bluuebunny/crossref_metadata_2025 huggingface dataset:
Site-specific C-terminal fluorescent labeling of Tau protein
Authors: Bryan Louise , Awasthi Saurabh , Li Yuanjie , Nirmalraj Peter Niraj , Balog Sandor , Yang Jerry , Mayer Michael
Published: June 2022

Formation of Tau protein aggregates in neurons is a pathological hallmark of several neurodegenerative diseases, including Alzheimer’s disease. Fluorescently-labeled Tau protein is therefore useful to study the aggregation of these pathological proteins and to identify potential therapeutic targets. Conventionally, cysteine residues are used for labeling Tau proteins, however, the full-length Tau isoform contains two cysteine residues in the microtubule-binding region, which are implicated in Tau aggregation by forming intermolecular disulfide bonds. In order to prevent the fluorescent label from disturbing the microtubule binding region, we developed a strategy to fluorescently label Tau at its C-terminus while leaving cysteine residues unperturbed. We took advantage of a Sortase A-mediated transpeptidation approach to bind a short peptide (GGGH6-Alexa647) with a His-tag and a covalently attached Alexa 647 fluorophore to the C-terminus of Tau. This reaction relies on the presence of a Sortase recognition motif (LPXTG), which we attached to the C-terminus of recombinantly expressed Tau. We determined the possible effects of the resulting C-terminal modification on the secondary structure of Tau protein, its aggregation kinetics, and fibril morphology compared to the unlabeled native Tau protein in vitro. The results showed no significant differences between the native and C-terminally labeled Tau monomer with regard to aggregation kinetics, secondary structure, and morphology.
Deciphering Hematopoiesis at single cell level through the lens of reduced dimensions
Authors: Singh Prashant , Zhai Yanan
Published: June 2022

Hematopoiesis plays a critical role in maintaining a diverse pool of blood cells throughout human life. Despite recent efforts with single-cell data analyses, the nature of the early cell fate decisions and compartmentalization of progenitors remains contentious due to the sparsity and noise of the data. Using publically available single-cell RNA-Seq hematopoietic data from bone marrow, with three different matrix factorization approaches to recover associated gene modules from cell clusters reveals a tri-directional and hierarchically-structured transcriptional landscape of hematopoietic differentiation. We also devised a bootstrap method, which in combination with the above can better characterize the progenitor compartments and retrieve cellular hierarchies.
Quantifying the Human Cost of Global Warming
Authors: Lenton Timothy M. , Xu Chi , Abrams Jesse F. , Ghadiali Ashish , Loriani Sina , Sakschewski Boris , Zimm Caroline , Ebi Kristie L. , Dunn Robert R. , Svenning Jens-Christian , Scheffer Marten
Published: June 2022

The costs of climate change are often estimated in monetary terms1,2but this raises ethical issues3. Here we express them in terms of numbers of people left outside the ‘human climate niche’4– defined as the historically highly-conserved distribution of relative human population density with respect to mean annual temperature (MAT). We show that climate change has already put ∼8% of people (>500 million) outside this niche. By end-of-century (2080-2100), current policies leading to around 2.7 °C global warming5-9could leave one third (21-42% or 2-4 billion) of a future 9.5 billion population outside the niche. Limiting global warming to 1.5 °C could halve this exposure, reducing it by ∼1.5 billion people. For the two countries with the most people affected – India and Nigeria – reducing global warming from 2.7 to 1.5 °C results in a >6-fold decrease in the population exposed to unprecedented temperatures, MAT ≥29 °C. The lifetime emissions of ∼3.5 global average citizens today (or ∼1.2 average US citizens) expose 1 future person to MAT ≥29 °C by end-of-century. That person comes from a place where emissions today are around half of the global average. These results highlight the need for more decisive policy action to limit the human costs and inequities of climate change.
Investigating mouse motor coordination using quantitative trait locus analysis to model the genetic underpinnings of developmental coordination disorder
Authors: Gill Kamaldeep , Rajan Jeffy Rajan Soundara , Chow Eric , Ashbrook David G. , Williams Robert W. , Zwicker Jill G. , Goldowitz Daniel
Published: June 2022

The fundamental skills for motor coordination and motor control emerge through development, from infancy to late childhood years. Neurodevelopmental disorders such as Developmental Coordination Disorder (DCD) lead to impaired acquisition of motor skills. This study investigated motor behaviors that reflect the core symptoms of human DCD through the use of BXD recombinant inbred lines of mice that are known to have divergent phenotypes in many behavioral traits, including motor activity. We sought to correlate behavior in basic motor control tasks with the known genotypes of these reference populations of mice using quantitative trait locus (QTL) mapping. We used twelve BXD lines with an average of 16 mice per group to assess the onset of reflexes during the early neonatal stage of life and differences in motor coordination using the open field, rotarod, and gait analyses during the adolescent/young adulthood period. Results indicated significant variability between lines in as to when neonatal reflexes appeared as well as significant line differences for all measures of motor coordination. Five lines (BXD15, BXD27, BXD28, BXD75, and BXD86) struggled with sensorimotor coordination as seen in gait analysis, rotarod, and open field, similar to human presentation of DCD. We identified three significant quantitative trait loci for gait on proximal Chr 3, Chr 4 and distal Chr 6. Based on expression, function, and polymorphism within the mapped QTL intervals, 7 candidate genes(Gpr63, Spata5, Trpc3, Cntn6, Chl1, Grm7andOgg1)emerged. This study offers new insights into mouse motor behavior which promises to be a first murine model to explore the genetics and neural correlates of DCD.
EZH2/ hSULF1 axis mediates receptor tyrosine kinase signaling to shape cartilage tumor progression
Authors: Lin Zong-Shin , Chung Chiao-Chen , Liu Yu-Chia , Chang Chu-Han , Liu Hui-Chia , Liang Yung-Yi , Huang Teng-Le , Chen Tsung-Ming , Lee Che-Hsin , Tang Chih-Hsin , Chen Ya-Huey , Hung Mien-Chie
Published: June 2022

Background

Chondrosarcomas are primary cancers of cartilaginous tissue and capable of alteration to highly aggressive, metastatic, and treatment-refractory states, leading to a poor prognosis with a five-year survival rate at 11 months for the dedifferentiated subtype. At present, the surgical resection of chondrosarcoma is the only effective treatment, and no other treatment options including targeted therapies, conventional chemotherapies, or immunotherapies are available for these patients.

Methods

A non-biased ChIP sequence, cDNA microarray analysis, and validation of chondrosarcoma cell lines identified sulfatase 1(SULF1) as the top EZH2-targeted gene to regulate chondrosarcoma progression. Receptor tyrosine kinase (RTK) array of chondrosarcoma cells with vector control or ectopically expressed SULF1 revealed that cMET was the downstream signal. The regulation of the EZH2/SULF1/cMET axis was further validated in mice and patient samples with mice models and chondrosarcoma tissue array, respectively.

Results

The EZH2/SULF1/cMET axis is identified, which contributes to the malignancy of chondrosarcoma and provides a potential therapeutic option for the disease. Ectopically expressed SULF1 or pharmaceutical inhibition of the cMET pathway significantly retards the chondrosarcoma growth and extends mice survival.

Conclusions

The results not only established a signal pathway promoting the malignancy of chondrosarcoma but also provided a therapeutic potential for further development of effective target therapy to treat chondrosarcoma.
MARSY: A multitask deep learning framework for prediction of drug combination synergy scores
Authors: El Khili Mohamed Reda , Memon Safyan Aman , Emad Amin
Published: June 2022

Motivation

Combination therapies have emerged as a treatment strategy for cancers to reduce the probability of drug resistance and to improve outcome. Large databases curating the results of many drug screening studies on preclinical cancer cell lines have been developed, capturing the synergistic and antagonistic effects of combination of drugs in different cell lines. However, due to the high cost of drug screening experiments and the sheer size of possible drug combinations, these databases are quite sparse. This necessitates the development of transductive computational models to accurately impute these missing values.

Results

Here, we developed MARSY, a deep learning multi-task model that incorporates information on gene expression profile of cancer cell lines, as well as the differential expression signature induced by each drug to predict drug-pair synergy scores. By utilizing two encoders to capture the interplay between the drug-pairs, as well as the drug-pairs and cell lines, and by adding auxiliary tasks in the predictor, MARSY learns latent embeddings that improve the prediction performance compared to state-of-the-art and traditional machine learning models. Using MARSY, we then predicted the synergy scores of 133,722 new drug-pair cell line combinations, which we have made available to the community as part of this study. Moreover, we validated various insights obtained from these novel predictions using independent studies, confirming the ability of MARSY in making accurate novel predictions.

Availability and Implementation

An implementation of the algorithms in Python and cleaned input datasets are provided inhttps://github.com/Emad-COMBINE-lab/MARSY.

Contact

amin.emad@mcgill.ca

Supplementary Information

Online-only supplementary data is available at the journal’s website.
Uncovering features of synapses in primary visual cortex through contrastive representation learning
Authors: Wilson Alyssa M. , Babadi Mehrtash
Published: June 2022

3D EM connectomics image volumes are now surpassing sizes of 1 mm3, and are therefore beginning to contain multiple meaningful spatial scales of brain circuitry simultaneously. However, the sheer density of information in such datasets makes the development of unbiased, scalable machine learning techniques a necessity for extracting novel insights without extremely time-consuming, intensive labor. In this paper, we present SynapseCLR, a self-supervised contrastive representation learning method for 3D electron microscopy (EM) data, and use the method to extract feature representations of synapses from a 3D EM dataset from mouse visual cortex. We show that our representations separate synapses according to both their overall physical appearance and structural annotations of known functional importance. We further demonstrate the utility of our methodology for several valuable downstream tasks for the growing field of 3D EM connectomics. These include one-shot identification of defective synapse segmentations, dataset-wide similarity-based querying, and accurate imputation of annotations for unlabeled synapses, using only manual annotation of 0.2% of synapses in the dataset. In particular, we show that excitatory vs. inhibitory neuronal cell types can be assigned to individual synapses and highly truncated neurites with accuracy exceeding 99.8%, making this population accessible to connectomics analysis. Finally, we present a data-driven and unsupervised study of the manifold of synaptic structural variation, revealing its intrinsic axes of variation and showing that synapse structure is also strongly correlated with inhibitory neuronal subtypes.
The potent Toxoplasma gondii growth inhibitor QQ-437 does not bind to its predicted target, parasite adaptin3β, in yeast three-hybrid assays
Authors: Tran Fanny , Foderaro Jenna E. , Westwood Nicholas J. , Ward Gary E.
Published: June 2022

New drugs are needed to treat infections with Toxoplasma gondii, a ubiquitous protozoan parasite that can cause miscarriage, blindness, and life-threatening encephalitis in its human hosts. A novel N-benzoyl-2-hydroxybenzamide named QQ-437 was recently shown to be a potent inhibitor of T. gondii growth in vitro (EC50 = 16nM), and to reduce parasite load in a mouse model of infection. Intriguingly, mutations in the parasite protein TgAP3β, which is thought to play an important role in intracellular protein trafficking, confer resistance to QQ-437. In this report, we use yeast three-hybrid analysis to test the hypothesis that QQ-437 inhibits parasite growth via a direct effect on TgAP3β. We see no evidence for the ability of QQ-437 to bind directly to TgAP3β, suggesting that the mutation in TgAP3β that confers resistance does so through an indirect mechanism. Further studies are needed to identify the direct molecular target(s) of this promising class of compounds.
Characterization of two conserved cell death elicitor families from the Dothideomycete fungal pathogens Dothistroma septosporum and Fulvia fulva (syn. Cladosporium fulvum)
Authors: Tarallo Mariana , McDougal Rebecca L. , Chen Zhiyuan , Wang Yan , Bradshaw Rosie E. , Mesarich Carl H.
Published: June 2022

Dothistroma septosporum (Ds) and Fulvia fulva (Ff; previously called Cladosporium fulvum) are two closely related Dothideomycete fungal species that cause Dothistroma needle blight in pine and leaf mold in tomato, respectively. During host colonization, these pathogens secrete virulence factors termed effectors to promote infection. In the presence of corresponding host immune receptors, however, these effectors activate plant defenses, including a localized cell death response that halts pathogen growth. We identified two effector protein families, Ecp20 and Ecp32, which are conserved between the two pathogens. The Ecp20 family has four paralogues in both species, while the Ecp32 family has four paralogues in D. septosporum and five in F. fulva. Both families have members that are highly expressed during host infection. Members of the Ecp20 family have predicted structural similarity to proteins with a β-barrel fold, including the Alt a 1 allergen from Alternaria alternata, while members of the Ecp32 family have predicted structural similarity to proteins with a β-trefoil fold, such as trypsin inhibitors and lectins. Using Agrobacterium tumefaciens-mediated transient transformation assays, each family member was assessed for its ability to trigger cell death in leaves of the non-host species Nicotiana benthamiana and N. tabacum. Using this approach, FfEcp20-2, DsEcp20-3 and FfEcp20-3 from the Ecp20 family, and all members from the Ecp32 family, except for the Ds/FfEcp32-4 pair, triggered cell death in both species. This cell death was dependent on secretion of the effectors to the apoplast. In line with recognition by an extracellular immune receptor, cell death triggered by Ds/FfEcp20-3 and FfEcp32-3 was compromised in N. benthamiana silenced for BAK1 or SOBIR1, which encode extracellular co-receptors involved in transducing defense response signals following apoplastic effector recognition. We then investigated whether DsEcp20-3 and DsEcp20-4 triggered cell death in the host species Pinus radiata by directly infiltrating purified protein into pine needles. Strikingly, as in the non-host species, DsEcp20-3 triggered cell death, while DsEcp20-4 did not. Collectively, our study describes two new candidate effector families with cell death-eliciting activity from D. septosporum and F. fulva and provides evidence that members of these families are recognized by plant immune receptors.
The lingering effects of Neanderthal introgression on human complex traits
Authors: Wei Xinzhu , Robles Christopher R. , Pazokitoroudi Ali , Ganna Andrea , Gusev Alexander , Durvasula Arun , Gazal Steven , Loh Po-Ru , Reich David , Sankararaman Sriram
Published: June 2022

The mutations introduced into the ancestors of modern humans from interbreeding with Neanderthals have been suggested to contribute an unexpected extent to complex human traits. However, testing this hypothesis has been challenging due to the idiosyncratic population genetic properties of introgressed mutations. We developed rigorous methods to assess the contribution of introgressed Neanderthal mutations to heritable trait variation relative to that of modern human variants. We applied these methods to analyze 235,592 introgressed Neanderthal mutations and 96 distinct phenotypes measured in about 300,000 unrelated white British individuals in the UK Biobank. Introgressed Neanderthal mutations have a significant contribution to trait variation consistent with the polygenic architecture of complex phenotypes (contributing 0.1% of heritable variation averaged across phenotypes; p = 9.59×10-9). However, the contribution of introgressed mutations tends to be significantly depleted relative to modern human mutations matched for allele frequency and linkage disequilibrium (about 57% depletion on average), consistent with purifying selection on introgressed mutations. Different from previous studies (McArthur 2021), we find no evidence for elevated heritability across the phenotypes examined. We identified 348 independent significant associations of introgressed Neanderthal mutations with 64 phenotypes (p < 1 ×10-10). Previous work (Skov 2021) has suggested that a majority of such associations are likely driven by statistical association with nearby modern human variants that are the true causal variants. We therefore developed a customized statistical fine-mapping methodology for introgressed mutations that led us to identify 112 regions (at a false discovery proportion of 16%) across 47 phenotypes containing 4,303 unique genetic variants where introgressed mutations are highly likely to have a phenotypic effect. Examination of these mutations reveal their substantial impact on genes that are important for the immune system, development, and metabolism. Our results provide the first rigorous basis for understanding how Neanderthal introgression modulates complex trait variation in present-day humans.
Strongly bactericidal all-oral β-lactam combinations for the treatment of Mycobacterium abscessus lung disease
Authors: Negatu Dereje A. , Zimmerman Matthew D. , Dartois Véronique , Dick Thomas
Published: June 2022

Bioactive forms of oral β-lactams were screened in vitro against Mycobacterium abscessus with and without the bioactive form of the oral β-lactamase inhibitor avibactam-ARX1796. Sulopenem was equally active without avibactam, while tebipenem, cefuroxime and amoxicillin required avibactam for optimal activity. Systematic pairwise combination of the four β-lactams revealed strong bactericidal synergy for each of sulopenem, tebipenem and cefuroxime combined with amoxicillin in the presence of avibactam. These all-oral β-lactam combinations warrant clinical evaluation.
Impact of imperfect diagnosis in ME/CFS association studies
Authors: Malato João , Graça Luís , Sepúlveda Nuno
Published: June 2022

The absence of an objective disease biomarker puts studies of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) under the curse of imperfect diagnosis. This problem leads to frequent reports that fail to reproduce prior published studies. To address the impact of imperfect diagnosis on the robustness of studies’ conclusions, we conducted a simulation study to quantify the statistical power to detect a disease association with a hypothetical binary factor in the presence of imperfect diagnosis. Using the classical case-control design, studies with sample sizes of less than 500 individuals per group could not reach the target power of at least 80% to detect realistic disease associations. We then recreated serological association studies in which the chance of imperfect diagnosis was combined with the probability of misclassifying a binary factor, as it happens in a typical serological association study. In this case, the target power of 80% could only be achieved for studies with more than 1000 individuals per group. Given the current sample sizes of ME/CFS studies, our results suggest that most studies are likely to be underpowered due to imperfect diagnosis alone. To increase reproducibility across studies, we provided some practical recommendations, such as the use of standard case definitions together with multi-centric study designs, and routine reporting of power calculations under a non-negligible chance of misdiagnosis. Our results can also inform the design of future studies under the assumption of misdiagnosis.
Luminal progenitors undergo partial epithelial-to-mesenchymal transition at the onset of basal-like breast tumorigenesis
Authors: Landragin Camille , Saichi Melissa , Prompsy Pacôme , Durand Adeline , Mesple Jérémy , Trouchet Amandine , Faraldo Marisa , Salmon Hélène , Vallot Céline
Published: June 2022

Defects in double-strand repair mechanisms - both through germline or somatic inactivation of repair genes - is a hallmark of basal-like breast cancers. In this genetically-unstable context, a recurrent shift in cell identity occurs within the mammary epithelium. Basal-like tumors have indeed been proposed to originate from luminal progenitor (LP) cells yet tumor-initiating events remain poorly understood. Here, we map state transitions at the onset of basal-like tumorigenesis, using a Brca-1 deficient mouse model launching tumorigenesis in multiple LP cells. Combining single-cell transcriptomics to spatial multiplex imaging, we identify a population of cycling p16-expressing cells, emerging from the luminal progenitor compartment, undergoing partial epithelial-to-mesenchymal transition and losing luminal identity. Pseudo-temporal analyses position these cells as a transitory pre-tumoral state between aberrant Brca1-deficient luminal progenitors and growing tumor cells. In patients, the p16 pre-tumoral signature is found in early stage basal-like tumors, that rarely recur. Concomitant to p16 activation, we show that LP cells undergo an epigenomic crisis attested by the general re-organization of their heterochromatin. They accumulate multiple H3K27me3 micro-foci - reminiscent of the formation of senescence-associated heterochromatin foci (SAHFs) - and lose their inactive X (Xi). Both p16 activation and heterochromatin reorganization are hallmarks of human basal-like breast tumors; we propose that these events occur during initial LP transformation and are scars of an initial transitory senescent-like state.
Mathematical modeling and biochemical analysis support partially ordered CaM-MLCK binding
Authors: MacEwen Melissa JS , Rusnac Domnita-Valeria , Ermias Henok , Locke Timothy M , Gizinski Hayden E , Dexter Joseph P , Sancak Yasemin
Published: June 2022

Activation of myosin light-chain kinase (MLCK) by calcium ions (Ca2+) and calmodulin (CaM) plays an important role in numerous cellular functions including vascular smooth muscle contraction and cellular motility. Despite extensive biochemical analysis of this system, aspects of the mechanism of activation remain controversial, and competing theoretical models have been proposed for the binding of Ca2+and CaM to MLCK. The models are analytically solvable for an equilibrium steady state and give rise to distinct predictions that hold regardless of the numerical values assigned to parameters. These predictions form the basis of a recently proposed, multi-part experimental strategy for model discrimination. Here we implement this strategy by measuring CaM-MLCK binding using anin vitroFRET system. This system uses the CaM-binding region of smooth muscle MLCK protein to link two fluorophores to form an MLCK FRET Reporter (FR). Biochemical and biophysical experiments have established that FR can be reliably used to analyze MLCK-CaM binding. We assessed the binding of either wild-type CaM, or mutant CaM with one or more defective EF-hand domains, to FR. Interpretation of binding data in light of the mathematical models suggests a partially ordered mechanism for binding of CaM to MLCK. Complementary data collected using orthogonal approaches that directly quantify CaM-MLCK binding further supports our conclusions.
Socio-economic disadvantage is associated with alterations in brain wiring economy
Authors: Siugzdaite Roma , Akarca Danyal , Johnson Amy , Carozza Sofia , Anwyl-Irvine Alexander L , Uh Stepheni , Smith Tess , Bignardi Giacomo , Dalmaijer Edwin , Astle Duncan E.
Published: June 2022

The quality of a child’s social and physical environment is a key influence on brain development, educational attainment and mental wellbeing. However, there still remains a mechanistic gap in our understanding of how environmental influences converge on changes in the brain’s developmental trajectory. In a sample of 145 children with structural diffusion tensor imaging data, we used generative network modelling to simulate the emergence of whole brain network organisation. We then applied data-driven clustering to stratify the sample according to socio-economic disadvantage, with one of the resulting clusters containing mostly children living below the poverty line. A formal comparison of the simulated networks from the generative model revealed that the computational principles governing network formation were subtly different for children experiencing socio-economic disadvantage, and that this resulted in significantly altered developmental timing of network modularity emergence. Children in the low socio-economic status (SES) group had a significantly slower time to peak modularity, relative to the higher SES group (t(69) = 3.02, P = 3.50 × 10-4, d = 0.491). In a subsequent simulation we showed that the alteration in generative properties increases the variability in wiring probabilities during network formation (KS test: D = 0.012, P < 0.001). One possibility is that multiple environmental influences such as stress, diet and environmental stimulation impact both the systematic coordination of neuronal activity and biological resource constraints, converging on a shift in the economic conditions under which networks form. Alternatively, it is possible that this stochasticity reflects an adaptive mechanism that creates “resilient” networks better suited to unpredictable environments.

Author Summary

We used generative network models to simulate macroscopic brain network development in a sample of 145 children. Within these models, network connections form probabilistically depending on the estimated “cost” of forming a connection, versus topological “value” that the connection would confer. Tracking the formation of the network across the simulation, we could establish the changes in global brain organisation measures such as integration and segregation. Simulations for children experiencing socio-economic disadvantage were associated with a shift in emergence of a topologically valuable network property, namely modularity.
Significance tests for R2 of out-of-sample prediction using polygenic scores
Authors: Momin Md. Moksedul , Lee Soohyun , Wray Naomi R , Lee S. Hong
Published: June 2022

The coefficient of determination (R2) is a well-established measure to indicate the predictive ability of polygenic scores (PGS). However, the sampling variance of R2 is rarely considered so that 95% confidence intervals (CI) are not usually reported. Moreover, when comparisons are made between PGS based on different discovery samples, the sampling covariance of R2 is necessary to test the difference between them. Here, we show how to estimate the variance and covariance of R2 values to assess the 95% CI and p-value of the R2 difference. We apply this approach to real data to predict into 28,880 European participants using UK Biobank (UKBB) and Biobank Japan (BBJ) GWAS summary statistics for cholesterol and BMI. We quantify the significantly higher predictive ability of UKBB PGS compared to BBJ PGS (p-value 7.6e-31 for cholesterol and 1.4e-50 for BMI). A joint model of UKBB and BBJ PGS significantly improves the predictive ability, compared to a model of UKBB PGS only (p-value 3.5e-05 for cholesterol and 1.3e-28 for BMI). The proposed approach can also be applied to testing a significant difference between R2 values across different p-value thresholds. We also show that the predictive ability of regulatory SNPs is significantly enriched than non-regulatory SNPs for cholesterol (p-value 2.6e-19 for UKBB and 8.7e-08 for BBJ). We suggest that the proposed approach (available in R package ‘r2redux’) should be used to test the statistical significance of difference between pairs of PGS, which may help to draw a correct conclusion about the predictive ability of PGS.
An intelligent synthetic bacterium with sound-integrated ability for chronological toxicant detection, degradation, and lethality
Authors: Liu Huan , Zhang Lige , Wang Weiwei , Hu Haiyang , Xu Ping , Tang Hongzhi
Published: June 2022

Modules, toolboxes, and systems of synthetic biology are being designed to solve environmental problems. However, weak and decentralized functional modules require complicated controls. To address this issue, we investigated an integrated system that can complete detection, degradation, and lethality, in chronological order without exogenous inducers. Biosensors were optimized by regulating expression of receptor and reporter to get higher sensitivity and output signal. Several stationary-phase promoters were selected and compared, while promoter Pficwas chosen to express the degradation enzyme. We created two concepts of lethal circuits by testing various toxic proteins, with a toxin/antitoxin circuit showing a potent lethal effect. Three modules were coupled, step-by-step. Detection, degradation, and lethality were sequentially completed, and the modules had partial attenuation compared to pre-integration, except for degradation. Our study provides a novel concept for integrating and controlling functional modules, which can accelerate the transition of synthetic biology from a concept to practical applications.

Teaser

We provide new ideas for integration and chronological control of multiple modules in synthetic biology.
Comparison of urine proteomes from tumor-bearing mice with those from tumor-resected mice
Authors: Ziqi Heng , Chenyang Zhao , Youhe Gao
Published: June 2022

[Objective]

This study focuses on the most important concern of surgeons - whether they resected all of the tumor. Urine can reflect early changes associated with physiological or pathophysiological processes. Based on the above ideas, we conducted experiments to explore changes in the urine proteome between tumor-bearing mice and tumor-resected mice.

[Method]

The tumor-bearing mouse model was established with MC38 mouse colon cancer cells, and the mice were divided into the healthy control group, complete resection group, and nonresection group. Urine was collected 7 days and 30 days after resection. Liquid chromatography coupled with tandem mass spectrometry (LC–MS/MS) was used to identify the urine proteome and then analyze differentially expressed proteins and biological pathways.

[Results]

(1) Seven days after tumor resection, there were 20 differentially expressed proteins that could distinguish between the complete resection group and the nonresection group. The biological process includes circadian rhythm, Notch signaling pathway, leukocyte cell–cell adhesion, and heterophilic cell–cell adhesion via plasma membrane cell adhesion molecules. (2) Thirty days after tumor resection, there were 33 differentially expressed proteins that could distinguish between the complete resection group and the nonresection group. The biological process includes cell adhesion, complement activation, the alternative pathway, the immune system process, and angiogenesis. (3) There was no significant difference between the two groups at 30 days after tumor resection between the complete resection group and the healthy control group.

[Conclusion]

Changes in the urine proteome can reflect tumors with or without complete resection.
Heterogeneity in viral replication dynamics shapes the antiviral response
Authors: Bruurs L.J.M. , Müller M. , Schipper J.G. , Rabouw H.H. , Boersma S. , van Kuppeveld F.J.M. , Tanenbaum M.E.
Published: June 2022

In response to virus infection, host cells can activate antiviral signaling to restrict virus replication and communicate viral infection to neighboring cells. For poorly understood reasons, antiviral response activation is highly heterogeneous among infected cells; both quantitatively (level of pathway activation) and qualitatively (transcribed antiviral gene set). Here, we used live-cell single-molecule imaging to simultaneously visualize viral infection and antiviral signaling, providing quantitative insights into antiviral response activation in single cells; first, the probability of activating an antiviral response varies throughout infection, with most efficient activation occurring several hours after the first viral replication. Second, cell-to-cell heterogeneity in viral replication rates early in infection determine the efficiency of antiviral response activation. Finally, variation in signaling strength of the viral sensing pathway result in qualitatively distinct antiviral responses. Together, this works identifies key parameters that shape the antiviral response and provides quantitative insights into the origin of heterogeneity in the antiviral response.
Benzo(a)pyrene regulates chaperone-mediated autophagy via heat shock protein 90
Authors: Su Min , Zhou Shuhong , Li Jun , Lin Nan , Chi Tao , Zhang Mengdi , Lv Xiaoli , Hu Yuxia , Bai Tuya , Chang Fuhou
Published: June 2022

The Benzo(a)pyrene (Bap) exposure induced oxidative damage, DNA damage and autophagy. To explore the molecular mechanism of BaP-induced autophagy. In these studies, we first found that heat shock protein 90 (HSP90), heat shock cognate 70 (HSC70) and lysosomal-associated membrane protein type 2 receptor (Lamp-2a) expressions of C57BL mice lung tissue and A549 cells exposed to BaP were significant increase, as well as Bap induced DNA double-strand breaks (DSBs) and activated DNA damage responses, as evidenced by comet assay and γ-H2AX foci analysis in A549 cells. Our results demonstrated BaP induced CMA and caused DNA damage. Next, we knocked down HSP90 expression by the HSP90 Inhibitor, NVP-AUY 922, exposed or HSP90α shRNA lentivirus transduction in A549 cells. HSC70 and Lamp-2a expressions of these cells exposed to BaP were not significant increase, which showed that BaP inducted CMA was mediated by HSP90. Further, HSP90α shRNA prevented BaP induced of Bap which suggested BaP regulated CMA and caused DNA damage by HSP90. Our results elucidated a new mechanism of BaP regulated CMA through HSP90.
Gene expression signature for predicting homologous recombination deficiency in triple-negative breast cancer
Authors: Pan Jia-Wern , Tan Zi-Ching , Ng Pei-Sze , Zabidi Muhammad Mamduh Ahmad , Nur Fatin Putri , Teo Jie-Ying , Hasan Siti Norhidayu , Islam Tania , Teoh Li-Ying , Jamaris Suniza , See Mee-Hoong , Yip Cheng-Har , Rajadurai Pathmanathan , Looi Lai-Meng , Taib Nur Aishah Mohd , Rueda Oscar M. , Caldas Carlos , Chin Suet-Feung , Lim Joanna , Teo Soo-Hwang
Published: June 2022

Triple-negative breast cancers (TNBCs) are a subset of breast cancers that have remained difficult to treat. Roughly 1 in 10 of TNBCs arise in individuals with pathogenic variants inBRCA1orBRCA2, and treating BRCA-associated TNBCs with PARP inhibitors results in improved survival. A proportion of TNBCs arising in non-carriers ofBRCApathogenic variants have genomic features that are similar toBRCAcarriers, and we postulated that gene expression may identify individuals with such features who might also benefit from PARP inhibitor treatment. Using genomic data from 129 TNBC samples from the Malaysian Breast Cancer (MyBrCa) cohort, we classified tumours as having high or low homologous recombination deficiency (HRD) and developed a gene expression-based machine learning classifier for HRD in TNBCs. The classifier identified samples with HRD mutational signature at an AUROC of 0.94 in the MyBrCa validation dataset, and strongly segregated HRD-associated genomic features in TNBCs from TCGA and METABRIC. Further validation of the classifier using the NanoString nCounter platform showed that the RNA-seq results correlated strongly with NanoString results (r= 0.90) from fresh frozen tissue as well as NanoString results from FFPE tissue (r= 0.84). Thus, our gene expression classifier may identify triple-negative breast cancer patients with homologous recombination deficiency, suggesting an alternative method to identify individuals who may benefit from treatment with PARP inhibitors or platinum chemotherapy.

Novelty/Impact statement

We developed a gene expression-based classifier for homologous recombination deficiency (HRD) in breast cancer patients using WES and RNA-seq data obtained from 129 TNBC samples from a Malaysian hospital-based cohort (MyBrCa). This classifier was able to predict for HRD status at an AUC of 0.94 in the MyBrCa cohort, and was also able to segregate HRD-associated features in TNBCs from TCGA. We also validated the classifier on a NanoString platform with both fresh frozen and FFPE tissue.
Uncovering the universality of self-replication in protein aggregation and its link to disease
Authors: Meisl Georg , Xu Catherine K , Taylor Jonathan D , Michaels Thomas C T , Levin Aviad , Otzen Daniel , Klenerman David , Matthews Steve , Linse Sara , Andreasen Maria , Knowles Tuomas P J
Published: June 2022

Fibrillar protein aggregates are a hallmark of the pathology of a range of human disorders, from prion diseases to dementias. Yet, the same aggregated structures that are formed in disease are also encountered in several functional contexts. The fundamental properties that determine whether these protein assembly processes are functional or, by contrast, pathological, have remained elusive. Here, we address this question by analysing the aggregation kinetics of a large set of self-assembling proteins, from those associated with disease, over those whose aggregates fulfil functional roles in biology, to those that aggregate only under artificial conditions. Remarkably, we find that essentially all systems that assemble by a nucleated-growth mechanism are capable of significant self-replication on experimentally accessible timescales. However, comparing the intrinsic timescales of self-replication with the timescales over which the corresponding aggregates form in a biological context yields a clear distinction; for aggregates which have evolved to fulfil a structural role, the rate of self-replication is too low to be significant on the biologically relevant timescale. By contrast, all analysed proteins that aggregate in the context of disease are able to self-replicate quickly compared to the timescale of the associated disease. Our findings establish the ability to self-replicate as both a ubiquitous property of protein aggregates and one that has the potential to be a key process across aggregation-related disorders.
Matrin3 regulates cell proliferation and spindle dynamics by regulating CDC14B alternative splicing
Authors: Muys Bruna R. , Shrestha Roshan L. , Anastasakis Dimitrios G. , Pongor Lorinc , Li Xiao Ling , Grammatikakis Ioannis , Polash Ahsan , Harris Curtis C , Aladjem Mirit I. , Basrai Munira A. , Hafner Markus , Lal Ashish
Published: June 2022

Matrin3 is an RNA-binding protein that affects diverse RNA-related processes, including mRNA splicing. While Matrin3 has been intensively studied in neurodegenerative diseases, its function in cancer remains unclear. Here, we discovered Matrin3-mediated regulation of mitotic spindle dynamics in colorectal cancer (CRC) cells. We identified bound and regulated Matrin3-target RNAs transcriptome-wide in CRC cells and found that Matrin3 broadly modulates mRNA splicing patterns. Among the top Matrin3 targets, we focused on CDC14B and found that Matrin3 loss increased inclusion of an exon containing a premature termination codon into the CDC14B transcript and simultaneous down-regulation of the standard CDC14B transcript. Selective knockdown of the CDC14B standard transcript phenocopied Matrin3 knockdown and exhibited reduced proliferation and defects in mitotic spindle formation, suggesting that CDC14B is a key downstream effector of Matrin3. Collectively, these data reveal an important role for the Matrin3/CDC14B axis in control of CRC cell proliferation and mitotic spindle formation.
Proposal and extensive test of a calibration protocol for crop phenology models
Authors: Wallach Daniel , Palosuo Taru , Thorburn Peter , Mielenz Henrike , Buis Samuel , Hochman Zvi , Gourdain Emmanuelle , Andrianasolo Fety , Dumont Benjamin , Ferrise Roberto , Gaiser Thomas , Garcia Cecile , Gayler Sebastian , Harrison Matthew , Hiremath Santosh , Horan Heidi , Hoogenboom Gerrit , Jansson Per-Erik , Jing Qi , Justes Eric , Kersebaum Kurt-Christian , Launay Marie , Lewan Elisabet , Liu Ke , Mequanint Fasil , Moriondo Marco , Nendel Claas , Padovan Gloria , Qian Budong , Schütze Niels , Seserman Diana-Maria , Shelia Vakhtang , Souissi Amir , Specka Xenia , Srivastava Amit Kumar , Trombi Giacomo , Weber Tobias K.D. , Weihermüller Lutz , Wöhling Thomas , Seidel Sabine J.
Published: June 2022

A major effect of environment on crops is through crop phenology, and therefore, the capacity to predict phenology for new environments is important. Mechanistic crop models are a major tool for such predictions, but calibration of crop phenology models is difficult and there is no consensus on the best approach. Here we propose an original, detailed approach, a protocol, for calibration of such models. The protocol covers all the steps in the calibration work-flow, namely choice of default parameter values, choice of objective function, choice of parameters to estimate from the data, calculation of optimal parameter values and diagnostics. The major innovation is in the choice of which parameters to estimate from the data, which combines expert knowledge and data-based model selection. First, almost additive parameters are identified and estimated. This should make bias (average difference between observed and simulated values) nearly zero. These are “obligatory” parameters, that will definitely be estimated. Then candidate parameters are identified, which are parameters likely to explain the remaining discrepancies between simulated and observed values. A candidate is only added to the list of parameters to estimate if it leads to a reduction in BIC (Bayesian Information Criterion), which is a model selection criterion. A second original aspect of the protocol is the specification of documentation for each stage of the protocol. The protocol was applied by 19 modeling teams to three data sets for wheat phenology. All teams first calibrated their model using their “usual” calibration approach, so it was possible to compare usual and protocol calibration. Evaluation of prediction error was based on data from sites and years not represented in the training data. Compared to usual calibration, calibration following the new protocol reduced the variability between modeling teams by 22% and significantly reduced prediction error.
A circular zone of attachment to the extracellular matrix provides directionality to the motility ofToxoplasma gondiiin 3D
Authors: Stadler Rachel V. , Nelson Shane R. , Warshaw David M. , Ward Gary E.
Published: June 2022

Toxoplasma gondiiis a protozoan parasite that infects 30-40% of the world’s population. Infections are typically subclinical but can be severe and, in some cases, life threatening. Central to the virulence ofT. gondiiis an unusual form of substrate-dependent motility that enables the parasite to invade cells of its host and to disseminate throughout the body. A hetero-oligomeric complex of proteins that functions in motility has been characterized, but how these proteins work together to drive forward motion of the parasite remains controversial. A key piece of information needed to understand the underlying mechanism(s) is the directionality of the forces that a moving parasite exerts on the external environment. The linear motor model of motility, which has dominated the field for the past two decades, predicts continuous anterior-to-posterior force generation along the length of the parasite. We show here using three-dimensional traction force mapping that the predominant forces exerted by a moving parasite are instead periodic and directed in towards the parasite at a fixed circular location within the extracellular matrix. These highly localized forces, which are generated by the parasite pulling on the matrix, create a visible constriction in the parasite’s plasma membrane. We propose that the ring of inward-directed force corresponds to a circumferential attachment zone between the parasite and the matrix, through which the parasite propels itself to move forward. The combined data suggest a closer connection between the mechanisms underlying parasite motility and host cell invasion than previously recognized. In parasites lacking the major surface adhesin, TgMIC2, neither the inward-directed forces nor the constriction of the parasite membrane are observed. The trajectories of the TgMIC2-deficient parasites are less straight than those of wild-type parasites, suggesting that the annular zone of TgMIC2-mediated attachment to the extracellular matrix normally constrains the directional options available to the parasite as it migrates through its surrounding environment.
Hallucinating protein assemblies
Authors: Wicky B. I. M. , Milles L. F. , Courbet A. , Ragotte R. J. , Dauparas J. , Kinfu E. , Tipps S. , Kibler R. D. , Baek M. , DiMaio F. , Li X. , Carter L. , Kang A. , Nguyen H. , Bera A. K. , Baker D.
Published: June 2022

Deep learning generative approaches provide an opportunity to broadly explore protein structure space beyond the sequences and structures of natural proteins. Here we use deep network hallucination to generate a wide range of symmetric protein homo-oligomers given only a specification of the number of protomers and the protomer length. Crystal structures of 7 designs are very close to the computational models (median RMSD: 0.6 Å), as are 3 cryoEM structures of giant rings with up to 1550 residues, C33 symmetry, and 10 nanometer in diameter; all differ considerably from previously solved structures. Our results highlight the rich diversity of new protein structures that can be created using deep learning, and pave the way for the design of increasingly complex nanomachines and biomaterials.

One-sentence summary

Deep network-based protein design enables the generation of cyclic homo-oligomers across the nanoscopic scale.
Bacteriophage anti-defense genes that neutralize TIR and STING immune responses
Authors: Ho Peiyin , Chen Yibu , Biswas Subarna , Canfield Ethan , Feldman Douglas E.
Published: June 2022

Programmed cell suicide of infected bacteria, known as abortive infection (Abi), serves as a central immune defense strategy to prevent the spread of bacteriophage viruses and other invasive genetic elements across a population. Many Abi systems utilize bespoke cyclic nucleotide immune messengers generated upon infection to rapidly mobilize cognate death effectors. Here, we identify a large family of bacteriophage nucleotidyltransferases (NTases) that synthesize competitor cyclic dinucleotide (CDN) ligands, inhibiting NAD-depleting TIR effectors activated by a linked STING CDN sensor domain. Virus NTase genes are positioned within genomic regions containing other anti-defense genes, and through a functional screen, we uncover candidate anti-TIR defense (Atd) genes that confer protection against TIR-STING cytotoxicity. We show that a virus MazG-like nucleotide pyrophosphatase identified in the screen, Atd1, depletes the starvation alarmone (p)ppGpp, revealing a role for the alarmone-activated host toxin MazF as a key executioner of TIR-directed abortive infection. Phage NTases and counter-defenses like Atd1 preserve host viability to ensure virus propagation, and may be exploited as tools to modulate TIR and STING immune responses.