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Respiratory syncytial virus (RSV) and human metapneumovirus (hMPV) cause human respiratory diseases and are major targets for vaccine development. In this study, we design uncleaved prefusion-closed (UFC) trimers for the fusion protein (F) of both viruses by examining mutations critical to F metastability. For RSV, we assess four previous prefusion F designs, including the first and second generations of DS-Cav1, SC-TM, and 847A. We then identify key mutations that can maintain prefusion F in a native-like, closed trimeric form (up to 76%) without introducing any interprotomer disulfide bond. For hMPV, we develop a stable UFC trimer with a truncated F 2 -F 1 linkage and an interprotomer disulfide bond. Dozens of UFC constructs are characterized by negative-stain electron microscopy (nsEM), x-ray crystallography (11 RSV-F structures and one hMPV-F structure), and antigenic profiling. Using an optimized RSV-F UFC trimer as bait, we identify three potent RSV neutralizing antibodies (NAbs) from a phage-displayed human antibody library, with a public NAb lineage targeting sites Ø and V and two cross-pneumovirus NAbs recognizing site III. In mouse immunization, rationally designed RSV-F and hMPV-F UFC trimers induce robust antibody responses with high neutralizing titers. Our study provides a foundation for future prefusion F-based RSV and hMPV vaccine development. RSV and hMPV infections pose significant health risks in vulnerable populations. Here, the authors used a systematic approach to identify mutations critical for fusion protein metastability and rationally design uncleaved prefusion-closed trimers for RSV and hMPV F proteins that induce robust antibody responses in vivo.

Observational studies on COVID-19 vaccine effectiveness (VE) have provided critical real-world data, informing public health policy globally. These studies, primarily using pre-existing data sources, have been indispensable in assessing VE across diverse populations and developing sustainable vaccination strategies. Cohort design is frequently employed in VE research. The rapid implementation of vaccination campaigns during the COVID-19 pandemic introduced differential vaccination influenced by sociodemographic disparities, public policies, perceived risks, health-promoting behaviors, and health status, potentially resulting in biases such as healthy user bias, healthy vaccinee effect, frailty bias, differential depletion of susceptibility bias, and confounding by indication. The overwhelming burden on healthcare systems has escalated the risk of data inaccuracies, leading to outcome misclassifications. Additionally, the extensive array of diagnostic tests used during the pandemic has also contributed to misclassification biases. The urgency to publish quickly may have further influenced these biases or led to their oversight, affecting the validity of the findings. These biases in studies vary considerably depending on the setting, data sources, and analytical methods and are likely more pronounced in low- and middle-income country (LMIC) settings due to inadequate data infrastructure. Addressing and mitigating these biases is essential for accurate VE estimates, guiding public health strategies, and sustaining public trust in vaccination programs. Transparent communication about these biases and rigorous improvement in the design of future observational studies are essential.

B cells recognize antigens via membrane‐expressed B‐cell receptors (BCR) and antibodies. Similar human BCR sequences are frequently found at a significantly higher frequency than that theoretically calculated. Patients infected with SARS‐CoV2 and HIV or with autoimmune diseases share very similar BCRs. Therefore, in silico reconstitution of BCR repertoires and identification of stereotypical BCR sequences related to human pathology have diagnostic potential. Furthermore, monitoring changes of clinically significant BCR sequences and isotype conversion has prognostic potential. For BCR repertoire analysis, peripheral blood (PB) is the most convenient source. However, the optimal human PB volume for in silico reconstitution of the BCR repertoire has not been studied in detail. Here, we sampled 5, 10, and 20 mL PB from the left arm and 40 mL PB from the right arm of two volunteers, reconstituted in silico PB BCR repertoires, and compared their composition. In both volunteers, PB sampling over 20 mL resulted in slight increases in functional unique sequences (FUSs) or almost no increase in repertoire diversity. All FUSs with a frequency above 0.08% or 0.03% in the 40 mL PB BCR repertoire were detected even in the 5 mL PB BCR repertoire from each volunteer. FUSs with a higher frequency were more likely to be found in BCR repertoires from reduced PB volume, and those coexisting in two repertoires showed a statistically significant correlation in frequency irrespective of sampled anatomical site. The correlation was more significant in higher‐frequency FUSs. These observations support the potential of BCR repertoire analysis for diagnosis. Peripheral blood (PB) is the most convenient source for BCR repertoire analysis. However, the optimal volume for in silico analysis of the BCR repertoire has not been studied in detail. We sampled various volumes of human PB from different anatomical sites to compare the functional unique sequences that coexist among the repertoires in order to suggest optimal PB volume.

c-Met is a promising target in cancer therapy for its intrinsic oncogenic properties. However, there are currently no c-Met-specific inhibitors available in the clinic. Antibodies blocking the interaction with its only known ligand, hepatocyte growth factor, and/or inducing receptor internalization have been clinically tested. To explore other therapeutic antibody mechanisms like Fc-mediated effector function, bispecific T cell engagement, and chimeric antigen T cell receptors, a diverse panel of antibodies is essential. We prepared a chicken immune scFv library, performed four rounds of bio-panning, obtained 641 clones using a high-throughput clonal retrieval system (TrueRepertoireTM, TR), and found 149 antigen-reactive scFv clones. We also prepared phagemid DNA before the start of bio-panning (round 0) and, after each round of bio-panning (round 1–4), performed next-generation sequencing of these five sets of phagemid DNA, and identified 860,207 HCDR3 clonotypes and 443,292 LCDR3 clonotypes along with their clonal abundance data. We then established a TR data set consisting of antigen reactivity for scFv clones found in TR analysis and the clonal abundance of their HCDR3 and LCDR3 clonotypes in five sets of phagemid DNA. Using the TR data set, a random forest machine learning algorithm was trained to predict the binding properties of in silico HCDR3 and LCDR3 clonotypes. Subsequently, we synthesized 40 HCDR3 and 40 LCDR3 clonotypes predicted to be antigen reactive (AR) and constructed a phage-displayed scFv library called the AR library. In parallel, we also prepared an antigen non-reactive (NR) library using 10 HCDR3 and 10 LCDR3 clonotypes predicted to be NR. After a single round of bio-panning, we screened 96 randomly-selected phage clones from the AR library and found out 14 AR scFv clones consisting of 5 HCDR3 and 11 LCDR3 AR clonotypes. We also screened 96 randomly-selected phage clones from the NR library, but did not identify any AR clones. In summary, machine learning algorithms can provide a method for identifying AR antibodies, which allows for the characterization of diverse antibody libraries inaccessible by traditional methods.

Phage display is one of the most frequently used platform technologies utilized to screen and select therapeutic antibodies, and has contributed to the development of more than 10 therapeutic antibodies used in the clinic. Despite advantages like efficiency and low cost, it has intrinsic technical limitations, such as the asymmetrical amplification of the library after each round of biopanning, which is regarded as a reason for it yielding a very limited number of antigen binders. In this study, we developed a high-throughput single-clonal screening system comprised of fluorescence immunoassays and a laser-driven clonal DNA retrieval system using microchip technology. Using this system, from a single-chain variable fragment (scFv) library displayed on phages with a complexity of 5.21 × 105 harboring random mutations at five amino acid residues, more than 70,000 clones—corresponding to ~14% of the library complexity—were screened, resulting in 78 antigen-reactive scFv sequences with mutations restricted to the randomized residues. Our results demonstrate that this system can significantly reduce the number of biopanning rounds, or even eliminate the need for this process for libraries with lower complexity, providing an opportunity to obtain more diverse clones from the library.

Respiratory syncytial virus (RSV) and human metapneumovirus (hMPV) cause human respiratory diseases and are major targets for vaccine development. In this study, we designed uncleaved prefusion-closed (UFC) trimers for the fusion (F) proteins of both viruses by examining mutations critical to F metastability. For RSV, we assessed four previous prefusion F designs, including the first and second generations of DS-Cav1, SC-TM, and 847A. We then identified key mutations that can maintain prefusion F in a native-like, closed trimeric form (up to 76%) without introducing any interprotomer disulfide bond. For hMPV, we developed a stable UFC trimer with a truncated F -F linkage and an interprotomer disulfide bond. Tens of UFC constructs were characterized by negative-stain electron microscopy (nsEM), x-ray crystallography (11 RSV-F and one hMPV-F structures), and antigenic profiling. Using an optimized RSV-F UFC trimer as bait, we identified three potent RSV neutralizing antibodies (NAbs) from a phage-displayed human antibody library, with a public NAb lineage targeting sites Ø and V and two cross-pneumovirus NAbs recognizing site III. In mouse immunization, rationally designed RSV-F and hMPV-F UFC trimers induced robust antibody responses with high neutralizing titers. Our study provides a foundation for future prefusion F-based RSV and hMPV vaccine development.

In antibody discovery, in-depth analysis of an antibody library and high-throughput retrieval of clones in the library are crucial to identifying and exploiting rare clones with different properties. However, existing methods have several technical limitations such as low process throughput from laborious cloning process and waste of the phenotypic screening capacity from unnecessary repetitive tests on the dominant clones. To overcome the limitations, we developed a new high-throughput platform for the identification and retrieval of clones in the library, TrueRepertoire . TrueRepertoire provides highly accurate sequences of the clones with linkage information between heavy and light chains of the antibody fragment. Additionally, the physical DNA of clones can be retrieved in high throughput based on the sequence information. We validated the high accuracy of the sequences and demonstrated that there is no platform-specific bias. Moreover, the applicability of TrueRepertoire was demonstrated by a phage-displayed single-chain variable fragment (scFv) library targeting human hepatocyte growth factor (hHGF) protein.

Both F17-like and type 1 pili promote intestinal colonization in mouse colonic crypts, and the high-affinity mannoside M4284 reduces intestinal colonization of uropathogenic Escherichia coli while simultaneously treating urinary tract infections without disrupting the composition of the gut microbiota. UTI reduction by mannoside Uropathogenic E. coli (UPEC) are responsible for 80% of community-acquired and 65% of nosocomial urinary tract infections (UTI), which together affect 150 million people annually. UPEC establishes reservoirs in the gut, but the factors involved in this process have remained unknown. Here, Scott Hultgren and colleagues show that both F17-like and type 1 pili promote intestinal colonization and bind to distinct glycans on epithelial cells distributed along colonic crypts. Using the high-affinity mannose analogue, mannoside M4284, which inhibits the adhesive function of type 1 pili, the authors demonstrate that it effectively reduces intestinal colonization of UPEC, while simultaneously treating UTI without significantly disrupting the composition of the gut microbiota. The authors suggest that this selective depletion of intestinal UPEC by mannosides could be used to reduce the occurrence of UTIs. Urinary tract infections (UTIs) caused by uropathogenic Escherichia coli (UPEC) affect 150 million people annually 1 , 2 . Despite effective antibiotic therapy, 30–50% of patients experience recurrent UTIs 1 . In addition, the growing prevalence of UPEC that are resistant to last-line antibiotic treatments, and more recently to carbapenems and colistin, make UTI a prime example of the antibiotic-resistance crisis and emphasize the need for new approaches to treat and prevent bacterial infections 3 , 4 , 5 . UPEC strains establish reservoirs in the gut from which they are shed in the faeces, and can colonize the periurethral area or vagina and subsequently ascend through the urethra to the urinary tract, where they cause UTIs 6 . UPEC isolates encode up to 16 distinct chaperone-usher pathway pili, and each pilus type may enable colonization of a habitat in the host or environment 7 . For example, the type 1 pilus adhesin FimH binds mannose on the bladder surface, and mediates colonization of the bladder. However, little is known about the mechanisms underlying UPEC persistence in the gut 5 . Here, using a mouse model, we show that F17-like and type 1 pili promote intestinal colonization and show distinct binding to epithelial cells distributed along colonic crypts. Phylogenomic and structural analyses reveal that F17-like pili are closely related to pilus types carried by intestinal pathogens, but are restricted to extra-intestinal pathogenic E. coli . Moreover, we show that targeting FimH with M4284, a high-affinity inhibitory mannoside, reduces intestinal colonization of genetically diverse UPEC isolates, while simultaneously treating UTI, without notably disrupting the structural configuration of the gut microbiota. By selectively depleting intestinal UPEC reservoirs, mannosides could markedly reduce the rate of UTIs and recurrent UTIs.

The COVID-19 pandemic caused a critical public health crisis worldwide, and policymakers are using lockdowns to control the virus. However, there has been a noticeable increase in aggressive social behaviors that threaten social stability. Lockdown measures might negatively affect mental health and lead to an increase in aggressive emotions. Discovering the relationship between lockdown and increased aggression is crucial for formulating appropriate policies that address these adverse societal effects. We applied natural language processing (NLP) technology to internet data, so as to investigate the social and emotional impacts of lockdowns. This research aimed to understand the relationship between lockdown and increased aggression using NLP technology to analyze the following 3 kinds of aggressive emotions: anger, offensive language, and hate speech, in spatiotemporal ranges of tweets in the United States. We conducted a longitudinal internet study of 11,455 Twitter users by analyzing aggressive emotions in 1,281,362 tweets they posted from 2019 to 2020. We selected 3 common aggressive emotions (anger, offensive language, and hate speech) on the internet as the subject of analysis. To detect the emotions in the tweets, we trained a Bidirectional Encoder Representations from Transformers (BERT) model to analyze the percentage of aggressive tweets in every state and every week. Then, we used the difference-in-differences estimation to measure the impact of lockdown status on increasing aggressive tweets. Since most other independent factors that might affect the results, such as seasonal and regional factors, have been ruled out by time and state fixed effects, a significant result in this difference-in-differences analysis can not only indicate a concrete positive correlation but also point to a causal relationship. In the first 6 months of lockdown in 2020, aggression levels in all users increased compared to the same period in 2019. Notably, users under lockdown demonstrated greater levels of aggression than those not under lockdown. Our difference-in-differences estimation discovered a statistically significant positive correlation between lockdown and increased aggression (anger: P=.002, offensive language: P<.001, hate speech: P=.005). It can be inferred from such results that there exist causal relations. Understanding the relationship between lockdown and aggression can help policymakers address the personal and societal impacts of lockdown. Applying NLP technology and using big data on social media can provide crucial and timely information for this effort.

The World Health Organization's end TB strategy promotes the use of symptom and chest radiograph screening for tuberculosis (TB) disease. However, asymptomatic early states of TB beyond latent TB infection and active disease can go unrecognized using current screening criteria. We conducted a longitudinal cohort study enrolling household contacts initially free of TB disease and followed them for the occurrence of incident TB over 1 year. Among 1,747 screened contacts, 27 (52%) of the 52 persons in whom TB subsequently developed during follow-up had a baseline abnormal radiograph. Of contacts without TB symptoms, persons with an abnormal radiograph were at higher risk for subsequent TB than persons with an unremarkable radiograph (adjusted hazard ratio 15.62 [95% CI 7.74-31.54]). In young adults, we found a strong linear relationship between radiograph severity and time to TB diagnosis. Our findings suggest chest radiograph screening can extend to detecting early TB states, thereby enabling timely intervention.