Clostridium tanneri sp. nov., isolated from the faecal material of an alpaca.

A strictly anaerobic, Gram-stain-negative rod-shaped bacterium, designated A1-XYC3T, was isolated from the faeces of an alpaca (Lama pacos). On the basis of the results of a comparative 16S rRNA gene sequence analysis, the isolate was assigned to the genus Clostridium with the highest sequence similarities to Clostridium magnum DSM 2767T (96.8 %), Clostridium carboxidivorans P7T (96.3 %) and Clostridium aciditolerans JW/YJL-B3T (96.1 %). The average nucleotide identity between A1-XYC3T, C. magnum, C. carboxidivorans and C. aciditolerans was 77.4, 76.1 and 76.6  %, respectively. The predominant components of the cellular fatty acids of A1-XYC3T were C14 : 0, C16 : 0 and summed feature 10, containing C18:0/C17:0 cyclo. The DNA G+C content was 32.4 mol%. On the basis of biochemical, phylogenetic, genotypic and chemotaxonomic criteria, this isolate represents a novel species within Clostridium sensu stricto for which the name Clostridium tanneri sp. nov. is proposed. The type strain of this species is strain A1-XYC3T (=CCM 9376T=NRRL B-65691T).

Development, High-Throughput Profiling, and Biopanning of a Large Phage Display Single-Domain Antibody Library.

Immunoglobulin G-based monoclonal antibodies (mAbs) have been effective in treating various diseases, but their large molecular size can limit their penetration of tissue and efficacy in multifactorial diseases, necessitating the exploration of alternative forms. In this study, we constructed a phage display library comprising single-domain antibodies (sdAbs; or "VHHs"), known for their small size and remarkable stability, using a total of 1.6 × 109 lymphocytes collected from 20 different alpacas, resulting in approximately 7.16 × 1010 colonies. To assess the quality of the constructed library, next-generation sequencing-based high-throughput profiling was performed, analyzing approximately 5.65 × 106 full-length VHH sequences, revealing 92% uniqueness and confirming the library's diverse composition. Systematic characterization of the library revealed multiple sdAbs with high affinity for three therapeutically relevant antigens. In conclusion, our alpaca sdAb phage display library provides a versatile resource for diagnostics and therapeutics. Furthermore, the library's vast natural VHH antibody repertoire offers insights for generating humanized synthetic sdAb libraries, further advancing sdAb-based therapeutics.

Nemabiome metabarcoding shows a high prevalence of Haemonchus contortus and predominance of Camelostrongylus mentulatus in alpaca herds in the northern UK.

Gastrointestinal nematodes (GINs) are a common threat faced by pastoral livestock. Since their major introduction to the UK in the early 1990s, South American camelids have been cograzed with sheep, horses, and other livestock, allowing exposure to a range of GIN species. However, there have been no molecular-based studies to investigate the GIN populations present in these camelids. In the current study, we sampled nine alpaca herds from northern England and southern Scotland and used high-throughput metabarcoded sequencing to describe their GIN species composition. A total of 71 amplicon sequence variants (ASVs) were identified representing eight known GIN species. Haemonchus contortus was the most prevalent species found in almost all herds in significant proportions. The identification of H. contortus in other livestock species is unusual in the northern UK, implying that alpacas may be suitable hosts and potential reservoirs for infection in other hosts. In addition, the camelid-adapted GIN species Camelostrongylus mentulatus was identified predominantly in herds with higher faecal egg counts. These findings highlight the value of applying advanced molecular methods, such as nemabiome metabarcoding to describe the dynamics of gastrointestinal nematode infections in novel situations. The results provide a strong base for further studies involving cograzing animals to confirm the potential role of alpacas in transmitting GIN species between hosts.

Development of nanobodies targeting hepatocellular carcinoma and application of nanobody-based CAR-T technology.

BACKGROUND: Chimeric antigen receptor T (CAR-T) cell therapy, as an emerging anti-tumor treatment, has garnered extensive attention in the study of targeted therapy of multiple tumor-associated antigens in hepatocellular carcinoma (HCC). However, the suppressive microenvironment and individual heterogeneity results in downregulation of these antigens in certain patients' cancer cells. Therefore, optimizing CAR-T cell therapy for HCC is imperative. METHODS: In this study, we administered FGFR4-ferritin (FGFR4-HPF) nanoparticles to the alpaca and constructed a phage library of nanobodies (Nbs) derived from alpaca, following which we screened for Nbs targeting FGFR4. Then, we conducted the functional validation of Nbs. Furthermore, we developed Nb-derived CAR-T cells and evaluated their anti-tumor ability against HCC through in vitro and in vivo validation. RESULTS: Our findings demonstrated that we successfully obtained high specificity and high affinity Nbs targeting FGFR4 after screening. And the specificity of Nbs targeting FGFR4 was markedly superior to their binding to other members of the FGFR family proteins. Furthermore, the Nb-derived CAR-T cells, targeting FGFR4, exhibited significantly enhanced anti-tumor efficacy in both experiments when in vitro and in vivo. CONCLUSIONS: In summary, the results of this study suggest that the CAR-T cells derived from high specificity and high affinity Nbs, targeting FGFR4, exhibited significantly enhanced anti-tumor efficacy in vitro and in vivo. This is an exploration of FGFR4 in the field of Nb-derived CAR-T cell therapy for HCC, holding promise for enhancing safety and effectiveness in the clinical treatment of HCC in the future.

Intra-horn insemination in the alpaca Vicugna pacos: Copulatory wounding and deep sperm deposition.

Alpacas (Vicugna pacos) are reported to be the rare mammal in which the penis enters the uterus in mating. To date, however, only circumstantial evidence supports this assertion. Using female alpacas culled for meat, we determined that the alpaca penis penetrates to the very tips of the uterine horns, abrading the tract and breaking fine blood vessels. All female alpacas sacrificed one hour or 24 hours after mating showed conspicuous bleeding in the epithelium of some region of their reproductive tract, including the hymen, cervix and the tips of each uterine horn, but typically not in the vagina. Unmated females showed no evidence of conspicuous bleeding. Histological examination of mated females revealed widespread abrasion of the cervical and endometrial epithelium, injuries absent in unmated females. Within one hour of mating, sperm were already present in the oviduct. The male alpaca's cartilaginous penis tip with a hardened urethral process is likely responsible for the copulatory abrasion. The entire female reproductive tract interacts with the penis, functioning like a vagina. Alpacas are induced ovulators, and wounding may hasten delivery of the seminal ovulation-inducing factor beta-NGF into the female's blood stream. There is no evidence of sexual conflict in copulation in alpaca, and thus wounding may also be one of a variety of mechanisms devised by mammals to induce a beneficial, short-term inflammatory response that stimulates blastocyst implantation, the uterine remodeling associated with placental development, and thus the success of early pregnancy.

Functional insights of Tyr37 in framework region 2 directly contributing to the binding affinities and dissociation kinetics in single-domain VHH antibodies.

Single-domain VHH antibody is regarded as one of the promising antibody classes for therapeutic and diagnostic applications. VHH antibodies have amino acids in framework region 2 that are distinct from those in conventional antibodies, such as the Val37Phe/Tyr (V37F/Y) substitution. Correlations between the residue type at position 37 and the conformation of the CDR3 in VHH antigen recognition have been previously reported. However, few studies focused on the meaning of harboring two residue types in position 37 of VHH antibodies, and the concrete roles of Y37 have been little to be elucidated. Here, we investigated the functional states of position 37 in co-crystal structures and performed analyses of three model antibodies with either F or Y at position 37. Our analysis indicates that Y at position 37 enhances the dissociation rate, which is highly correlated with drug efficacy. Our findings help to explain the molecular mechanisms that distinguish VHH antibodies from conventional antibodies.

Quality of Answers of Generative Large Language Models Versus Peer Users for Interpreting Laboratory Test Results for Lay Patients: Evaluation Study.

BACKGROUND: Although patients have easy access to their electronic health records and laboratory test result data through patient portals, laboratory test results are often confusing and hard to understand. Many patients turn to web-based forums or question-and-answer (Q&A) sites to seek advice from their peers. The quality of answers from social Q&A sites on health-related questions varies significantly, and not all responses are accurate or reliable. Large language models (LLMs) such as ChatGPT have opened a promising avenue for patients to have their questions answered. OBJECTIVE: We aimed to assess the feasibility of using LLMs to generate relevant, accurate, helpful, and unharmful responses to laboratory test-related questions asked by patients and identify potential issues that can be mitigated using augmentation approaches. METHODS: We collected laboratory test result-related Q&A data from Yahoo! Answers and selected 53 Q&A pairs for this study. Using the LangChain framework and ChatGPT web portal, we generated responses to the 53 questions from 5 LLMs: GPT-4, GPT-3.5, LLaMA 2, MedAlpaca, and ORCA_mini. We assessed the similarity of their answers using standard Q&A similarity-based evaluation metrics, including Recall-Oriented Understudy for Gisting Evaluation, Bilingual Evaluation Understudy, Metric for Evaluation of Translation With Explicit Ordering, and Bidirectional Encoder Representations from Transformers Score. We used an LLM-based evaluator to judge whether a target model had higher quality in terms of relevance, correctness, helpfulness, and safety than the baseline model. We performed a manual evaluation with medical experts for all the responses to 7 selected questions on the same 4 aspects. RESULTS: Regarding the similarity of the responses from 4 LLMs; the GPT-4 output was used as the reference answer, the responses from GPT-3.5 were the most similar, followed by those from LLaMA 2, ORCA_mini, and MedAlpaca. Human answers from Yahoo data were scored the lowest and, thus, as the least similar to GPT-4-generated answers. The results of the win rate and medical expert evaluation both showed that GPT-4's responses achieved better scores than all the other LLM responses and human responses on all 4 aspects (relevance, correctness, helpfulness, and safety). LLM responses occasionally also suffered from lack of interpretation in one's medical context, incorrect statements, and lack of references. CONCLUSIONS: By evaluating LLMs in generating responses to patients' laboratory test result-related questions, we found that, compared to other 4 LLMs and human answers from a Q&A website, GPT-4's responses were more accurate, helpful, relevant, and safer. There were cases in which GPT-4 responses were inaccurate and not individualized. We identified a number of ways to improve the quality of LLM responses, including prompt engineering, prompt augmentation, retrieval-augmented generation, and response evaluation.

Current safeguards, risk mitigation, and transparency measures of large language models against the generation of health disinformation: repeated cross sectional analysis.

OBJECTIVES: To evaluate the effectiveness of safeguards to prevent large language models (LLMs) from being misused to generate health disinformation, and to evaluate the transparency of artificial intelligence (AI) developers regarding their risk mitigation processes against observed vulnerabilities. DESIGN: Repeated cross sectional analysis. SETTING: Publicly accessible LLMs. METHODS: In a repeated cross sectional analysis, four LLMs (via chatbots/assistant interfaces) were evaluated: OpenAI's GPT-4 (via ChatGPT and Microsoft's Copilot), Google's PaLM 2 and newly released Gemini Pro (via Bard), Anthropic's Claude 2 (via Poe), and Meta's Llama 2 (via HuggingChat). In September 2023, these LLMs were prompted to generate health disinformation on two topics: sunscreen as a cause of skin cancer and the alkaline diet as a cancer cure. Jailbreaking techniques (ie, attempts to bypass safeguards) were evaluated if required. For LLMs with observed safeguarding vulnerabilities, the processes for reporting outputs of concern were audited. 12 weeks after initial investigations, the disinformation generation capabilities of the LLMs were re-evaluated to assess any subsequent improvements in safeguards. MAIN OUTCOME MEASURES: The main outcome measures were whether safeguards prevented the generation of health disinformation, and the transparency of risk mitigation processes against health disinformation. RESULTS: Claude 2 (via Poe) declined 130 prompts submitted across the two study timepoints requesting the generation of content claiming that sunscreen causes skin cancer or that the alkaline diet is a cure for cancer, even with jailbreaking attempts. GPT-4 (via Copilot) initially refused to generate health disinformation, even with jailbreaking attempts-although this was not the case at 12 weeks. In contrast, GPT-4 (via ChatGPT), PaLM 2/Gemini Pro (via Bard), and Llama 2 (via HuggingChat) consistently generated health disinformation blogs. In September 2023 evaluations, these LLMs facilitated the generation of 113 unique cancer disinformation blogs, totalling more than 40 000 words, without requiring jailbreaking attempts. The refusal rate across the evaluation timepoints for these LLMs was only 5% (7 of 150), and as prompted the LLM generated blogs incorporated attention grabbing titles, authentic looking (fake or fictional) references, fabricated testimonials from patients and clinicians, and they targeted diverse demographic groups. Although each LLM evaluated had mechanisms to report observed outputs of concern, the developers did not respond when observations of vulnerabilities were reported. CONCLUSIONS: This study found that although effective safeguards are feasible to prevent LLMs from being misused to generate health disinformation, they were inconsistently implemented. Furthermore, effective processes for reporting safeguard problems were lacking. Enhanced regulation, transparency, and routine auditing are required to help prevent LLMs from contributing to the mass generation of health disinformation.

Bias of AI-generated content: an examination of news produced by large language models.

Large language models (LLMs) have the potential to transform our lives and work through the content they generate, known as AI-Generated Content (AIGC). To harness this transformation, we need to understand the limitations of LLMs. Here, we investigate the bias of AIGC produced by seven representative LLMs, including ChatGPT and LLaMA. We collect news articles from The New York Times and Reuters, both known for their dedication to provide unbiased news. We then apply each examined LLM to generate news content with headlines of these news articles as prompts, and evaluate the gender and racial biases of the AIGC produced by the LLM by comparing the AIGC and the original news articles. We further analyze the gender bias of each LLM under biased prompts by adding gender-biased messages to prompts constructed from these news headlines. Our study reveals that the AIGC produced by each examined LLM demonstrates substantial gender and racial biases. Moreover, the AIGC generated by each LLM exhibits notable discrimination against females and individuals of the Black race. Among the LLMs, the AIGC generated by ChatGPT demonstrates the lowest level of bias, and ChatGPT is the sole model capable of declining content generation when provided with biased prompts.

Classifying alkaliphilic proteins using embeddings from protein language model.

Alkaliphilic proteins have great potential as biocatalysts in biotechnology, especially for enzyme engineering. Extensive research has focused on exploring the enzymatic potential of alkaliphiles and characterizing alkaliphilic proteins. However, the current method employed for identifying these proteins that requires web lab experiment is time-consuming, labor-intensive, and expensive. Therefore, the development of a computational method for alkaliphilic protein identification would be invaluable for protein engineering and design. In this study, we present a novel approach that uses embeddings from a protein language model called ESM-2(3B) in a deep learning framework to classify alkaliphilic and non-alkaliphilic proteins. To our knowledge, this is the first attempt to employ embeddings from a pre-trained protein language model to classify alkaliphilic protein. A reliable dataset comprising 1,002 alkaliphilic and 1,866 non-alkaliphilic proteins was constructed for training and testing the proposed model. The proposed model, dubbed ALPACA, achieves performance scores of 0.88, 0.84, and 0.75 for accuracy, f1-score, and Matthew correlation coefficient respectively on independent dataset. ALPACA is likely to serve as a valuable resource for exploring protein alkalinity and its role in protein design and engineering.

Systematic analysis of ChatGPT, Google search and Llama 2 for clinical decision support tasks.

It is likely that individuals are turning to Large Language Models (LLMs) to seek health advice, much like searching for diagnoses on Google. We evaluate clinical accuracy of GPT-3·5 and GPT-4 for suggesting initial diagnosis, examination steps and treatment of 110 medical cases across diverse clinical disciplines. Moreover, two model configurations of the Llama 2 open source LLMs are assessed in a sub-study. For benchmarking the diagnostic task, we conduct a naïve Google search for comparison. Overall, GPT-4 performed best with superior performances over GPT-3·5 considering diagnosis and examination and superior performance over Google for diagnosis. Except for treatment, better performance on frequent vs rare diseases is evident for all three approaches. The sub-study indicates slightly lower performances for Llama models. In conclusion, the commercial LLMs show growing potential for medical question answering in two successive major releases. However, some weaknesses underscore the need for robust and regulated AI models in health care. Open source LLMs can be a viable option to address specific needs regarding data privacy and transparency of training.

Advancing entity recognition in biomedicine via instruction tuning of large language models.

MOTIVATION: Large Language Models (LLMs) have the potential to revolutionize the field of Natural Language Processing, excelling not only in text generation and reasoning tasks but also in their ability for zero/few-shot learning, swiftly adapting to new tasks with minimal fine-tuning. LLMs have also demonstrated great promise in biomedical and healthcare applications. However, when it comes to Named Entity Recognition (NER), particularly within the biomedical domain, LLMs fall short of the effectiveness exhibited by fine-tuned domain-specific models. One key reason is that NER is typically conceptualized as a sequence labeling task, whereas LLMs are optimized for text generation and reasoning tasks. RESULTS: We developed an instruction-based learning paradigm that transforms biomedical NER from a sequence labeling task into a generation task. This paradigm is end-to-end and streamlines the training and evaluation process by automatically repurposing pre-existing biomedical NER datasets. We further developed BioNER-LLaMA using the proposed paradigm with LLaMA-7B as the foundational LLM. We conducted extensive testing on BioNER-LLaMA across three widely recognized biomedical NER datasets, consisting of entities related to diseases, chemicals, and genes. The results revealed that BioNER-LLaMA consistently achieved higher F1-scores ranging from 5% to 30% compared to the few-shot learning capabilities of GPT-4 on datasets with different biomedical entities. We show that a general-domain LLM can match the performance of rigorously fine-tuned PubMedBERT models and PMC-LLaMA, biomedical-specific language model. Our findings underscore the potential of our proposed paradigm in developing general-domain LLMs that can rival SOTA performances in multi-task, multi-domain scenarios in biomedical and health applications. AVAILABILITY AND IMPLEMENTATION: Datasets and other resources are available at https://github.com/BIDS-Xu-Lab/BioNER-LLaMA.

Histological evaluation of the alpaca (Vicugna pacos) vomeronasal organ.

Little is known about the neuronal structure of the vomeronasal organ (VNO), a receptor organ responsible for pheromone perception, in the alpaca (Vicugna pacos). This study was performed to determine the localization of neuronal elements, including protein gene product 9.5 (PGP 9.5), a pan-neuronal marker, olfactory marker protein (OMP), a marker of mature olfactory receptor cells, and phospholipase C beta 2 (PLC-ß2), a marker of solitary chemoreceptor cells (SCCs), in the VNO. OMP was identified in receptor cells of the vomeronasal sensory epithelium (VSE), while PGP 9.5 and PLC-ß2 were localized in both the VSE and vomeronasal non-sensory epithelium. Collectively, these results suggested that the alpaca VNO possesses SCCs and olfactory receptor cells, which recognize both harmful substances and pheromones.

The influence of selected factors on wool cortisol concentration in alpacas (Vicugna pacos).

Several internal and external factors can influence animals' hormonal activity. Cortisol level in hair and wool determines chronic stress, which is connected with the long-term HPA axis effect. Wool cortisol levels in alpacas have never been determined to this time. The study aimed to assess the influence of selected factors on wool cortisol concentration in alpacas. The study included 36 alpacas. Wool samples were collected during shearing in June 2021, cut with an electric clipper from the right shoulder and the rump. Wool samples were fragmented into proximal (winter-spring regrowth) and distal (summer-fall regrowth) segments. Alpacas' Heat Stress Index (HSI) for the summer of 2020 was 139.4, and 116 for the winter of 2021. The cortisol levels in the wool samples were determined with the General Cortisol ELISA Kit assay. The most significant differences in wool cortisol concentrations were caused by two factors: the wool segment (P < 0.001; η2 = 0.889) and the region on the body (P < 0.001; η2 = 0.876). Wool cortisol level was higher in the distal segment (referring to the summer-fall season) than in the proximal one (referring to the winter-spring season). It is suggested that alpacas can feel heat stress in summer (HSI = 139.4), which could influence higher cortisol levels in the distal segment. The wool cortisol level was higher in the rump samples than the shoulder ones. Therefore, it is essential in future studies that wool samples from all tested animals should be completed from the same body region. Differences among age and sex groups were also observed. Wool cortisol level was higher in older animals, as differences between age groups were observed in samples from the rump in the distal and proximal segments (distal, the rump younger*older: P < 0.001; η2 = 0.321; proximal, the rump older*younger: P = 0.007; η2 = 0.195). Males showed higher cortisol levels than females, as a difference between sexes was observed in samples from the rump in the proximal segment (P = 0.001, η2 = 0.271). This study emphasizes that various factors may significantly influence wool cortisol levels, which can be helpful in alpacas' welfare estimation using this hormonal indicator as a noninvasive long-term stress assessment method.

Effect of chemical activators after intracytoplasmic sperm injection (ICSI) on embryo development in alpacas.

Low motility and low sperm concentration are characteristics of alpaca semen. Thus, the intracytoplasmic sperm injection (ICSI) technique represents an alternative to improve the reproductive capacity of the male. However, the effect of post-ICSI activation in alpaca is not yet known. The aim of the present study was to compare the effect of chemical activators on alpaca embryo development after ICSI. Alpaca ovaries were collected from a local slaughterhouse and transported to the laboratory. Category I, II and III oocytes were matured for 30 h at 38.5 °C. After ICSI, injected oocytes were randomly divided and activated as follows: i) 5 µM ionomycin for 5 min, ii) 7% ethanol for 4 min, iii) 5 µM ionomycin for 5 min, window period 3 h plus 7% ethanol for 4 min, iv) 5 µM ionomycin for 5 min, window period 3 h, a second ionomycin treatment for 5 min, followed by 1.9 mM 6-DMAP for 3 h, v) 10 mM SrCl2 for 3 h. Culture was carried out for 5 days in SOFaa at 38.5 °C. The cleavage rate was the lowest in the SrCl2 group, morula development was the lowest in the SrCl2 and without activation groups, and blastocyst stage was not different between groups (P<0.05). The rates with SrCl2 were lower in total embryos produced, whereas in transferable embryos they were lower with 2Io/6-DMAP and with SrCl2 (P<0.05). In conclusion, alpaca oocyte activation is more efficient with ionomycin and ethanol to produce transferable embryos.

SARS-CoV-2 Specific Nanobodies Neutralize Different Variants of Concern and Reduce Virus Load in the Brain of h-ACE2 Transgenic Mice.

Since the beginning of the COVID-19 pandemic, there has been a significant need to develop antivirals and vaccines to combat the disease. In this work, we developed llama-derived nanobodies (Nbs) directed against the receptor binding domain (RBD) and other domains of the Spike (S) protein of SARS-CoV-2. Most of the Nbs with neutralizing properties were directed to RBD and were able to block S-2P/ACE2 interaction. Three neutralizing Nbs recognized the N-terminal domain (NTD) of the S-2P protein. Intranasal administration of Nbs induced protection ranging from 40% to 80% after challenge with the WA1/2020 strain in k18-hACE2 transgenic mice. Interestingly, protection was associated with a significant reduction in virus replication in nasal turbinates and a reduction in virus load in the brain. Employing pseudovirus neutralization assays, we identified Nbs with neutralizing capacity against the Alpha, Beta, Delta, and Omicron variants, including a Nb capable of neutralizing all variants tested. Furthermore, cocktails of different Nbs performed better than individual Nbs at neutralizing two Omicron variants (B.1.529 and BA.2). Altogether, the data suggest the potential of SARS-CoV-2 specific Nbs for intranasal treatment of COVID-19 encephalitis.

Vicuña antipredator diel movement drives spatial nutrient subsidies in a high Andean ecosystem.

Large animals could be important drivers of spatial nutrient subsidies when they ingest resources in some habitats and release them in others, even moving nutrients against elevational gradients. In high Andean deserts, vicuñas (Vicugna vicugna) move daily between nutrient-rich wet meadows, where there is abundant water and forage but high risk of predation by pumas (Puma concolor), and nutrient-poor open plains with lower risk of predation. In all habitats, vicuñas defecate and urinate in communal latrines. We investigated how these latrines impacted soil and plant nutrient concentrations across three habitats in the Andean ecosystem (meadows, plains, and canyons) and used stable isotope analysis to explore the source of fecal nutrients in latrines. Latrine soils had higher concentrations of nitrogen, carbon, and other nutrients than did nonlatrine soils across all habitats. These inputs corresponded with an increase in plant quality (lower C:N) at latrine sites in plains and canyons, but not in meadows. Stable isotope mixing models suggest that ~7% of nutrients in plains latrines originated from vegetation in meadows, which is disproportionately higher than the relative proportion of meadow habitat (2.6%) in the study area. In contrast, ~68% of nutrients in meadow latrines appear to originate from plains and canyon vegetation, though these habitats made up nearly 98% of the study area. Vicuña diel movements thus appear to concentrate nutrients in latrines within habitats and to drive cross-habitat nutrient subsidies, with disproportionate transport from low-lying, nutrient-rich meadows to more elevated, nutrient-poor plains. When these results are scaled up to the landscape scale, the amount of nitrogen and phosphorus subsidized in soil at plains latrines was of the same order of magnitude as estimates of annual atmospheric nitrogen and phosphorus deposition for this region (albeit far more localized and patchy). Thus, vicuña-mediated nutrient redistribution and deposition appears to be an important process impacting ecosystem functioning in arid Andean environments, on par with other major inputs of nutrients to the system.

Noninvasive Evaluation of Tumoral PD-L1 Using a Novel 99mTc-Labeled Nanobody Tracer with Rapid Renal Clearance.

The expression level of PD-L1 in tumor tissue is considered one of the effective biomarkers to guide PD-1/PD-L1 therapy. Quantifying whole-body PD-L1 expression by SPECT imaging may help in selecting patients that potentially respond to PD-1/PD-L1 therapy. Nanobody is the smallest antibody fragment with antigen-binding ability that is well suited for radionuclide imaging. Nevertheless, high retention of radioactivity in the kidney may limit its clinical translation. The present study aimed to screen, design, and prepare a nanobody-based SPECT probe with rapid renal clearance to evaluate the PD-L1 expression level in vivo noninvasively. A phage library was constructed by immunizing alpaca with recombinant human PD-L1 protein, and 17 anti-PD-L1 nanobodies were screened by the phage display technique. After sequence alignment and flow cytometry analysis, APN09 was selected as the candidate nanobody, and a GGGC chelator was attached to its C-terminus for 99mTc labeling to prepare a SPECT imaging probe. The affinity and specificity of 99mTc-APN09 were evaluated by protein and cell-binding experiments, and SPECT imaging and biodistribution were performed in a mouse model with bilateral transplantation of A549 and A549PD-L1 tumors. The ability of 99mTc-APN09 to quantify the PD-L1 expression level in vivo was validated in tumor models with different PD-L1 expression levels. 99mTc-APN09 had a radiochemical purity higher than 99% and a binding equilibrium dissociation constant of 21.44 ± 1.65 nM with hPD-L1, showing high affinity. SPECT imaging results showed that 99mTc-APN09 could efficiently detect PD-L1-positive tumors within 0.5 h, and the quantitative results of SPECT were well correlated with the expression level of PD-L1 in cell lines. SPECT imaging and biodistribution results also showed that 99mTc-APN09 was rapidly cleared from the kidney in 2 h postinjection. 99mTc-APN09 was a simple and stable tool for visualizing PD-L1 expression in the whole body. In addition, due to its significant reduction in renal retention, it has better prospects for clinical translation.

Development and characterization of a novel nanobody with SRMV neutralizing activity.

Peste des petits ruminants (PPR) is an acute, contact infectious disease caused by the small ruminant morbillivirus (SRMV), and its morbidity in goats and sheep can be up to 100% with significant mortality. Nanobody generated from camelid animals such as alpaca has attracted wide attention because of its unique advantages compared with conventional antibodies. The main objective of this study was to produce specific nanobodies against SRMV and identify its characteristics. To obtain the coding gene of SRMV-specific nanobodies, we first constructed an immune phage-displayed library from the VHH repertoire of alpaca that was immunized with SRMV-F and -H proteins. By using phage display technology, the target antigen-specific VHHs can be obtained after four consecutive rounds of biopanning. Results showed that the size of this VHH library was 2.26 × 1010 CFU/mL and the SRMV-F and -H specific phage particles were greatly enriched after four rounds of biopanning. The positive phage clones were selected and sequenced, and total of five independent different sequences of SRMV-specific nanobodies were identified. Subsequently, the DNA fragments of the five nanobodies were cloned into E. coli BL21(DE3), respectively, and three of them were successfully expressed and purified. Specificity and affinity towards inactivated SRMV of these purified nanobodies were then evaluated using the ELISA method. Results demonstrated that NbSRMV-1-1, NbSRMV-2-10, and NbSRMV-1-21 showed no cross-reactivity with other antigens, such as inactivated BTV, inactivated FMDV, His-tag labeled protein, and BSA. The ELISA titer of these three nanobodies against inactivated SRMV was up to 1:1000. However, only NbSRMV-1-21 displayed SRMV neutralizing activity at a maximum dilution of 1:4. The results indicate that the nanobodies against SRMV generated in this study could be useful in future applications. This study provided a novel antibody tool and laid a foundation for the treatment and detection of SRMV.

Whole genome sequencing analysis of alpaca suggests TRPV3 as a candidate gene for the suri phenotype.

BACKGROUND: Alpaca is a domestic South American camelid probably arising from the domestication of two wild camelids, the vicugna and the guanaco. Two phenotypes are described for alpaca, known as huacaya and suri. Huacaya fleece is characterized by compact, soft, and highly crimped fibers, while suri fleece is longer, straight, less crimped, and lustrous. The gene variants determining these phenotypes are still unknown, although previous studies suggested a dominant inheritance of the suri. Based on that, the aim of this study was the identification of the gene variants determining alpaca coat phenotypes through whole genome sequencing (WGS) analysis. RESULTS: The sample used includes two test-cross alpaca families, suri × huacaya, which produced two offspring, one with the suri phenotype and one with the huacaya phenotype. The analyzed sample was expanded through the addition of WGS data from six vicugnas and six guanacos; this because we assumed the absence of the gene variants linked to the suri phenotype in these wild species. The analysis of gene variant segregation with the suri phenotype, coupled with the filtering of gene variants present in the wild species, disclosed the presence in all the suri samples of a premature termination codon (PTC) in TRPV3 (transient receptor potential cation channel subfamily V member 3), a gene known to be involved in hair growth and cycling, thermal sensation, cold tolerance and adaptation in several species. Mutations in TRPV3 were previously associated with the alteration of hair structure leading to an impaired formation of the hair canal and the hair shaft in mouse. This PTC in TRPV3, due to a G > T substitution (p.Glu475*), results in a loss of 290 amino acids from the canonical translated protein, plausibly leading to a physiological dysfunction. CONCLUSION: The present results suggest that the suri phenotype may arise from a TRPV3 gene variant which may explain some of the suri features such as its longer hair fibre with lower number of cuticular scales compared to huacaya.