PNAS nexus最新文献

Infertility is associated with the alteration of the seminal microbiome. However, the onset of dysbiosis remains controversial and the involvement of host factors remains elusive. This study investigates the alterations of the seminal microbiome in male infertility and examines the association and function of DEFB119, a reproductive-tract-specific host antimicrobial peptide, on the seminal microbiome and male fertility. While we observed comparable genera, diversity and evenness of bacterial communities, a marked decrease in the modularity of the metacommunities was observed in patients with abnormal spermiogram (n = 57) as compared to the control (n = 30). A marked elevation of DEFB119 was observed in a subpopulation of male infertile patients (n = 5). Elevated seminal DEFB119 was associated with a decrease in the observed genera, diversity and evenness of bacterial communities, and further distortion of the metacommunities. Mediation analysis suggests the involvement of elevated DEFB119 and dysbiosis of the seminal microbiome in mediating the abnormalities in the spermiogram. Functional experiments showed that recombinant DEFB119 significantly decrease the progressive motility of sperm in patients with abnormal spermiogram. Moreover, DEFB119 demonstrated species-specific antimicrobial activity against common seminal and nonseminal species. Our work identifies an important host factor that mediates the host-microbiome interaction and stratifies the seminal microbiome associated with male infertility. These results may lead to a new diagnostic method for male infertility and regimens for formulating the microbiome in the reproductive tract and other organ systems.

Neuronal systems maintain stable functions despite large variability in their physiological components. Ion channel expression, in particular, is highly variable in neurons exhibiting similar electrophysiological phenotypes, which raises questions regarding how specific ion channel subsets reliably shape intrinsic properties of neurons. Here, we use detailed conductance-based modeling to explore how stable neuronal function is achieved despite variability in channel composition among neurons. Using dimensionality reduction, we uncover two principal dimensions in the channel conductance space that capture most of the variance of the observed variability. These two dimensions correspond to two sources of variability that originate from distinct physiologically relevant mechanisms underlying the regulation of neuronal activity, providing quantitative insights into how channel composition is linked to the electrophysiological activity of neurons. These insights allow us to understand and design a model-independent, reliable neuromodulation rule for variable neuronal populations.

People now commonly interact with Artificial Intelligence (AI) agents. How do these interactions shape how humans perceive each other? In two preregistered studies (total N = 1,261), we show that people evaluate other humans more harshly after interacting with an AI (compared with an unrelated purported human). In Study 1, participants who worked on a creative task with AIs (versus purported humans) subsequently rated another purported human's work more negatively. Study 2 replicated this effect and demonstrated that the results hold even when participants believed their evaluation would not be shared with the purported human. Exploratory analyses of participants' conversations show that prior to their human evaluations they were more demanding, more instrumental and displayed less positive affect towards AIs (versus purported humans). These findings point to a potentially worrisome side effect of the exponential rise in human-AI interactions.

Podophages that infect gram-negative bacteria, such as Pectobacterium pathogen ΦM1, encode tail assemblies too short to extend across the complex gram-negative cell wall. To overcome this, podophages encode a large protein complex (ejectosome) packaged inside the viral capsid and correspondingly ejected during infection to form a transient channel that spans the periplasmic space. Here, we describe the ejectosome of bacteriophage ΦM1 to a resolution of 3.32 Å by single-particle cryo-electron microscopy (cryo-EM). The core consists of tetrameric and octameric ejection proteins which form a ∼1.5-MDa ejectosome that must transition through the ∼30 Å aperture created by the short tail nozzle assembly that acts as the conduit for the passage of DNA during infection. The ejectosome forms several grooves into which coils of genomic DNA are fit before the DNA sharply turns and goes down the tunnel and into the portal. In addition, we reconstructed the icosahedral capsid and hybrid tail apparatus to resolutions between 3.04 and 3.23 Å, and note an uncommon fold adopted by the dimerized decoration proteins which further emphasize the structural diversity of podophages. These reconstructions have allowed the generation of a complete atomic model of the ΦM1, uncovering two distinct decoration proteins and highlighting the exquisite structural diversity of tailed bacteriophages.

Large language models (LLMs) are a potential substitute for human-generated data and knowledge resources. This substitution, however, can present a significant problem for the training data needed to develop future models if it leads to a reduction of human-generated content. In this work, we document a reduction in activity on Stack Overflow coinciding with the release of ChatGPT, a popular LLM. To test whether this reduction in activity is specific to the introduction of this LLM, we use counterfactuals involving similar human-generated knowledge resources that should not be affected by the introduction of ChatGPT to such extent. Within 6 months of ChatGPT's release, activity on Stack Overflow decreased by 25% relative to its Russian and Chinese counterparts, where access to ChatGPT is limited, and to similar forums for mathematics, where ChatGPT is less capable. We interpret this estimate as a lower bound of the true impact of ChatGPT on Stack Overflow. The decline is larger for posts related to the most widely used programming languages. We find no significant change in post quality, measured by peer feedback, and observe similar decreases in content creation by more and less experienced users alike. Thus, LLMs are not only displacing duplicate, low-quality, or beginner-level content. Our findings suggest that the rapid adoption of LLMs reduces the production of public data needed to train them, with significant consequences.

Hyperalgesic priming, a form of pain plasticity initiated by initial injury, leads to heightened sensitivity to subsequent noxious stimuli, contributing to chronic pain development in animals. While astrocytes play active roles in modulating synaptic transmission in various pain models, their specific involvement in hyperalgesic priming remains elusive. Here, we show that spinal astrocytes are essential for hyperalgesic priming formation in a mouse model of acid-induced muscle pain. We observed spinal astrocyte activation 4 h after initial acid injection, and inhibition of this activation prevented chronic pain development upon subsequent acid injection. Chemogenetic activation of spinal astrocytes mimicked the first acid-induced hyperalgesic priming. We also demonstrated that spinal phosphorylated extracellular regulated kinase (pERK)-positive neurons were mainly vesicular glutamate transporter-2 positive (Vglut2⁺) neurons after the first acid injection, and inhibition of spinal pERK prevented astrocyte activation. Furthermore, pharmacological inhibition of astrocytic glutamate transporters glutamate transporter-1 and glutamate-aspartate transporter abolished the hyperalgesic priming. Collectively, our results suggest that pERK activation in Vglut2⁺ neurons activate astrocytes through astrocytic glutamate transporters. This process eventually establishes hyperalgesic priming through spinal D-serine. We conclude that spinal astrocytes play a crucial role in the transition from acute to chronic pain.

With climate extremes hitting nations across the globe, disproportionately burdening vulnerable developing countries, the prompt operation of the Loss and Damage fund is of paramount importance. As decisions on resource disbursement at the international level, and investment strategies at the national level, loom, the climate science community's role in providing fair and effective evidence is crucial. Attribution science can provide useful information for decision makers, but both ethical implications and deep uncertainty cannot be ignored. Considering these aspects, we articulate a vision that integrates established attribution methods and multiple lines of evidence within a coherent logical framework.

While the formalization of chronobiology as a scientific discipline occurred in the mid-20th century, the exploration of rhythmic phenomena has a longer history, notably exemplified by De Mairan's observations of Mimosa pudica in darkness in 1729. In this historical narrative, Charles Darwin is known for his investigations into the "sleep movements" of plants. Nevertheless, the complete scope of Darwin's exploration of biological rhythms remains incompletely understood. Through a detailed examination of Darwin's writings, meticulous observations, experiments, and conceptualizations, we unveil a depth of engagement that surpasses his widely acknowledged work on plants, revealing a more extensive interest in and insight into biological rhythms than traditionally recognized.

Acute lung injury (ALI) is a serious adverse event in the management of acute type A aortic dissection (ATAAD). Using a large-scale cohort, we applied artificial intelligence-driven approach to stratify patients with different outcomes and treatment responses. A total of 2,499 patients from China 5A study database (2016-2022) from 10 cardiovascular centers were divided into 70% for derivation cohort and 30% for validation cohort, in which extreme gradient boosting algorithm was used to develop ALI risk model. Logistic regression was used to assess the risk under anti-inflammatory strategies in different risk probability. Eight top features of importance (leukocyte, platelet, hemoglobin, base excess, age, creatinine, glucose, and left ventricular end-diastolic dimension) were used to develop and validate an ALI risk model, with adequate discrimination ability regarding area under the receiver operating characteristic curve of 0.844 and 0.799 in the derivation and validation cohort, respectively. By the individualized treatment effect prediction, ulinastatin use was significantly associated with significantly lower risk of developing ALI (odds ratio [OR] 0.623 [95% CI 0.456, 0.851]; P = 0.003) in patients with a predicted ALI risk of 32.5-73.0%, rather than in pooled patients with a risk of <32.5 and >73.0% (OR 0.929 [0.682, 1.267], P = 0.642) (Pinteraction = 0.075). An artificial intelligence-driven risk stratification of ALI following ATAAD surgery were developed and validated, and subgroup analysis showed the heterogeneity of anti-inflammatory pharmacotherapy, which suggested individualized anti-inflammatory strategies in different risk probability of ALI.

The coronavirus disease (COVID-19) pandemic has occurred in Massachusetts in multiple waves led by a series of emerging variants. While the evidence has linked obesity with severe symptoms of COVID-19, the effect of obesity on susceptibility to SARS-CoV-2 infection remains unclear. Identification of intrinsic factors, which increase the likelihood of exposed individuals succumbing to productive SARS-CoV-2 infection could help plan mitigation efforts to curb the illness. We aim to investigate whether obese individuals have a higher susceptibility to developing productive SARS-CoV-2 infection given comparable exposure to nonobese individuals. This case-control study leveraged data from the Mass General Brigham's (MGB) electronic medical records (EMR), containing 687,813 patients, to determine whether obesity at any age increases the proportion of infections. We used PCR results of 72,613 subjects who tested positive to SARS-CoV-2 or declared exposure to the virus independently of the result of the test. For this study, we defined susceptibility as the likelihood of testing positive upon suspected exposure. We demonstrate evidence that SARS-CoV-2 exposed obese individuals were more prone to become COVID positive than nonobese individuals [adjusted odds ratio = 1.34 (95% CI: 1.29-1.39)]. Temporal analysis showed significantly increased susceptibility in obese individuals across the duration of the pandemic in Massachusetts. Obese exposed individuals are at a higher risk of getting infected with SARS-CoV-2. This indicates that obesity is not only a risk factor for worsened outcomes but also increases the risk for infection upon exposure. Identifying such populations early will be crucial for curbing the spread of this infectious disease.