PAM-Dependent Target DNA Recognition and Cleavage by C2c1 CRISPR-Cas Endonuclease.

C2c1 is a newly identified guide RNA-mediated type V-B CRISPR-Cas endonuclease that site-specifically targets and cleaves both strands of target DNA. We have determined crystal structures of Alicyclobacillus acidoterrestris C2c1 (AacC2c1) bound to sgRNA as a binary complex and to target DNAs as ternary complexes, thereby capturing catalytically competent conformations of AacC2c1 with both target and non-target DNA strands independently positioned within a single RuvC catalytic pocket. Moreover, C2c1-mediated cleavage results in a staggered seven-nucleotide break of target DNA. crRNA adopts a pre-ordered five-nucleotide A-form seed sequence in the binary complex, with release of an inserted tryptophan, facilitating zippering up of 20-bp guide RNA:target DNA heteroduplex on ternary complex formation. Notably, the PAM-interacting cleft adopts a "locked" conformation on ternary complex formation. Structural comparison of C2c1 ternary complexes with their Cas9 and Cpf1 counterparts highlights the diverse mechanisms adopted by these distinct CRISPR-Cas systems, thereby broadening and enhancing their applicability as genome editing tools.

A Binary Cre Transgenic Approach Dissects Microglia and CNS Border-Associated Macrophages.

The developmental and molecular heterogeneity of tissue macrophages is unravelling, as are their diverse contributions to physiology and pathophysiology. Moreover, also given tissues harbor macrophages in discrete anatomic locations. Functional contributions of specific cell populations can in mice be dissected using Cre recombinase-mediated mutagenesis. However, single promoter-based Cre models show limited specificity for cell types. Focusing on macrophages in the brain, we establish here a binary transgenic system involving complementation-competent NCre and CCre fragments whose expression is driven by distinct promoters: Sall1ncre: Cx3cr1ccre mice specifically target parenchymal microglia and compound transgenic Lyve1ncre: Cx3cr1ccre animals target vasculature-associated macrophages, in the brain, as well as other tissues. We imaged the respective cell populations and retrieved their specific translatomes using the RiboTag in order to define them and analyze their differential responses to a challenge. Collectively, we establish the value of binary transgenesis to dissect tissue macrophage compartments and their functions.

JASPER: Fast, powerful, multitrait association testing in structured samples gives insight on pleiotropy in gene expression.

Joint association analysis of multiple traits with multiple genetic variants can provide insight into genetic architecture and pleiotropy, improve trait prediction, and increase power for detecting association. Furthermore, some traits are naturally high-dimensional, e.g., images, networks, or longitudinally measured traits. Assessing significance for multitrait genetic association can be challenging, especially when the sample has population sub-structure and/or related individuals. Failure to adequately adjust for sample structure can lead to power loss and inflated type 1 error, and commonly used methods for assessing significance can work poorly with a large number of traits or be computationally slow. We developed JASPER, a fast, powerful, robust method for assessing significance of multitrait association with a set of genetic variants, in samples that have population sub-structure, admixture, and/or relatedness. In simulations, JASPER has higher power, better type 1 error control, and faster computation than existing methods, with the power and speed advantage of JASPER increasing with the number of traits. JASPER is potentially applicable to a wide range of association testing applications, including for multiple disease traits, expression traits, image-derived traits, and microbiome abundances. It allows for covariates, ascertainment, and rare variants and is robust to phenotype model misspecification. We apply JASPER to analyze gene expression in the Framingham Heart Study, where, compared to alternative approaches, JASPER finds more significant associations, including several that indicate pleiotropic effects, most of which replicate previous results, while others have not previously been reported. Our results demonstrate the promise of JASPER for powerful multitrait analysis in structured samples.

Low-latency gravitational wave alert products and their performance at the time of the fourth LIGO-Virgo-KAGRA observing run.

Multimessenger searches for binary neutron star (BNS) and neutron star-black hole (NSBH) mergers are currently one of the most exciting areas of astronomy. The search for joint electromagnetic and neutrino counterparts to gravitational wave (GW)s has resumed with ALIGO's, AdVirgo's and KAGRA's fourth observing run (O4). To support this effort, public semiautomated data products are sent in near real-time and include localization and source properties to guide complementary observations. In preparation for O4, we have conducted a study using a simulated population of compact binaries and a mock data challenge (MDC) in the form of a real-time replay to optimize and profile the software infrastructure and scientific deliverables. End-toend performance was tested, including data ingestion, running online search pipelines, performing annotations, and issuing alerts to the astrophysics community. We present an overview of the low-latency infrastructure and the performance of the data products that are now being released during O4 based on the MDC. We report the expected median latency for the preliminary alert of full bandwidth searches (29.5 s) and show consistency and accuracy of released data products using the MDC. We report the expected median latency for triggers from early warning searches (-3.1 s), which are new in O4 and target neutron star mergers during inspiral phase. This paper provides a performance overview for LIGO-Virgo-KAGRA (LVK) low-latency alert infrastructure and data products using theMDCand serves as a useful reference for the interpretation of O4 detections.

Technological advances in mosquito olfaction neurogenetics.

Gene-editing technologies have revolutionized the field of mosquito sensory biology. These technologies have been used to knock in reporter genes in-frame with neuronal genes and tag specific mosquito neurons to detect their activities using binary expression systems. Despite these advances, novel tools still need to be developed to elucidate the transmission of olfactory signals from the periphery to the brain. Here, we propose the development of a set of tools, including novel driver lines as well as sensors of neuromodulatory activities, which can advance our knowledge of how sensory input triggers behavioral outputs. This information can change our understanding of mosquito neurobiology and lead to the development of strategies for mosquito behavioral manipulation to reduce bites and disease transmission.

On the core segmentation algorithms of copy number variation detection tools.

Shotgun sequencing is a high-throughput method used to detect copy number variants (CNVs). Although there are numerous CNV detection tools based on shotgun sequencing, their quality varies significantly, leading to performance discrepancies. Therefore, we conducted a comprehensive analysis of next-generation sequencing-based CNV detection tools over the past decade. Our findings revealed that the majority of mainstream tools employ similar detection rationale: calculates the so-called read depth signal from aligned sequencing reads and then segments the signal by utilizing either circular binary segmentation (CBS) or hidden Markov model (HMM). Hence, we compared the performance of those two core segmentation algorithms in CNV detection, considering varying sequencing depths, segment lengths and complex types of CNVs. To ensure a fair comparison, we designed a parametrical model using mainstream statistical distributions, which allows for pre-excluding bias correction such as guanine-cytosine (GC) content during the preprocessing step. The results indicate the following key points: (1) Under ideal conditions, CBS demonstrates high precision, while HMM exhibits a high recall rate. (2) For practical conditions, HMM is advantageous at lower sequencing depths, while CBS is more competitive in detecting small variant segments compared to HMM. (3) In case involving complex CNVs resembling real sequencing, HMM demonstrates more robustness compared with CBS. (4) When facing large-scale sequencing data, HMM costs less time compared with the CBS, while their memory usage is approximately equal. This can provide an important guidance and reference for researchers to develop new tools for CNV detection.

FastBiCmrMLM: a fast and powerful compressed variance component mixed logistic model for big genomic case-control genome-wide association study.

Large sample datasets have been regarded as the primary basis for innovative discoveries and the solution to missing heritability in genome-wide association studies. However, their computational complexity cannot consider all comprehensive effects and all polygenic backgrounds, which reduces the effectiveness of large datasets. To address these challenges, we included all effects and polygenic backgrounds in a mixed logistic model for binary traits and compressed four variance components into two. The compressed model combined three computational algorithms to develop an innovative method, called FastBiCmrMLM, for large data analysis. These algorithms were tailored to sample size, computational speed, and reduced memory requirements. To mine additional genes, linkage disequilibrium markers were replaced by bin-based haplotypes, which are analyzed by FastBiCmrMLM, named FastBiCmrMLM-Hap. Simulation studies highlighted the superiority of FastBiCmrMLM over GMMAT, SAIGE and fastGWA-GLMM in identifying dominant, small α (allele substitution effect), and rare variants. In the UK Biobank-scale dataset, we demonstrated that FastBiCmrMLM could detect variants as small as 0.03% and with α ≈ 0. In re-analyses of seven diseases in the WTCCC datasets, 29 candidate genes, with both functional and TWAS evidence, around 36 variants identified only by the new methods, strongly validated the new methods. These methods offer a new way to decipher the genetic architecture of binary traits and address the challenges outlined above.

Crystal structure and activity of a de novo enzyme, ferric enterobactin esterase Syn-F4.

Producing novel enzymes that are catalytically active in vitro and biologically functional in vivo is a key goal of synthetic biology. Previously, we reported Syn-F4, the first de novo protein that meets both criteria. Syn-F4 hydrolyzed the siderophore ferric enterobactin, and expression of Syn-F4 allowed an inviable strain of Escherichia coli (Δfes) to grow in iron-limited medium. Here, we describe the crystal structure of Syn-F4. Syn-F4 forms a dimeric 4-helix bundle. Each monomer comprises two long α-helices, and the loops of the Syn-F4 dimer are on the same end of the bundle (syn topology). Interestingly, there is a penetrated hole in the central region of the Syn-F4 structure. Extensive mutagenesis experiments in a previous study showed that five residues (Glu26, His74, Arg77, Lys78, and Arg85) were essential for enzymatic activity in vivo. All these residues are located around the hole in the central region of the Syn-F4 structure, suggesting a putative active site with a catalytic dyad (Glu26-His74). The complete inactivity of purified proteins with mutations at the five residues supports the putative active site and reaction mechanism. Molecular dynamics and docking simulations of the ferric enterobactin siderophore binding to the Syn-F4 structure demonstrate the dynamic property of the putative active site. The structure and active site of Syn-F4 are completely different from native enterobactin esterase enzymes, thereby demonstrating that proteins designed de novo can provide life-sustaining catalytic activities using structures and mechanisms dramatically different from those that arose in nature.

TREM2 on microglia cell surface binds to and forms functional binary complexes with heparan sulfate modified with 6-O-sulfation and iduronic acid.

The Triggering Receptor Expressed on Myeloid Cells-2 (TREM2), a pivotal innate immune receptor, orchestrates functions such as inflammatory responses, phagocytosis, cell survival, and neuroprotection. TREM2 variants R47H and R62H have been associated with Alzheimer's disease, yet the underlying mechanisms remain elusive. Our previous research established that TREM2 binds to heparan sulfate (HS) and variants R47H and R62H exhibit reduced affinity for HS. Building upon this groundwork, our current study delves into the interplay between TREM2 and HS and its impact on microglial function. We confirm TREM2's binding to cell surface HS and demonstrate that TREM2 interacts with HS, forming HS-TREM2 binary complexes on microglia cell surfaces. Employing various biochemical techniques, including Surface Plasmon Resonance, low molecular weight HS microarray screening, and serial HS mutant cell surface binding assays, we demonstrate TREM2's robust affinity for HS, and the effective binding requires a minimum HS size of approximately 10 saccharide units. Notably, TREM2 selectively binds specific HS structures, with 6-O-sulfation and, to a lesser extent, the iduronic acid residue playing crucial roles. N-sulfation and 2-O-sulfation are dispensable for this interaction. Furthermore, we reveal that 6-O-sulfation is essential for HS-TREM2 ternary complex formation on the microglial cell surface, and HS and its 6-O-sulfation are necessary for TREM2-mediated ApoE3 uptake in microglia. By delineating the interaction between HS and TREM2 on the microglial cell surface and demonstrating its role in facilitating TREM2-mediated ApoE uptake by microglia, our findings provide valuable insights that can inform targeted interventions for modulating microglial functions in Alzheimer's disease.

Prediction of RNA-interacting residues in a protein using CNN and evolutionary profile.

This paper describes a method Pprint2, which is an improved version of Pprint developed for predicting RNA-interacting residues in a protein. Training and independent/validation datasets used in this study comprises of 545 and 161 non-redundant RNA-binding proteins, respectively. All models were trained on training dataset and evaluated on the validation dataset. The preliminary analysis reveals that positively charged amino acids such as H, R and K, are more prominent in the RNA-interacting residues. Initially, machine learning based models have been developed using binary profile and obtain maximum area under curve (AUC) 0.68 on validation dataset. The performance of this model improved significantly from AUC 0.68 to 0.76, when evolutionary profile is used instead of binary profile. The performance of our evolutionary profile-based model improved further from AUC 0.76 to 0.82, when convolutional neural network has been used for developing model. Our final model based on convolutional neural network using evolutionary information achieved AUC 0.82 with Matthews correlation coefficient of 0.49 on the validation dataset. Our best model outperforms existing methods when evaluated on the independent/validation dataset. A user-friendly standalone software and web-based server named 'Pprint2' has been developed for predicting RNA-interacting residues (https://webs.iiitd.edu.in/raghava/pprint2 and https://github.com/raghavagps/pprint2).

Phylo2Vec: a vector representation for binary trees.

Binary phylogenetic trees inferred from biological data are central to understanding the shared history among evolutionary units. However, inferring the placement of latent nodes in a tree is computationally expensive. State-of-the-art methods rely on carefully designed heuristics for tree search, using different data structures for easy manipulation (e.g., classes in object-oriented programming languages) and readable representation of trees (e.g., Newick-format strings). Here, we present Phylo2Vec, a parsimonious encoding for phylogenetic trees that serves as a unified approach for both manipulating and representing phylogenetic trees. Phylo2Vec maps any binary tree with n leaves to a unique integer vector of length n - 1. The advantages of Phylo2Vec are fourfold: i) fast tree sampling, (ii) compressed tree representation compared to a Newick string, iii) quick and unambiguous verification if two binary trees are identical topologically, and iv) systematic ability to traverse tree space in very large or small jumps. As a proof of concept, we use Phylo2Vec for maximum likelihood inference on five real-world datasets and show that a simple hill-climbing-based optimisation scheme can efficiently traverse the vastness of tree space from a random to an optimal tree.

A de novo protein catalyzes the synthesis of semiconductor quantum dots.

De novo proteins constructed from novel amino acid sequences are distinct from proteins that evolved in nature. Construct K (ConK) is a binary-patterned de novo designed protein that rescues Escherichia coli from otherwise toxic concentrations of copper. ConK was recently found to bind the cofactor PLP (pyridoxal phosphate, the active form of vitamin B6). Here, we show that ConK catalyzes the desulfurization of cysteine to H2S, which can be used to synthesize CdS nanocrystals in solution. The CdS nanocrystals are approximately 3 nm, as measured by transmission electron microscope, with optical properties similar to those seen in chemically synthesized quantum dots. The CdS nanocrystals synthesized using ConK have slower growth rates and a different growth mechanism than those synthesized using natural biomineralization pathways. The slower growth rate yields CdS nanocrystals with two desirable properties not observed during biomineralization using natural proteins. First, CdS nanocrystals are predominantly of the zinc blende crystal phase; this is in stark contrast to natural biomineralization routes that produce a mixture of zinc blende and wurtzite phase CdS. Second, in contrast to the growth and eventual precipitation observed in natural biomineralization systems, the CdS nanocrystals produced by ConK stabilize at a final size. Future optimization of CdS nanocrystal growth using ConK-or other de novo proteins-may help to overcome the limits on nanocrystal quality typically observed from natural biomineralization by enabling the synthesis of more stable, high-quality quantum dots at room temperature.

Enhanced localized dipole of Pt-Au single-site catalyst for solar water splitting.

Solar water splitting is regarded as holding great potential for clean fuels production. However, the efficiency of charge separation/transfer of photocatalysts is still too low for industrial application. This paper describes the synthesis of a Pt-Au binary single-site loaded g-C3N4 nanosheet photocatalyst inspired by the concept of the dipole. The existent larger charge imbalance greatly enhanced the localized molecular dipoles over adjacent Pt-Au sites in contrast to the unary counterparts. The superposition of molecular dipoles then further strengthened the internal electric field and thus promoted the charge transportation dynamics. In the modeling photocatalytic hydrogen evolution, the optimal Pt-Au binary site photocatalysts (0.25% loading) showed 4.9- and 2.3-fold enhancement of performance compared with their Pt and Au single-site counterparts, respectively. In addition, the reaction barrier over the Pt-Au binary sites was lowered, promoting the hydrogen evolution process. This work offers a valuable strategy for improving photocatalytic charge transportation dynamics by constructing polynary single sites.

Synergism in Binary Nanocrystals Enables Top-Illuminated HgTe Colloidal Quantum Dot Short-Wave Infrared Imager.

Thanks to their tunable infrared absorption, solution processability, and low fabrication costs, HgTe colloidal quantum dots (CQDs) are promising for optoelectronic devices. Despite advancements in device design, their potential for imaging applications remains underexplored. For integration with Si-based readout integrated circuits (ROICs), top illumination is necessary for simultaneous light absorption and signal acquisition. However, most high-performing traditional HgTe CQD photodiodes are p-on-n stack and bottom-illuminated. Herein, we report top-illuminated inverted n-on-p HgTe CQD photodiodes using a robust p-type CQD layer and a thermally evaporated Bi2S3 electron transport layer. The p-type CQD solid is achieved by exploring the synergism in binary HgTe and Ag2Te CQDs. These photodetectors show a room-temperature detectivity of 3.4 × 1011 jones and an EQE of ∼44% at ∼1.7 µm wavelength, comparable to the p-on-n HgTe CQD photodiodes. A top-illuminated HgTe CQD short-wave infrared imager (640 × 512 pixels) was fabricated, demonstrating successful infrared imaging.

Nanostructured Molecular Packing of Polymer Films Formed on Water Surfaces.

In this study, we examined the nanostructured molecular packing and orientations of poly[[N,N'-bis(2-octyldodecyl)-naphthalene-1,4,5,8-bis(dicarboximide)-2,6-diyl]-alt-5,5'-(2,2'-bithiophene)] (P(NDI2OD-T2)) films formed on water for the application of nanotechnology-based organic electronic devices. First, the nanoscale molecule-substrate interaction between the polymer and water was modulated by controlling the alkyl side chain length in NDI-based copolymers. Increasing alkyl side chain lengths induced a nanomorphological transition from face-on to edge-on orientation, confirmed by molecular dynamics simulations revealing nanostructural behavior. Second, the nanoscale intermolecular interactions of P(NDI2OD-T2) were controlled by varying the volume ratio of the high-boiling-point additive solvent in the binary solvent blends. As the additive solvent ratio increased, the nanostructured molecular orientation of the P(NDI2OD-T2) films on water changed remarkably from edge-on to bimodal with more face-on crystallites, thereby affecting charge transport. Our finding provides essential insights for precise nanoscale morphological control on water substrates, enabling the formation of high-performance polymer films for organic electronic devices.

Distinct dynamics and proximity networks of hub proteins at the prey-invading cell pole in a predatory bacterium.

In bacteria, cell poles function as subcellular compartments where proteins localize during specific lifecycle stages, orchestrated by polar "hub" proteins. Whereas most described bacteria inherit an "old" pole from the mother cell and a "new" pole from cell division, generating cell asymmetry at birth, non-binary division poses challenges for establishing cell polarity, particularly for daughter cells inheriting only new poles. We investigated polarity dynamics in the obligate predatory bacterium Bdellovibrio bacteriovorus, proliferating through filamentous growth followed by non-binary division within prey bacteria. Monitoring the subcellular localization of two proteins known as polar hubs in other species, RomR and DivIVA, revealed RomR as an early polarity marker in B. bacteriovorus. RomR already marks the future anterior poles of the progeny during the predator's growth phase, during a precise period closely following the onset of divisome assembly and the end of chromosome segregation. In contrast to RomR's stable unipolar localization in the progeny, DivIVA exhibits a dynamic pole-to-pole localization. This behavior changes shortly before the division of the elongated predator cell, where DivIVA accumulates at all septa and both poles. In vivo protein interaction networks for DivIVA and RomR, mapped through endogenous miniTurbo-based proximity labeling, further underscore their distinct roles in cell polarization and reinforce the importance of the anterior "invasive" cell pole in prey-predator interactions. Our work also emphasizes the precise spatiotemporal order of cellular processes underlying B. bacteriovorus proliferation, offering insights into the subcellular organization of bacteria with filamentous growth and non-binary division.IMPORTANCEIn bacteria, cell poles are crucial areas where "hub" proteins orchestrate lifecycle events through interactions with multiple partners at specific times. While most bacteria exhibit one "old" and one "new" pole, inherited from the previous division event, setting polar identity poses challenges in bacteria with non-binary division. This study explores polar proteins in the predatory bacterium Bdellovibrio bacteriovorus, which undergoes filamentous growth followed by non-binary division inside another bacterium. Our research reveals distinct localization dynamics of the polar proteins RomR and DivIVA, highlighting RomR as an early "hub" marking polar identity in the filamentous mother cell. Using miniTurbo-based proximity labeling, we uncovered their unique protein networks. Overall, our work provides new insights into the cell polarity in non-binary dividing bacteria.

Noisecut: a python package for noise-tolerant classification of binary data using prior knowledge integration and max-cut solutions.

BACKGROUND: Classification of binary data arises naturally in many clinical applications, such as patient risk stratification through ICD codes. One of the key practical challenges in data classification using machine learning is to avoid overfitting. Overfitting in supervised learning primarily occurs when a model learns random variations from noisy labels in training data rather than the underlying patterns. While traditional methods such as regularization and early stopping have demonstrated effectiveness in interpolation tasks, addressing overfitting in the classification of binary data, in which predictions always amount to extrapolation, demands extrapolation-enhanced strategies. One such approach is hybrid mechanistic/data-driven modeling, which integrates prior knowledge on input features into the learning process, enhancing the model's ability to extrapolate. RESULTS: We present NoiseCut, a Python package for noise-tolerant classification of binary data by employing a hybrid modeling approach that leverages solutions of defined max-cut problems. In a comparative analysis conducted on synthetically generated binary datasets, NoiseCut exhibits better overfitting prevention compared to the early stopping technique employed by different supervised machine learning algorithms. The noise tolerance of NoiseCut stems from a dropout strategy that leverages prior knowledge of input features and is further enhanced by the integration of max-cut problems into the learning process. CONCLUSIONS: NoiseCut is a Python package for the implementation of hybrid modeling for the classification of binary data. It facilitates the integration of mechanistic knowledge on the input features into learning from data in a structured manner and proves to be a valuable classification tool when the available training data is noisy and/or limited in size. This advantage is especially prominent in medical and biomedical applications where data scarcity and noise are common challenges. The codebase, illustrations, and documentation for NoiseCut are accessible for download at https://pypi.org/project/noisecut/ . The implementation detailed in this paper corresponds to the version 0.2.1 release of the software.

MR-BOIL: Causal inference in one-sample Mendelian randomization for binary outcome with integrated likelihood method.

Mendelian randomization is a statistical method for inferring the causal relationship between exposures and outcomes using an economics-derived instrumental variable approach. The research results are relatively complete when both exposures and outcomes are continuous variables. However, due to the noncollapsing nature of the logistic model, the existing methods inherited from the linear model for exploring binary outcome cannot take the effect of confounding factors into account, which leads to biased estimate of the causal effect. In this article, we propose an integrated likelihood method MR-BOIL to investigate causal relationships for binary outcomes by treating confounders as latent variables in one-sample Mendelian randomization. Under the assumption of a joint normal distribution of the confounders, we use expectation maximization algorithm to estimate the causal effect. Extensive simulations demonstrate that the estimator of MR-BOIL is asymptotically unbiased and that our method improves statistical power without inflating type I error rate. We then apply this method to analyze the data from Atherosclerosis Risk in Communications Study. The results show that MR-BOIL can better identify plausible causal relationships with high reliability, compared with the unreliable results of existing methods. MR-BOIL is implemented in R and the corresponding R code is provided for free download.

Development of a V5-tag-directed nanobody and its implementation as an intracellular biosensor of GPCR signaling.

Protein-protein interactions (PPIs) form the foundation of any cell signaling network. Considering that PPIs are highly dynamic processes, cellular assays are often essential for their study because they closely mimic the biological complexities of cellular environments. However, incongruity may be observed across different PPI assays when investigating a protein partner of interest; these discrepancies can be partially attributed to the fusion of different large functional moieties, such as fluorescent proteins or enzymes, which can yield disparate perturbations to the protein's stability, subcellular localization, and interaction partners depending on the given cellular assay. Owing to their smaller size, epitope tags may exhibit a diminished susceptibility to instigate such perturbations. However, while they have been widely used for detecting or manipulating proteins in vitro, epitope tags lack the in vivo traceability and functionality needed for intracellular biosensors. Herein, we develop NbV5, an intracellular nanobody binding the V5-tag, which is suitable for use in cellular assays commonly used to study PPIs such as BRET, NanoBiT, and Tango. The NbV5:V5 tag system has been applied to interrogate G protein-coupled receptor signaling, specifically by replacing larger functional moieties attached to the protein interactors, such as fluorescent or luminescent proteins (∼30 kDa), by the significantly smaller V5-tag peptide (1.4 kDa), and for microscopy imaging which is successfully detected by NbV5-based biosensors. Therefore, the NbV5:V5 tag system presents itself as a versatile tool for live-cell imaging and a befitting adaptation to existing cellular assays dedicated to probing PPIs.

Effect of testosterone therapy on breast tissue composition and mammographic breast density in trans masculine individuals.

BACKGROUND: The effect of gender-affirming testosterone therapy (TT) on breast cancer risk is unclear. This study investigated the association between TT and breast tissue composition and breast tissue density in trans masculine individuals (TMIs). METHODS: Of the 444 TMIs who underwent chest-contouring surgeries between 2013 and 2019, breast tissue composition was assessed in 425 TMIs by the pathologists (categories of lobular atrophy and stromal composition) and using our automated deep-learning algorithm (% epithelium, % fibrous stroma, and % fat). Forty-two out of 444 TMIs had mammography prior to surgery and their breast tissue density was read by a radiologist. Mammography digital files, available for 25/42 TMIs, were analyzed using the LIBRA software to obtain percent density, absolute dense area, and absolute non-dense area. Linear regression was used to describe the associations between duration of TT use and breast tissue composition or breast tissue density measures, while adjusting for potential confounders. Analyses stratified by body mass index were also conducted. RESULTS: Longer duration of TT use was associated with increasing degrees of lobular atrophy (p < 0.001) but not fibrous content (p = 0.82). Every 6 months of TT was associated with decreasing amounts of epithelium (exp(ß) = 0.97, 95% CI 0.95,0.98, adj p = 0.005) and fibrous stroma (exp(ß) = 0.99, 95% CI 0.98,1.00, adj p = 0.05), but not fat (exp(ß) = 1.01, 95%CI 0.98,1.05, adj p = 0.39). The effect of TT on breast epithelium was attenuated in overweight/obese TMIs (exp(ß) = 0.98, 95% CI 0.95,1.01, adj p = 0.14). When comparing TT users versus non-users, TT users had 28% less epithelium (exp(ß) = 0.72, 95% CI 0.58,0.90, adj p = 0.003). There was no association between TT and radiologist's breast density assessment (p = 0.58) or LIBRA measurements (p > 0.05). CONCLUSIONS: TT decreases breast epithelium, but this effect is attenuated in overweight/obese TMIs. TT has the potential to affect the breast cancer risk of TMIs. Further studies are warranted to elucidate the effect of TT on breast density and breast cancer risk.