocytes ultimately influence brain function and behavior. We previously showed that astrocytic Gi-GPCR activation is sufficient to increase slow-wave neural activity (SWA) during sleep when activated in cortical astrocytes1. Here, we investigate the outputs of astrocytic Gi-GPCRs, focusing on the regulation of extracellular glutamate and GABA, by combining in vivo fiber photometry recordings of the extracellular indicators iGluSnFR and iGABASnFR with astrocyte-specific chemogenetic Gi-GPCR activation. We find that Gi-GPCR activation does not change spontaneous dynamics of extracellular glutamate or GABA. However, Gi-GPCR activation does specifically increase visual stimulus-evoked extracellular glutamate. Together, these data point towards a complex relationship between astrocytic inputs and outputs in vivo that may depend on behavioral context. Further, they suggest an extracellular glutamate-specific mechanism underlying some astrocytic Gi-GPCR-dependent behaviors, including the regulation of sleep SWA.
Insulin-like Growth Factor-1 Synergizes with IL-2 to Induce Homeostatic Proliferation of Regulatory T cells
Authors: Shapiro Melanie R. , Peters Leeana D. , Brown Matthew E. , Cabello-Kindelan Cecilia , Posgai Amanda L. , Bayer Allison L. , Brusko Todd M.
Published: May 2022

IL-2 has been proposed to restore tolerance via regulatory T cell (Treg) expansion in autoimmunity, yet off-target effects necessitate identification of a combinatorial approach. We recently reported reduced levels of immunoregulatory insulin-like growth factor-1 (IGF1) during type 1 diabetes (T1D) progression. Thus, we hypothesized that IGF1 would synergize with IL-2 to expand Tregs. We observed IGF1R was elevated on murine memory and human naïve Treg subsets. IL-2 and IGF1 promoted murine PI3K/Akt and human STAT5 signaling in Tregs. IL-2 and IGF1 treatment expanded Tregs beyond either agent alone in NOD mice. Incubation of naïve human CD4+T cells with IL-2 and IGF1 enhanced Treg proliferationin vitro, without the need for T cell receptor ligation. This synergism was attributed to increased high-affinity IL-2Rα expression on naïve Tregs, in contrast to intermediate-affinity IL-2Rβ and IL-2Rγ subunit enhancement on naïve conventional T cells (Tconv). We then demonstrated that IGF1 and IL-2 or the IL2Rγ-chain-dependent cytokine, IL-7, can be used to induce proliferation of genetically-engineered naïve Treg or Tconv cells, respectively. These data support the potential use of IGF1 in combination with common γ-chain cytokines to drive T cell expansions bothin vitroandin vivofor cellular therapeutics and genetic modifications.
Using methylation data to improve transcription factor binding prediction
Authors: Morgan Daniel , DeMeo Dawn L. , Glass Kimberly
Published: May 2022

Scanning the genome for the sequence patterns defined by Position Weight Matrices (PWM) is commonly used to estimate transcription factor (TF) binding “motif” locations. However, the PWM-based scores assigned to these inferred locations are associated with only modest accuracy when benchmarked against in vivo TF binding. One reason for this limited performance may be because PWMs do not incorporate information regarding the epigenetic context necessary for TF binding. To investigate this, we developed a framework to score inferred TF binding locations using CpG methylation data. We intersected motif locations identified using PWMs with methylation information captured in both whole genome bisulfite sequencing and Illumina EPIC array data for six cell lines, scored motif locations based on these data, and compared with experimental data characterizing TF binding (ChIP-seq). We found that, for most TFs, binding is better predicted using methylation-based scoring compared to standard PWM scores. In addition, our analysis shows that, while most TFs do not bind to methylated promoter regions, there are several exceptions to this rule, indicating that the role of methylation in TF binding may be cell-type and context specific. Finally, we also illustrate that our approach can be generalized to infer TF binding when methylation information is only proximally available, i.e. measured for nearby CpGs that do not directly overlap with a motif location. Overall, our approach provides a framework for inferring context specific TF binding using methylation data, a crucial initial step for understanding the impact of methylation on gene regulatory processes.
Deep learning-based approach for the characterization and quantification of histopathology in mouse models of colitis
Authors: Kobayashi Soma , Shieh Jason , de Sabando Ainara Ruiz , Kim Julie , Liu Yang , Zee Sui Y. , Prasanna Prateek , Bialkowska Agnieszka B. , Saltz Joel H. , Yang Vincent W.
Published: May 2022

Inflammatory bowel disease (IBD) is a chronic immune-mediated disease of the gastrointestinal tract. While therapies exist, response can be limited within the patient population. As such, researchers have studied mouse models of colitis to further understand its pathogenesis and identify new treatment targets. Although bench methods like flow cytometry and RNA-sequencing can characterize immune responses with single-cell resolution, whole murine colon specimens are processed at once. Given the simultaneous presence of colonic regions that are involved or uninvolved with abnormal histology, processing whole colons may lead to a loss of spatial context. Detecting these regions in hematoxylin and eosin (H&E)-stained colonic tissues offers the downstream potential of quantifying immune populations in areas with and without disease involvement by immunohistochemistry on serially sectioned slides. This could provide a complementary, spatially-aware approach to further characterize populations identified by other methods. However, detection of such regions requires expert interpretation by pathologists and is a tedious process that may be difficult to perform consistently across experiments. To this end, we have trained a deep learning model to detect ‘Involved’ and ‘Uninvolved’ regions from H&E-stained colonic slides across controls and three mouse models of colitis – the dextran sodium sulfate (DSS) chemical induction model, the recently established intestinal epithelium-specific, inducible Klf5ΔIND (Villin-CreERT2;Klf5fl/fl) genetic model, and one that combines both induction methods. The trained classifier allows for extraction of ‘Involved’ colonic regions across mice to cluster and identify histological classes. Here, we show that quantification of ‘Involved’ and ‘Uninvolved’ image patch classes in swiss rolls of colonic specimens can be utilized to train a linear determinant analysis classifier to distinguish between mouse models. Such an approach has the potential for revealing histological links and improving synergy between various colitis mouse model studies to identify new therapeutic targets and pathophysiological mechanisms.
Fob1-dependent condensin recruitment and loop extrusion on yeast chromosome III
Authors: Dinda Manikarna , Fine Ryan D. , Saha Shekhar , Wang Zhenjia , Zang Chongzhi , Li Mingguang , Smith Jeffrey S.
Published: May 2022

Despite recent advances in single-molecule and structural analysis of condensin activity in vitro, mechanisms of functional condensin loading and loop extrusion that lead to specific chromosomal organization remain unclear. In Saccharomyces cerevisiae, the most prominent condensin loading site is the rDNA locus on chromosome XII, but its repetitiveness deters rigorous analysis of individual genes. An equally prominent non-rDNA condensin site is located on chromosome III (chrIII). It lies in the promoter of a putative non-coding RNA gene called RDT1, which is in a segment of the recombination enhancer (RE) that dictates MATa-specific chrIII organization. Here, we unexpectedly find that condensin is recruited to RDT1 through interactions with Fob1, Tof2, and cohibin (Lrs4/Csm1), a set of nucleolar factors that also recruit condensin to the rDNA. Using Micro-C XL, we uncover evidence for condensin-driven loop extrusion anchored by Fob1 and cohibin at RDT1 that extends toward MATa on the right arm of chrIII, supporting donor preference during mating-type switching. S. cerevisiae chrIII therefore provides a new platform for the study of programmed condensin-mediated chromosome conformation.
SPACE-GM: geometric deep learning of disease-associated microenvironments from multiplex spatial protein profiles
Authors: Wu Zhenqin , Trevino Alexandro E. , Wu Eric , Swanson Kyle , Kim Honesty J. , D’Angio H. Blaize , Preska Ryan , Charville Gregory W. , Dalerba Piero D. , Egloff Ann Marie , Uppaluri Ravindra , Duvvuri Umamaheswar , Mayer Aaron T. , Zou James
Published: May 2022

Multiplexed immunofluorescence imaging enables high-dimensional molecular profiling at subcellular resolution. However, learning disease-relevant cellular environments from these rich imaging data is an open challenge. We developed SPAtial CEllular Graphical Modeling (SPACE-GM), a geometric deep learning framework that flexibly models tumor microenvironments (TMEs) as cellular graphs. We applied SPACE-GM to 658 head-and-neck and colorectal human cancer samples assayed with 40-plex immunofluorescence imaging to identify spatial motifs associated with cancer recurrence and patient survival after immunotherapy. SPACE-GM is substantially more accurate in predicting patient outcomes than previous approaches for modeling spatial data using neighborhood cell-type compositions. Computational interpretation of the disease-relevant microenvironments identified by SPACE-GM generates insights into the effect of spatial dispersion of tumor cells and granulocytes on patient prognosis.
Profile of Mycobacterium tuberculosis-specific CD4 T cells at the site of disease and blood in pericardial tuberculosis
Authors: Du Bruyn Elsa , Ruzive Sheena , Howlett Patrick , Jacobs Ashley J. , Lindestam Arlehamn Cecilia S. , Sette Alessandro , Sher Alan , Mayer-Barber Katrin D. , Barber Daniel L. , Mayosi Bongani , Ntsekhe Mpiko , Wilkinson Robert J. , Riou Catherine
Published: May 2022

Our understanding of the immune response at the site of disease in extra-pulmonary tuberculosis (EPTB) is still limited. In this study, using flow cytometry, we defined the pericardial fluid (PCF) cellular composition and compared the phenotypic and functional profile of Mycobacterium tuberculosis (Mtb)-specific T cells between PCF and whole blood in 16 patients with pericardial TB (PCTB). We found that lymphocytes were the predominant cell type in PCF in PCTB, with a preferential influx of CD4 T cells. The frequencies of TNF-α producing myeloid cells and Mtb-specific T cells were significantly higher in PCF compared to blood. Mtb-specific CD4 T cells in PCF exhibited a distinct phenotype compared to those in blood, with greater GrB expression and lower CD27 and KLRG1 expression. We observed no difference in the production IFNγ, TNF or IL-2 by Mtb-specific CD4 T cells between the two compartments, but MIP-1β production was lower in the PCF T cells. Bacterial loads in the PCF did not relate to the phenotype or function of Mtb-specific CD4 T cells. Upon anti-tubercular treatment completion, HLA-DR, Ki-67 and GrB expression was significantly decreased, and relative IL-2 production was increased in peripheral Mtb-specific CD4 T cells. Overall, using a novel and rapid experimental approach to measure T cell response ex vivo at site of disease, these results provide novel insight into molecular mechanisms and immunopathology at site of TB infection of the pericardium.
Quantifying the adaptive landscape of commensal gut bacteria using high-resolution lineage tracking
Authors: Wong Daniel P.G.H. , Good Benjamin H.
Published: May 2022

Gut microbiota can adapt to their host environment by rapidly acquiring new mutations. However, the dynamics of this process are difficult to characterize in dominant gut species in their complexin vivoenvironment. Here we show that the fine-scale dynamics of genome-wide transposon libraries can enable quantitative inferences of thesein vivoevolutionary forces. By analyzing >400,000 lineages across four humanBacteroidesstrains in gnotobiotic mice, we observed positive selection on thousands of previously hidden mutations – most of which were unrelated to their original gene knockouts. The spectrum of fitness benefits varied between species, and displayed diverse tradeoffs over time and in different dietary conditions, enabling inferences of their underlying function. These results suggest that within-host adaptations arise from an intense competition between numerous contending mutations, which can strongly influence their emergent evolutionary tradeoffs.
Climatic niche evolution of infectious diseases driving amphibian declines
Authors: Ellepola Gajaba , Herath Jayampathi , Dan Sun , Pie Marcio R. , Murray Kris A. , Pethiyagoda Rohan , Hanken James , Meegaskumbura Madhava
Published: May 2022

Climate change and infectious diseases continue to drive global amphibian population declines, contributing to one of the greatest vertebrate extinctions of the Anthropocene. Currently around 16% amphibian species across the world are affected by four pathogens –Batrachochytrium dendrobatidis(Bd),B. salamandrivorans(Bsal),RanavirusandPerkinsea. A climatic context behind the dispersal of some of these diseases is hypothesized. However, the interplay between niche conservatism (NC) and climatic niche evolution (CNE), essential to understand disease evolution and dispersal, has so far received little attention. Here we show that the impacts of amphibian pathogens are intensifying as their climatic niches evolve. NC-based analyses suggest that niches of these diseases overlap, especially in Europe and East/southeast Asia (ESEA), and that all four pathogens will continue to devastate amphibians through seasonality shifts and range expansions, penetrating deeper into temperate regions and global amphibian diversity hotspots.Bdwill spread over diversity-rich mountain ranges and ranaviruses will overwhelm lowlands. CNE-based analyses suggest that the earliest lineages of these diseases originated in colder regions and that some lineages subsequently evolved towards warmer climatic niches. We caution that quiescent, warm-adapted strains are likely to become widespread and novel ranaviruses adapted to local climatic conditions and new hosts are likely to emerge. These results portend the dangers of introducing pathogens into new regions given their ability to adapt to changing climate scenarios. In a climatic background conducive to most of these diseases, frequent monitoring, enhanced biosecurity measures and policy reforms are needed for disease control and mitigation.
In situ architecture and membrane fusion of SARS-CoV-2 Delta variant
Authors: Song Yutong , Yao Hangping , Wu Nanping , Xu Jialu , Zhang Zheyuan , Peng Cheng , Li Shibo , Kong Weizheng , Chen Yong , Zhu Miaojin , Wang Jiaqi , Shi Danrong , Zhao Chongchong , Lu Xiangyun , Galindo Martín Echavarría , Li Sai
Published: May 2022

Among the current five Variants of Concern, infections caused by the SARS-CoV-2 B.1.617.2 (Delta) variant are often associated with the greatest severity. Despite recent advances on the molecular basis of elevated pathogenicity using recombinant proteins, architecture of intact Delta virions remains veiled. Moreover, molecular evidences for the detailed mechanism of S-mediated membrane fusion are missing. Here we reported the in situ structure and distribution of S on the authentic Delta variant, and discovered invagination in the distinctive Delta architecture. We also captured fusion snapshots from the virus-virus fusion events, provided structural evidences for Delta’s attenuated dependency on cellular factors for fusion activation, and proposed a model of S-mediated membrane fusion. Site-specific glycan analysis revealed increased oligomannose-type glycosylation of native Delta S over that of the Wuhan-Hu-1 S. Together, these results disclose distinctive factors of Delta being the most virulent SARS-CoV-2 variant.

In Brief

Cryo-ET of intact SARS-CoV-2 Delta variant revealed its distinctive architecture and captured snapshots of its membrane fusion in action.
Low spontaneous mutation rate in a complex multicellular eukaryote with a haploid-diploid life cycle
Authors: Krasovec Marc , Lipinska Agnieszka P. , Coelho Susana M.
Published: May 2022

The spontaneous mutation rate µ is a crucial parameter driving evolution and biodiversity. Mutation rates are highly variable across species, suggesting that µ is susceptible to selection and drift and that species life cycle and life history may impact its evolution. In particular, asexual reproduction and haploid selection are expected to affect mutation rate, but very little empirical data is available to test this expectation. Here, we sequence 30 genomes of a parent-offspring pedigree in the brown algae Ectocarpus to test the impact of its life cycle on mutation rate. Ectocarpus alternates between a haploid and a diploid stage, both multicellular and free living, and utilizes both sexual and asexual reproduction. Ectocarpus is therefore a unique model to empirically test expectations of the effect of asexual reproduction and haploid selection on mutation rate evolution. We estimate that Ectocarpus has a nucleotide mutation rate of µbs=4.07×10−10 per site per generation, a surprisingly low number for a multicellular complex eukaryote. Effective population size (Ne) and genome size could not explain this low mutation rate. We propose that the haploid-diploid life cycle, combined with extensive asexual reproduction may be key drivers of mutation rate.
Integration of single cell gene expression data in Bayesian association analysis of rare variants
Authors: Zhong Guojie , Choi Yoolim A. , Shen Yufeng
Published: May 2022

We present VBASS, a Bayesian method that integrates single-cell expression and de novo variant (DNV) data to improve power of disease risk gene discovery. VBASS models disease risk prior as a function of expression profiles, approximated by deep neural networks. It learns the weights of neural networks and parameters of Poisson likelihood models of DNV counts jointly from expression and genetics data. On simulated data, VBASS shows proper error rate control and better power than state-of-the-art methods. We applied VBASS to published datasets and identified more candidate risk genes with supports from literature or data from independent cohorts.
An algorithmic framework for isoform-specific functional analysis
Authors: Karlebach Guy , Carmody Leigh , Sundaramurthi Jagadish Chandrabose , Casiraghi Elena , Hansen Peter , Reese Justin , Mungall Chris J , Valentini Giorgio , Robinson Peter N.
Published: May 2022

Gene Ontology (GO) overrepresentation analysis characterizes the biological mechanisms common to sets of differentially expressed genes identified by high-throughput experiments. To date, GO overrepresentation analysis has mainly been used to evaluate differentially expressed genes, but short- and long-read RNA-seq technologies now allow increasingly accurate identification of differential alternative splicing. The function of most splice isoforms remain unknown, but if acccurate predictions could be made, overrepresentation analysis could be applied to differentially spliced isoforms to assess the functional implications of alternative splicing in RNA-seq experiments. We present isopret (Isoform Interpretation), a new paradigm for isoform function prediction based on the expectation-maximization framework. isopret leverages the relationships between sequence and functional isoform similarity to infer isoform specific functions in a highly accurate fashion. This enabled us to adapt GO overrepresentation analysis, which to date has been limited to differential gene expression, to be extended to assess overrepresentation of GO annotations in differentially spliced isoforms. An analysis of 100 RNA-seq studies including investigations of development, cancer, and common disease demonstrated that expression and splicing regulate different sets of biological functions. We make isopret predictions freely available in a desktop application that can be used to analyze differential expression and splicing in any bulk RNA-seq dataset.
Machine Learning Predicts Treatment Response in Bipolar & Major Depressive Disorders
Authors: Salman Mustafa S. , Verner Eric , Bockholt H. Jeremy , Fu Zening , Misiura Maria , Baker Bradley T. , Osuch Elizabeth , Sui Jing , Calhoun Vince D.
Published: May 2022

Diagnosis of bipolar disorder (BD) patients with complex symptoms presents a challenge to clinicians. Patients tend to spend more time in a depressive state than a manic state. In such complex cases, the current Diagnostic and Statistical Manual (DSM), which is not based on pathophysiology, can lead to misdiagnosis as major depressive disorder (MDD) and an imperfect or even harmful medication response. A biologically-based classification algorithm is needed to improve the accuracy of diagnosis. Osuch et al. (2018) presented a kernel support vector machine (SVM) algorithm to predict the medication-class of response from new patient samples whose diagnoses were unclear. Here we also utilize the kernel support vector machine (SVM) algorithm but with a few novel contributions. We applied the robust, fully automated neuromark independent component analysis (ICA) framework to extract comparable features in a multi-dataset setting and learn a kernel function for support vector machine (SVM) on multiple feature subspaces. The neuromark framework successfully replicates the prior result with 95.45% accuracy (sensitivity 90.24%, specificity 92.3%). To further evaluate the generalizability of our approach, we incorporated two additional datasets comprising bipolar disorder (BD) and major depressive disorder (MDD) patients. We validated the trained algorithm on these datasets, resulting in a testing accuracy of up to 89% (sensitivity 0.88, specificity 0.89) without using site or scanner harmonization techniques. We also translated the model to predict improvement scores of major depressive disorder (MDD) with up to 70% accuracy. This approach reveals some salient biological markers of medication-class of response within mood disorders.

Highlights

We demonstrate a DSM-free approach for predicting treatment response from resting-state functional magnetic resonance imaging (fMRI) data.

We identify several replicable biomarkers using the approach.

Our work has potential for clinical application by replacing trial-and-error in treating complex psychiatric disorders.
Maturation kinetics of phagosomes depends on their transport switching from actin to microtubule tracks
Authors: Yu Yanqi , Zhang Zihan , Yu Yan
Published: May 2022

Phagosomes, where internalized pathogens are ingested, mature into a degradative unit through a sequence of dynamical and biochemical changes. The maturation process requires the active transport of phagosomes on actin and microtubules, but how phagosome maturation is kinetically dependent on their actin- and microtubule-mediated transport is poorly characterized. Here, we focused on the early-to-late phagosome maturation and investigated how the kinetic rate of this early maturation process depends on the timing of phagosome transition from actin to microtubules. We performed single-phagosome tracking of their sequential maturation activities, including recruitment of phagosome markers and acidification. Simultaneously, we measured their transport dynamics. We showed that the timing of phagosome transport from actin cortex to microtubules controls the kinetics of early phagosome assembly and transition to late phagosomes. This ultimately determines the fate of phagosome maturation. Our results reveal distinct mechanisms by which the actin- and microtubule-based transport of phagosomes temporally controls the maturation progression of phagosomes during their degradative function.
Novel epidemiological model of gastrointestinal-nematode infection to assess grazing cattle resilience by integrating host growth, parasite, grass and environmental dynamics
Authors: Filipe J.A.N. , Kyriazakis I. , McFarland C. , Morgan E.R.
Published: May 2022

Gastrointestinal nematode (GIN) infections are ubiquitous and often cause morbidity and reduced performance in livestock. Emerging anthelmintic resistance and increasing change in climate patterns require evaluation of alternatives to traditional treatment and management practices. Mathematical models of parasite transmission between hosts and the environment have contributed towards the design of appropriate control strategies in ruminants, but have yet to account for relationships between climate, infection pressure, immunity, resources, and growth. Here, we develop a new epidemiological model of GIN transmission in a herd of grazing cattle, including host tolerance (body weight and feed intake), parasite burden and acquisition of immunity, together with weather-dependent development of parasite free-living stages, and the influence of grass availability on parasite transmission. Dynamic host, parasite and environmental factors drive a variable rate of transmission. Using literature sources, the model was parametrised forOstertagia ostertagi, the prevailing pathogenic GIN in grazing cattle populations in temperate climates. Model outputs were validated on published empirical studies from first season grazing cattle in Northern Europe. These results show satisfactory qualitative and quantitative performance of the model; they also indicate the model may approximate the dynamics of grazing systems under co-infection byO. ostertagiandCooperia oncophora, a second GIN species common in cattle. In addition, model behaviour was explored under illustrative anthelmintic treatment strategies, considering impacts on parasitological and performance variables. The model has potential for extension to explore altered infection dynamics as a result of management and climate change, and to optimise treatment strategies accordingly. As the first mechanistic model to combine parasitic and free-living stages of GIN with host feed-intake and growth, it is well suited to predict complex system responses under non-stationary conditions. We discuss the implications, limitations and extensions of the model, and its potential to assist in the development of sustainable parasite control strategies.

HIGHLIGHTS

Nematode control in cattle is complicated by drug resistance and climate change

A model was developed to predict GIN epidemiology under varying conditions

The model incorporates cattle growth, infection and immunity, grass availability, weather

Predictions were validated against empirical studies of GIN in N Europe

The model applies toOstertagia ostertagi, and possibly to co-infectingCooperia

Abstract Figure

https://www.lmcni.com/
Identification by cryoEM of a densovirus causing mass mortality in mass-reared larval darkling beetles (Zophobas morio)
Authors: Penzes Judit J , Kaelber Jason T
Published: May 2022

A reared colony of larval superworms (Zophobas morio) experienced the swift and unexplained death of about 90% of its population. We isolated a high-abundance virus from dead larvae and, using cryoEM, identified it as a novel densovirus, which we name Zophobas morio black wasting virus (ZmBWV). Densoviruses (DVs) are small, ssDNA viruses of the family Parvoviridae that infect protostome and deuterostome invertebrates. The vast majority of DVs have been associated with severe pathology, especially in larval stage insects. By cryoEM we resolved the high-resolution capsid structure of this new DV at 2.9 Å resolution for the genome-packaging, infectious particles and at a resolution of 3.3 Å in case of the empty particles, both purified directly from the infected Z. morio larvae. The capsid structure of ZmBWV provides the first insights into the capsid morphology of a structurally previously-uncharacterized genus, Blattambidensovirus. Consequently, the ZmBWV capsid harbors a unique surface morphology within the family, yet shows the T=1 icosahedral symmetry, the eight-stranded jelly roll core, as well as general features of multimer interactions previously found typical of subfamily Densovirinae. Although we have not inoculated healthy larvae with ZmBWV, on the basis of its prodigious abundance in infected larvae and the prior probability of larval pathology with viruses of this subfamily, ZmBWV is the most probable cause of the observed mass mortality event.
Structural-guided fragment-based drug discovery applied to antitoxin, MAB3862 opens a new possibility of exploring the Toxin and Antitoxin for antibiotics
Authors: Kim So Yeon , Eun Hyun-Jong , Lee JooYeon , Lee Wonsuk , Thomas Sherine Elizabeth , Brear Paul , Vedithi Sundeep Chaitanya , Abell Chris , Lee Bong-Jin , Blundell Tom L.
Published: May 2022

Mycobacterium abscessus (Mab) is a rapidly growing multidrug-resistant species among nontuberculous mycobacteria (NTM). Pulmonary infections caused by M. abscessus are difficult to treat and often result in an accelerated condition and premature death of immunosuppressed patients such as those with cystic fibrosis, putting them at a greater risk of other infections and increasing the urgency of developing a novel class of antibiotics. Here, we explore the use of toxin and antitoxin (TA) as an interesting and promising new class of target in a fragment-based drug-discovery approach. A de novo structure of Mab3862, an antitoxin of type 2 TA class in M. abscessus was elucidated and followed by drug discovery work. Very small molecules (fragments) are used to bind the antitoxin and then elaborated into drug-sized molecule that potentially trigger conformational change to prevent formation of toxin-antitoxin complex. Biophysical screening methods and binding-mode guidance from in silico docking were conducted. Grouping of fragments based on the binding site, creating a pharmacophore model, can facilitate further studies for rational design of inhibitors. Although targeting the TA complex for developing antibiotics is relatively novel and challenging, this could possibly open the gate for exploring it as potential drug target. Targeting one of the TA pairs might not be completely bactericidal. However, using this approach strategically with other antibiotics for synergic effect might be effective for patients with a persistence phenotype requiring prolonged therapy.
Site-specific decreases in DNA methylation in replicating cells following exposure to oxidative stress
Authors: Seddon Annika R. , Das Andrew B. , Hampton Mark B. , Stevens Aaron J.
Published: May 2022

Oxidative stress is a common feature of inflammation-driven cancers, and promotes genomic instability and aggressive tumour phenotypes. It is known that oxidative stress transiently modulates gene expression through the oxidation of transcription factors and associated regulatory proteins. Activated neutrophils produce hypochlorous acid and chloramines that can disrupt DNA methylation via methionine oxidation. The goal of the current study was to determine whether chloramine exposure results in sequence-specific modifications in DNA methylation that enable long-term alterations in transcriptional output. Proliferating Jurkat T-lymphoma cells were exposed to sublethal doses of glycine chloramine and differential methylation patterns were compared using Illumina EPIC 850K bead chip arrays. There was a substantial genome-wide decrease in methylation four hours after exposure that correlated with altered RNA expression for 24 and 48 hours, indicating sustained impacts on exposed cells. A large proportion of the most significant differentially methylated CpG sites were situated towards chromosomal ends, suggesting that these regions are most susceptible to inhibition of maintenance DNA methylation. This may contribute to epigenetic instability of chromosomal ends in rapidly dividing cells, with potential implications for the regulation of telomere length and cellular longevity.
Onsager regression characterizes living systems in passive measurements
Authors: Muenker Till M. , Knotz Gabriel , Krüger Matthias , Betz Timo
Published: May 2022

Understanding life is arguably among the most complex scientific problems faced in modern research. From a physics perspective, living systems are complex dynamic entities that operate far from thermodynamic equilibrium.1–3This active, non-equilibrium behaviour, with its constant hunger for energy, allows life to overcome the ever dispersing forces of entropy, and drives cellular organisation.4, 5Unfortunately, most analysis methods provided by the toolbox of statistical mechanics cannot be used in such non-equilibrium situations, forcing researchers to use sophisticated and often invasive approaches to study the mechanistic processes inside living organisms. Here we introduce a new observable coined the mean back relaxation. Based on three-point probabilities, and exploiting Onsager’s regression hypothesis, it extracts additional information from passively observed trajectories compared to classical observables such as the mean squared displacement. We mathematically prove that the mean back relaxation is able to detect broken detailed balance in systems confined in stationary or actively diffusing potentials. We show in experiment and theory that it gives access to the non-equilibrium generating energy and the viscoelastic material properties of a well controlled artificial system, and we experimentally demonstrate that it does so even for a variety of living systems, revealing an astonishing relation between the mean back relaxation and the active mechanical energy. Based on these findings, we conclude that it acts as a new marker of non-equilibrium dynamics. Combining, in a next step, passive fluctuations with the extracted active energy allows to overcome a fundamental barrier in the study of living systems; it gives access to the viscoelastic material properties from passive measurements.
PAX4 loss of function alters human endocrine cell development and influences diabetes risk
Authors: Lau Hwee Hui , Krentz Nicole A. J. , Abaitua Fernando , Perez-Alcantara Marta , Chan Jun-Wei , Ajeian Jila , Ghosh Soumita , Champon Benoite , Sun Han , Jha Alokkumar , Hoon Shawn , Tan Nguan Soon , Gardner Daphne , Kao Shih Ling , Tai E Shyong , Gloyn Anna L , Teo Adrian Kee Keong
Published: May 2022

Diabetes is a major chronic disease with an excessive healthcare burden on society1. A coding variant (p.Arg192His) in the transcription factorPAX4is uniquely and reproducibly associated with an altered risk for type 2 diabetes (T2D) in East Asian populations2–7, whilst rarePAX4alleles have been proposed to cause monogenic diabetes8. In mice,Pax4is essential for beta cell formation but neither the role of diabetes-associated variants inPAX4nor PAX4 itself on human beta cell development and/or function are known. Here, we demonstrate that non-diabetic carriers of either thePAX4p.Arg192His or a newly identified p.Tyr186X allele exhibit decreased pancreatic beta cell function. In the human beta cell model, EndoC-βH1,PAX4knockdown led to impaired insulin secretion, reduced total insulin content, and altered hormone gene expression. Deletion ofPAX4in isogenic human induced pluripotent stem cell (hiPSC)-derived beta-like cells resulted in derepression of alpha cell gene expression whilstin vitrodifferentiation of hiPSCs from carriers ofPAX4p.His192 and p.X186 alleles exhibited increased polyhormonal endocrine cell formation and reduced insulin content.In silicoandin vitrostudies showed that thesePAX4alleles cause either reduced PAX4 expression or function. Correction of the diabetes-associatedPAX4alleles reversed these phenotypic changes. Together, we demonstrate the role of PAX4 in human endocrine cell development, beta cell function, and its contribution to T2D-risk.