Serum Proteomics Identifies Immune Pathways and Candidate Biomarkers of Coronavirus Infection in Wild Vampire Bats

The apparent ability of bats to harbor many virulent viruses without showing disease is likely driven by distinct immune responses that coevolved with mammalian flight and the exceptional longevity of this order. Yet our understanding of the immune mechanisms of viral tolerance is restricted to a small number of bat–virus relationships and remains poor for coronaviruses (CoVs), despite their relevance to human health. Proteomics holds particular promise for illuminating the immune factors involved in bat responses to infection, because it can accommodate especially low sample volumes (e.g., sera) and thus can be applied to both large and small bat species as well as in longitudinal studies where lethal sampling is necessarily limited. Further, as the serum proteome includes proteins secreted from not only blood cells but also proximal organs, it provides a more general characterization of immune proteins. Here, we expand our recent work on the serum proteome of wild vampire bats (Desmodus rotundus) to better understand CoV pathogenesis. Across 19 bats sampled in 2019 in northern Belize with available sera, we detected CoVs in oral or rectal swabs from four individuals (21.1% positivity). Phylogenetic analyses identified all RdRp gene sequences in vampire bats as novel α-CoVs most closely related to known human CoVs. Across 586 identified serum proteins, we found no strong differences in protein composition nor abundance between uninfected and infected bats. However, receiver operating characteristic curve analyses identified seven to 32 candidate biomarkers of CoV infection, including AHSG, C4A, F12, GPI, DSG2, GSTO1, and RNH1. Enrichment analyses using these protein classifiers identified downregulation of complement, regulation of proteolysis, immune effector processes, and humoral immunity in CoV-infected bats alongside upregulation of neutrophil immunity, overall granulocyte activation, myeloid cell responses, and glutathione processes. Such results denote a mostly cellular immune response of vampire bats to CoV infection and identify putative biomarkers that could provide new insights into CoV pathogenesis in wild and experimental populations. More broadly, applying a similar proteomic approach across diverse bat species and to distinct life history stages in target species could improve our understanding of the immune mechanisms by which wild bats tolerate viruses.

Relationships between bats and CoVs specifically have been of particular interest for zoonotic risk assessment (21,22). CoVs are RNA viruses found across mammals and birds, with at least seven known human viruses: two and five in the genera Alphacoronavirus and Betacoronavirus (23,24). A novel a-CoV with canine origins was recently detected in humans, but its role in zoonotic disease remains unclear (25). CoVs are highly diverse in bats, which are the likely ancestral hosts of aand b-CoVs (26)(27)(28). The evolutionary origins of highly pathogenic CoVs (i.e., SARS-CoV, MERS-CoV, SARS-CoV-2) have also been ascribed to bats, but spillover has typically involved intermediate hosts rather than direct bat-to-human transmission (5,29,30).
Our understanding of bat-CoV interactions and the bat immune response to infection remains limited and has stemmed mostly from experimental studies of a few select species, including Rousettus leschenaulti, R. aegyptiacus, Artibeus jamaicensis, and Eptesicus fuscus (10,11,28,31,32). In vivo studies have typically found short-term CoV replication and shedding without substantial weight loss or pathology, supporting viral tolerance, although some species seem to entirely resist infection. Tolerance appears driven by innate immune processes, such as increased expression of ISGs, with little adaptive immune response. In vitro studies have further supported bat receptor affinity for CoVs (i.e., susceptibility) but little host inflammatory response in bats (10,11,16,33).
Despite the clear insights afforded by experiments, model bat systems are heavily limited by logistical constraints (e.g., necessity for specialized facilities, colony maintenance) and have been mostly focused on frugivorous bats, which are relatively easy to keep in captivity compared to other dietary guilds (34,35). Transcriptomics of key tissues (e.g., spleen) has helped advance the field by identifying immune responses to infection in a wider array of species (36)(37)(38). However, such approaches are restrictive when lethal bat sampling is limited, such as when working with threatened species, in protected habitats, or in longitudinal studies to assess how these viruses persist in bat populations. Blood transcriptomes overcome such challenges to a degree and are increasingly feasible (39,40), yet such assays are only informative about the immune response in the blood itself. Proteomics, on the other hand, provides a unique and more nuanced perspective into the immune system, because the blood proteome includes proteins secreted from not only blood cells but also proximal organs such as the liver (41).
Proteomics holds special promise for illuminating the innate and adaptive immune factors involved in bat responses to infection, because it can accommodate the small bat blood volumes typical of field studies (e.g., <10 mL). Recently, we surveyed the serum proteome of wild vampire bats (Desmodus rotundus) (42). Owing to its diet of mainly mammal blood, this species is the primary reservoir host of rabies lyssavirus in Latin America, and alpha-and betacoronaviruses have also been identified in this species (43)(44)(45)(46). Using only 2 mL sera from these small (25-40 gram) bats, we identified 361 proteins across five orders of magnitude, including antiviral and antibacterial components, 20S proteasome complex, and redox activity proteins. Mass spectrometry-based proteomics can thus facilitate the relative quantification of classical immunological proteins while also providing insight into proteins yet to be fully recognized for their importance in resolving viral infection (47)(48)(49).
Here, we expand our recent work on serum proteomics in vampire bats in the context of CoV infection. First, by profiling the serum proteome of the same host species in an additional year of study, we provide a more general and comprehensive characterization of the wild bat immune phenotype. Second, owing to changes in regulations for importing vampire bat samples into the United States, we also assess the impact of heat inactivation, a common method of inactivating bat sera (50), on the proteome. Lastly, and most importantly, we assess differences in the serum proteomes of wild bats with and without acute CoV infection. We aimed to identify up-and downregulated immune responses of wild bats to CoV infection, with particular interest in comparisons with results from experimental infections. We also aimed to guide the discovery of candidate serum biomarkers of viral infection. By taking an agnostic approach via discovery proteomics (51), such biomarkers could provide new and mechanistic insight into CoV pathogenesis in wild bats (52).

Vampire Bat Sampling
As part of an ongoing longitudinal study (53), we sampled vampire bats in April 2019 in the Lamanai Archeological Reserve, northern Belize. This same population was sampled in 2015 for our earlier proteomic analysis (42). For the 19 individuals included in this study, we used a harp trap and mist nets to capture bats upon emergence from a tree roost. All individuals were issued a unique incoloy wing band (3.5 mm, Porzana Inc) and identified by sex, age, and reproductive status. For sera, we collected blood by lancing the propatagial vein with 23-gauge needles followed by collection with heparinized capillary tubes. Blood was stored in serum separator tubes (BD Microtainer) for 10−20 minutes before centrifugation. Following recent CDC guidelines, all sera were inactivated for importation to the United States by heating at 56°C for one hour. We also collected saliva and rectal samples using sterile miniature rayon swabs (1.98 mm; Puritan) stored in DNA/RNA Shield (Zymo Inc). Samples were held at −80°C using a cryoshipper (LabRepCo) prior to long-term storage. Bleeding was stopped with styptic gel, and all bats were released at their capture location. Field protocols followed guidelines for safe and humane handling of bats from the American Society of Mammalogists (54) and were approved by the Institutional Animal Care and Use Committee of the American Museum of Natural History (AMNHIACUC-20190129). Sampling was further authorized by the Belize Forest Department via permit FD/WL/1/19(09). Serum specimens used for proteomic analysis were approved by the National Institute of Standards and Technology Animal Care and Use Coordinator under approval MML-AR19-0018.

CoV Screening and Phylogenetics
As part of a larger viral surveillance project, we extracted and purified RNA from oral and rectal swabs using the Quick-RNA Viral Kit (Zymo Research). With exception of one bat, RNA from both swab types was available for all sera. We used a seminested PCR targeting the RNA-dependent RNA polymerase gene (RdRp) of alpha-and betacoronaviruses, following previous protocols (55). Amplicons were submitted to GENEWIZ for sequencing. Resulting sequences were aligned using Geneious (Biomatters; (56), followed by analysis using NCBI BLAST (57). PhyML 3.0 was used to build a maximum-likelihood phylogeny of these and additional CoV sequences (58).
To assess possible risk factors for CoV infection (in oral or rectal swab samples), we fit univariate logistic regression models with bat age, sex, and reproductive status as separate predictors. Because the small sample sizes used here could bias our estimates of odds ratios, we used the logistf package in R to implement Firth's bias reduction (59).

Proteome Profiling
In addition to profiling serum from the 19 bats described above, we also performed another proteomic experiment to evaluate effects of heat inactivation with previously analyzed samples collected in 2015 (42). From four non-inactivated sera, we submitted an aliquot of each sample to the heat inactivation process used for our 2019 samples (56°C for one hour). We then processed the paired non-inactivated and heat-inactivated sera and the 19 sera in two batches using the S-Trap method for digestion with the S-Trap micro column (ProtiFi; ≤ 100 mg binding capacity). Full details are provided in the Supplemental Information. Briefly, we used 2 mL (approximately 100 mg protein) of each serum sample for digestion, and a pooled bat serum was digested across the two batches. Proteins were reduced with DL-Dithiothreitol (DTT) and alkylation with 2chloroacetamide (CAA). Digestion was performed with trypsin at an approximate 1:30 mass ratio, followed by incubation at 47°C for one hour. These resulting peptide mixtures were then reduced to dryness in a vacuum centrifuge at low heat before long-term storage at -80°C. Before analysis, samples were then reconstituted with 100 mL 0.1% formic acid (volume fraction) and vortexed, followed by centrifugation at 10000 x g n for 10 minutes at 4°C. The sample peptide concentrations were determined via the Pierce quantitative colorimetric peptide assay with a Molecular Devices SpectraMax 340PC384 microplate reader.
We used the same LC-MS/MS method earlier applied for vampire bat serum proteomics (42). Using the original sample randomization gave a randomized sample order, and injection volumes were determined for 0.5 mg loading (0.21-0.44 mL sample). The run order and data key are provided in Table S1. Peptide mixtures were analyzed using an UltiMate 3000 Nano LC coupled to a Fusion Lumos Orbitrap mass spectrometer (ThermoFisher Scientific). A trap elute setup was used with a PepMap 100 C18 trap column (ThermoFisher Scientific) followed by separation on an Acclaim PepMap RSLC 2 µm C18 column (ThermoFisher Scientific) at 40°C. Following 10 minutes of trapping, peptides were eluted along a 60 minute twostep gradient of 5-30% mobile phase B (80% acetonitrile volume fraction, 0.08% formic acid volume fraction) over 50 minutes, followed by a ramp to 45% mobile phase B over 10 minutes, ramped to 95% mobile phase B over 5 minutes, and held at 95% mobile phase B for 5 minutes, all at a flow rate of 300 nL per minute. The data-independent acquisition (DIA) settings are briefly described here. The full scan resolution using the orbitrap was set at 120000, the mass range was 399 to 1200 m/z (corresponding to the DIA windows used), with 40 DIA windows that were 21 m/z wide, with 1 m/z overlap on each side covering the range of 399 to 1200 m/z. Each DIA window used higher-energy collisional dissociation at a normalized collision energy of 32 with quadrupole isolation width at 21 m/ z. The fragment scan resolution using the orbitrap was set at 30000, and the scan range was specified as 200 to 2000 m/z. Full details of the LC-MS/MS settings are in Supplementary Material. The method file (85min_DIA_40x21mz.meth) and mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (60) partner repository with the dataset identifier PXD031075.
In our earlier analysis of vampire bat serum proteomes (42), we used Spectronaut software to analyze our DIA data. Here, we instead used the DIA-NN software suite, which uses deep neural networks for the processing of DIA proteomic experiments (61). To search the bat samples, we used the NCBI RefSeq Desmodus rotundus Release 100 GCF_002940915.1_ASM294091v2 FASTA (29,845 sequences). Our full DIA-NN settings are provided in the Supplementary Material, and search settings and the generated spectral library (*.speclib) are included in the PRIDE submission (PXD031075). Briefly, 0.01 precursor false discovery was used, search parameters were chosen based on DIA settings, trypsin (cut at K*,R* but excluded cuts at *P) was selected, and fixed modification of cysteine carbamidomethylation. Since DIA-NN was made to handle protein inference and grouping assuming a UniProtKB-formatted FASTA file, and the NCBI RefSeq Desmodus rotundus Release 100 was used (RefSeq format), settings were chosen such that DIA-NN effectively ignored protein grouping, which can be performed on the backend following ontology mapping.
To additionally search for CoV proteins (42), we performed a secondary search using the same settings and the addition of a Coronaviridae FASTA (117709 sequences) retrieved from UniProtKB (2021_03 release) using taxon identifier 1118 with all SwissProt and TrEMBL entries. Search settings are included in the PRIDE submission (PXD031075).
Our identified bat proteins were then mapped to human orthologs using BLAST+ (62) and a series of python scripts described previously (42) to facilitate downstream analysis using human-centric databases (see Supplementary Material for full details). In those cases where human orthologs do not exist, such as mannose-binding protein A (MBL1), we used ad hoc ortholog identifiers. Specifically, eight identified vampire bat proteins are not found in humans (APOR, Bpifb9a, HBE2, ICA, LGB1, MBL1, Patr-A, and REG1), and we thus used UniProt identifiers from chimpanzee, cow, horse, mouse, and pig (Table S2).

Proteomic Data Analyses
The final data matrix of relative protein abundance for all identified proteins was stratified into two datasets for differential analysis: (i) the four 2015 samples analyzed before and after heat inactivation (Table S3) and (ii) the 19 samples collected in 2019 analyzed for CoV infection (Table S4). Our analysis also included a pooled serum sample as a quality control between the two digestion batches (Table S2), and digestion was evaluated by the number of peptide spectral matches. For formal statistical analyses, missing abundance values were imputed as half the minimum observed intensity of each given protein; however, for summary statistics (e.g., means, log 2 -fold change [LFC]), missing values were excluded (63,64).
For the inactivation analyses, log 2 -transformed protein abundance ratios were used for each paired sample. These ratios were used in a moderated t-test with the limma package in R to evaluate protein changes within sera samples before and after heat treatment (65), followed by Benjamini-Hochberg (BH) correction (66).
For the CoV infection analyses, we first reduced dimensionality of our protein dataset using a principal components analysis (PCA) of all identified proteins, with abundances scaled and centered to have unit variance. We then used a permutation multivariate analysis of variance (PERMANOVA) with the vegan package to test for differences in protein composition between uninfected and infected bats (67). Next, we used a two-sided Wilcoxon rank sum test in MATLAB to detect differentially abundant proteins between uninfected and infected bats. We again used the BH correction to adjust for the inflated false discovery rate. We also calculated LFC as the difference of mean log 2 -transformed counts between uninfected and infected bats. To next identify candidate biomarkers of CoV infection, we used receiver operating characteristic (ROC) curve analysis. We used a modified function (https://github.com/dnafinder/roc) in MATLAB to generate the area under the ROC curve (AuROC) as a measure of classifier performance with 95% confidence intervals, which we calculated with standard error, a = 0.05, and a putative optimum threshold closest to 100% sensitivity and specificity (68,69). We considered proteins with AuROC ≥ 0.9 to be strict classifiers of CoV positivity, whereas proteins with AuROC ≥ 0.8 but less than 0.9 were considered less conservative; all other proteins were considered to be poor classifiers (70). Variation in the abundance of strict classifiers by CoV infection status was visualized using boxplots. We also visualized the matrix of all candidate serum biomarkers with the pheatmap package, using log 2 -transformed protein abundances (scaled and centered around zero) and Ward's hierarchical clustering method (71,72).
Lastly, we interrogated up-and down-regulated responses to CoV infection using gene ontology (GO) analysis. First, we programmatically accessed GO terms for all identified proteins using their associated UniProt identifiers and the UniprotR package (73). Next, we used the gprofiler2 package as an interface to the g:Profiler tool g:GOSt for functional enrichment tests (74,75). Enrichment was performed for all candidate protein biomarkers based on AuROC, with up-and downregulated proteins determined using log 2 -fold change. We ranked our proteins by AuROC to conduct incremental enrichment testing, with the resulting p-values adjusted by the Set Counts and Sizes (SCS) correction. We restricted our data sources to GO biological processes, the Kyoto Encyclopedia of Genes and Genomes (KEGG), and WikiPathways (WP). We ran the enrichment tests for both our strict and less-conservative protein classifiers. We note that the eight bat proteins lacking human orthologs all had AuROC < 0.8 and therefore did not require any manual GO and pathway mapping.

Bat Demographics and CoV Positivity
Our sample of 19 vampire bats included for proteomic analyses consisted predominantly of females (84%) and adults (79%). One male was reproductively active, whereas four females were lactating (n = 3) or pregnant (n = 1). Four of the 19 sera samples had paired oral or rectal swabs test positive through PCR for CoVs (21.1%); sequences are available in GenBank under accession numbers OM240577−80. Phylogenetic analyses of the four sequenced amplicons confirmed all positives in the genus Alphacoronavirus ( Figure 1A). All four vampire bat sequences are novel a-CoVs and displayed the most genetic similarity (94.6 −97.3%) to human CoVs (HCoVs; HCoV-NL63 and HCoV-229E) rather than known bat a-CoVs, including those previously found in other vampire bat colonies and other Neotropical bat species more broadly (76,77). In particular, NCBI BLAST did not identify any closely related a-CoV sequences from one of the PCR-positive vampire bat rectal swabs (BZ19-95; OM240579). Univariate logistic regressions did not find significant (unadjusted) effects of any bat demographic variables on CoV positivity. Males were no more likely than females to be infected (OR = 2.31, p = 0.49), although non-reproductive individuals (OR = 6.16, p = 0.17) and subadult bats (OR = 5.40, p = 0.13) were weakly more likely to be positive for CoVs ( Figure 1B).

Serum Proteome Characterization
Bottom-up proteomics using DIA identified 586 proteins in the 19 bat sera samples, with relative quantification covering 5.6 orders of magnitude ( Figure 2A; Table S4). The overall number of identified proteins in our former analysis (i.e., 361 proteins) (42) and the current dataset was within the same order of magnitude and had a similar dynamic range (approximately 10 3 −10 8 in our prior analysis versus 10 4 −10 9 in the current analysis). There was also high overlap in identified proteins between the datasets (91% of the original 361 proteins were included in our analysis here; Figure S1) and similar protein ranks (Table S5). Although the prior and current study have low sample sizes (n = 17 and 19, respectively) and were sampled across different years, the similarity in protein abundance, composition, and ranks suggest that these proteomic patterns (e.g., guanylate-binding proteins, circulating 20S proteasome, and hyaluronidase-1) are not the result of sampling bias and are instead likely a consistent vampire bat phenotype, with improved protein identifications differences driven by technical advances.

Effects of Heat Inactivation
One key difference between analyses here and our prior proteomic study of this vampire bat population is unknown technical artifacts from heat inactivation. To assess these possible effects, we compared proteomes before and after treatment of four serum samples used in our prior study (42). Using a moderated t-test of the four paired sera samples, 34 proteins showed significant changes after heat inactivation (unadjusted p < 0.05), but no differences remained after BH adjustment (even using a liberal adjusted p < 0.3). Although we found no statistically significant changes in protein abundance with heat inactivation, we observed a mean 28% absolute change across the proteome, with a maximum mean 500% absolute change. Most proteins (52.6%) changed less than 17% in response to heat inactivation ( Figure S2; Table S3).

Mining for CoV Proteins
Given prior proteomic identification of putative viral proteins in undepleted serum, including CoVs (42), we broadened our search space to include any CoV proteins. As observing nonhost proteins is a rare event, we used additional stringent criteria to verify any initial CoV peptide spectral matches (see Supplementary Material). Of the 749 CoV peptide spectral matches, none passed these more stringent criteria (Table S6).
Thus we cannot firmly say that viral proteins were identified in this set of undepleted sera, regardless of CoV status.

Proteomic Differences With CoV Infection
To assess differences in the serum proteome between CoVinfected and uninfected bats, we first used multivariate tests. Across the 586 identified proteins, the first two PCs explained 25.46% of the variance in serum proteomes ( Figure S3). A PERMANOVA found no difference in proteome composition by viral infection status (F 1,17 = 0.99, R 2 = 0.05, p = 0.46), although variation was greater in infected bats. Using Wilcoxon rank sum tests, we initially identified 22 proteins with significantly different abundance in CoV-infected bats (unadjusted p < 0.05), but no differences likewise remained after BH adjustment (even using a liberal adjusted p < 0.3; Figure 2B). In contrast to multivariate and differential abundance tests, ROC curve analyses identified 32 candidate protein biomarkers of CoV infection using strict (n = 7, AuROC ≥ 0.9) and lessconservative (n = 25, 0.9 > AuROC ≥ 0.  Figure 2C). The total 32 candidate biomarkers provided clear discriminatory power in differentiating the protein phenotypes of uninfected and infected bats (Figure 3).

DISCUSSION
Despite an increasing interest in bat-virus interactions, especially for CoVs given their human health relevance (22,23), we still have limited insights into the immune mechanisms involved in infection of bats (28). Here, we used serum proteomics to broadly profile the immune phenotype of wild vampire bats in the presence of relatively common CoV infections. Novel a-CoVs detected in these bats had little association with serum protein composition nor abundance, although ROC curve analyses identified 7-32 candidate biomarkers of CoV infection, including AHSG, C4A, F12, GPI, DSG2, GSTO1, and RNH1. Enrichment analyses using these protein classifiers identified strong downregulation of complement, regulation of proteolysis, immune effector processes, and humoral immunity in CoV-infected bats alongside an upregulation of neutrophil immunity, overall granulocyte activation, myeloid cell responses, and glutathione processes. Such results denote a mostly cellular immune response of vampire bats to CoVs and further identify putative biomarkers that could provide new insights into CoV pathogenesis in both wild and experimental populations.
Much bat immunology work to date has understandably focused on model bat systems under captive conditions (14,18). However, identifying the immune correlates of infection is especially important for wild populations, where susceptibility and tolerance to infection, alongside other immunological processes, can vary based on habitat quality and host life history (e.g., reproduction) (78). Such efforts could provide a mechanistic basis for establishing when and where pathogen pressure from bats is greatest and thus help predict viral spillover (79,80). Unlike some other global profiling techniques, proteomics has the benefit of leveraging the small blood volumes that can be obtained non-lethally from most small bats (e.g., members of the Yangochiroptera, including over 1000 species) and thus is especially amenable to the long-term, mark-recapture studies required to study bat virus dynamics (52,81). To facilitate this work, we here built on our prior proteomic characterization of Desmodus rotundus (42). We identified a core serum protein phenotype for this species from multiple years of sampling that could serve as a reference for long-term proteomic studies, although technical advances (e.g., DIA-NN) likely contributed to an expanded protein repertoire in the current study. We also assessed the impacts of heat treatment, a common inactivation method for sera, given recent shifts in United States importation regulations. Artifacts from heat inactivation were not sufficiently conserved to be statistically significant, and most serum proteins had small changes in abundance before and after this treatment. Yet given the extent of such changes, we suggest original samples should typically not be analyzed with heated samples for comparative purposes. In contexts where sera inactivation is required, however, heat treatment across samples should not bias characterization of bat serum proteomes. Such optimizations could next be applied across longitudinal timepoints to more broadly study bat-virus interactions in the wild.
We here focused this initial study on CoVs, which have been previously characterized as genetically diverse (a-and b-CoVs) in Neotropical bats, including but not limited to Desmodus rotundus (43,45,82,83). Our detection of CoVs from a northern Belize colony of vampire bats in swab samples with paired sera (4/19) was higher than other CoV surveys in this species (46,76,82,84), although future studies with larger sample sizes are needed to test if this represents a true geographic difference in virus prevalence. Yet despite many available CoV sequences in GenBank from diverse bat surveys, all CoVs detected here fell within the genus Alphacoronavirus but outside of known bat a-CoV clades. Instead, these viruses were either entirely novel or more closely related to human a-CoVs, specifically HCoV-NL63 and HCoV-229E, suggesting greater genetic diversity of CoVs within vampire bats than previously recognized. Such results could be further interrogated through whole-genome sequencing of these viruses (45). Similarly, such results further suggest the possibility of high zoonotic potential (or spillback from humans to bats) for vampire bat CoVs, which likely varies based on geography given the high degree of genetic differentiation across the broad distribution of Desmodus rotundus (85)(86)(87). Such findings should be confirmed with larger sample sizes and characterization, including in vitro assessments and attempts at virus isolation.
Despite identifying CoV infection in a small number of bat samples, we were unable to detect CoV proteins in the serum proteomes. Previously, we detected two CoV peptides in sera from this population, but these were likely at the edge of detection limits (42). As such detection limits are susceptible to technical artifacts, including but not limited to sample handling, protein processing, or instrument performance, heat inactivation could have affected our ability to identify similar peptides in these bat samples, especially for bats positive for CoVs by PCR. Additionally, our ability to detect viral proteins may have been further restricted by ongoing limitations in applying proteomics to wild species. In humans, over 3000 serum proteins can be detected by mass spectrometry after depletion of the most abundant proteins (41). However, using antibody-based depletion techniques is not an effective strategy in non-human mammals (47), such that undepleted serum proteomics in bats will be limited to the top 300-600 proteins, with false negatives for low abundance proteins such as those of viruses (88). Alternatively, lack of detection of CoV proteins in sera despite detection of CoV RNA in oral and rectal swabs could indicate tropism, as CoVs have been more readily detected in bat feces and saliva than in blood (89).
Using our novel a-CoVs, we then tested for differential composition and abundance of serum proteins between uninfected and infected vampire bats. In both cases, we found negligible overall differences in serum proteomes with CoV infection. This lack of differences is not necessarily surprising, because bats and their aand b-CoVs share a long coevolutionary history (27,90). However, such null results should also be qualified by the challenges posed to differential abundance tests by sample imbalance, given the small number of infected relative to uninfected bats (91). To partly address this imbalance, we used ROC curve analyses to identify proteins with strict (AuROC ≥ 0.9; n = 7) and less conservative (AuROC ≥ 0.8; n = 25) classifier ability for infection (47,92). The unbiased query of proteins in relation to infection through proteomics can in turn result in detection of unexpected candidate biomarkers and new insights into CoV pathogenesis in bats. For example, we identified increased ribonuclease inhibitor (RNH1) as a putative biomarker. RNH1 inhibits RNase 1 and blocks extracellular RNA degradation, possibly resulting in increased tumor necrosis factor (TNF)-a activation (93). Prior cell line studies of Eptesicus fuscus have shown limited production of TNF-a upon stimulation (94), whereas those of Pteropus alecto have suggested an induced TNF-a response (16). Whether greater abundance of proinflammatory cytokines such as TNF-a occur with CoV infection in bats would thus be a fruitful area for future work based on RNH1 differences here. We also identified lower complement C4A (one of two C4 isotypes) as another putative biomarker. In humans, lower complement C4A and C3 can signal elevated autoimmunity (95,96), and decreased complement C4 and C3 in COVID-19 patients also corresponds to disease severity (97). The processes that shape serum complement, namely complement synthesis, activation, and clearance, remain poorly characterized in bats (17), but the identification of C4A as a classifier could suggest specific explorations into how complement affects CoV infection.
Other candidate biomarkers also had more direct implications for the antiviral response in bats. AHSG (alpha-2-HS-glycoprotein) is a negative acute phase reactant (98) and here was a positive predictor of CoV infection. In humans, elevated AHSG is accordingly protective against progression of disease caused by SARS-CoV (99), and decreased inflammation could also contribute to viral tolerance in bats. We also identified poly (rC)-binding protein 1 (PCBP1) as a positive, albeit weaker, predictor of CoV infection (AuROC = 0.87). This RNA-binding protein is upregulated in activated T cells to control effector T cells converting into regulatory T cells and thus stabilizes the innate immune response (100, 101); PCBP1 may also prevent virus-related inflammation (102). Whereas human patients with prolonged SARS-CoV-2 infections showed lower PCB1 compared to patients with short-term infections (103), bats with CoV infection here had elevated PCBP1 and more generally harbor more PCBP1 than humans (42). Despite focusing on two different viral genera, such results suggest both similarities and differences in how bats and humans may respond to CoV infection. More generally, the list of such candidate biomarkers identified here can be used to create accurate, sensitive, quantitative, and bat-specific parallel reaction monitoring mass spectrometry-based protein assays (104,105). Such assays could facilitate more thorough investigations into bat immune response to CoV infection. Further, such putative biomarkers are only observational correlates of naturally occurring CoV infection, but bats can be infected by a high diversity of viruses that could elicit similar immune responses (106,107). Experimental infection with CoVs would be an important future step to identify the roles of such proteins in pathogenesis.
In addition to identifying candidate biomarkers, we also leveraged these proteins to more generally assess broad upand downregulated biological processes with CoV infection through enrichment analyses. Using all candidate biomarkers, we found that CoV-infected bats displayed downregulation of the complement system, regulation of proteolysis, immune effector processes, and humoral immunity while also showing upregulated neutrophil-mediated immunity, overall granulocyte activation, myeloid cell responses, and glutathione processes. These results in part support findings from limited experimental infections of select bat species, which have shown little humoral response to CoVs (11,31). Yet while Rousettus aegyptiacus challenged with SARS-like CoVs did not show hematological changes following infection (31), CoV-infected vampire bats here had largely upregulated cellular immune responses. Similarly, experimental studies have suggested bat tolerance of CoVs to be driven by upregulation of cytokine responses and a downregulated inflammatory response (11,16,33), but we did not find GO terms related to cytokines or inflammation in our analyses. Such discrepancies could again result from our ability to only detect the top 300-600 proteins without antibody-based depletion, which could cause low-abundance proteins (including but not limited to IFNs) being especially difficult to characterize here (88). Alternatively, these differences could reflect distinct immune responses of bats for a-CoV infection, given that experimental studies to date have focused on b-CoVs. Additionally, these findings could also signal immunological variation within and among bat clades, given that Desmodus rotundus and the closely related Artibeus jamaicensis may respond differently to CoVs (11).
Future proteomic analyses across bat species in the wild could provide a tractable means to broadly characterize host responses to viruses, including but not limited to hypothesized immune mechanisms of tolerance in this order of mammals and to infection with diverse CoVs. By leveraging the benefits of proteomics to quantify hundreds of proteins from the small sera volumes that can be obtained from most bat species (41,42), such analyses could evaluate whether particular immune responses to viruses such as CoVs are conserved across bats (e.g., downregulation of humoral immunity) and which may be a feature of particular bat clades. In particular, further comparative proteomic analyses across Neotropical bats, including both additional members of the Phyllostomidae as well as sister families such as the Mormoopidae (108), would illuminate whether vampire bats have particular immunological relationships with CoVs that may facilitate viral tolerance. As suggested in our work here on Desmodus rotundus, such studies could also identify putative biomarkers that may suggest novel mechanisms of pathogenesis and facilitate development of protein-specific assays to improve the resource base for studying the immunology of wild bats and bat-virus dynamics.

DATA AVAILABILITY STATEMENT
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.

ETHICS STATEMENT
The animal study was reviewed and approved by the Institutional Animal Care and Use Committee of the American Museum of Natural History (AMNHIACUC-20190129).

AUTHOR CONTRIBUTIONS
DB, MF, and NS conducted fieldwork. G-SL and RR ran CoV diagnostics. MJ, AB, and BN conducted proteomic analyses and bioinformatics. DB and BN analyzed data. DB and BN wrote the manuscript with contributions from all co-authors. All authors contributed to the article and approved the submitted version.

ACKNOWLEDGMENTS
For assistance with bat sampling logistics and research permits, we thank Mark Howells, Melissa Ingala, Kelly Speer, Neil Duncan, and the staff of the Lamanai Field Research Center. Identification of certain commercial equipment, instruments, software, or materials does not imply recommendation or endorsement by the National Institute of Standards and Technology, nor does it imply that the products identified are necessarily the best available for the purpose.


s2fieldsofstudy: Biology Psychology

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Looking for “fNIRS Signature” in Autism Spectrum: A Systematic Review Starting From Preschoolers

Accumulating evidence suggests that functional Near-Infrared Spectroscopy (fNIRS) can provide an essential bridge between our current understanding of neural circuit organization and cortical activity in the developing brain. Indeed, fNIRS allows studying brain functions through the measurement of neurovascular coupling that links neural activity to subsequent changes in cerebral blood flow and hemoglobin oxygenation levels. While the literature offers a multitude of fNIRS applications to typical development, only recently this tool has been extended to the study of neurodevelopmental disorders (NDDs). The exponential rise of scientific publications on this topic during the last years reflects the interest to identify a “fNIRS signature” as a biomarker of high translational value to support both early clinical diagnosis and treatment outcome. The purpose of this systematic review is to describe the updating clinical applications of fNIRS in NDDs, with a specific focus on preschool population. Starting from this rationale, a systematic search was conducted for relevant studies in different scientific databases (Pubmed, Scopus, and Web of Science) resulting in 13 published articles. In these studies, fNIRS was applied in individuals with Autism Spectrum Disorder (ASD) or infants at high risk of developing ASD. Both functional connectivity in resting-state conditions and task-evoked brain activation using multiple experimental paradigms were used in the selected investigations, suggesting that fNIRS might be considered a promising method for identifying early quantitative biomarkers in the autism field.

INTRODUCTION
Functional Near-Infrared Spectroscopy (fNIRS) allows performing in vivo a continuous and non-invasive monitoring of oxygenation levels of chromophores like hemoglobin ( Van de Rijt et al., 2018), thanks to the transparency of biological tissues -including the human brainto the light in the near-infrared spectrum. Indeed, fNIRS quantifies regional changes in the concentration of oxygenated, deoxygenated, and total hemoglobin (OxyHb, DeoxyHb, and TotHb). The blood-oxygen-level-dependent (BOLD) signal detected by fNIRS provides a direct measure of modifications in the brain blood flow, which are coupled to neuronal activity by the complex process known as "neurovascular coupling" (Villringer and Chance, 1997;Logothetis and Wandell, 2004). Typically, an increase of neural activity is paralleled by a peak of OxyHb and TotHb, with a drop of DeoxyHb. fNIRS devices consist of red light-sources paired to specific detectors, which can be placed into a textile EEG-like cap to form an array of multi-distant channels. For data acquisition, the system executes the parallel reading of optical sensors. fNIRS systems give the opportunity to perform recording with different wavelengths of the nearinfrared spectrum (700-1000 nm). However, for functional activation studies in humans the devices typically focus on paired wavelengths of approximately 690 and 830 nm to optimize the accuracy of OxyHb and DeoxyHb measurements (Lloyd-Fox et al., 2010). Indeed, separability between the chromophore signals seems to be optimal if one wavelength is below 720 nm and the other is above 730 nm (Uludag et al., 2004).
Although several neuroimaging methods can directly detect the electrical activity of brain circuits (EEG and MEG) or record the related hemodynamic response (fMRI), most of them have limiting factors restricting their use in the developmental time window (for instance, sensitivity to motion artifacts, requiring high grade of subject compliance). Indeed, fMRI is the gold standard for in vivo imaging of the human brain, but fNIRS stands out for its high portability, robustness to noise, relatively low costs and small size, bringing functional imaging into much more realistic environments (Cui et al., 2011;Duan et al., 2012;Scarapicchia et al., 2017). In particular, strength to motion artifacts make this technique an ideal candidate for research in preschool children (until the age of 6), even in infancy and toddlerhood (Lloyd-Fox et al., 2010). Despite lower spatial resolution and imaging depth, fNIRS offers a higher temporal resolution than fMRI. On the other hand, EEG and fNIRS share high portability, low-cost features and recording depth, but they measure different perspectives of brain activity: indeed, EEG signal reflects the bioelectrical activation of cortical neurons, while fNIRS assesses hemodynamic responses (Chiarelli et al., 2017;Berger et al., 2019). In order to obtain a more detailed picture of brain activity, fNIRS can be combined with other neuroimaging and electrophysiological methods (e.g., fMRI and EEG), without causing any measurement interference (Chen et al., 2015) and potentially gaining a multimodal data set (Yuan et al., 2010;Chiarelli et al., 2017). Notably, EEG and fNIRS use similar experimental settings (they are both scalplocated procedures), but show different temporal resolutions that might allow dissecting the electrical from the hemodynamic contribution to the recorded signal.
Although introduced into clinical care almost 40 years ago, only recently, fNIRS gained much popularity in the study of brain development and neurodevelopmental disorders (NDDs) (Greisen, 2006;Vanderwert and Nelson, 2014). FNIRS has been widely used to investigate the typical maturation of speech perception and language development, sensory and motor functions, social communication and interaction, object processing, human action processing in both toddlers and children (Lloyd-Fox et al., 2010). In contrast, the application of fNIRS in the field of NDDs is a growing research area and studies looking for a "fNIRS signature" as a brain biomarker useful for the diagnosis and the treatment outcome are still underrepresented (Gervain et al., 2011). NDDs are a heterogeneous group of complex disorders resulting from the interaction of genetic, epigenetic, neurobiological and environmental factors (from alterations in utero to postnatal environment), and characterized by early disruption in cognition, emotion, and behavior. NDDs include attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), communication and specific learning disorders, intellectual disability (ID) and motor disorders (American Psychiatric Association, 2013). NDDs often present overlapping clinical features or may occur simultaneously in the same patient, making the differential diagnosis potentially challenging (Hansen et al., 2018). Moreover, NDDs show fairly broad behavioral phenotypes, reflecting also etiological heterogeneity of these disorders that can occur as "idiopathic" or "secondary" conditions (e.g., well documented causes of secondary ASD include monogenic disorders such as tuberous sclerosis and Fragile-X syndrome) (Muhle et al., 2004). Thus, reliable functional biomarkers would be very helpful to support early clinical diagnosis and to monitor developmental trajectories through in vivo longitudinal studies, also focused on the potential outcome of tailored intervention. Moreover functional biomarkers might help clinicians to separate behavioral symptoms of NDDs into more stable and objective phenotypes, including gender-related endophenotypes.
Clinically useful neuroimaging markers for NDDs are sorely missing yet (Vasung et al., 2019;Azhari et al., 2020), but emerging evidence suggests that fNIRS might be exploited to generate unbiased and reliable measures to explore brain functions, thus possibly driving clinicians toward a faster and more precise diagnosis (Zhang and Roeyers, 2019).
Moreover, fNIRS offers the opportunity to study cortical networks focusing not only on neuronal activation, but also on the neuromodulatory action of non-neuronal cells (such as glial cells or astrocytes that contribute to neurovascular and metabolic homeostasis of the brain). Thus, fNIRS is emerging as a fundamental tool, alone or in combination with other techniques, to dissect the key players in brain physiology and pathophysiology (Lines et al., 2020).
The main purpose of this systematic review has been to investigate the recent literature covering the use of fNIRS in the field of NDDs. We decided to focus on the preschool population in order to highlight the promising value of fNIRS as a reliable and non-invasive tool to detect brain biomarkers in clinical settings, even in populations commonly showing reduced compliance to experimental environments. This specific inclusion criterium led to narrow the focus of this review to children with idiopathic ASD or high-risk (HR) infants.

MATERIALS AND METHODS
A systematic search was conducted for relevant studies in three databases (PubMed, Scopus, and Web of Science), using the following search terms that were refined from previous reviews (Liu et al., 2019;Zhang and Roeyers, 2019) and according to PICOS framework: ("fNIRS" OR "functional nirs" OR "nearinfrared spectroscopy") AND ("neurodevelopmental disorder" OR "ASD" OR "ADHD") AND ("children" OR "toddler" OR "preschooler").
Additionally, manual searches were conducted among the reference sections of the retrieved studies and reviews. Publication year was not restricted, and the latest database search was performed in March 2021. Reviews were not included in the selection, but were only used to collect original studies. After removing duplicate records, articles emerged from the search were required to meet pre-established criteria. Inclusion and exclusion criteria, established prior to the literature search, are outlined below.

Inclusion Criteria
(i) Studies applying fNIRS; (ii) Studies including a clinical population with a confirmed diagnosis (according to DSM-5) or a well-established condition of risk for NDDs; (iii) Studies including preschool children (sample mean age < 7 years).

Exclusion Criteria
(i) Articles published in languages other than English; (ii) Meta-analyses or literature reviews; (iii) Other reasons referring to report characteristics, as not published clinical studies (e.g., conference paper, thesis, book chapter) or studies that do not involve human participants (e.g., focused on methodological issues).

Study Selection
One hundred and sixty-three potentially relevant studies were identified from PubMed, 136 from Scopus, 127 from Web of Science; in addition, four articles that met the selection criteria were retrieved from screened review articles. After omitting duplicates, 224 articles were examined. On the basis of the title, a total of 52 studies that did not meet the inclusion criteria were disregarded. Finally, within the172 articles screened for abstract or full-text evaluation, a total of 13 studies were selected for this review. An indepth detail of paper selection is shown in the PRISMA diagram (Figure 1).
Crucial information was extrapolated from included papers. Tables 1, 2 summarize the mean age of participants, the sample size and gender ratio, the experimental procedures (technical aspects of recording sessions and experimental design) used during fNIRS measurement.
Although the initial aim was to include different type of NDDs (see keywords used for the search), only studies focused on children with idiopathic ASD or high-risk (HR) infants (infants at high familial risk of autism) satisfied the inclusion and exclusion criteria. Regarding other NDDs, studies on ADHD included children older than 7 years, due to the pharmacological aim of their experimental design: at the time of our last search, indeed, 55 studies focused on fNIRS and ADHD, but none of them was performed on the preschool population. Moreover, within the growing bulk of literature that uses fNIRS for evaluating the emerging language ability in infants, only two studies (Chang, 2014;Hosseini et al., 2018) focused on children with Language Disorders; however, both of them, did not meet the inclusion criteria (mean age of participants older than 7 years) and were therefore excluded from this review. fNIRS was rarely used to explore neuropsychological features in learning disorders such as Dyslexia Pecyna and Pokorski, 2013;Song et al., 2013;Sela et al., 2014;Cutini et al., 2016), but this field obviously includes only school-aged children. Application of fNIRS to Intellectual Disability (ID) is only anecdotal yet n.a. Significant associations between age and network efficiency, especially evident in the deoxy-and total-Hb-based-networks, indicating the network efficiency decreases with age in young ASD children.
Significant associations between levels of autistic behaviors and network efficiency in the oxy-Hb-based-network, indicating decreased functional global and local network efficiency in ASDs with a relatively higher level of autistic behaviors. R-S fNIRS measurement while watching cartoon ASD 29 (23:9) 6 years TD 29 (20:9) 6.5 years Spatial complexity of functional connectivity (SCFC) differs between ASD and control group. Global SCFC was significantly higher in ASDs, along with considerably higher intraregion SCFCs in the prefrontal and temporal lobes. Moreover, higher interregion SCFCs between right PFC and the other brain regions were found in young children with ASD compared to TD children. The long-range temporal correlations (LRTCs) of hemoglobin concentration signals were attenuated in young children with ASD over left temporal region (for oxy-Hb signal) and bilateral temporo-occipital regions (for deoxy-Hb signal). Detrended fluctuation analysis (DFA), used to quantify LRTCs, of oxy-Hb in left temporal region were negatively correlated with autistic symptom severity. Relationship between age and LRTCs of Hb concentration signals differs between two groups, correlating with autistic symptom severity.
Sample mean age not available: age range of participants is shown. N.A. means not applicable: no comparison group is included in this study.
Frontiers in Neuroscience | www.frontiersin.org Face processing task HR 10 (4:6) 7 months LR 10 (4:6) 6.9 months Greater OxyHb responses in lateral regions for LR as compared to HR, and significantly greater DeoxyHb responses in frontal channels for HR as compared to LR during face processing task. Greater DeoxyHb response to mother's face as opposed to stranger face within the HR group across both frontal and lateral regions. Greater OxyHb response to smiling versus neutral conditions only for the LR. Social perception task HR2 0 (10:10) * 5 months LR 16 (10:6) * 5 months Early fNIRS measurements (4-6 months) in response to social perception task correlate with later clinical outcome and symptoms level at 36 months. Reduced activation to visual social stimuli in those infants with later diagnosis of ASD. Reduced activation to vocal relative to non-vocal sounds in those infants with later diagnosis of ASD compared to LR and HR-without later diagnosis confirmation.

Bhat et al., 2019
Hitachi ETG-4000 system 24 10:8 optodes probe (covering temporal areas, bilaterally) Naturalistic social interactions HR 9 (7:2) 7 months LR 6 (1:5) 7.5 months HR infants showed reduced right and left-hemispheric activation compared to LR infants based on OxyHb and DeoxyHb signal trends. Indeed, HR infants had greater functional connectivity than LR infants during the pre-and post-social periods and showed a drop in connectivity during the social period. Keehn et al., 2013 Hitachi ETG-4000 system 24 10:8 optode probe (covering anterior and posterior temporal areas, bilaterally) Language-related task HR1 4 (7:7) 7 months LR 18 (9:9) 7 months LR infants exhibited strongest activation in bilateral anterior ROIs, while HR exhibited similar activation across all brain regions in study. Compared to LRs, HR-ASD infants had reduced brain response in the bilateral anterior ROIs, while HR-noASD had increased brain response in the right posterior ROI. This atypical brain response was not predictive of 24-month language abilities in HR infants.
*Note that 32 of the 36 infants (16 low risk and 16 high risk) contributed data to a previous publication (Lloyd . Frontiers in Neuroscience | www.frontiersin.org (only one scientific report found in literature database consulted) (Bembich et al., 2021).

Characteristics of Participants
As mentioned above, all studies included in this review applied fNIRS in preschool children with ASD, a clinical condition characterized by socio-communicative impairment associated with restricted interests and repetitive behaviors, or HR infants (younger siblings of children with ASD). Only four studies included more than 40 participants (Keehn et al., 2013;Jia et al., 2018;; in most cases subjects were equally distributed between the clinical population and the control group. Interestingly, the cohorts included both males and females in all studies, but the two populations were not homogeneously distributed in terms of sample size, except for two investigations Pecukonis et al., 2021). Five studies included in this review (Kikuchi et al., 2013;Yu, 2016, 2018;Jia et al., 2018; comprised ASD children with a mean age was between 3 and 7 years and compared the ASD group with age-and gender-matched typically developing (TD) peers in resting state condition (see Table 1). It is to note that all selected studies included children with an average IQ score and no comorbidity with ADHD.
The remaining eight studies Keehn et al., 2013;Lloyd-Fox et al., 2013Edwards et al., 2017;Braukmann et al., 2018;Bhat et al., 2019;Pecukonis et al., 2021) focused on infants/toddlers (children younger than 3 years old), and compared fNIRS data between HR group and in Low-Risk groups (LR; condition defined as subjects with no family history of ASD) in task-related study designs (see Table 2). Of these studies, only two (Lloyd- Pecukonis et al., 2021) analyzed the correlation between the fNIRS brain responses of infants during the first year of life and their diagnostic outcome at 24 or 36 months.

fNIRS Measurement: Technical Issues and Experimental Design
A total of four different fNIRS recording systems were employed in the reviewed studies (Foire 3000, LABNIRS, Hitachi ETG-4000 system, UCL-NIRS topography system). The number of channels used ranged from a minimum of 2 (Kikuchi et al., 2013) to a maximum of 44 Yu, 2016, 2018;Jia et al., 2018;. The remaining investigations used an intermediate number of channels (24-26 pairs of optodes), but the density of montages may be considered comparable among almost all studies, due to the younger age of the cohort investigated and the relatively smaller dimension of the probe. Despite the variability of probe geometry, in all cases optodes placement followed the international 10-20 EEG system.
Within the 13 articles included, eight studies explored the brain activation evoked by specific tasks (task-related paradigm or T-R; an overview of experimental procedures is reported in Table 2) and the remaining five investigated functional brain organization in resting-state (R-S) condition (an overview of experimental procedures is reported in Table 1). Considering the significant difficulties to remain still under stimulationfree conditions for young children, all resting-state data were recorded while participants watched a cartoon or, only in one case, a picture-card show (Kikuchi et al., 2013) in order to optimize subject compliance. Papers exploring functional cortical activation in task-related condition can be sub-grouped into two categories that reflect the nature of the task proposed: (i) social stimuli, including social perception (Lloyd-Fox et al., 2013Braukmann et al., 2018), face processing  and naturalistic social interactions (Bhat et al., 2019); (ii) languagerelated stimuli (Keehn et al., 2013;Edwards et al., 2017;Pecukonis et al., 2021).

DISCUSSION
Papers systematically selected for this review allow discussing some research questions that might pave the way toward the fNIRS application in the field of NDDs. The final selection, mainly based on age criteria, focused our attention on ASD and HR populations. This field is characterized by huge heterogeneity both regarding the phenotypic and the neurobiological level, and a great amount of literature is devoted to uncover biomarkers dissecting sub-phenotypes and supporting the clinical assessment (Loth et al., 2016;Lombardo et al., 2019). Indeed, while an early detection of ASD is highly recommended to receive the services and supports the children need to reach their full potential (Dawson et al., 2010;Hyman et al., 2020), a final diagnosis is still obtained approximately during the third year of life (Salomone et al., 2016). However, neuroimaging and electrophysiological evidence consistently suggested that ASD-related atypical brain pattern in cortical regions crucial for socio-communicative skills (such as fronto-temporal areas) could be detected before the fullblown expression of symptoms (Conti et al., 2015;Ha et al., 2015;O'Reilly et al., 2017;Pagnozzi et al., 2018).
Is "fNIRS Signature" Useful as a Brain Biomarker for Autism Spectrum Disorder?
Resting-state fNIRS measurements have been proposed as auxiliary indexes for the objective assessment of ASD in both young (Xu et al., 2021) and adult population (Yanagisawa et al., 2016).
Regarding preschoolers, Kikuchi et al. (2013) first demonstrated an aberrant functional connectivity between the right and the left anterior prefrontal cortex (aPFC) in young children with ASD under resting state conditions. Notably, they found a significantly higher inter-hemispheric connectivity in the ASD compared to the TD group, reinforcing previous data about the aberrant cortical organization at structural level in ASD children (McCaffery and Deutsch, 2005;Courchesne et al., 2011). The higher inter-hemispheric connectivity in ASD reported by Kikuchi et al. (2013) was positively correlated with the severity of social deficit, as scored with the Autism Diagnostic Observation Schedule (Lord et al., 2012). It is to note that the anterior prefrontal cortex has already been implicated in ASD pathophysiology in previous structural and functional studies, as being involved in social perception and processing abilities that are altered in the ASD condition (Uddin et al., 2013;Pagnozzi et al., 2018).
In contrast, studies from Yu (2016, 2018) and  showed weaker functional network efficiency in young children with ASD compared to TD peers, mainly in short-and long-range connectivity of the right prefrontal cortex (Li and Yu, 2016). Moreover, a subsequent study of the same research group  showed an inverse association between network efficiency, age (especially evident in the Deoxy-and TotHbbased networks) and severity of autistic behaviors (especially evident in the OxyHb-based-network) in high-functioning ASD subjects. Statistically significant findings were also obtained when comparing ASD and TD groups through spatial complexity of functional connectivity (SCFC) analysis .
Other approaches to analyze resting state recordings also confirmed that fNIRS might be a candidate tool to investigate the pathophysiological mechanism of ASD, looking at the temporal dynamics of neuronal oscillations . Specifically, the long-range temporal correlations (LRTCs) of hemoglobin concentration signals were studied by quantifying a detrended fluctuation analysis (DFA) exponent. Comparing data between ASD and TD group, significant differences were found over the left temporal region for OxyHb signal, and over bilateral temporo-occipital regions for DeoxyHb signals, suggesting a shift-to-randomness of brain oscillations in children with ASD. Moreover, the relationship between age and DFA exponents revealed that this association could be modulated by autism. Furthermore, the DFA exponents of OxyHb in the left temporal region were negatively correlated with autistic symptom severity. Between-group differences remained significant also looking at correlation coefficients between age and DFA.
Altogether, these results confirm that fNIRS can detect a different pattern of functional connectivity in the brain of ASD children. Accordingly, the considerable bulk of prospective studies focused on neurostructural and neurofunctional measures of brain development validated the concept of ASD as characterized by early altered connectivity patterns (Bosl et al., 2011;Wolff et al., 2012;Solso et al., 2016). In particular, early social brain network alterations were previously reported in structural (Shen et al., 2018) and functional MRI (Emerson et al., 2017) as well in observational studies (Elsabbagh and Johnson, 2016). However, no resting-state fNIRS data are yet available in cohorts younger than 3 years, thus restricting the predictive value of resting state analyses so far. Moreover, the mathematical algorithms used for the interpretation of resting-state data are very complex to be applied in the clinical setting and the limited coherence among models still prevents from drawing conclusive interpretations.
Is "fNIRS Signature" Useful as an Early Predictor in "At Risk" Population?
Younger siblings of children with ASD are considered as HR infants because around 20% of them receive a diagnosis of ASD at the age of 3 years (Ozonoff et al., 2011) and a further 20-30% exhibit other neurodevelopmental problems (Messinger et al., 2013). In addition, a high rate of behavioral traits overlapping with those observed in children with an ASD diagnosis (referred as the Broader Autism Phenotype) has been documented in the sibling population (Charman et al., 2017). Thus, the prospective study of the HR population allows detecting behavioral risk signs or biomarkers of NDDs at a very early age, prior to the full clinical manifestation (Zwaigenbaum et al., 2015).
Studies on younger siblings of children with ASD compared HR infants with infants without familial history of ASD or NDDs (LR infants), regardless their developmental outcome or differentiating HR infants with or without a later diagnosis of ASD. In this review, we included both types of studies as we are interested in understanding how fNIRS signal can be used as a potential biomarker of atypical development, and not only as an ASD related outcome.
In the past decade, the number of studies exploring the applicability of fNIRS technique in HR infants has rapidly grown, focusing especially on core ASD neurocognitive domains. Accordingly, our study selection includes five papers testing social perception and three others assessing possible alterations of language development in the HR group.
With regard to social processing, starting from previous data in typically developing infants (Lloyd-Fox et al., 2012), Lloyd-Fox et al. (2013) examined whether the temporal lobe specialization for processing visual and auditory social stimuli during the first months of life differed between HR and LR infants. They found significantly diminished evoked hemodynamic responses in HR compared to LR infants in the superior temporal sulcus (STS), both in visual and auditory trials, suggesting a lack of cortical specialization to social stimuli, already within the first 6 months of life. Notably, a more recent prospective study in the same cohort of HR infants (increased by only 2 more subjects) demonstrated that the reduced activation to visual social and vocal stimuli across cortical regions of interest was specific for infants with a later diagnosis of ASD .
Moreover, Braukmann et al. (2018) provided further evidence for a social processing difference in infants at risk of autism, highlighting a reduced hemodynamic response evoked by social visual stimuli in the right posterior temporal cortex. Similarly, a reduced bilateral cortical activation in HR children compared to LR infants was reported in the social perception within the infantmother dyad (Bhat et al., 2019). Of note, this is the only study using a fNIRS experimental protocol with live stimuli (face-toface naturalistic social interaction between infant and mother).
Social behavior was also explored by Fox et al. (2013), recording fNIRS hemodynamic responses to a face perception task (facial identity and emotion perception on video clips performed by infant's mothers) during the first year of life. This study suggested differences in patterns of functional connectivity in the frontal lobe (short-and long-range connections) between the HR and LR groups. HR infants showed significantly greater DeoxyHb responses in frontal channels and lower OxyHb responses in right temporal regions during face processing task; a significant difference between groups was detectable in response to mother's face as to a stranger face (greater DeoxyHb response within the HR group across both frontal and lateral regions). For this cohort, no longitudinal data were available for discriminating infants with or without later ASD diagnosis and establishing the predictive value of early fNIRS recordings.
To summarize, these results suggest that socially evoked fNIRS signals might be a suitable and predictive biomarker in early assessment of HR infants; however, further studies are needed to confirm these promising findings.
Growing evidence on the validity of fNIRS for studying early aberrant functional brain networks in HR infants emerged from the language domain as well. Keehn et al. (2013) explored functional connectivity during the first year of life in HR infants using a taskrelated experimental paradigm (language processing task). They found that from 3 to 12 months HR children exhibited a pattern of decreasing connectivity with reduced intrahemispheric connectivity for the left hemisphere compared to LR infants at 12 months.
Moreover, an alteration of language processing can be detected very precociously with fNIRS: Edwards et al. (2017) analyzed precursors of language development in HR and LR 3-monthold infants: they found that HR females exhibited significantly higher OxyHb responses to auditory stimuli containing syllable repetitions than HR males, LR females or LR males in both left and right anterior brain regions. It is worth noting that HR females showed different temporal response patterns with respect to LR females across different type of auditory pattern (repeating vs. non-repeating syllabic sequences) presented randomly (speech-like stimuli), indicating a potential gender endophenotype in ASD. Using the same experimental protocol, Pecukonis et al. (2021) performed a longitudinal study on functional specialization of language-related brain regions during the first year of life in HR and LR group, suggesting a possible role of language-evoked fNIRS measurements to predict the diagnostic outcome of infants at 24 months.
Altogether, these data demonstrate that fNIRS studies on siblings can provide a unique window into the earliest neurobiological atypical trajectories and have the potential to shed light on possible ASD-related endophenotypes and biomarkers of brain function. In particular, studies correlating fNIRS brain responses in the first year of life to the diagnostic outcome at 24-36 months Pecukonis et al., 2021) highlight that atypical brain responses are detectable in ASD infants as early as 6 months, thus indicating that fNIRS might be the an useful tool to early predict ASD.

Is Hemispheric Asymmetry an Informative Index in fNIRS Recordings?
Theoretical speculations about possible left-hemisphere dysfunction (Fein et al., 1984) or predominant right-hemisphere impairment (Happé, 1999) have historical roots in autism research. Many reports of atypical hemispheric asymmetries in ASD came from anatomical (Herbert et al., 2005) and functional imaging studies (Kleinhans et al., 2008), and typically focused on the abnormal lateralization of language domain (Just et al., 2004), even at very early stages of infants' development (Eyler et al., 2012). Nevertheless, it is only in the last decade that attention on functional asymmetries related to non-verbal processing and the possibility to consider atypical brain asymmetry as a candidate for clinically meaningful stratification in ASD has grown (Floris et al., 2021). Indeed, recent evidence from resting-state fMRI studies supported the hypothesis that atypical rightward asymmetry shift may be a pervasive feature of functional brain organization in ASD, affecting not only the language network, but also the sensorimotor function and higher cognitive domains (Cardinale et al., 2013). Task-related -fNIRS studies focusing on hemispheric asymmetry in ASD individuals are still very limited and evidence reported are quite inconsistent (Doi and Shinohara, 2017). No systematic findings on hemispheric asymmetry in ASD population during resting-state fNIRS recording are currently available. Even in the studies included in this review, hemispheric asymmetry was not highlighted as a primary research purpose, allowing us to prompt only a few speculations on this topic.
Functional near-infrared spectroscopy studies on language perception and processing seem to confirm previous literature about ASD atypical lateralization for the language domain, with a reduced functional connectivity in the left hemisphere of HR infants compared to the LR peers, both at 6- (Pecukonis et al., 2021) and 12-months of age (Keehn et al., 2013). In contrast, Edwards and colleagues (Edwards et al., 2017) found left-or even hyper-lateralization of neural activity in 3-month-old HR females in response to a language auditory task, speculating about a gender-specific ASD endophenotype related to language processing. However, the limited spatial resolution of fNIRS prevent these results from being conclusive.
Similarly, task-related fNIRS studies of social processing were not consistent in defining hemispheric asymmetry. Indeed, Lloyd-Fox et al. (2013 found cortical activation patterns in response to social vs. non-social stimuli showing maximal differences between HR and LR groups in the left STS region. Moreover, Braukmann et al. (2018) detected different evoked responses in the right temporal region between groups. In contrast other studies, despite reporting cortical hypoactivation in HR both in naturalistic social interactions (Bhat et al., 2019) and face processing , did not highlight a clear hemispheric asymmetry of responses.
Finally, resting-state fNIRS studies found a hypoactivation with rightward asymmetry shift in the prefrontal cortical area of ASD preschool children (Li and Yu, 2016;. Only two studies reported ASD-related alterations bilaterally (Kikuchi et al., 2013;Jia et al., 2018). However, these results could be influenced by technical aspects, such as the different number of channels used.
Even if no systematic assessment of hemispheric asymmetry has been performed with fNIRS in preschoolers, these findings and evidence from other neuroimaging technique such as structural  and functional MRI (Müller et al., 2011) would encourage studying more in depth atypical lateralization in ASD population, also at early stage.
Could fNIRS Be the Right Tool to Dissect the Vascular Contribution to ASD Condition?
Over the last years of research, the biological dimension of ASD has been extensively investigated both in the preclinical (Ebert and Greenberg, 2013;Del Pino et al., 2018) and the clinical settings (Billeci et al., 2016;Bralten et al., 2018). However, most studies only focused on neuronal mechanisms underlying ASD.
Preclinical studies on animal models can offer important insights about specific mechanisms underlying the pathophysiology of ASD (Bartz et al., 2008). Recent studies suggested the presence of structural and functional neurovascular abnormalities in ASD. Indeed, an early dysfunction of endothelial cells, altered angiogenesis and impaired vasodilation reactivity have been shown in a mouse model of 16p11.2 deletion ASD syndrome (Ouellette et al., 2020). Similarly, cerebrovascular deficiencies have been recognized in a mouse model of Creatine Transporter Deficiency, an inherited metabolic condition characterized by intellectual disability and autistic-like features (Mazziotti et al., 2020). The vascular hypothesis is also supported by clinical studies. Indeed, postmortem analysis of ASD brains suggested an impairment of cerebral angiogenesis (Azmitia et al., 2016) and resting-state imaging highlighted the alteration of cerebral blood flow in distinct brain regions (Jann et al., 2015;Bjørklund et al., 2018). Interestingly, it has been speculated that an exacerbated inflammatory response might be one of the possible causes of the deteriorated integrity of the blood-brain barrier (Fiorentino et al., 2016;Kumar and Sharma, 2016) and of the defective blood flow documented in the ASD brain (Morgan et al., 2010;Bjørklund et al., 2018). Moreover, there is evidence of glial dysfunction in ASD (Petrelli et al., 2016), with the alteration of astrocyte population (Fatemi et al., 2008;Edmonson et al., 2014;Wang et al., 2021) potentially contributing to the deregulation of neurovascular homeostasis.
We believe that the potential vascular contribution in the etiology of ASD should be explored more in-depth, since the interplay with the vascular system is crucial for the proper maturation and function of neuronal networks (Andreone et al., 2015;Segarra et al., 2018). In this framework, the combined analysis of hemodynamic responses measured with fNIRS and bioelectrical signals obtained through EEG recordings might allow dissecting vascular vs. neuronal aspects of ASD.

CONCLUSION
This review highlights theoretical and methodological advantages of fNIRS that encourage its application for identifying quantitative biomarkers in NDDs, and in particular in the autism field. Even if available papers in preschoolers are yet not conclusive to let us claim a "fNIRS signature" of autism, we believe that the use of this technique as an auxiliary diagnostic tool is very promising. Consistently, we recently reported that the variability of fNIRS visually evoked hemodynamic responses correlates with autistic traits in typically developing children, setting the background for testing the diagnostic value of fNIRS visual measurements in the ASD and HR clinical population (Mazziotti et al., 2022).
Nevertheless, some critical technical issues should be taken into account, because they might limit the interpretation of data and the comparison of results among different studies. First, the availability of reliable methods to detect and remove motion artifacts is fundamental for applying fNIRS in a very young population. Only a few of the studies reviewed (Edwards et al., 2017;Bhat et al., 2019;Pecukonis et al., 2021) systematically applied motion correction algorithms to the data recorded, while most of them simply removed corrupted data (caused by large head or body movements or other unexpected behaviors) by visual inspection of experiment video recordings. This methodological heterogeneity leads not only to a considerable failure rate of fNIRS data collection, but also to a possible bias in the results. Since this topic is debated, wide agreement on subtle technical parameters to apply is still missing even if they could significantly affect data outputs (Di Lorenzo et al., 2019). Moreover, in restingstate measurements complex algorithms to extract reliable data from the fNIRS raw output are often required, representing a potential bias and limiting factor for their clinical use. Indeed, several methodological models have been applied, like functional network efficiency Yu, 2016, 2018), detrended fluctuation analysis , coherence analysis of spontaneous hemodynamic fluctuations (Kikuchi et al., 2013) and a combination of principal component analysis and normalized entropy . It is to note that all resting-state studies reviewed compared ASD vs. TD cohorts (3-7-year-old children) and included in the clinical sample only high-functioning autism, probably due to recording constraints (predictable low-grade of compliance during the experimental procedure) that prevent such experimental design in younger and more impaired subjects.
On the other hand, the analysis of articles reviewed in this survey offers several cues of topics to systematically stress in the future fNIRS research within the autism field. Only one of the 13 articles included a focus on the suitability of fNIRS to highlight gender differences in HR infants (Edwards et al., 2017) and the small sample size of other studies (or the lack of a gender-balanced population) did not allow separating gender analyses. However, since consistent findings reported sexual dimorphism of specific brain regions and networks in ASD (see Mo et al., 2021 for a recent review), a specific focus on gender-related brain differences in well-powered cohorts might be an added value of next fNIRS assessments. Similarly, more weight should be given to the extrapolation of asymmetry indexes from fNIRS signals that might represent intra-subject measures potentially useful as condition biomarkers. Moreover, previous studies (Burzi et al., 2015;Jones et al., 2015) explored the importance of imitative learning during early development, focusing on differences in brain activation evoked from live (real time actions) rather than artificial stimuli (videos or photos of actions). While the others used artificial stimuli was applied (Bhat et al., 2019), while in the others artificial stimuli were used. The putative role of stimulus "quality" is still challenging to assess and it might deserve attention in future studies.
Finally, the possibility to measure hemodynamic response with fNIRS would allow the investigation of the possible role of neurovascular coupling in the pathogenesis of ASD, in particular when paired with simultaneous EEG recordings. In this framework, the evolution of hemodynamic patterns from the developing to the adult brain (Kozberg and Hillman, 2016) needs to be considered in the interpretation of typical and aberrant fNIRS signals.

AUTHOR CONTRIBUTIONS
EC and ES equally contributed to conceptualization of the manuscript, studies selection, interpretation of results, and manuscript writing. CB contributed to systematic search of the literature. VM, VC, VD, and RM contributed to the conceptualization of the study and interpretation of results. LD, CC, and FM supervised the manuscript. LB contributed to conceptualization, interpretation of technical results, and manuscript writing. SC and RB contributed to conceptualization and supervised the manuscript. All authors have read and agreed the final version of the manuscript.

FUNDING
This work has been supported by grant from the IRCCS Fondazione Stella Maris (Ricerca Corrente -RC 2021: Early functional and neurobiological markers of rare diseases associated to neurodevelopmental disorders -RB-and the 5 × 1000 voluntary contributions, Italian Ministry of Health), and from the University of Pisa, "Bando PRA-Progetti di Ricerca di Ateneo" (Institutional Research Grants) 2020-2021 "Multimodal non-invasive approach in young females with autism and their parents: a feasibility study."