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X-ray crystal diffraction has provided atomic-level structural information on biological macromolecules. Data quality determines the reliability of structural models. In most cases, multiple data sets are available from different crystals and/or collected with different experimental settings. Reliable metrics are critical to rank and select the data set with the highest quality. Many measures have been created or modified for data quality evaluation. However, some are duplicate in functionality, and some are likely misused due to misunderstanding, which causes confusion or problems, especially at synchrotron beamlines where experiments proceed quickly. In this work, these measures are studied through both theoretical analysis and experimental data with various characteristics, which demonstrated that: 1). {Rmerg, Rmeas, Rpim, CC1/2} all measure the equivalence of reflections, and the low-shell values of these metrics can be used as reliable indicators for correctness (or trueness) of Laue symmetry; 2). High-shell I/σI is a reliable and better indicator to select resolution cutoff while the overall value measures the overall strength of the data.

Plasmids are the workhorse of biotechnology. These small DNA molecules are used to produce recombinant proteins and to engineer living organisms. They can be regarded as the blueprints of many biotechnology products. It is, therefore, critical to ensure that the sequences of these DNA molecules match their intended designs. Yet, plasmid verification remains challenging. To secure the exchange of plasmids in research and development workflows, we have developed self-documenting plasmids that encode information about themselves in their own DNA molecules. Users of self-documenting plasmids can retrieve critical information about the plasmid without prior knowledge of the plasmid identity. The insertion of documentation in the plasmid sequence does not adversely affect their propagation in bacteria and does not compromise protein expression in mammalian cells. This technology simplifies plasmid verification, hardens supply chains, and has the potential to transform the protection of intellectual property in the life sciences.

Region-specific synapse loss is an early pathological hallmark in Alzheimer's disease (AD). Emerging data in mice and humans highlight microglia, the brain-resident macrophages, as cellular mediators of synapse loss; however, the upstream modulators of microglia-synapse engulfment remain elusive. Here, we report a distinct subset of astrocytes, which are glial cells essential for maintaining synapse homeostasis, appearing in a region-specific manner with age and amyloidosis at onset of synapse loss. These astrocytes are distinguished by their peri-synaptic processes which are 'bulbous' in morphology, contain accumulated p62-immunoreactive bodies, and have reduced territorial domains, resulting in a decrease of astrocyte-synapse coverage. Using integrated in vitro and in vivo approaches, we show that astrocytes upregulate and secrete phagocytic modulator, milk fat globule-EGF factor 8 (MFG-E8), which is sufficient and necessary for promoting microglia-synapse engulfment in their local milieu. Finally, we show that knocking down Mfge8 specifically from astrocytes using a viral CRISPR-saCas9 system prevents microglia-synapse engulfment and ameliorates synapse loss in two independent amyloidosis mouse models of AD. Altogether, our findings highlight astrocyte-microglia crosstalk in determining synapse fate in amyloid models and nominate astrocytic MFGE8 as a potential target to ameliorate synapse loss during the earliest stages of AD.

During fertilization, mammalian sperm undergo a winnowing selection process that reduces the candidate pool of potential fertilizers from ∼10 ⁶ -10 ¹¹ cells to 10 ¹ -10 ² cells (depending on the species). Classical sperm competition theory addresses the positive or 'stabilizing' selection acting on sperm phenotypes within populations of organisms but does not strictly address the developmental consequences of sperm traits among individual organisms that are under purifying selection during fertilization. It is the latter that is of utmost concern for improving assisted reproductive technologies (ART) because 'low fitness' sperm may be inadvertently used for fertilization during interventions that rely heavily on artificial sperm selection, such as intracytoplasmic sperm injection (ICSI). Importantly, some form of sperm selection is used in nearly all forms of ART (e.g., differential centrifugation, swim-up, or hyaluronan binding assays, etc.). To date, there is no unifying quantitative framework (i.e., theory of sperm selection) that synthesizes causal mechanisms of selection with observed natural variation in individual sperm traits. In this report, we reframe the physiological function of sperm as a collective diffusive search process and develop multi-scale computational models to explore the causal dynamics that constrain sperm 'fitness' during fertilization. Several experimentally useful concepts are developed, including a probabilistic measure of sperm 'fitness' as well as an information theoretic measure of the magnitude of sperm selection, each of which are assessed under systematic increases in microenvironmental selective pressure acting on sperm motility patterns.

Author summary: Fertilization in mammals requires many orders of magnitude more sperm than eggs to be fertilized. This study models collective sperm movement as a diffusive search process, combining experiments and simulations to explore how heterogeneity in motility patterns and microenvironmental complexity shape fertilization success. We introduce simple metrics to quantify sperm fitness and the magnitude of selection pressure imposed by the microenvironment, revealing that sperm phenotype distributions interact with environmental constraints to determine the range of sperm traits that ultimately support successful egg contact. These insights improve our understanding of sperm population dynamics and offer practical tools for optimizing assisted reproductive technologies in clinical and agricultural settings.

Head movements are sensed by the vestibular organs. Unlike classical senses, signals from vestibular organs are not conveyed to a dedicated cortical area but are broadcast throughout the cortex. Surprisingly, the routes taken by vestibular signals to reach the cortex are still largely uncharted. Here we show that the primary visual cortex (V1) receives real-time head movement signals - direction, velocity, and acceleration - from the ipsilateral pulvinar and contralateral visual cortex. The ipsilateral pulvinar provides the main head movement signal, with a bias toward contraversive movements (e.g. clockwise movements in left V1). Conversely, the contralateral visual cortex provides head movement signals during ipsiversive movements. Crucially, head movement variables encoded in V1 are already encoded in the pulvinar, suggesting that those variables are computed subcortically. Thus, the convergence of inter- and intrahemispheric signals endows V1 with a rich representation of the animal's head movements.

In the first postnatal month, the developing mouse intestine shifts from an immature to a mature intestine that will sustain the organism throughout the lifespan. Here, we surveyed the mouse intestine in C57Bl/6 mice by RNA-Seq to evaluate the changes in gene expression over time from the day of birth through 1 month of age in both the duodenum and ileum. We analyzed gene expression for changes in gene families that correlated with the periods of NEC susceptibility or resistance. We highlight that increased expression of DNA processing genes and vacuolar structure genes, tissue development and morphogenesis genes, and cell migration genes all correlated with NEC susceptibility, while increases in immunity gene sets, intracellular transport genes, ATP production, and intracellular metabolism genes correlated with NEC resistance. Using trends identified in the RNA-Seq analyses, we further evaluated expression of cellular markers and epithelial regulators, immune cell markers, and adenosine metabolism components. We confirmed key changes with qRT-PCR and immunofluorescence. In addition, we compared some findings to humans using human intestinal biopsies and organoids. This dataset can serve as a reference for other groups considering the role of single molecules or molecular families in early intestinal and postnatal development.

The authors have withdrawn this manuscript because they identified problems with how some figure panels were processed. Those experiments will be repeated before deposition of a new manuscript. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding authors.

Understanding biomolecular interactions is fundamental to advancing fields like drug discovery and protein design. In this paper, we introduce Boltz-1, an open-source deep learning model incorporating innovations in model architecture, speed optimization, and data processing achieving AlphaFold3-level accuracy in predicting the 3D structures of biomolecular complexes. Boltz-1 demonstrates a performance on-par with state-of-the-art commercial models on a range of diverse benchmarks, setting a new benchmark for commercially accessible tools in structural biology. By releasing the training and inference code, model weights, datasets, and benchmarks under the MIT open license, we aim to foster global collaboration, accelerate discoveries, and provide a robust platform for advancing biomolecular modeling.

The goal of proteomics is to identify and quantify peptides and proteins within a biological sample. Almost all algorithms for the identification of peptides in LC-MS/MS data employ two steps: peptide/spectrum matching and peptide-identity-propagation (PIP), also known as match-between-runs. PIP was originally envisioned as a backup method to overcome measurement stochasticity. However, current PIP implementations can routinely account for up to 40% of all results, with that proportion rising as high as 75% in single-cell proteomics. Unlike peptide identities derived through peptide/spectrum matches, for which error estimation has been strictly enforced for decades, peptide identities derived through PIP have not historically been subject to statistical evaluation. As an indispensable component of label free quantification, PIP needs a simple and statistically rigorous method for estimating its error rates. Although several tools claim to control the false discovery rate (FDR) of PIP, these claims cannot be validated as there is currently no accepted method to assess the accuracy of the stated FDR. We present a method for FDR control of PIP, called PIP-ECHO, and devise a rigorous protocol for evaluating FDR control of any PIP method. Using three different benchmark datasets, we evaluate PIP-ECHO alongside the PIP procedures implemented by FlashLFQ, IonQuant, and MaxQuant. These analyses show that only PIP-ECHO can accurately control the FDR of PIP at 1% across all datasets, including single cell. When analyzing spike-in datasets where different known amounts of yeast or E. coli peptides are added to a constant background of human peptides, PIP-ECHO increases both the accuracy and sensitivity of differential expression analysis, yielding 53% more differentially abundant proteins than MaxQuant and 146% more than IonQuant.

Mammalian gut microbiomes are highly dynamic communities that shape and are shaped by host aging, including age-related changes to host immunity, metabolism, and behavior. As such, gut microbial composition may provide valuable information on host biological age. Here we test this idea by creating a microbiome-based age predictor using 13,563 gut microbial profiles from 479 wild baboons collected over 14 years. The resulting "microbiome clock" predicts host chronological age. Deviations from the clock's predictions are linked to some demographic and socio-environmental factors that predict baboon health and survival: animals who appear old-for-age tend to be male, sampled in the dry season (for females), and have high social status (both sexes). However, an individual's "microbiome age" does not predict the attainment of developmental milestones or lifespan. Hence, in our host population, gut microbiome age largely reflects current, as opposed to past, social and environmental conditions, and does not predict the pace of host development or host mortality risk. We add to a growing understanding of how age is reflected in different host phenotypes and what forces modify biological age in primates.