The Microbiome, an Important Factor That Is Easily Overlooked in Male Infertility

Humankind has been interested in reproduction for millennia. Infertility, in which male factors contribute to approximately 50%, is estimated to concern over 72 million people worldwide. Despite advances in the diagnosis, medical treatment, and psychosocial management of male infertility over the past few decades, approximately 30% of male infertility is still thought to be idiopathic. Despite emerging advances in the microbiome associated with male infertility have indicated that the microbiome may be a key factor to the management of male infertility, roles, and mechanisms of the microbiome remain ambiguous. Here, we mainly discussed the association between microbial infection in the genital tract and male infertility, effect of antimicrobial therapy on male reproduction, association between microbial dysbiosis and male infertility, and effect of probiotic intervention on male reproduction. This review made progress toward establishing a relationship between the microbiome and male infertility, and explored the role of the microbiome in male infertility. We call for more high-quality studies to focus on the relationship between microbes and male infertility, and strongly suggest increasing awareness among sterile males with microbial infection and/or microbial dysbiosis when they seek fertility help.

INTRODUCTION
Infertility is defined as couples who have had regular sex for more than 1 year without contraception failing to conceive and has been regarded as a major global public health issue. Infertility constitutes an emotional, social, and financial burden, yet appropriate services directed toward preventing and addressing infertility are often inaccessible, unaffordable, or non-existent (Dierickx et al., 2021). Compared with female infertility, too little attention has been given to male infertility.
The topic of worldwide decline in sperm parameters is contentious (Mann et al., 2020). Manuscripts reported heterogeneous findings, with some studies confirming a decreasing trend in semen quality while others did not (Huang et al., 2017). A systematic review reported that sperm counts in western countries decreased by 50-60% between 1973 and 2011, and approximately one-third of cases remained idiopathic (Levine et al., 2017;Fainberg and Kashanian, 2019). Another study conducted a retrospective analysis of 119,972 men looking at total motile sperm count trends from 2002 to 2017 and revealed a decline of approximately 10% over the past 16 years (Tiegs et al., 2019;Mann et al., 2020). Jorgensen et al. (2012) showed an increasing trend in sperm concentration and total sperm count in 4,867 young men in Copenhagen, Denmark (Huang et al., 2017). Despite the decline in male fertility continues unabated, recent high-quality studies have demonstrated that there is indeed a decline in sperm parameters and have shown possible links for this decline in sperm parameters such as obesity, diet, and environmental toxins (Mann et al., 2020). Many efforts have been made to identify the deeper causes and develop new approaches to improve fertility in infertile men over the past few decades. However, there is currently few effective treatment to slow the decline in idiopathic male infertility.
The human microbiome is the focus of one of the most dynamic research fields of our time. The microbiome is a tiny ecosystem made up of thousands of species of microbes that mainly exist in external cavities, such as the reproductive tract, oral cavity, and gastrointestinal tract. Recent improvements in technology for collecting and analyzing DNA sequence data give a much deeper cognition of the diverse human microbiome. The complex composition of the human microbiome often poses challenges for scientists to classify and understand its effects on health. Studies in the microbiome field have found that the human microbiome plays a role in important physiological processes, such as host nutrient absorption immune system development, etc., and changes in human microbiome are closely related to the occurrence, development and treatment of a variety of diseases (Cani, 2018).
The "culture-negative" status of male reproductive tract samples in microbiological tests was once considered free of microbial infection, which has resulted in the male reproductive tract microbiota not being well described for many years. Yet, recent studies have found that a microbiota indeed exists in the male healthy reproductive tract and its body fluids, such seminal fluid and urine (Allen-Vercoe, 2013). As 16S ribosomal RNA sequencing has been applied to the microbiome of infertile males, correlations between human microbiome and semen parameters/fertility have been gradually revealed, which may be one of the important reasons why this disease has been so hard to track down in the clinic for so many years and is reluctantly called idiopathic male infertility (Lundy et al., 2020). Supplements of probiotics to modulate the microbiome have become a hotspot. It is well known that probiotics and their metabolites can alter the composition of the human microbiome and further influence the body's metabolism and disease status. Considering that strategies using probiotics may benefit the microbial balance of the host, the influence of microbiota and probiotics on male infertility is gaining attention. Therefore, the potential of probiotics to affect the microbiome associated with male infertility has recently led to a significant increase in research interest.
Thus, the goal of this review was to give a current cognition of the microbiome's potential effects on male infertility. In general, this review mainly included the following three aspects: the association between microbial infection in the genital tract and male infertility as well as the effect of antimicrobial therapy on male reproduction, the association between microbial dysbiosis and male infertility as well as the effect of probiotic intervention on male reproduction, and the limitations of current research as well as the suggestions for future research.

MICROBIAL INFECTION IN THE GENITAL TRACT AND MALE INFERTILITY
Studies devoted to understanding the role of microorganisms isolated in seminal fluid are conducted as early as 1976 (Kundsin et al., 1967;Busolo et al., 1984). The role of microbial infections in male infertility has been debatable . However, the impact of some microorganisms on male infertility has been extensively studied and established in the literature. Here, we will provide an overview of the relationship between microbial infections and male infertility. The relationship between bacterial infection, genital mycoplasmas or trachomatis infection, viral infection, and fungal infection with male infertility will be described in turn. In addition, we will present the effects of antimicrobial intervention on fertility in men with infection.

Bacterial Infection Influences Male Infertility
Escherichia coli (E. coli) is among the most common pathogens having negative effects on male infertility (Berjis et al., 2018). E. coli is the most common cause of urogenital infection (Farsimadan and Motamedifar, 2020). Studies have provided compelling evidence indicating that E. coli infection has a detrimental effect on male fertility by reducing sperm motility or vitality and changing sperm morphology (Table 1). Semen quality in patients with E. coli infection can improve after the eradication of this infection, indirectly proving the detrimental role of E. coli in the genesis of male infertility (Vicari et al., 2016). As far as is known, the detrimental effect of E. coli on sperm may be via its adhesion to sperm and/or the expression of hemolysin A, a virulence factor involved in premature acrosomal activation and sperm nuclear DNA damage (Lang et al., 2013).
As a kind of symbiotic bacteria in human body, Staphylococcal species can attack reproductive tissues directly or through hematogenous pathway, and mediate the inflammatory response in reproductive system through innate immune pathway triggered by TLR2 (Dutta et al., 2020). Among Staphylococcal species, Staphylococcus aureus (S. aureus) is most closely related to male infertility. S. aureus may be an important adverse factor for deteriorating male reproductive function by decreasing sperm motility and increasing abnormal morphology, as well as decreasing sperm concentration (Esmailkhani et al., 2018). S. aureus along with other bacteria (E. coli, UUR, Neisseria gonorrhoeae, Enterococcus faecalis, Gardnerella vaginalis, and Streptococcus agalactiae) in male reproductive system is proved to be related to the lowering of sperm concentration, normal Frontiers in Microbiology | www.frontiersin.org sperm morphology, sperm volume, and sperm vitality, as well as leads to the decreased fertilization rate, increased sperm protamine deficiency, and decreased sperm chromatin condensation (Table 1). In addition, high concentrations of S. aureus in seminal vesicles have been reported to be associated with sperm abnormalities (Esmailkhani et al., 2018). Also, S. aureus is able to immobilize and agglutinate spermatozoa, and thus colonization of S. aureus in male reproductive system should not be neglected in the treatment of male infertility (Dutta et al., 2020). Enterococcus faecalis is found to negatively affect both sperm motility and morphology ( Table 1). As mentioned in the previous paragraph, Enterococcus faecalis with other bacteria is proved to be related to the decreased semen quality.
In recent years, many other bacteria have been (e.g., Enterococcus faecalis, Gardnerella vaginalis, Helicobacter pylori, etc.) reported to be associated with male infertility, but independent studies on the relationship between single bacteria and male infertility are often lacking, so that we cannot tell which bacteria are really associated with male infertility (Merino et al., 1995;Gizzo et al., 2014;Moretti et al., 2017). Some studies have even drawn opposite conclusions about the relationship between the same bacteria and male infertility.

Infection With Genital Mycoplasmas or Trachomatis Influences Male Infertility
Since 1967, Ureaplasma spp., infection in the male reproductive system has been considered as one of the causes of male infertility (Kundsin et al., 1967). It was reported that Ureaplasma was more frequently isolated from semen of infertile men (76%) than those of fertile men (19%) as early as 1974 (Friberg and Gnarpe, 1974). Since then, the correlation between Ureaplasma spp. infection and male reproductive function has been widely studied based on standard PCR based culture. In several recent studies, Ureaplasma spp. has been isolated from the semen of infertile men with frequencies ranging from 5 to 58% and from fertile men with frequencies ranging from 3 to 31% (Andrade-Rocha, 2003; Knox et al., 2003). Most previous studies did not divide Ureaplasma spp. into these two categories when discussing its role in male reproductive function: Ureaplasma parvum (UPA) and Ureaplasma urealyticum (UUR), among which the former comprises four serovars and the latter comprises ten serovars (Pitcher et al., 2001;Burrello et al., 2004). With further indepth research, UUR has been shown to be a pathogen with a potential etiologic role in both genital infections and male infertility . Evidence suggests that a negative impact on sperm parameters, including sperm concentration, sperm motility, sperm volume, sperm vitality, normal sperm morphology, pH, and reactive oxygen species (ROS), was found in infertile men with UUR infection ( Table 1). There are several possible pathophysiological mechanisms of UUR infection in infertile men, including direct pathogen-related DNA damage, membrane damage by cross-reactive antigens, or alterations in nitric oxide and interleukin-17/interleukin-18 expression (Shi et al., 2007;Gimenes et al., 2014;Qian et al., 2016). Nevertheless, UPA is more common than UUR in the semen of infertile and infertile men. Compared with UUR, UPA has higher pathogenicity and affects progressive movement and secretion function of semen (Zhou et al., 2018). Thus, the role of Ureaplasma spp. in the development of male infertility is beginning to be recognized.
Chlamydia trachomatis (C. trachomatis) is considered to be one of the most common sexually transmitted pathogens of the male reproductive tract (La Vignera et al., 2014). The detection rate of C. trachomatis in infertile men was several times higher than that in healthy fertile men (Ahmadi et al., 2018). Several studies agree with the negative impact of C. trachomatis infection on sperm count, sperm motility, normal sperm morphology, ROS production, total antioxidant capacity, and the egg penetration ability of sperm of infertile men (Table 1). Furthermore, C. trachomatis infection may lead to a higher incidence of male infertility by inducing the production of anti-sperm antibodies (Soffer et al., 1990).
Mycoplasma hominis (M. hominis) is also a common sexually transmitted pathogen with global distribution. The detection rate for M. hominis in infertile men was approximately more than three times higher than that in healthy fertile men (Huang et al., 2016;Ahmadi et al., 2017). A decrease in sperm count, motility, and rates of normal morphology, along with elevated ROS levels, was found in infertile men with M. hominis infection ( Table 1). As a possible mechanism to explain the male infertility caused by M. hominis infection, Francisco Javier Diaz- Garcia et al. (2006) showed that M. hominis can adhere to the head, midpiece, or tail of the spermatozoa and can be internalized within cytosolic spaces (Diaz-Garcia et al., 2006).

Viral Infection Influences Male Infertility
Human papillomavirus (HPV) is one of the well-known sexually transmitted viruses, which may lead to HPV-associated cancers in both men and women. Most studies on HPV have only focused on HPV-related diseases in women. Few data were available about HPV infection in men until a recent study showed the presence of HPV in semen (Wang et al., 2010). The findings have drawn intense attention to the effect of HPV infection on male fertility impairment (Gizzo et al., 2014). Lyu et al. (2017) showed that the pooled HPV prevalence was higher in fertility clinic attendees (20.4%, 95% CI = 16.2-24.6%) than in the general population (11.4%, 95% CI = 7.8-15.0%), providing clues to HPV prevalence in semen and its relationship with male reproductive function. HPV16 is a high-risk type of HPV commonly found in male anogenital sites, prostate, bladder, and oropharynx (Ndiaye et al., 2014;Yang et al., 2015). According to Lyu's research, HPV16 is the most common type of HPV in semen, accounting for about 1/5 HPV-positive samples (Lyu et al., 2017). They also found that in semen, the positive rate of high-risk HPV56 was second only to that of HPV16. HPV56 is less common in HPV-related cancers (Lyu et al., 2017). Their conclusions are consistent with other evidence that HPV infection in semen may contribute to the risk of male infertility (La Vignera et al., 2015;Garolla et al., 2016). Baleful effects of HPV infection on male infertility have also been reported (Table 1), such as decreases in sperm count, sperm motility, sperm volume, and normal sperm morphology, which partly indicates that HPV infection is a risk factor for male infertility. Impaired sperm parameters, including a decrease in semen volume, sperm count, and progressive sperm motility, as well as an increase in abnormal sperm morphology in male patients with hepatitis B virus (HBV)/hepatitis C virus (HCV) infection, have been found in several studies ( Table 1). Exposure of human spermatozoa to HBV S protein enhances the early events of the apoptotic cascade and reduces sperm fertilizing capacity in vitro, which may explain the harmful effects of HBV on male infertility (J. Huang et al., 2013). Deleterious effects of HCV on male fertility are reflected not only by impaired sperm parameters but also by the lower total serum testosterone and higher serum E 2 and prolactin levels than those of healthy controls (Hofny et al., 2011).
Although human immunodeficiency virus (HIV) can be detected in semen shortly after infection and during all subsequent stages of disease, current research suggests that sperm alterations can be attributed to the effects of anti-HIV therapy rather than HIV infection itself (Garolla et al., 2013;Liu et al., 2018). Nevertheless, other viruses, such as herpesviruses, cytomegalovirus, and adeno-associated virus, were also reported to be connected with male infertility; currently available evidence does not support a clear association between these factors and male fertility (Garolla et al., 2013).

Fungal Infection Influences Male Infertility
Candida albicans (C. albicans) is one of the opportunistic pathogens of genitourinary infections, and its effect on male infertility has been poorly understood until a previous study reported that C. albicans is isolated from semen samples of asymptomatic men attending an infertility clinic (Rehewy et al., 1979). Then, the role of C. albicans on male infertility has been evaluated in several studies. C. albicans as well as its filtrates had an inhibitory effect on human sperm motility and impaired the ultrastructure of human spermatozoa in vitro, which could be associated with male infertility (Tian et al., 2007). Another study showed that the presence of C. albicans inhibited fertilization of oocytes and increased sperm DNA debris (Burrello et al., 2004). Fungal communities are likely closely tied to male infertility in ways we have only begun to characterize.

Therapeutic Effects of Antimicrobial Agents on Male Reproductive Tract Infection
Asymptomatic infection, accompanied by recurrent and latent infections, is one of the major challenges in the treatment of infertility. Raising awareness on cleanliness of genital area among infertile patients and adopting appropriate means of infection prevention or control could be considered for the management of infertile men (Farsimadan and Motamedifar, 2020). Antibiotic therapy may be beneficial in maintaining or restoring normal sperm parameters in infertile men with infections and several classes of antibiotics may be used, such as quinolones, trimethoprim, tetracyclines. macrolides, βlactam antibiotics, etc. Improvements in spermatozoal motility, including the speed of forward progression and the percentage of motile cells, were found in men who were successfully treated for UUR infection (Swenson et al., 1979). One study suggested that most semen parameters of male infertility associated with C. trachomatis infection returned to normal levels, and 42.9% of wives of C. trachomatis-infected infertile men became pregnant after antibiotic treatment (Ahmadi et al., 2018). Another study indicated that antibiotic therapy can improve semen parameters and treat male infertility induced by Mycoplasma hominis, resulting in 58.3% of female partners becoming pregnant after antibiotic therapy (Ahmadi et al., 2017). Although antibiotic treatment remains to be the cornerstone of treating bacterial/mycoplasma/chlamydia mediated infections, some antibiotics may damage sperm. Sperm damage has not been directly shown in humans in randomized clinical trials, but there is some data suggesting toxicity of certain antibiotics to testes and/or sperm in rats or mice, such as ciprofloxacin and pefloxacin, ofloxacin, lomefloxacin, tetracycline, cephalosporin and other cephalosporins, and norfloxacin (Calogero et al., 2017).
Few studies have been conducted to evaluate the efficacy of antiviral or antifungal therapy on sterile males with viral or fungal infections. Even still, researchers found that the treatment of HCV with antiviral drugs worsens semen parameters in sterile males (Hofer et al., 2010;Lorusso et al., 2010;Bukhari et al., 2018). More researches are needed to elucidate the pathologic mechanisms of the harmful effects of different microorganisms on the male reproductive system and how to stop microbial infection from damaging the male reproductive system, which will help to produce new treatments to save infertile men in the future.

MICROBIAL DYSBIOSIS AND MALE INFERTILITY
Recent studies have suggested that the composition of the human microbiota is closely related to health outcomes (Natarajan and Bhatt, 2020;Tett et al., 2021). Since then, there is growing concern about the role of microbiota in reproductive health. Here, we will give an overview of the major findings and discuss the close relationship between microbiota and male infertility. Besides, we will also present some studies on the effects of probiotic supplementation on male reproduction.

Gut Microbial Dysbiosis and Male Infertility
Most efforts on the microbiome are directed at the gastrointestinal tract, which harbors most of our microbes. Recent studies have found a direct relationship between dysbiosis of gut microbiota and male infertility, which may be an overlooked factor in clinical practice (Ding et al., 2020;Zhao et al., 2020;Lundy et al., 2021). It has been proven that the sperm concentration and motility were significantly decreased in mice treated with a high-fat diet, along with a decreased abundance of Bacteroidetes and Verrucomicrobia and an increased abundance of Firmicutes and Proteobacteria in their gut microbiota (Ding et al., 2020). Increased sperm concentration (twofold) and sperm motility (20-fold) were found after fecal microbiota transplantation from alginate oligosaccharide-dosed mice to busulfan-treated mice, and this was accompanied by an increase in the "beneficial" bacteria Bacteroidales and Bifidobacteriales (Zhang et al., 2021). Another relevant study suggested that alginate oligosaccharides can rescue busulfan-disrupted spermatogenesis in mice along with an increase in "beneficial" bacteria such as Bacteroidales and Lactobacillaceae and a decrease in "harmful" bacteria such as Desulfovibrionaceae in the gut microbiota (Zhao et al., 2020).
Although direct studies on the relationship of gut microbiota and male infertility are few, these current studies provide clues for further studies on the modification of gut microbiota in sterile male, which may provide valuable clues for treating male infertility.

Microbial Dysbiosis in the Reproductive System and Male Infertility
Compared to microbiota in other body sites, the study on microbiota in the male reproductive system is relatively lacking. Previous studies have mainly focused on the detection of known pathogens based on traditional culture-dependent methods, targeted PCR amplification, and microscope observation. With the appearance of next-generation sequencing, it has become easier to determine the complex microbiota in the male reproductive system with high accurate and more detailed understanding of its interaction with male infertility.
A large number of studies have found that the disturbance of the female reproductive tract microbiota can lead to a series of reproductive diseases (Ravel et al., 2011;Phukan et al., 2013;Petrova et al., 2015). Although the social impact of reduced male fertility has been recognized, the male reproductive tract microbiota has been poorly studied and. As mentioned above, the "culture-negative" status of male reproductive tract samples in microbiological tests was once considered free of microbial infection, which has resulted in the male reproductive tract microbiota not being well described for many years. Yet, recent emerging evidence indicates that a microbiota indeed exists in the healthy reproductive tract and its body fluids, e.g., seminal fluid and urine (Allen-Vercoe, 2013). Microbiome investigations of male infertility focused on semen samples, and the results were relatively consistent for the microbiota in infertile men's semen. Increased abundance of Prevotella and Staphylococcus, as well as decreased abundance of Lactobacillus and Pseudomonas, were shown in some studies (Weng et al., 2014;Baud et al., 2019;Farahani et al., 2021). Additionally, the abundance of Prevotella was negatively correlated with sperm motility and abnormal sperm morphology was directly correlated with the decreased abundance of Lactobacillus (Baud et al., 2019;Farahani et al., 2021).
Studies on the testicular microbiome are rare. Massimo Alfano et al. (2018) presented the first research evidence of a link between male infertility and alterations in the testicular microbiome.
Compared with normal reproductive men, infertile men lacked Bacteroidetes and Proteobacteria in the testes. Tests with negative sperm retrieval at micro-testicular sperm extraction showed a decrease in Firmicutes and Clostridium, a complete lack of Peptoniphilus asaccharolyticus, and an increase in Actinobacteria. However, the fact that the control samples were from nontumor regions of a tumor-bearing testis made it impossible to ignore the actual influence of the tumor microenvironment on the testicular microbiome. In addition, the small sample size of each group in this study further reduces its credibility. Lundy et al. (2021) found that both Collinsella (phylum Actinobacteria) and Staphylococcus (phylum Firmicutes) were depleted in semen after vasectomy, indirectly indicating the correlation between testicular microbiota and male infertility and providing further evidence for the relationship between male infertility and testicular microbiota. This study conducted by Lundy et al. (2021) also provided the first comprehensive survey of the rectum, semen, and urine microbiota of infertile men, in which similarities were found between the semen microbiota and the urine microbiota. Compared with the samples from fertile men, the rectum samples of infertile men showed decreased abundance of Anaerococcus and increased abundance of Lachnospiraceae, Collinsella, and Coprococcus; conversely, urine samples from infertile men contained increased Anaerococcus; semen samples of infertile men contained decreased Collinsella and increased Aerococcus (Lundy et al., 2021). Further study found a statistically negative correlation between Aerococcus abundance and both leukocytospermia and semen viscosity, a statistically negative correlation between Prevotella abundance and semen concentration, and a statistically direct correlation between Pseudomonas abundance and sperm count but inversely proportional to the pH of semen (Lundy et al., 2021).
Although the link between alterations in the male reproductive tract microbiota and male infertility is supported by the above studies, larger, multi-institution longitudinal studies are still needed to verify the accuracy of these results.

Probiotic Supplementation for the Management of Male Infertility
With growing knowledge of the relationship between the microbial dysbiosis and male infertility, it is also necessary to make clear how probiotic supplementation affects male infertility. Here, we will show some outcomes of probiotic intervention in sterile males.
Probiotic supplementation has gained increasing attention as a treatment in various medical fields, including reproductive health, due to its low side effects. In recent years, the effect of probiotic supplementation on the human gastrointestinal microbiota has been fully demonstrated (Unno et al., 2015). Probiotic supplementation have also been studied during pregnancy, the treatment of bacterial vaginitis, and assisted reproduction technology (Larsson et al., 2008;Barbonetti et al., 2011;Hemalatha et al., 2012;Gille et al., 2016;Chenoll et al., 2019).
In addition to the effects on roosters and murine models, probiotics have also been investigated for possible male reproductive impacts in zebrafish models. Feeding zebrafish with Lactobacillus rhamnosus CECT8361 and Bifidobacterium longum CECT7347 positively increased their sperm concentration, total motility, progressive motility, and fast spermatozoa subpopulations (Valcarce et al., 2019).
Therefore, oral intake of probiotics has the potential to be one of the ways to address male infertility. The main features and results obtained in studies involving probiotics and male infertility are shown in Table 2.

Limitations of the Current Studies on Microbiome and Male Infertility
Although many studies in this review are labeled randomized controlled trials, the randomization methods were not described in detail, the vast majority of controls were lacking or were blank controls, and almost no double-blind clinical studies were available, which greatly reduces the effectiveness of the research evidence. However, the accurate evaluation of the microbiome in male infertility is exceptionally difficult because of many confounding factors, such as age, body mass index, drinking and smoking habits, diet profile, scale and term of study, and medication exposure, may also affect microbiome and reproductive parameters. Standardization remains a critical issue hampering the implementation of microbiome analysis in clinical practice. Microbiome analysis may yield questionable conclusions of relevance to human disease if basic research information between cases and controls does not match (Vujkovic-Cvijin et al., 2020). This review is also limited by the small sample sizes in each study, application of different animal models, heterogeneity of the study types, discordance of reported outcomes, large variation in antimicrobial drug or probiotic administration, and limitation of sequencing range of 16S rRNA technology. Most studies have not examined the effect of the microbiome on clinical fertility outcomes and progeny of infertile men, such as clinical pregnancy rate and live birth rate.
Given the lack of evidence in the literature on this topic, further large-cohort prospective studies are needed to corroborate the importance of the microbiome as a cause of male infertility. Nonetheless, further work exploring the deeper mechanisms, as well as appropriate criteria for identifying patients with microbiome-induced infertility is required.

Future Trends in the Microbiome Association With Male Infertility
Although microbial infection or dysbiosis can affect male infertility, to date, only scant information is available about the influence of microbial infection or dysbiosis or its exact molecular mechanisms. Research in this field is just getting started, and there is still much work to be done in this area. For example, authoritative studies in this direction are expected to find out whether antibiotics cure infertile men by eliminating microbes, or whether there are other mechanisms involved, such as infection of the body's microbiome or immune system. Also, the study of female infertility and microbiota may have many implications for us.
Given the lack of research on the relationship between the microbiome and male infertility, the three steps of correlations, causality, and mechanisms need to be fully explored traditionally. Specifically,correlation analysis mainly involves sequencing samples from different sources to analyze the changes in microbiome structure,abundance, and species between infertile men and fertile men/cured infertile men. In the study of causality, methods of fecal microbiota transplantation or probiotic supplementation are often used to study the relationship between the microbiome and diseases from a macroscopic perspective. Mechanistic exploration is based on multiomics methods (metagenomics, transcriptomics and metabolomics,etc.) to identify differential metabolites and targets for further intervention. The rapid development of nextgeneration sequencing will give us a more detailed understanding of the microbiome in infertile men. Subsequent work must use a large number of samples from multiple sampling centers in order to gain a clear understanding of the microbiome and its changes during adolescence, first sexual activity, adulthood, and old age, as well as its impact on reproductive function and reproductive disease processes (Farahani et al., 2021). Nevertheless, there is also room for bold research innovations in this area, and integrated studies will thereby allow us to truly advance research on the microbiome and male infertility, moving from association to modulation. In the process of studying microbiome and male infertility, factors such as genetic mutations, diet, environmental factors, drugs, female infertility, fertility outcome, and mental condition can be included to make the research results more comprehensive and convincing. Therefore, the design of future studies should attempt to eliminate confounding factors in order to better understand the relationship and regulatory mechanisms between microbiota and infertile male (Bordalo Tonucci et al., 2017). Each study should provide a comprehensive description of its entire protocol and procedure for microbiome assessment, including experimental design, sequencing platform, sequencing area and applied database, to enhance the usefulness of these studies to other investigators. Therefore, it is recommended that future studies in this area should be conducted in a multicentre, randomized controlled trial or with a randomized double-blind placebo-controlled design. In addition, future studies should also focus on non-pathogenic organisms that may have a protective role, and how these organisms can be developed as therapeutic options (e.g., probiotics), and attention should be especially paid to the safety monitoring of probiotics in sterile males. The key process in studying the microbiome of infertile males is from understanding correlation to determining causal molecular mechanisms. We are entering an era of personalized microbiome medicine, where treatments can be tailored not only to human genetic polymorphisms but also to the make-up of individual microbes.

CONCLUSION
There is growing evidence that an imbalance of the human microbiome can cause a range of diseases, some of which can be treated by restoring the symbiotic microbiome. Breakthrough research on the microbiome has opened up new ideas for disease treatment, and microbiome therapy has emerged. This review provides a new perspective for the prevention and treatment of male infertility, especially the previously unknown etiology of idiopathic male infertility, and proposes a new idea for its precise treatment with probiotics. With increasing evidence of the relationship between the human microbiome and various diseases, the use of these results to prevent, treat, and predict the prognosis of diseases has become a research hotspot. Research on the microbiome of sterile males can provide a more comprehensive understanding of the pathogenesis of male infertility, and individualized treatment of the microbiome can be carried out to meet the fertility needs of sterile males.

AUTHOR CONTRIBUTIONS
XL designed the review, wrote the manuscript, performed the literature research, and drafted the manuscript. HW and AX performed revisions and critically discussed the complete manuscript.
LG was responsible for the concept and final revision of the manuscript. BZ and ZC participated in the content design, data supplement, and manuscript modification during the process of revising this review. All authors contributed to the article and approved the submitted version.


Limited cross-variant immune response from SARS-CoV-2 Omicron BA.2 in naïve but not previously infected outpatients

Summary Omicron is currently the dominant SARS-CoV-2 variant and several sublineages have emerged. Questions remain about the impact of previous SARS-CoV-2 exposure on cross-variant immune responses elicited by the SARS-CoV-2 Omicron sublineage BA.2 compared to BA.1. Here we show that without previous history of COVID-19, BA.2 infection induces a reduced immune response against all variants of concern (VOC) compared to BA.1 infection. The absence of ACE2 binding in sera of previously naïve BA.1 and BA.2 patients indicates a lack of meaningful neutralization. In contrast, anti-spike antibody levels and neutralizing activity greatly increased in the BA.1 and BA.2 patients with a previous history of COVID-19. Transcriptome analyses of peripheral immune cells showed significant differences in immune response and specific antibody generation between BA.1 and BA.2 patients as well as significant differences in the expression of specific immune genes. In summary, prior infection status significantly impacts the innate and adaptive immune response against VOC following BA.2 infection.

The ability of BA.2 infection to induce neutralizing antibodies in unvaccinated individuals, either without or with previous SARS-CoV-2 infection, is pending definition. Here we conducted a comparative investigation of the innate and humoral immune response elicited in 74 Omicron outpatients infected with BA.1 (Lee et al., 2022b) or BA.2 that were either naive prior to Omicron infection or had prior exposure to other SARS-CoV-2 variants. We measured antibody titers and ACE2 binding inhibition as a proxy for neutralization. In addition, we investigated the immune transcriptome and the repertoire of antibody germline alleles in these cohorts.

Antibody response after omicron subvariant infection
We measured antibody titers and neutralizing antibody responses in 74 outpatients infected with either Omicron BA.1 or BA.2 sublineages. Demographic and clinical information are provided in Table 1. Although none of the patients had been vaccinated, 24 had been infected by an earlier variant. More than 20% of the BA.1 infected, but none of the BA.2 infected patients, had moderate or severe disease.
Humoral immune response was measured between 7 and 21 days (median: days 13-14) post-documented Omicron BA.1 or BA.2 infection. In previously uninfected and unvaccinated individuals, anti-Omicron BA.1 spike IgG titers were approximately 40-fold lower following BA.2 infection compared to BA.1 infection ( Figure 1A; Table S1). IgG titers against the ancestral spike were almost 70-fold lower following BA.2 as compared to BA.1 infection. Anti-spike IgG antibody titers in individuals previously infected with earlier SARS-CoV-2 variants were at least 5-fold higher with no difference in titers between those infected with the BA.1 versus BA.2 variants ( Figure 1A; Table S1). This highlights that previous infection with variants has a particularly strong impact on the humoral immune response in BA.2 infected patients.
For reference, titers from individuals infected with the Omicron Beta variant  are shown. These approximate those obtained in individuals previously infected with an earlier variant who then experienced BA.1 or BA.2 infection. The differential response in antibody generation following BA.1 and BA.2 infection was consistent across all SARS-CoV-2 variants tested ( Figure 1B). We next measured neutralization capacity using the angiotensin-converting enzyme 2 (ACE2) binding inhibition assay ( Figure 1C). Neutralizing activities against the ancestral strain and Omicron BA.1 were similarly low in antigen-naive individuals infected with either BA.1 or BA.2. Like the overall antibody titers, iScience Article neutralizing activity was higher in those previously infected. Activity from BA.1 and BA.2 infected individuals with previous SARS-CoV-2 infection approximated that found for individuals infected with the Omicron Beta variant and was similar across all SARS-CoV-2 variants tested against ( Figure 1D).

Transcriptome response
Next, we investigated the impact of BA.1 and BA.2 infection on the immune transcriptome in Omicron patients with or without a previous infection history ( Figure 2; Table S2: Significantly differential genes between BA.1 and BA.2 infected cohorts with no prior infection; Table S3: Significantly differential genes between no prior and prior infection in BA.2 infected cohorts). Buffy coats were isolated within the first three days after validated Omicron infection and bulk RNA-seq was conducted with an average sequencing depth of 200 million reads per sample. First, we directly compared the transcriptomes of the BA.1 and BA.2 infected Omicron cohorts without prior infection. In a direct comparison of the two cohorts, expression of 38 and 15 genes was induced significantly in patients infected with BA.1 and BA.2, respectively ( Figure 2A; Table S2: Significantly differential genes between BA.1 and BA.2 infected cohorts with no prior infection). Although genes activated upon BA.1 infection are enriched in neutrophil degranulation and innate immune system, the ones activated upon BA.2 infection are categorized in complement pathway ( Figure 2B; Table S2: Significantly differential genes between BA.1 and BA.2 infected cohorts with no prior infection; Figure S1). Genes induced at significantly higher levels in BA.1 patients, such as C4BPA, initially identified by RNA-seq analyses ( Figure 2C) and validated by qRT-PCR ( Figure S1A), have also been reported to be regulated in COVID-19 patients (Gutmann et al., 2021;Jabeen et al., 2022). Of note, the complement genes C1QA and C1QB that show elevated expression in nonclassical monocytes from COVID-19 patients (Stephenson et al., 2021) are preferentially expressed in BA.2 patients as initially identified by RNA-seq analyses ( Figure 2C) and validated by qRT-PCR ( Figure S1B). We then compared the immune transcriptomes from BA.2 patients with and without prior infection and identified 342 and 145 significantly induced genes, respectively ( Figure 2D; Table S3: Significantly differential (C and D) Neutralization response to virus spike protein of the ancestral strain and Omicron variants (C) as well as the SARS-CoV-2 RBD (spike) of former variants (D). p-value between the two groups is from the one-tailed Mann-Whitney t-test. Percentage of samples with zero inhibition activity for each group is shown above the X axis. Asterisks indicate significance between groups. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. Line at median, dotted line at 90%.

OPEN ACCESS
iScience 25, 105369, November 18, 2022 3 iScience Article genes between no prior and prior infection in BA.2 infected cohorts). GSEA analyses linked the induced genes in the ''no prior infection'' cohort to innate immune responses, including interferon and inflammatory pathways ( Figure 2E). Genes activated in the ''no prior infection'' cohort such as CCL2 and IL6, initially identified by RNAseq ( Figure 2F) and validated by qRT-PCR ( Figure S1C), are part of the interferon response in severe COVID-19 (Cao, 2021;Munnur et al., 2021). Similar to what has been previously reported for transcriptome analysis of peripheral blood mononuclear cells of SARS-CoV-2 infected patients, in general, there was good agreement between RNA-seq ( Figure 2) and qRT-PCR results for genes differentially expressed between different cohorts ( Figure S1) (Zhang et al., 2021b).

Antibody germline repertoire
The higher level of anti-spike antibodies and neutralization activity in BA.1 patients of the ''no prior infection'' group compared to BA.2 infection, led us to dig deeper and interrogate the germline antibody gene usage. Specifically, we determined the range of immunoglobulin heavy chain (IGHV) gene usage elicited by BA.1 and BA.2 sublineage infection in the ''no previous infection'' cohort ( Figure 3). RNA-seq was conducted 13-16 days after the positive PCR test. The number of total immunoglobulin genes (heavy chain variable (IGHV), light chain variable (IGKV and IGLV) and T cell receptor alpha and beta variable (TRAV and TRBV)) revealed the use of a broad range of germlines in all cohorts without statistically significance ( Figure 3A). However, we observed an increased frequency of transcription of eight VH genes, including IGHV3-11,

DISCUSSION
While the Omicron BA.1 and BA.2 sublineages share mutations they also carry unique mutations, 28 in BA.2, which can explain their differential response to vaccine-induced and therapeutic antibodies (Ai et al., 2022;Arora et al., 2022aArora et al., , 2022bArora et al., , 2022b. Previous studies have also demonstrated that BA.1 infection of naive persons yields a very limited antibody response to SARS-CoV-2 VOCs other than Omicron itself (Lee et al., 2022b;Rö ssler et al., 2022). However, prior vaccination or prior infection with older SARS-CoV-2 variants augment the immune response upon BA.1 breakthrough infection (Lee et al., 2022b;Rö ssler et al., 2022). Our study now reveals that in antigen-naive individuals the immunologic response following BA.2 infection is even lower than that elicited by BA.1 infection as indicated by significantly lower antibody titers against all VOCs.
While other studies have focused on the humoral response including neutralizing activity induced by Omicron infection (Suryawanshi et al., 2022), we have extended the investigations and explored the presence of germline immunoglobulin variants induced by BA.1 and BA.2 infection. RBD-targeting antibodies can block SARS-CoV-2 binding to ACE2 and neutralizing immunoglobulin G heavy-chain variable (IGHV) gene usage has been reported (Yuan et al., 2020). Our study identified several IGHV germline alleles in Omicron patients that have been identified in neutralizing antibodies in COVID-19 patients (Yan et al., 2021;Yuan et al., 2020).
In conclusion, our study demonstrates that Omicron BA.2 infection of unvaccinated individuals without prior COVID-19 does not elicit effective antibodies against BA.1 and non-Omicron variants. In previously infected individuals, however, BA.2 infection successfully induces immunity against other variants.

Limitations of the study
There are several limitations to the current study. First, the study was conducted on volunteers from a specific geographical area, Tyrol (Austria). Secondly, the limitation is the confinement of the study to a timeframe of three weeks following Omicron infection. Third, the number of BA.2 patients with prior infection was very limited. Additional individuals for this cohort could not be recruited due to the replacement of BA.2 infection by BA.4 and BA.5 in Tyrol. Similar pandemic-related cohort size limitations have been noted for other transcriptome-based COVID studies (Sureshchandra et al., 2021;Zhang et al., 2021b). Lastly, some of the data collected on breakthrough infections were reliant on retrospective chart review and not collected as part of a prospective study.   Our gratitude goes to the participants who contributed to this study to advance our understanding of SARS-CoV-2 Omicron infection. We thank Yuhai Dai from the NIDDK clinical core for helping antibody assay. This work was utilized the computational resources of the NIH HPC Biowulf cluster (http://hpc.nih. gov). RNA-sequencing and single-cell RNA-sequencing were conducted at the NIH Intramural Sequencing Center, NISC (https://www.nisc.nih.gov/contact.htm).

DECLARATION OF INTERESTS
The authors declare no competing interests.

INCLUSION AND DIVERSITY
We worked to ensure gender balance in the recruitment of human subjects. We worked to ensure ethnic or other types of diversity in the recruitment of human subjects. We worked to ensure that the study questionnaires were prepared in an inclusive way. One or more of the authors of this paper self-identifies as an underrepresented ethnic minority in their field of research or within their geographical location. One or more of the authors of this paper self-identifies as a gender minority in their field of research. One or more of the authors of this paper self-identifies as living with a disability. While citing references scientifically relevant for this work, we also actively worked to promote gender balance in our reference list. We avoided ''helicopter science'' practices by including the participating local contributors from the region where we conducted the research as authors on the paper. iScience 25, 105369, November 18, 2022 iScience Article followed the 'Sex and Gender Equity in Research -SAGER -guidelines' and included sex and gender considerations where relevant.

Antibody assay
Whole blood was collected by medical personnel after subjects had tested positive for SARS-CoV-2. Antibody containing sera were obtained by centrifuging EDTA blood samples for 10 min at 4,000 g. End-point binding IgG antibody titers to various SARS-CoV-2-derived antigens were measured using the Meso Scale Discovery (MSD) platform. SARS-CoV-2 spike, nucleocapsid, Alpha, Beta, Gamma, Delta, and Omicron spike subdomains were assayed using the V-plex multispot COVID-19 serology kits (Panel 23 (IgG) Kit, K15567U). Plates were coated with the specific antigen on spots in the 96 well plate and the bound antibodies in the samples (1:50,000 dilution) were then detected by anti-human IgG antibodies conjugated with the MSD SULPHO-TAG which is then read on the MSD instrument which measures the light emitted from the tag.

ACE2 binding inhibition (neutralization) ELISA
The V-PLEX COVID-19 ACE2 Neutralization kit (Meso Scale Discovery, Panel 23 (ACE2) Kit, K15570U) was used to quantitatively measure antibodies that block the binding of ACE2 to its cognate ligands (SARS-CoV-2 and variant spike subdomains). Plates were coated with the specific antigen on spots in the 96 well plate and the bound antibodies in the samples (1:10 dilution) were then detected by Human ACE2 protein conjugated with the MSD SULPHO-TAG which is then read on the MSD instrument which measures the light emitted from the tag.

Extraction of the buffy coat and purification of RNA
Whole blood was collected, and total RNA was extracted from the buffy coat and purified using the Maxwell RSC simply RNA Blood Kit (Promega) according to the manufacturer's instructions. The concentration and quality of RNA were assessed by an Agilent Bioanalyzer 2100 (Agilent Technologies, CA). mRNA sequencing (mRNA-seq) and data analysis The Poly-A containing mRNA was purified by poly-T oligo hybridization from 1 mg of total RNA and cDNA was synthesized using SuperScript III (Invitrogen, MA). Libraries for sequencing were prepared according to the manufacturer's instructions with TruSeq Stranded mRNA Library Prep Kit (Illumina, CA, RS-20020595) and paired-end sequencing was done with a NovaSeq 6000 instrument (Illumina) yielding 200-350 million reads per sample.
For T-or B-cell receptor repertoire sequencing analysis, trimmed fastq files from bulk RNA-seq were aligned against human V, D and J gene sequences using the default settings with MiXCR (Bolotin et al., 2015(Bolotin et al., , 2017.

QUANTIFICATION AND STATISTICAL ANALYSIS
Differential expression gene (DEG) identification used Bioconductor package DESeq2 in R. P-values were calculated using a paired, two-side Wilcoxon test and adjusted p-value (pAdj) corrected using the Benjamini-Hochberg method. The cut-off value for the false discovery rate was pAdj >0.05. Genes with log 2 fold change >1 or < -1, pAdj <0.05 and without 0 value from all sample were considered significant. For significance of each GSEA category, significantly regulated gene sets were evaluated with the Kolmogorov-Smirnov statistic. p-values of antibody and qRT-PCR between two groups were calculated using one-tailed Mann-Whitney t-test on GraphPad Prism software (version 9.0.0). A value of *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001 was considered statistically significant.


s2fieldsofstudy: Biology Medicine

text:
ERBB2 as a prognostic biomarker correlates with immune infiltrates in papillary thyroid cancer

Epidermal growth factor receptor 2 (ERBB2) is commonly over-expressed in advanced or metastatic tissues of papillary thyroid cancer (PTC) with poor prognosis, while it remains unknown whether ERBB2 plays a role in the progression of PTC. Thus, we analyzed the data derived from online repositories, including TCGA, KEGG, GO, GeneMANIA, and STRING, to explore the relationship between ERBB2 expression and prognosis, tumor phenotypes of interest, and immune infiltrates in PTC. Compared to normal thyroid tissue, ERBB2 was up-regulated in PTC samples (p < 0.001); In comparison with the group with low expression of ERBB2, the group with high expression of ERBB2 had poorer progression-free interval in stage III/IV patients (p = 0.008) and patients aged >45 years (p = 0.019). The up-regulated ERBB2 was associated with iodine metabolism dysfunction, proliferation, metastasis, angiogenesis, and drug resistance. The expression of ERBB2 negatively correlated with enrichment scores of B cells (r = −0.176, p < 0.001), CD8+ T cells (r = −0.160, p < 0.001), cytotoxic cells (r = −0.219, p < 0.001), NK CD56dim cells (r = −0.218, p < 0.001), plasmacytoid dendritic cells (r = −0.267, p < 0.001), T cells (r = −0.164, p < 0.001), T follicular helper cells (r = −0.111, p = 0.012), gamma delta T cells (r = −0.105, p = 0.017), and regulatory T cells (r = −0.125, p = 0.005). In conclusion, ERBB2 may serve as a prognostic biomarker and an immunotherapeutic target in PTC, deserving further exploration.

Introduction
Thyroid cancer is one of the most common endocrine neoplasms, and the incidence rate is overgrowing, with 53,815 new cases expected in the US (Siegel et al., 2021) and 224,023 in China (Xia et al., 2022) in 2022. Papillary thyroid cancer (PTC) is the most widely recognized thyroid tumor, accounting for around 84% of all thyroid cancer patients (Fagin and Wells, 2016). Alarmingly, the incidence of invasive and metastatic PTC has risen 1.5-5-fold over the past 30 years (Lim et al., 2017). Within 10 years after initial surgery and radioiodine therapy, approximately 1/10 to 1/5 of PTCs developed local recurrence and distant metastasis; Once distant metastases of PTC occurred, the 5-year survival rate of patients would decrease from 98% to 63-75% (Cancer Facts 2022). The high rates of recurrence, metastasis, and radioiodine refractoriness have become the key bottleneck, stymying the cure of PTC (Jin et al., 2018).
Epidermal growth factor receptor 2 (ERBB2), a well-known oncogene of multiple cancers, such as breast cancer and ovarian cancer (Baxevanis et al., 2004), was found to be firmly connected with cancer occurrence, proliferation, metastasis, drug resistance, immune escape and poor prognosis (Dawood et al., 2009;Bates et al., 2018). Owe to the research on ERBB2, multiple diagnostic and therapeutic methods were established for tumor management (Mitri et al., 2012). In thyroid cancer, however, the deep-going research on ERBB2 is limited. Although ERBB2 is over-expressed in progressive and metastatic PTC cases (Kremser et al., 2003), and high expression of ERBB2 was found to be responsible for resistance to mitogen-activated extracellular signal-regulated kinase inhibitors (MEKi) (Montero-Conde et al., 2013), the ERBB2 upregulation-related unfavorable phenotypes, such as tumor occurrence, proliferation, metastasis, and poor progression-free interval (PFI), have not been comprehensively explored in PTC, and the relations between ERBB2 expression and the immune microenvironment in PTC remains unknown.
Given that the application of ERBB2-related management approaches, such as imaging probes and immunotherapeutic drugs, is limited by lacking knowledge of the association between ERBB2 expression and oncological features of PTC; we, therefore, conducted the bioinformatic study with integrated data from online repositories, including TCGA, KEGG, GO, GeneMANIA, and STRING to explore the potency and translational value of ERBB2 as a prognostic biomarker and immunotherapeutic target for PTC management.

Data collection
The mRNA data and clinical characteristics of PTC patients were downloaded from the TCGA database (https://www.cancer. gov/tcga/), an open data portal that compiled clinical information and RNA-Seq data of 33 cancers. The thyroid cancer (THCA) project of the TCGA database includes 568 samples (510 PTC and 58 paracancerous normal samples). Clinical data collection for the THCA project was mainly completed in 2014; thus, PTC staging was adopted in AJCC 7 th edition (Agrawal et al., 2014). The mRNA sequencing data of all samples were converted to the format of transcripts per million (TPM) for subsequent analysis.

OncoPrint analysis online
OncoPrint is a method for visualizing samples by integrating gene expression heat map and gene variant distribution map. The gene expression and gene mutation data in OncoPrint analysis were derived from the TCGA-THCA dataset and analyzed online (https://www.cbioportal.org). The OncoPrint analysis was utilized to show the association between ERBB2 expression and BRAF and RAS mutant PTC sample distribution.

Progression-free interval analysis
The Kaplan-Meier Progression-free interval (PFI) curve compared prognosis differences between patients with high and low expression of ERBB2. Since the death reports of PTC in the TCGA database were few (n = 16), the clinical prognosis data included in this study were PFI. Due to the limited number of patients with distant metastases in the database (n = 9), we could not analyze PFI in the subgroup of patients with distant metastases.

Analysis of immune infiltrates
The enrichment scores of 24 immune cells were based on the reported literature (Bindea et al., 2013). The correlation analysis between ERBB2 expression and enrichment scores of immune infiltration was tested with a single sample gene set enrichment analysis (ssGSEA) with R packages of clusterProfiler (version 3.8.0).

Analysis of ERBB2-related differentially expressed genes (DEGs)
To further understand the role of ERBB2 in the progression of PTC, we screened the DEGs between samples with high and Frontiers in Genetics frontiersin.org low expression of ERBB2. We applied Log 2 (fold change) > 1.5 and adjusted p < 0.001 to select DEGs. The analysis is performed using the R package of DESeq2 (version 3.8). All the DEGs were graphed in a volcano plot.

Enrichment analysis of KEGG and GO terms
GO analysis is a widely applied bioinformatics tool for determining ERBB2-related biological processes, cellular components, and molecular functions. KEGG analysis is used to analyze ERBB2-related signal path changes. We applied GO and KEGG to analyze the biological function of ERBB2 in PTC. To understand how ERBB2 is involved in tumorigenesis, we used KEGG and GO online tools to analyze the signaling pathways and gene functions in which ERBB2-related DEGs participated. Gene set enrichment analysis (GSEA) with R package clusterProfiler (version 3.8.0) evaluated the ERBB2-related DEGs contributing to annotated gene functions, cell phenotypes, signal pathways, and diseases.

GeneMANIA and string analysis online
The network of gene-gene relations was constructed with the online tool GeneMANIA (https://genemania.org/). The website extensively integrated data on gene-gene interactions, gene co-expression networks, and gene function enrichment. To understand the ERBB2-involved mechanisms, the STRING tool (version 11.5, https://stringdb.org/) was utilized to analyze the protein-protein interaction (PPI) network of corresponding proteins of ERBB2-related DEGs. The PPI network was displayed with Cytoscape software (version 3.9.1).

Statistical analysis
All statistical analyses and graphs were performed using R software (version 3.6.3). The ERBB2 expression in unpaired and paired samples was analyzed with Wilcoxon rank-sum test and Wilcoxon signed-rank test, respectively. Kruskal-Wallis test was tested to assess the relationship between clinical/ cytogenetic features and ERBB2 expression. The differences in enrichment scores of immune infiltrate were analyzed using the Wilcoxon rank sum tests. The Spearman statistical method was adopted for the correlation analysis between ERBB2 expression and the gene expression of interest or enrichment scores of 24 immune cells. Hazard ratio, log-rank tests, and the Kaplan-Meier curve were applied to assess the role of ERBB2 as a prognostic biomarker. P < 0.05 was considered statistically significant.

Aberrantly upregulated ERBB2 expression in PTC samples
By analyzing the RNA-Seq data downloaded from TCGA data, we found that the ERBB2 expression in PTC samples was generally higher than that in normal thyroid tissue (unpaired test: p < 0.001, Figure 1A; paired test: p < 0.001, Supplementary Figure S1). OncopPrint plot displayed that PTC tissues with high expression of ERBB2 were mainly distributed in BRAF mutation samples rather than RAS mutation samples ( Figure 1B). Subgroup analysis showed that the ERBB2 expression in BRAF mutant PTC, classic PTC, and PTC with bilateral foci were significantly higher than those in corresponding subgroups of RAS mutant PTC (p < 0.001), follicular variant PTC (FVPTC) (p < 0.001), and PTC with unilateral foci (p = 0.02), respectively ( Figure 2). To determine the ERBB2-related clinical value and the underlying mechanism, PTC samples with high and low expression of ERBB2 were divided based on the median TPM value. The characteristics of patients with the high and low expression of ERBB2 were summarized in Supplementary Table S1.

Elevated ERBB2 expression was associated with the poor prognosis
The relations between ERBB2 expression and PFI were analyzed using the Kaplan-Meier curve. As is shown in Figure 3, the group with high expression of ERBB2 had a significantly poorer prognosis than that with low expression of ERBB2 in stage III/IV patients (HR: 2.7, CI: 1.29-5.66, p = 0.008) and patients aged >45

Highly expressed ERBB2 was correlated to unfavorable tumor phenotypes
The association between ERBB2 expression and clinical phenotypes was performed with correlation analysis. The coexpression heat map showed that the expression of ERBB2 positively correlated with TSHR but negatively correlated with SLC5A8, SLC26A4, and TPO ( Figure 4A

Up-regulated ERBB2 was associated with suppressed tumor-infiltration of immune cells
The tumor-infiltrating immune cells were quantified by ssGSEA. Compared to the samples with low expression of ERBB2, the enrichment scores of T cells, CD8 + T cells, cytotoxic cells, NK CD56dim cells, plasmacytoid dendritic (pDC) cells, gamma delta T cells (Tgd), and regulatory T cells (TReg) were lower in samples with high expression of ERBB2 ( Figure 5A). The correlation analysis showed that ERBB2 expression was negatively correlated with the enrichment scores of B cells, CD8 + T cells, cytotoxic cells, NK CD56dim cells, pDC, T cells, follicular helper T cells (TFH), Tgd cells, and TReg cells ( Figure 5B, Supplementary Table S2).

Identification of DEGs between samples with high and low expression of ERBB2
A total of 146 DEGs between samples with high and low expression of ERBB2 were yielded (Supplementary Table S3). The five most up-regulated genes and the five most downregulated genes were shown in the volcano plot ( Figure 6). Spearman correlation analysis found that the expression of ERBB2 negatively correlated with KLK15, KLK1, ARSF, and FGF21; and positively correlated with TAGLN3, GLRA1, RSPO1, SPAG11B, and SPAG11A (Supplementary Figure S5).

ERBB2-related DEGs participated in the primary immunodeficiency signaling pathway
Annotations from KEGG and GO were enriched with 146 ERBB2-related DEGs, showing that the ERBB2-related DEGs were involved in the KEGG term of primary immunodeficiency and GO term of the humoral immune response, regulation of execution phase of apoptosis, and Frontiers in Genetics frontiersin.org negative regulation of execution phase of apoptosis ( Figure 7A). Thereinto, the majority of ERBB2-related DEGs (n = 23) were enriched into the primary immunodeficiency signaling pathway, attracting more attention. GSEA analysis adds more insight into the 23 ERBB2-related DEGs and primary immunodeficiency signaling pathway, showing that ERBB2-related DEGs associated with primary immunodeficiency were mainly down-regulated ERBB2related DEGs, such as CD79A, CD19, CD3D, CD3E, CD8A, and CD4 ( Figure 7B).

ERBB2-related DEGs constructed the core PPI networks
Network analysis showed that the 23 ERBB2-related DEGs had gene interaction and co-expression relationships with each other; the functional enrichment found that the 23 ERBB2related DEGs involved in the function of a variety of immune cells, including the functions of B cells, monocytes, and T cells (Supplementary Figure S6). The PPI network of corresponding proteins of the ERBB2-related DEGs was screened with the  Figure S7).

Discussion
Despite ERBB2 being a well-known oncogene and regarded as a crucial diagnostic/prognostic biomarker and therapeutic target in multiple tumors, such as breast cancer (Wynn and Tang, 2022), the exact function of ERBB2 in tumors remains largely unknown. Only a few data are available in PTC, showing an 18%-100% rate of positive expression of ERBB2 protein via immunohistochemistry in 43-45 cases (Kremser et al., 2003;Ruggeri et al., 2016). Thus, expression profiles of the ERBB2 gene in the PTC need to be comprehensively investigated in the context of a large-scale study. In the present study, we took advantage of a large-scale analysis of gene expression profiles in 510 PTC patients in the TCGA database. The bioinformatic analysis adds the knowledge of detailed relations between ERBB2 expression and the clinical characteristics, prognosis, and immunoenvironment of PTC. Moreover, it is a novel finding that the expression levels of ERBB2 were significantly correlated to the markers of the immune cells in the PTC tissues, suggesting that ERB2 may play a crucial role in regulating the tumorimmunoenvironment. The present study uncovered the biological and clinical roles of ERBB2 in PTC, which might help imply novel ERBB2-based management strategies to improve the prognosis of PTC patients.
ERBB2 is up-regulated in multiple tumors located in breast, bladder, pancreas, ovary, and esophagus, especially in tumors with poor prognostic characteristics; in breast cancer, high expression of ERBB2 has become one of the hallmarks of poor prognosis (Dawood et al., 2009). Similarly, our study showed that the expression of ERBB2 in PTC was aberrantly up-regulated. A step further, we found that ERBB2 expression in patients with bilateral PTCs was higher than that in those with unilateral foci, which may be related to the fact that bilateral PTC is more aggressive and metastatic than the PTC with unilateral foci (Qu et al., 2016). Meanwhile, we found that the expression of ERBB2 in classic PTC was higher than that in FVPTC, which may be related to the poorer prognosis of PTC than that of FVPTC . The prognosis analysis in our study uncovered that the high expression of ERBB2 is a risk factor for poor prognosis in PTC.
The highly expressed ERBB2 was deemed to participate in cancer progression, promoting tumor proliferation, invasion, and metastasis (Mitri et al., 2012). PTC patients with high expression of ERBB2 were prone to suffer from distant metastasis (Kremser et al., 2003). In our study, the expression of ERBB2 was found to be positively correlated with VEGFassociated genes of VEGFA, FLT, KDR, and FLT4 (Shibuya, 2011), vascular endothelial cell markers of PECAM1 and VWF (Bauer et al., 2015), and vascular support-related genes of TEK, ANGPT1, ANGPT2, CDH5, CLDN5, and JAM2 (Oshi et al., 2021). Furthermore, the expression of ERBB2 was positively correlated with lymph node metastatic signature genes such as EVA1A, TIMP1, SERPINA1, FAM20A, FN1, TNC, and MXRA8 (Wu et al., 2021), and distant metastatic signature genes of Kaplan-Meier curves of progression-free interval comparing the high and low expression of ERBB2 in thyroid cancer patients. (A) In stage III/IV patients, the progression-free interval of the high ERBB2 expression group was poorer than that of the low expression group (p = 0.008). (B) In patients aged >45 years, the progression-free survival of the high ERBB2 expression group was poorer than that of the low expression group (p = 0.019).
Frontiers in Genetics frontiersin.org MMP2, PTTG1, VEGFC, CXCR4, and FGF2 (Liang et al., 2011). Besides, ERBB2 expression was also positively correlated with proliferation-related genes, such as MKI67, PCNA, and MCM2 (Juríková et al., 2016). Collectively, highly expressed ERBB2 is involved in progressive oncological behaviors of PTC, which might be responsible for the poor prognosis. Interestingly, high expression of ERBB2 was found to be associated with dysfunction of iodine metabolism of PTC in our study. The expression of ERBB2 was negatively correlated with SLC5A8, SCL26A4, and TPO, which relate to iodine absorption, transport, and organization. The downregulated SLC5A8 was associated with impaired resorption of organic iodine (Porra et al., 2005), making it challenging to transport iodine across the basal membrane to cell cytoplasm in thyroid follicular cells (Anekpuritanang et al., 2021). Meanwhile, TPO is the crucial protein to organize iodine with the participation of H 2 O 2 , and TPO deficiency would prevent iodine from taking part in the organization process (Kessler et al., 2008). Although the expression of ERBB2 was positively correlated with that of TSHR, the role of TSHR in radioiodine refractoriness remains Frontiers in Genetics frontiersin.org debated because the highly expressed TSHR is not only associated with high radioiodine uptake (Hou et al., 2010) but also causes fast thyroid tumor growth (Lu et al., 2010). Therefore, the high expression of ERBB2 is likely to associate with the poor efficacy of radioactive iodine to some extent, and this mechanism needs to be further studied.
In recent years, targeted drugs, including commonly applied receptor tyrosine kinase inhibitors (RTKi) and novel MEKi, have become valuable antitumor drugs for advanced or progressive thyroid cancer (Jin et al., 2018). Notwithstanding the broad application of RTKi, the occurrence of RTKi-related severe adverse events is frequent, and drug resistance eventually develops in almost all PTC patients (Cheng et al., 2020). Compared to RTKi, the MEKi has the additional capability of inducing tumor differentiation, activating immune recognition, and possesses the characteristic of a lower incidence of severe adverse events (Neuzillet et al., 2014), attracting more attentions and expectations in the field of anti-PTC. Still, MEKi resistance is the typical scenario, often calling for a combination of MEKi and other therapeutics; nevertheless, the improvement of efficacy by the combination is generally limited (Shah et al., 2017), requiring a crucial key to solving the most pressing challenge. MEKi resistance has been proven to be closely related to the high expression of ERBB2 (Sa et al., 2022). For instance, high ERBB2 expression is one characteristic phenotype of thyroid cancer resistance to MEKi selumetinib (Montero-Conde et al., 2013). Our study further explored the correlation between the expression of ERBB2 and MEKi resistance genes of SPRY2, SPRY4, ETV4, ETV5, DUSP4, DUSP6, CCND1, EPHA2, and EPHA4 (Wagle et al., 2018). In BRAF or RAS mutant tumor cells, DUSP4, DUSP6, SPRY2, and SPRY4 tend to be highly expressed, allowing tumors to evade regular MAPK signaling pathway feedback (Pratilas et al., 2009). Other genes, such as ETV4, ETV5, CCND1, EPHA2, and EPHA4, also play a role in ERBB2 expression negatively correlated with the enrichment scores of CD8 + T cell, cytotoxic cell, NK CD56dim cell, pDC, T cell, TFH, Tgd, and TReg. DC, dendritic cell; aDC, activated dendritic cell; pDC, plasmacytoid dendritic cell; iDC, interdigitating dendritic cell; Tcm, central memory T cell; Tem, effector memory T cell; TFH, follicular helper T cell; Tgd, T gamma delta, γδ; Th1, T helper type 1; Th17, T helper type 17; Th2, T helper type 2; Treg, regulatory T cells; NK, natural killer. *p < 0.05, **p < 0.01, ***p < 0.001.

Frontiers in Genetics
frontiersin.org activating the MAPK pathway, promoting tumor resistance to MEKi (Chesnokov et al., 2022). Together with the previous study (Montero-Conde et al., 2013), our study confirmed the relationship between highly expressed ERBB2 and MEKi resistance in PTC. Over the past decade, immune-related diagnostic techniques have successfully become new tools in tumor management. For instance, Michael D Farwell et al. conducted a phase I trial of PET/CT scan with 89 Zr-labeled IAB22M2C minibody targeting CD8 + cells, which visualized the biodistribution of tumorinfiltrating CD8 + T cells and predicted early response to immunotherapy (Farwell et al., 2021). In the present research, ERBB2 expression was inversely correlated with the enrichment scores of CD8 + T cells. Therefore, radioactive or non-radioactive probes, such as 68 Ga-labeled anti-ERBB2 Nanobody (Keyaerts et al., 2016) or polyethylene glycol-conjugated anti-ERBB2 peptides targeting ERBB2 protein (Guan et al., 2018) might be of value for predicting the response to immunotherapy in PTCs. In addition, ERBB2-targeted imaging would overcome the false positive uptake of 89 Zr-labeled IAB22M2C in CD8 + normal tissues, such as the bone marrow and lymphnodes.
The finding, ERBB2 overexpression related to suppressed T cell infiltration in tumors, is not unique to PTC. Liu et al. (2021) also found that ERBB2 expression was negatively correlated with the infiltration of B cells and CD8 + T cells in cutaneous melanoma, similar to our findings. In breast cancer, ERBB2 is related to low expression of MHC class I surface antigen, causing impaired recognition between tumor cells and CD8 + T cells (Herrmann et al., 2004;Seliger and Kiessling, 2013). Besides, the PD-1/PD-L1 antibodies have been used in clinical trials to treat advanced, progressive, or metastatic thyroid cancer. However, the results were unsatisfactory; most patients still have an unfavorable response to PD-1 antibody therapy (Mehnert et al., 2019), related to the limited T-cell infiltration in PTC (Bastman et al., 2016). DEG analysis showed that in PTC samples with high expression of ERBB2, KLK15 and KLK1 were downregulated. The down-regulated KLKs were previously reported to be responsible for impaired immune microenvironment reprogramming during an antitumor immune response (Srinivasan et al., 2022). In our research, KEGG and GO annotation analysis found that the DEGs were mainly associated with the primary immunodeficiency pathway, which is closely related to tumor occurrence and the failure of antitumor immunotherapy (Tangye et al., 2020). Therefore, increasing tumor-infiltrating immune cells by applying bispecific antibodies or adding cytokines, e.g., PD-L1×CD3 bispecific antibody (Yang et al., 2021) or interleukin-17 (Nagaoka et al., 2020), in ERBB2 highly expressed tumors may be beneficial to improving the response to immunotherapy in PTC patients.
In addition, we found that NPY and ESR1 were located at the center of the ERBB2-related PPI network. NPY is a pleiotropic gene initially thought to be an endogenous anxiolytic peptide whose expression can be regulated by stress; however, NPY has been found to promote the growth and migration of breast cancer cells in recent years . ESR1 encodes an estrogen receptor, plays a crucial role in the occurrence and progression of breast and endometrial cancer, and is the main reason for resistance to estrogen suppression therapy (Piscuoglio et al., 2018). These results suggest that ERBB2 may be involved in the occurrence and progression of PTC together with NPY and ESR1; the mechanism needs further investigation.
Despite the fact that our data analysis enhanced the understanding of the roles of ERBB2 associated with PTC progression, there remain some limitations. First, the role of ERBB2 in follicular thyroid cancer, poorly differentiated thyroid cancer, and anaplastic thyroid cancer could not be investigated because those pathological types or endpoint information were lacking in online databases. Second, despite large sample studies in public databases prone to possess the superiority of strong evidence, public databases remain to lack some clinical information on PTC tissue around the prognostic tumor status of radioiodine uptake, angiogenesis, proliferation, drug resistance, and serum thyroglobulin levels; Therefore, we cannot perform a clinically based analysis comparing those prognostic factors above, and directly investigating their relations with ERBB2 expression.

FIGURE 6
Volcano plot of differentially expressed genes (DEGs) between high and low ERBB2 expression groups. The significantly up-regulated genes are red dots; the significantly down-regulated genes are blue dots. The dots marked with gene names are the most up-regulated five DEGs and the most down-regulated five DEGs.
Frontiers in Genetics frontiersin.org Thus, a more comprehensive prospective study is of value in the future to detail the role of ERBB2 in thyroid cancer. Lastly, this research mainly relied on mRNA data from the TCGA database; therefore, it is necessary to further experimentally investigate the direct evidence of ERBB2-related mechanisms in PTC tumorigenesis and progression in vitro or in vivo.
Because of the complex interaction between ERBB2 and mutant genes, we didn't separate poor prognosis contributed by ERBB2 overexpression and gene mutations. To step further, we have planned a more profound analysis in the subsequent study to separate their contributions to poor prognosis. As is known, BRAF mutation is a driver gene for tumorigenesis, which causes ERBB2 overexpression and poor prognosis of PTC (Kebebew et al., 2007;Caria et al., 2016). It remains unknown whether ERBB2 overexpression independently contributes to the poor prognosis of PTC, which need further validation.
The present work, the first to document, provides a comprehensive study of the relationship between the ERBB2 expression and clinical characteristics of a large-scale PTC cohort. We found that ERBB2 was highly expressed in PTC, and FIGURE 7 KEGG and GO enrichment analysis of differentially expressed genes between high and low expression of ERBB2 groups. (A) 146 ERBB2-related differentially expressed genes were enriched in KEGG signaling pathways and GO annotations, such as the primary immunodeficiency. (B) GSEA analysis of 19433 protein-coding genes and 146 ERBB2-related differentially expressed genes showed that the genes involved in the primary immunodeficiency pathway were mainly 23 down-regulated genes in the ERBB2 high expression group; Gene expression was sorted by log 2 (fold change).
Frontiers in Genetics frontiersin.org elevated ERBB2 expression was associated with iodine metabolism dysfunction, tumor proliferation, metastasis, angiogenesis, MEKi resistance, and poor prognosis. Meanwhile, ERBB2 expression was inversely correlated with the infiltration of immune cells in PTC tissues. It seems that ERBB2 might be a prognostic biomarker and an immunotherapeutic target in PTC, warranting further clinical validation. The study might also be a new start to expect future investigations on the latent mechanisms that bridge ERBB2 expression, clinical characteristics, and immunosuppressive environment in PTC.

Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.

Author contributions
Conceptualization, YJ, XQ, and ZH; methodology, YJ; software, YJ; validation, XQ, and ZH; formal analysis, YJ; investigation, YJ and ZH; resources, LC; data curation, ZH, and RS; writing-original draft preparation, YJ; writing-review and editing, XQ, ZH, JW, and RS; visualization, YJ; supervision, LC; project administration, LC; funding acquisition, LC. All authors have read and agreed to the published version of the manuscript.

Funding
The study was funded by the National Natural Science Foundation of China, grant numbers 81671711 and 82171981.