HBFP: a new repository for human body fluid proteome

Seminal plasma proteomics as putative biomarkers for male infertility diagnosis

Male infertility represents a significant global public health issue that is currently emerging as a prominent research focus. Presently, laboratories adhere to the guidelines outlined by the World Health Organization (WHO) manuals for conducting routine semen analysis to diagnose male infertility. However, the accuracy of results in predicting sperm quality and fertility is limited because some individuals with a normal semen analysis report, an unremarkable medical history, and a physical examination may still experience infertility. As a result, the importance of employing more advanced techniques to investigate sperm function and male fertility in the treatment of male infertility and/or subfertility becomes apparent. The standard test for evaluating human semen has been improved by more complex tests that look at things like reactive oxygen species (ROS) levels, total antioxidant capacity (TAC), sperm DNA fragmentation levels, DNA compaction, apoptosis, genetic testing, and the presence and location of anti-sperm antibodies. Recent discoveries of novel biomarkers have significantly enriched our understanding of male fertility. Moreover, the notable biological diversity among samples obtained from the same individual complicates the efficacy of routine semen analysis. Therefore, unraveling the molecular mechanisms involved in fertilization is pivotal in expanding our understanding of factors contributing to male infertility. By understanding how these proteins work and what role they play in sperm activity, we can look at the expression profile in men who can't have children to find diagnostic biomarkers. This review examines the various sperm and seminal plasma proteins associated with infertility, as well as proteins that are either deficient or exhibit aberrant expression, potentially contributing to male infertility causes.

Urinary concentration of Cathepsin D as a relievable marker of preeclampsia

BACKGROUND

The early and accurate diagnosis of preeclampsia is crucial to avoid serious complications for both the mother and baby. However, the current diagnostic methods are limited, and there is a need for new diagnostic biomarkers. Previous studies have shown that cathepsin D (CTD) participates in the pathophysiology of preeclampsia and is present in urine samples, making it a potential biomarker for the disease. This study aimed to compare urinary and serum levels of CTD in preeclamptic and normotensive women and analyze its potential role as a diagnostic biomarker in preeclampsia.

METHODS

The study included thirty-nine patients with preeclampsia and twelve normotensive pregnant women as controls. Biomarkers were determined using Multiplex Assay kit, and serum prolactin (Prl) and urinary TNF-α levels were also evaluated. Statistical analysis was conducted using the Mann-Whitney U test.

RESULTS

We found that urinary and serum CTD levels were significantly higher in the preeclampsia group than in the normotensive group, suggesting that CTD could be a diagnostic biomarker for preeclampsia. No significant differences were found in the levels of serum prolactin or urinary TNF-α between the two groups.

CONCLUSIONS

The study provides evidence that non-invasive biological samples such as urine can be used to improve new therapeutic strategies for the early management of preeclampsia.

Strategies for non-viral vectors targeting organs beyond the liver

In recent years, nanoparticles have evolved to a clinical modality to deliver diverse nucleic acids. Rising interest in nanomedicines comes from proven safety and efficacy profiles established by continuous efforts to optimize physicochemical properties and endosomal escape. However, despite their transformative impact on the pharmaceutical industry, the clinical use of non-viral nucleic acid delivery is limited to hepatic diseases and vaccines due to liver accumulation. Overcoming liver tropism of nanoparticles is vital to meet clinical needs in other organs. Understanding the anatomical structure and physiological features of various organs would help to identify potential strategies for fine-tuning nanoparticle characteristics. In this Review, we discuss the source of liver tropism of non-viral vectors, present a brief overview of biological structure, processes and barriers in select organs, highlight approaches available to reach non-liver targets, and discuss techniques to accelerate the discovery of non-hepatic therapies.

O2 plasma-modified carbon nanotube for sulfamethoxazole degradation via peroxymonosulfate activation: Synergism of radical and non-radical pathways boosting water decontamination and detoxification

Sulfamethoxazole (SMX), a widely used antibiotic, has triggered increasing attention due to its extensive detection in wastewater effluent, causing serious ecological threats. Herein, a carbon-based heterogeneous catalyst was developed by the O2 plasma-etching process, regulating oxygen-containing functional groups (OFGs) and defects of carbon nanotubes (O-CNT) to activate peroxymonosulfate (PMS) for highly efficient SMX abatement. Through adjusting the etching time, the desired active sites (i.e., C=O and defects) could be rationally created. Experiments collectively suggested that the degradation of SMX was owing to the contribution of synergism by radical (•OH (17.3%) and SO4•- (39.3%)) and non-radical pathways (1O2, 43.4%), which originated from PMS catalyzed by C=O and defects. In addition, the possible degradation products and transformation pathways of SMX in the system were inferred by combining the Fukui function calculations and the LC-MS/MS analysis. And the possible degradation pathway was effective in reducing the environmental toxicity of SMX, as evidenced by the T.E.S.T. software and the micronucleus experiment on Vicia faba root tip. Also, the catalytic system exhibited excellent performance for different antibiotics removal, such as amoxicillin (AMX), carbamazepine (CBZ) and isopropylphenazone (PRP). This study is expected to provide an alternative strategy for antibiotics removal in water decontamination and detoxification.

Urinary biomarkers for amyotrophic lateral sclerosis: candidates, opportunities and considerations

Amyotrophic lateral sclerosis is a relentless neurodegenerative disease that is mostly fatal within 3-5 years and is diagnosed on evidence of progressive upper and lower motor neuron degeneration. Around 15% of those with amyotrophic lateral sclerosis also have frontotemporal degeneration, and gene mutations account for ∼10%. Amyotrophic lateral sclerosis is a variable heterogeneous disease, and it is becoming increasingly clear that numerous different disease processes culminate in the final degeneration of motor neurons. There is a profound need to clearly articulate and measure pathological process that occurs. Such information is needed to tailor treatments to individuals with amyotrophic lateral sclerosis according to an individual's pathological fingerprint. For new candidate therapies, there is also a need for methods to select patients according to expected treatment outcomes and measure the success, or not, of treatments. Biomarkers are essential tools to fulfil these needs, and urine is a rich source for candidate biofluid biomarkers. This review will describe promising candidate urinary biomarkers of amyotrophic lateral sclerosis and other possible urinary candidates in future areas of investigation as well as the limitations of urinary biomarkers.

Prediction of Proteins in Cerebrospinal Fluid and Application to Glioma Biomarker Identification

Cerebrospinal fluid (CSF) proteins are very important because they can serve as biomarkers for central nervous system diseases. Although many CSF proteins have been identified with wet experiments, the identification of CSF proteins is still a challenge. In this paper, we propose a novel method to predict proteins in CSF based on protein features. A two-stage feature-selection method is employed to remove irrelevant features and redundant features. The deep neural network and bagging method are used to construct the model for the prediction of CSF proteins. The experiment results on the independent testing dataset demonstrate that our method performs better than other methods in the prediction of CSF proteins. Furthermore, our method is also applied to the identification of glioma biomarkers. A differentially expressed gene analysis is performed on the glioma data. After combining the analysis results with the prediction results of our model, the biomarkers of glioma are identified successfully.

Potential biomarker proteins for aspiration pneumonia detected by shotgun proteomics using buccal mucosa samples: a cross-sectional case-control study

BACKGROUND

Aspiration pneumonia (AP), which is a major cause of death in the elderly, does present with typical symptoms in the early stages of onset, thus it is difficult to detect and treat at an early stage. In this study, we identified biomarkers that are useful for the detection of AP and focused on salivary proteins, which may be collected non-invasively. Because expectorating saliva is often difficult for elderly people, we collected salivary proteins from the buccal mucosa.

METHODS

We collected samples from the buccal mucosa of six patients with AP and six control patients (no AP) in an acute-care hospital. Following protein precipitation using trichloroacetic acid and washing with acetone, the samples were analyzed by liquid chromatography and tandem mass spectrometry (LC-MS/MS). We also determined the levels of cytokines and chemokines in non-precipitated samples from buccal mucosa.

RESULTS

Comparative quantitative analysis of LC-MS/MS spectra revealed 55 highly (P values < 0.10) abundant proteins with high FDR confidence (q values < 0.01) and high coverage (> 50%) in the AP group compared with the control group. Among the 55 proteins, the protein abundances of four proteins (protein S100-A7A, eukaryotic translation initiation factor 1, Serpin B4, and peptidoglycan recognition protein 1) in the AP group showed a negative correlation with the time post-onset; these proteins are promising AP biomarker candidates. In addition, the abundance of C-reactive protein (CRP) in oral samples was highly correlated with serum CRP levels, suggesting that oral CRP levels may be used as a surrogate to predict serum CRP in AP patients. A multiplex cytokine/chemokine assay revealed that MCP-1 tended to be low, indicating unresponsiveness of MCP-1 and its downstream immune pathways in AP.

CONCLUSION

Our findings suggest that oral salivary proteins, which are obtained non-invasively, can be utilized for the detection of AP.

Placentophagia and the Tao of POEF

Placentophagia, ingestion of placenta and amniotic fluid, usually during parturition, is a behavioral feature of nearly all nonaquatic, placental mammals, and is a nexus for several interlocking behavioral phenomena. Placentophagia has not been typical of human cultures, but in recent years, some women in affluent societies have engaged in it, thereby bringing publicity to the behavior. First, we summarized benefits of placentophagia for nonhuman mammals, which include increased attractiveness of neonates, enhanced onset of maternal behavior, suppression of pseudopregnancy, and enhancement of opioid hypoalgesia by Placental Opioid-Enhancing Factor (POEF), a benefit that may extend well outside the context of parturition. The research on POEF in animals was discussed in detail. Then we discussed placentophagia (placentophagy) in humans, and whether there is validity to the claims of various benefits reported primarily in the pro-placentophagy literature, and, although human afterbirth shows POEF activity, the POEF effect has not yet been tested in humans. Finally, we discussed the general possible implications, for the management of pain and addiction, of isolating and characterizing POEF.

Bioinformatics tools and data resources for assay development of fluid protein biomarkers

Fluid protein biomarkers are important tools in clinical research and health care to support diagnosis and to monitor patients. Especially within the field of dementia, novel biomarkers could address the current challenges of providing an early diagnosis and of selecting trial participants. While the great potential of fluid biomarkers is recognized, their implementation in routine clinical use has been slow. One major obstacle is the often unsuccessful translation of biomarker candidates from explorative high-throughput techniques to sensitive antibody-based immunoassays. In this review, we propose the incorporation of bioinformatics into the workflow of novel immunoassay development to overcome this bottleneck and thus facilitate the development of novel biomarkers towards clinical laboratory practice. Due to the rapid progress within the field of bioinformatics many freely available and easy-to-use tools and data resources exist which can aid the researcher at various stages. Current prediction methods and databases can support the selection of suitable biomarker candidates, as well as the choice of appropriate commercial affinity reagents. Additionally, we examine methods that can determine or predict the epitope - an antibody’s binding region on its antigen - and can help to make an informed choice on the immunogenic peptide used for novel antibody production. Selected use cases for biomarker candidates help illustrate the application and interpretation of the introduced tools.

LiqBioer: a manually curated database of cancer biomarkers in body fluid

Cancer biomarkers are measurable indicators that play vital roles in clinical applications. Biomarkers in body fluids have gained considerable attention since the development of liquid biopsy, and their data volume is rapidly increasing. Nevertheless, current research lacks the compilation of published cancer body fluid biomarkers into a centralized and sustainable repository for researchers and clinicians, despite a handful of small-scale and specific data resources. To fulfill this purpose, we developed liquid biomarker (LiqBioer) containing 6231 manually curated records from 3447 studies, covering 3056 biomarkers and 74 types of cancer in 22 tissues. LiqBioer allows users to browse and download comprehensive information on body liquid biomarkers, including cancer types, source studies and clinical usage. As a comprehensive resource for body fluid biomarkers of cancer, LiqBioer is a powerful tool for researchers and clinicians to query and retrieve biomarkers in liquid biopsy.

MultiSec: Multi-Task Deep Learning Improves Secreted Protein Discovery in Human Body Fluids

Prediction of secreted proteins in human body fluids is essential since secreted proteins hold promise as disease biomarkers. Various approaches have been proposed to predict whether a protein is secreted into a specific fluid by its sequence. However, there may be relationships between different human body fluids when proteins are secreted into these fluids. Current approaches ignore these relationships directly, and therefore their performances are limited. Here, we present MultiSec, an improved approach for secreted protein discovery to exploit relationships between fluids via multi-task learning. Specifically, a sampling-based balance strategy is proposed to solve imbalance problems in all fluids, an effective network is presented to extract features for all fluids, and multi-objective gradient descent is employed to prevent fluids from hurting each other. MultiSec was trained and tested in 17 human body fluids. The comparison benchmarks on the independent testing datasets demonstrate that our approach outperforms other available approaches in all compared fluids.

DenSec: Secreted Protein Prediction in Cerebrospinal Fluid Based on DenseNet and Transformer

Cerebrospinal fluid (CSF) exists in the surrounding spaces of mammalian central nervous systems (CNS); therefore, there are numerous potential protein biomarkers associated with CNS disease in CSF. Currently, approximately 4300 proteins have been identified in CSF by protein profiling. However, due to the diverse modifications, as well as the existing technical limits, large-scale protein identification in CSF is still considered a challenge. Inspired by computational methods, this paper proposes a deep learning framework, named DenSec, for secreted protein prediction in CSF. In the first phase of DenSec, all input proteins are encoded as a matrix with a fixed size of 1000 × 20 by calculating a position-specific score matrix (PSSM) of protein sequences. In the second phase, a dense convolutional network (DenseNet) is adopted to extract the feature from these PSSMs automatically. After that, Transformer with a fully connected dense layer acts as classifier to perform a binary classification in terms of secretion into CSF or not. According to the experiment results, DenSec achieves a mean accuracy of 86.00% in the test dataset and outperforms the state-of-the-art methods.

Chemical Barrier Proteins in Human Body Fluids

Chemical barriers are composed of those sites of the human body where potential pathogens can contact the host cells. A chemical barrier is made up by different proteins that are part of the antimicrobial and immunomodulatory protein/peptide (AMP) family. Proteins of the AMP family exert antibacterial, antiviral, and/or antifungal activity and can modulate the immune system. Besides these proteins, a wide range of proteases and protease inhibitors can also be found in the chemical barriers maintaining a proteolytic balance in the host and/or the pathogens. In this review, we aimed to identify the chemical barrier components in nine human body fluids. The interaction networks of the chemical barrier proteins in each examined body fluid were generated as well.

Evaluation of the Suitability of Dried Saliva Spots for In-Depth Proteome Analyses for Clinical Applications

Previously, we performed nontargeted proteome analysis using dried blood spots (DBSs) that are widely used in newborn screening for the clinical diagnosis of congenital genetic diseases and immunodeficiency. We have developed an efficient and simple pretreatment method for DBSs that can detect more than 1000 proteins. To complement proteins that are difficult to detect via DBS analysis with less invasive alternative body fluids, we conducted this study to investigate the proteins detected from dried saliva spots (DSSs) using single-shot LC-MS/MS, which is practical in clinical settings. We also clarified whether DSSs have the same advantages as DBSs, and we investigated the influence of saliva collection conditions and the storage environment on their protein profile. As a result, we detected approximately 5000 proteins in DSSs and whole saliva, and we concluded that they were sufficient to complement the proteins lacking in the blood analysis. DSSs could be used as an alternative tool to DBSs for detecting the presence of causative proteins.

Artificial intelligence in clinical research of cancers

Several factors, including advances in computational algorithms, the availability of high-performance computing hardware, and the assembly of large community-based databases, have led to the extensive application of Artificial Intelligence (AI) in the biomedical domain for nearly 20 years. AI algorithms have attained expert-level performance in cancer research. However, only a few AI-based applications have been approved for use in the real world. Whether AI will eventually be capable of replacing medical experts has been a hot topic. In this article, we first summarize the cancer research status using AI in the past two decades, including the consensus on the procedure of AI based on an ideal paradigm and current efforts of the expertise and domain knowledge. Next, the available data of AI process in the biomedical domain are surveyed. Then, we review the methods and applications of AI in cancer clinical research categorized by the data types including radiographic imaging, cancer genome, medical records, drug information and biomedical literatures. At last, we discuss challenges in moving AI from theoretical research to real-world cancer research applications and the perspectives toward the future realization of AI participating cancer treatment.