Immunological Feature and Transcriptional Signaling of Ly6C Monocyte Subsets From Transcriptome Analysis in Control and Hyperhomocysteinemic Mice

Background Murine monocytes (MC) are classified into Ly6Chigh and Ly6Clow MC. Ly6Chigh MC is the pro-inflammatory subset and the counterpart of human CD14++CD16+ intermediate MC which contributes to systemic and tissue inflammation in various metabolic disorders, including hyperhomocysteinemia (HHcy). This study aims to explore molecule signaling mediating MC subset differentiation in HHcy and control mice. Methods RNA-seq was performed in blood Ly6Chigh and Ly6Clow MC sorted by flow cytometry from control and HHcy cystathionine β-synthase gene-deficient (Cbs -/-) mice. Transcriptome data were analyzed by comparing Ly6Chigh vs. Ly6Clow in control mice, Ly6Chigh vs. Ly6Clow in Cbs-/- mice, Cbs-/- Ly6Chigh vs. control Ly6Chigh MC and Cbs-/- Ly6Clow vs. control Ly6Clow MC by using intensive bioinformatic strategies. Significantly differentially expressed (SDE) immunological genes and transcription factor (TF) were selected for functional pathways and transcriptional signaling identification. Results A total of 7,928 SDE genes and 46 canonical pathways derived from it were identified. Ly6Chigh MC exhibited activated neutrophil degranulation, lysosome, cytokine production/receptor interaction and myeloid cell activation pathways, and Ly6Clow MC presented features of lymphocyte immunity pathways in both mice. Twenty-four potential transcriptional regulatory pathways were identified based on SDE TFs matched with their corresponding SDE immunological genes. Ly6Chigh MC presented downregulated co-stimulatory receptors (CD2, GITR, and TIM1) which direct immune cell proliferation, and upregulated co-stimulatory ligands (LIGHT and SEMA4A) which trigger antigen priming and differentiation. Ly6Chigh MC expressed higher levels of macrophage (MΦ) markers, whereas, Ly6Clow MC highly expressed lymphocyte markers in both mice. HHcy in Cbs -/- mice reinforced inflammatory features in Ly6Chigh MC by upregulating inflammatory TFs (Ets1 and Tbx21) and strengthened lymphocytes functional adaptation in Ly6Clow MC by increased expression of CD3, DR3, ICOS, and Fos. Finally, we established 3 groups of transcriptional models to describe Ly6Chigh to Ly6Clow MC subset differentiation, immune checkpoint regulation, Ly6Chigh MC to MΦ subset differentiation and Ly6Clow MC to lymphocyte functional adaptation. Conclusions Ly6Chigh MC displayed enriched inflammatory pathways and favored to be differentiated into MΦ. Ly6Clow MC manifested activated T-cell signaling pathways and potentially can adapt the function of lymphocytes. HHcy reinforced inflammatory feature in Ly6Chigh MC and strengthened lymphocytes functional adaptation in Ly6Clow MC.


INTRODUCTION
Monocytes (MC) are bone marrow (BM) derived mononuclear phagocytes that play an important role in innate immune response and are the major immune cell population in chronic tissue inflammatory (1,2). MC can be classified into inflammatory or anti-inflammatory subsets (1). Human MC were initially divided into three subsets based on the cell surface expression of CD14 and CD16, and recently classified based on CD40 expression (2)(3)(4)(5). Murine MC are divided into three subsets based on surface expression of lymphocyte antigen 6 complex, locus C (Ly6C) (3,4). Murine Ly6C high and Ly6C middle MC subsets perform pro-inflammatory functions, which are considered the counterpart of human CD14 ++ CD16 + intermediate MC or CD14 + CD40 + inflammatory MC (4,5). Murine Ly6C low MC perform patrolling and antiinflammatory function, similar to human CD14 + CD16 ++ nonclassical, CD14 ++ CD16classical MC, and CD14 + CD40antiinflammatory MC (4,5). Various studies support the notion that Ly6C high MC can be differentiated into Ly6C low MC (6)(7)(8). However, the selective impairment of Ly6C h i g h MC in Irf8 −/− mutant murine demonstrated an independent developmental pathway for Ly6C low MC (9). It was reported that certain transcription factors (TF) (e.g. NR4A1, CEBPb) controlled Ly6C low MC differentiation in the BM (10,11). TF CEBPb was shown to regulate Ly6C low MC differentiation by controlling orphan nuclear receptor NR4A1 expression (10,11). CEBPb-deficient mice lacked Ly6C low MC (11). However, the molecular mechanism underlying MC subset differentiation and transcriptional regulation remain to be elucidated.
To explore the immunological feature and transcriptional regulatory mechanism in MC subsets, we analyzed the expression pattern of four sets of immunological genes (secretome, cytokine, surface marker and immune checkpoint). Secretome is a new term to describe proteins secreted to the extracellular space mediating cell-cell interactions (17). Cytokines are small soluble signaling proteins secreted by cells, which determine immune response (26). Most cytokines have defined functions to regulate immune responses including proliferation, trafficking, and differentiation by binding to corresponding receptors (26). Cell surface markers, such as cluster of differentiation (CD) molecules, regulate adhesion, immune recognition and cell-cell interaction (27,28). Lineagespecific cell-surface markers are characteristic molecules used to define specific lineage and stage in the differentiation process (29,30). Recent progress in a single-cell RNA sequencing (scRNAseq) study proposed a group of new signature genes to define novel immune cell populations (31). Immune checkpoints are cell surface molecular pairs (receptors and their ligands) classified into co-stimulatory and co-inhibitory immune checkpoint (25,32). Co-stimulatory signals activate T-cell or antigen-presenting cell to regulate differentiation, proliferation, cytokines secretion, and receptor expression (33). Co-inhibitory signals are negative regulators of immune response to avoid immune injury or turn down the immune system (25,34).
We previously demonstrated that hyperhomocysteinemia (HHcy), an independent risk factor for cardiovascular, diabetic and Alzheimer's disease, induced Ly6C high inflammatory MC subsets differentiation, which contributed to tissue inflammatory and accelerated arteriosclerosis and chronic kidney disease (5,(35)(36)(37)(38)(39). The effect of HHcy on MC subset differentiation in patient would be an interesting topic for future clinical research. Discover of regulatory mechanisms mediating HHcy-induced MC subset differentiation may lead to the discovery of novel therapeutic target. This study aims to systemically examine mRNA expression profiles of key immunological genes in Ly6C high and Ly6C low MC subsets by intensive bioinformatic analysis and to develop models of molecule pathways and transcriptional regulatory signaling for subset differentiation.

RESEARCH DESIGN AND METHODS
We summarized the overall study approaches and strategies in Figure 1.

HHcy Mice
The Tg-hCBS Cbs -/mice were created as described previously (35,40). The human CBS transgene (Tg-hCBS) was introduced in Cbs -/mice to rescue neonatal lethality and is under the control of a Zn-inducible metallothionein promoter (40). Mice were all born to mothers drinking ZnCl 2 water (25 mM) to induce transgene expression (35,40). ZnCl 2 was withdrawn after weaning at 1 month of age to allow the development of HHcy. Animals were fed standard rodent chow diet and sacrificed at 22 weeks for blood collection after euthanization. Mouse protocols were approved by the Temple University Institutional Animal Care and Use Committee.

Hcy Measurement
Mouse blood was collected into 1 mM ethylenediaminetetraacetic acid (EDTA)-coated tubes. A total of 50 ml of plasma was batched and stored at -20°C for Hcy measurement as previously described (41). In brief, total Hcy levels were tested by liquid chromatographyelectrospray ionization-tandem mass spectrometry.

RNA Sequencing in Monocyte Subsets
Flow cytometry sorted CD11b + Ly6G -Ly6C h i g h and CD11b + Ly6G -Ly6C low cells from control and Cbs -/-WBC (200000/MC subset) were collected in 700 ml QIAzol Lysis FIGURE 1 | Overall strategy of the identification of Ly6C MC regulatory genes and molecule mechanism for Ly6C monocyte subset differentiation in control and Cbs -/mice. RNA-seq were performed in Ly6C high (CD11b + Ly6G − Ly6C high ) and Ly6C low (CD11b + Ly6G − Ly6C low ) MC isolated by flow cytometry sorting from peripheral blood of C57/BL6 control and Cbs -/mice. Transcriptome data were analyzed by performing four pairs of comparisons; (A) Ly6C high vs. Ly6C low (CT), (B) Ly6C high vs. Ly6C low (Cbs -/-), (C) Cbs -/vs. CT (Ly6C high ), (D) Cbs -/vs. CT (Ly6C low ). We identified 7928 SDE genes using the Bioconductor suite of packages in RStudio software with the criteria of |Log 2 FC| more than 1 (2-FC) and adjusted P value less than 0.01. Top ingenuity pathways were identified by top-down analysis using IPA with |Z-score|>2, P value<0.05. Immunological SDE gene sets, including secretome, cytokine and surface marker were overlapped analysis and matched with corresponding upstream SDE TF by IPA upstream analysis. Three molecular signaling model system were developed, 1) Transcriptional regulation for Ly6C high to Ly6C low MC subset differentiation, 2) Immune checkpoint regulation in Ly6C MC. 3) Transcriptional signaling for Ly6C high MC to MF subset differentiation and Ly6C low MC to lymphocyte functional adaptation, CT, control, HHcy, Hyperhomocysteinemia; RNA-seq, RNAsequencing; MC, monocyte; Cbs, Cystathionine b-synthase; SDE, significant differentially expressed; IPA, Ingenuity Pathway Analysis, TF, transcription factor, MF, macrophage. Reagent (Qiagen, Germantown, MD) for total RNA extraction. Samples were quality checked on an Agilent Bioanalyzer 2100 using pico RNA chip for RNA integrity number. Total RNA (50-100 ng/sample) were used for cDNA library construction after ribosomal cDNA depletion using Takara pico-input kit. Pooled samples were run for sequencing analysis in duplication on Illumina NextSeq 500 (CT) and Illumina Hiseq 4000 sequencer (HHcy).
RNA-seq data from this study are available from the corresponding author upon reasonable request in reference to recent similar publication (42). Details for major RNA-seq data resources can be found in Supplementary Material.

RNA Sequencing Data Processing
The raw reads were mapped to the mouse reference transcriptome (mouse cDNA FASTA from ensembl, website http://uswest.ensembl.org/info/data/ftp/index.html) using Kallisto, version 0.45. Genes with less than 1 count per million reads in at least 2 or more samples were filtered out. This reduced the number of genes to 16,476 normalized genes. The raw RNAseq data was analyzed using the statistical computing environment R, the Bioconductor suite of packages for R and RStudio (tidyverse, reshape2, tximport, biomaRt, RColorBrewer, genefilter, edgeR, matrixStats, hrbrthemes, gplots, limma, DT, gt, plotly, beepr, skimr, cowplot, data.table, sva).

Principle Component Analysis
PCA was performed to examine the variance of RNA-seq data. RNA-seq data from control and Cbs -/mice were produced at different times and processed to remove batch effects and other unwanted noise using ComBat approach (43,44). The first 2 principal components (PC1 and PC2) were used to depict the similarity between samples.

Volcano Plot and Heatmap
Volcano plot was used as a scatterplot to show the differential expression of genes that shows statistical significance (-Log 10 adjust P-value) versus magnitude of change (Log 2 FC). Heatmap was generated in RStudio using the pheatmap package to present the expression levels of SDE genes. The color density in the heatmap indicates the average expression levels of a given gene normalized by z-score.

Identification of Functional Pathways
We used Ingenuity Pathway Analysis (IPA) version 7.1 (IPA, Ingenuity Systems, https://www.ingenuity.com) to identify functional pathways. SDE genes were identified and uploaded into IPA for analysis. The general canonical functional pathways were established for SDE genes identified in above mentioned four comparison groups, as we have previously reported (48,49).

Overlap Analysis of SDE Genes
SDE genes and functional pathways identified from above mentioned four comparisons were subjected for overlapping analysis (http://bioinformatics.psb.ugent.be/webtools/Venn/). Venn diagrams were displayed to present SDE genes and pathways overlaps between comparisons. Further, functional pathways were also established for three sets of immunological SDE genes (secretome, cytokines and surface markers) and SDE TF. Functional pathways in Venn diagram were developed by using metascape website software (https://metascape.org/) for SDE gene set (>20 SDE genes).

Identification of Transcriptional Signaling
We identified SDE TFs and matched with their corresponding SDE immunological genes by referencing TF-matched gene sets using IPA upstream analysis. The significate matches were recognized as potential transcriptional signaling (TF/targeted molecule axis) based on p-values < 0.01, |z-scores|>2, calculated by using Fisher's Exact Test.

Identification of 7928 Significantly Differentially Expressed Genes Through Four Comparisons in Sorted Blood
Ly6C high and Ly6C low Monocytes From Control and Cbs -/-Mice We obtained 40 million reads and 16476 normalized genes from RNA-Seq analysis of 200000 sorted Ly6C high (CD11b + Ly6G -Ly6C high ) and Ly6C low (CD11b + Ly6G -Ly6C low ) MC from control C57/BL6 mice and HHcy Cbs -/mice (plasma Hcy 5.23 mM and 128.13 mM) (Figures 2A-C). PCA presented a clear separation between Ly6C high and Ly6C low in both control and Cbs -/samples ( Figure 2D). There was also a good separation in Ly6C high between control and Cbs -/mice which was absent in Ly6C low . The PC1 axis showed the largest variations and explained 44.1% of the variances between Ly6C high and Ly6C low MC subsets. The PC2 axis explains 21.1% of the variance between Cbs -/and control mice.
A total of 7,928 SDE genes with the criteria of |Log 2 FC| more than 1 (2-FC) and adjusted P-value less than 0.01 ( Figure 2E) were identified through the previously mentioned comparison pairs ( Figure 2F). We found 1,423 upregulated and 1,641 downregulated SDE genes in Ly6C high MC compared with Ly6C low MC in control mice (Comparison A). We identified 1,525 upregulated and 2,080 downregulated in Ly6C high MC compared with Ly6C low MC in Cbs -/mice (Comparison B). When compared between the same subset among the two mouse groups, we discovered that HHcy in Cbs -/mice upregulated 345 and downregulated 337 SDE genes in Ly6C high MC (Comparison C), and upregulated 201 and downregulated 366 SDE genes in Ly6C low MC (Comparison D).

Ly6C high Monocytes Enriched With Inflammatory Pathways and Ly6C low Monocytes Presented Features of T Cell Activation Based on All Significantly Differentially Expressed Genes
We recognized 23, 18, 2, and 3 canonical pathways that were significantly enriched by top-down analysis using SDE gene identified from comparison groups A, B, C, and D, respectively, by using IPA software ( Figures 3A-D). The details of the gene names, FC and molecular category of the top 40 up/down SDE genes involved in these pathways are listed in Supplementary  Table 1.
Through overlap analysis ( Figure 3E), we discovered 21 activated pathways in Ly6C high MC (16 in control mice, two in Cbs -/mice, and three in both) in Comparisons A and B. These activated pathways were derived from 2084 SDE genes (590 in control, 667 in Cbs -/and 826 in both). The top 3 pathways are depicted. Moreover, we found 15 suppressed pathways in Ly6C high MC (2 in control, 11 in Cbs -/and 2 in both). These suppressed pathways were derived from 2677 SDE genes (604 in control only, 1,068 in Cbs -/only and 1,005 in both). From comparison C and D, we discovered two activated pathways and three suppressed pathways in Ly6C high and Ly6C low MC in Cbs -/mice, respectively. The two activated pathways in Cbs -/-Ly6C high MC were derived from 294 SDE genes. The three suppressed pathways in Cbs -/-Ly6C low MC were derived from 249 SDE genes.
There were 3 activated pathways overlapped in Ly6C high MC from both control and Cbs -/mice. These include interferon, inflammasome and PD-1/PD-L1 checkpoint pathways. Two suppressed pathways, T-cell apoptosis and Th cell signaling, were overlapped in Ly6C high from both control and Cbs -/mice.
Specifically, sulfate degradation was activated, and Th1/B-cell pathway was suppressed only in Ly6C high from Cbs -/mice. Whereas, NK cell signaling were activated in Ly6C high and a few metabolic pathways, including xenobiotic metabolism and melatonin degradation, were suppressed in Ly6C low MC only in Cbs -/mice as detailed in Figures  In SDE gene-derived pathway overlap analysis, presented in Venn diagram in Figure 4B, we found 20-activated/20suppressed pathways from SDE secretome genes in Ly6C high MC from both control and Cbs -/mice (Comparisons A and B). The top pathways indicated the activation of lysosome and extracellular structure, and suppression of lymphocyte activation, IFN-g production and inflammatory response in Ly6C high MC. In addition, we identified secretome SDE genederived pathway specific for Ly6C high for each mouse. For example, protein glycosylation and ECM regulation were activated in Ly6C high only in Cbs -/mice. Moreover, HHcy in Cbs -/mice specifically activated extracellular structure organization and synaptic membrane adhesion, and suppressed external stimulus, MNC migration, cell adhesion and leukocyte proliferation pathways in Ly6C high MC, and suppressed myeloid leukocyte migration, collagen catabolic process and humoral immune response pathways in Ly6C low MC. A detailed list of SDE genes and pathway are presented in Supplementary  Table 3.
For the SDE cytokine genes, we identified 20-activated/20suppressed pathways in comparison A and B. The top pathways indicated the activation of cytokine production, response to lipopolysaccharide and locomotion, and the suppression of NK cell chemotaxis and leukocyte activation in Ly6C high MC. Specifically, HHcy activated responses to lipopolysaccharide, IL-17 signaling pathway and inflammatory response, and suppressed cytokine production/signaling pathways and adaptive immune response in Ly6C high only in Cbs -/mice.
In SDE surface marker gene set, we discovered 20-activated/ 20-suppressed pathways in comparison A and B. The top pathways displayed the activation of myeloid cell and cytokine production, and suppression of lymphocyte activation, hematopoietic cell lineage, and lymphocyte mediated immunity in Ly6C high MC. Specifically, HHcy suppressed regulation of cell adhesion, adaptive immune system and collagen metabolic process in Ly6C high only in Cbs -/mice.   As shown in volcano plots in Figure 5A, we identified 77upregulated/84-downregulated, 66-upregulated/115downregulated, 13-upregulated/13-downregulated, and 14upregulated/9-downregulated SDE TFs in comparisons A, B, C and D, respectively. From these SDE TFs, we discovered 20activated/20-suppressed pathways overlapped in Ly6C high MC from both control and Cbs -/mice (Comparisons A and B) ( Figure 4B). The top pathways displayed the activation of hemopoiesis, and suppression of cell fate commitment, proliferation and differentiation in Ly6C high MC. Specifically, HHcy activated RNA polymerase II transcription initiation, chordate embryonic development and myoblast differentiation pathways, and suppressed fat cell differentiation, cellular response to steroid hormone, and histone modification pathways in Ly6C high only in Cbs -/mice.

Ly6C high Monocyte Presented Downregulated Co-Stimulatory Receptors for Proliferation, and Upregulated Co-Stimulatory Ligands for Antigen Priming and Differentiation
To test the differential role of Ly6C MC subsets in regulating adaptive immunity, we examined the expression pattern of immune checkpoint molecules. As depicted in Figure 6A, 25 out of 49 checkpoint pairs displayed differential expression in Ly6C high and Ly6C low MC subsets. Ly6C high MC expressed relative low levels of both co-stimulatory and co-inhibitory immune checkpoint receptors. A detailed list of immune checkpoint expression was presented in Supplementary Table 4.

Ly6C high Monocyte Favored to MF Differentiation and Ly6C low Monocyte Shared Function With Lymphocyte Subsets
To examine the potential plasticity of Ly6C MC subsets, we first analyzed the expression pattern of newly suggested leukocyte signature genes from recent scRNA-seq studies (46,47). Ly6C high MC expressed high levels of myeloid cell (MF and DC) signature genes in both mice ( Figures 7A, B). Differently, Ly6C low MC expressed high levels of lymphocyte (T-and B-cell) signature genes, especially that of CD8 + T-cell and B-cell ( Figures 7A, B). Interestingly, Ly6C high MC expressed high levels of osteoclast TFs (Cebpa, Fos, Tfe3, and Mitf) and surface marker CD44, and  Table 5. Further, we examined the expression of established lineage/ subset TF and surface marker in Ly6C MC. MF surface markers (CXCL10, Ym1, and CD206) and myeloid lineage TFs (Cebpa, c-Fos, and Spi1) were highly expressed in Ly6C high MC in both mice. While, lymphocyte surface markers (CD4, CD25, CD161, CD5, CD19, CD21, CD79a, and CD79b) and lymphocyte lineage TFs (T-bet, Rog, Carma1, and Pax5) were preferentially expressed in Ly6C low MC in both mice (Figures 7C, D).

Specifically, CD3, a T-cell receptor involved in activating both cytotoxic T-cell and T helper (Th) cells, was upregulated by
Cbs -/in Ly6C low MC (Comparison D). Literature justification and designation of TFs and surface markers for leukocyte subsets are provided in the Supplementary Table 6. Expression change and function implication of SDE cytokine genes in Ly6C MC were presented in Supplementary Table 7.

DISCUSSION
Mouse MC are classified into inflammatory Ly6C high and antiinflammatory Ly6C low subsets. However, the molecular mechanism underlying MC subset differentiation remains unclear, and functional features of MC subsets have not been systemically investigated. This study established transcription profiles of flow cytometry sorted Ly6C high and Ly6C low MC subsets from control and HHcy Cbs -/mice and examined their functional features and transcriptional regulatory pathways by performing intensive bioinformatic analysis and literature integration. We have 6 major findings: 1) Ly6C high MC showed enriched inflammatory pathways, whereas Ly6C low MC displayed activated lymphocyte immunity pathways in both control and Cbs -/mice. 2) Identified SDE TFs and their corresponding targeted SDE genes in Ly6C MC subset from both mice. 3) Ly6C high MC presented downregulated immune checkpoint receptor-directed immune cell proliferation, and upregulated ligand-triggered antigen priming and differentiation. 4) Ly6C high MC preferentially expressed MF and osteoclast markers, whereas Ly6C low MC expressed higher levels of lymphocyte subsets markers. 5) HHcy in Cbs -/mice reinforced the inflammatory response in Ly6C high MC, but promoted functional adaptation of lymphocytes in Ly6C low MC. 6) We established 3 groups of hypothetic molecular signaling models. The first model described transcriptional regulatory mechanism of Ly6C high to Ly6C low MC subset differentiation. These include SDE immunological gene and their regulatory SDE TFs. The second model was for immune checkpoint molecular alteration and function connection in MC subset. The third model summarized the potential molecular mechanism regulating Ly6C high MC to MF subset differentiation and Ly6C low MC to lymphocyte functional adaptation. Our   Our study emphasized that inflammatory pathways were enriched in Ly6C high MC and Ly6C low MC presented features of lymphocyte immunity activation (Figures 3 and 4). Ly6C high MC from both mice displayed elevated interferon, inflammasome, neutrophil degranulation, lysosome, cytokine production/ receptor interaction and myeloid cell activation pathways. This is consistent with previous findings showing that Ly6C high MC are rapidly recruited to sites of inflammation and releasing proinflammatory cytokines, such as type I interferon (IFN-I), IL-1, IL-6, IL-8, TNF-a, and MCP-1 (4,(51)(52)(53)(54)(55). It was reported that Ly6C high MC coordinates the innate immune response through inflammasome activation following exposure to pathogen-, damage-associated molecular patterns (PAMP, DAMP) and metabolic-associated danger signals (MADS) (25,32,56). Lysosomal activity is a new feature of Ly6C high MC, which implies enhance function of endocytosis and autophagia, and molecule degradation (57). Phagocytic features of Ly6C high MC were connected with high lysosomal activity (3,58).
Our data suggested that 9 SDE TFs (Cebpa, Cebpd, Cebpe, Irf5/7, Ifi16, Spi1, and Stat1/2) are potentially involved in Ly6C high MC generation and responsible for the immunological features in control mice ( Figure 5C). We and others have reported that CEBPa and CEBPd were enriched in Ly6C high MC (11,38). CEBPa binds to the Ly6c promoter and its expression was elevated and synergistically increased in HHcy and Type 2 Diabetes Mellitus mice (38). We found PU.1 (encoded by Spi1 gene) was increased by 2.66-fold in Ly6C high MC in control mice. PU.1 was a critical lineage determining TF for both myeloid and lymphoid cell development as PU.1-deficient mice lack MC, granulocytes and B-cells (3,59). PU.1 can transactivate other TFs (e.g., CEBPa, CEBPb, IRF proteins, c-Jun, JunB) to regulate subset differentiation (60). Upregulation of Irf7 by 7.26-fold in Ly6C high MC in control mice may be related with their function towards MF differentiation. This is supported by IRF-7 overexpression-induced MC differentiation to MF in U937 and HL60 cells (61).
We found that CEBPa, Irf7, PU.1 and Stat1 were Ly6c TFs and positively associated with Ly6c expression. They are strong candidate determining Ly6C high MC generation. Other upregulated TFs in Ly6C high MC are also potentially responsible for Ly6C high MC generation, for example, the top 5 TFs (Ifi211, Tfec, Fos, Fam129b, and Id1) listed in Figure 5E. Under homeostasis, classical Ly6C high MC in blood reduces the expression of Ly6C and becomes non-classical Ly6C low MC (7,15). We proposed that downregulated TFs in Ly6C high MC are possible regulators determining Ly6C high MC to Ly6C low MC differentiation. The top 4 downregulated TFs (Neurod4, Asb2, Sox5 and Pou2af1) and 2 matched TFs Pax5 and Tbx21 represented potential general transcriptional mechanism for Ly6C high MC to Ly6C low MC differentiation. Pax5 plays a crucial role in the commitment of BM multipotent progenitor cells to the B-lymphoid lineages. It has been shown that, except for B-cell lineage, other hemopoietic lineages develop normally in Pax5-deficient mice (62). T-bet (encoded by the Tbx21 gene) controlled IFN-g expression in CD4 + T-cell, and was reported recently to be expressed in human MC (63). Lack of Tbx21 reduces monocytic interleukin-12 formation and accelerates thrombus resolution in deep vein thrombosis (64). Overall, TFs (Pax5 and Tbx21) were previously thought as lymphocyte lineage-specific TF, but their role in regulating MC differentiation remains to be addressed.
The upregulation of co-stimulatory ligands (LIGHT and SEMA4A) in Ly6C high MC led us to hypothesize that Ly6C high MC presents high activity of antigen priming and differentiation. LIGHT/HVEM engagement promotes T-cell priming and differentiation (73,74). During viral infection, LIGHT are induced by IFN-g on MC-derived cells (75). High level expression of Sema4A was found on Ly6C high MC (76). Sema4A-deficient mice exhibit defective Th1 responses and impaired antigen-specific T-cell priming and antibody response against T-cell-dependent antigens (76). These findings suggested a key role for Ly6C high MC in the regulation of T-cell immunity and may provide new insights into development of more effective therapies for diseases in which T-cell has an important role.
As illustrated in Figure 8A, our study provides evidence to support a model that Ly6C high MC favors to differentiate to MF,  Although the fate and mechanism underlying Ly6C high MC differentiation is unclear, a more common postulation is that Ly6C high MC tend to differentiate into M1 MF, but Ly6C low MC to M2 MF (37,77). It is suggested that Ly6C high MC may be primed to differentiate into Ly6C low MC, or infiltrated into tissues to develop specific tissue MC-derived cells (3,4,17,78). It was shown that continued recruitment of Ly6C high MC and their differentiation to M2 rather than M1 MF are required for resolution of atherosclerotic inflammation and plaque regression (46,79). The destiny of Ly6C high MC differentiation may vary under different microenvironment. Details presented in Figure  8A provide important insights for molecular pathways underlying Ly6C high MC to MF differentiation.
Based on the high levels of osteoclast TFs, surface marker and osteoclast-like TREM2 high MF signature genes in Ly6C high MC, we proposed that Ly6C high MC is a precursor of osteoclasts. Osteoclasts contribute to vascular calcification, which causes local tissue stress and plaque instability (80). Like MF, osteoclasts are derived from MC precursors in chronic inflammatory conditions and required 2 main cytokines (CSF1 and RANKL) and 4 TFs (Cebpa, Fos, Tfe3, and Mitf) (81,82). Our data is in good accordance with previous finding showing that Ly6C high MC, but not Ly6C low , differentiate into osteoclast in arthritis bone erosion (18,83). Taken together, we hypothesize that inflammatory MC subset can be differentiate to osteoclasts and contribute to tissue calcification in inflammatory condition and chronic disease. We promoted a model for Ly6C low MC to lymphocyte subsets functional adaptation according to their preferential express of T-cell specific surface markers, lineage TFs and checkpoint receptor, and their associated T-cell-related effector function ( Figure 8B). The classical road map of immune cell differentiation describes that lymphoid progenitor lineages segregate from myelo-erythroid (ME) in hematopoietic stem cells. However, the 'myeloid-based model' suggested that myeloid cell can also be generated from myeloid-T progenitor and myeloid-B progenitor, which is derived from common myelo-lymphoid progenitor (84,85). Recent evidence suggested that early pro-B-cell can give rise to either MCderived MF or tissue-specific MF during tissue homeostasis and inflammation (86). Evidence for myeloid to lymphoid lineage differentiation and function adaptation is absent. Our study, for the first time, provide evidence of Ly6C low MC to lymphocyte functional adaptation.
Our data also suggested that HHcy further strengthened Ly6C low MC to lymphocytes functional adaptation by upregulating surface marker CD3, co-stimulatory checkpoint (DR3, ICOS) and TF Fos. CD3 complexes with T-cell receptor contributing to antigen recognition (94). The ligation of immune checkpoint receptor DR3 with TL1A exerts activation and differentiation in immune cell, including Th and T-reg cell (95). ICOS regulates the differentiation and maintenance of Tfh cells (96), which helps B-cells to form germinal centers and differentiate into plasma cells and memory B-cell for high affinity antibody production (96,97). TF Fos plays a central role in nuclear factor of activated T-cell (NFAT) complex formation which involved in cell proliferation, differentiation and tumor progression (98)(99)(100). This evidence supports the notion that HHcy promoted lymphocytes functional adaptation in Ly6C low MC.
In conclusion, our study, for the first time, demonstrated that Ly6C high MC displayed enriched inflammatory pathways, immune checkpoint molecules for suppressed proliferation and increased antigen priming, and demonstrated the potential to differentiate into MF and osteoclast. Ly6C low MC manifested activated T-cell signal pathways and potentially can adapt the function of lymphocytes. HHcy in Cbs -/mice reinforced  Table 6. scRNA-seq, single-cell RNA sequencing; MC, monocyte, Cbs, Cystathionine b-synthase; MF, A B FIGURE 8 | Molecule signaling of Ly6C MC to MF subset differentiation and to lymphocyte subset functional adaptation. We established two models for molecule signaling of MC differentiation based on their preferential expression of lineage signature TF, surface marker and cytokine using information extracted from Figures 3, 5, and 7. (A) Ly6C high MC favors to MF subset differentiation and associated molecule signaling. Ly6C high MC preferentially expressed lineage signature TF genes of MF/DC subsets, suggesting their potential differentiation to MF. The indicated immunological and inflammatory pathways lead to various changes of cytokines production, and effector function including T/NK cell proliferation, inflammatory response and calcification. Cbs -/-Ly6C high MC exhibited inflammatory cytokine production. (B) Ly6C low MC shares function with lymphocyte subset (molecule signaling). Ly6C low MC preferentially expressed lineage signature TF genes of B/T cell subsets, suggesting their potential functional adaptation to lymphocyte subsets. The indicated immunological and inflammatory pathways lead to various changes of cytokines attributed to increased T/B cell activation, host defend, wound healing and anti-inflammatory responds. Cbs -/-Ly6C low MC exhibited enhance T/B cell activation potential. Expression change and function implication of SDE cytokine genes in Ly6C MC were presented in Supplementary Table 7 inflammatory response in Ly6C high MC and strengthened lymphocytes functional adaptation in Ly6C low MC.

DATA AVAILABILITY STATEMENT
The data present in the study are deposited in the Gene Expression Omnibus (GEO) repository under the accession number GEO:GSE165879.

ETHICS STATEMENT
The animal study was reviewed and approved by the Temple University Institutional Animal Care and Use Committee (IACUC).

AUTHOR CONTRIBUTIONS
PY analyzed the data, drafted and participated in preparing all figures and manuscript. LL conducted the bioinformatics analyses. LS participated in data analysis and some part of manuscript preparation. PF isolated MC subsets from mice and designed RNA-Seq analysis. JS and WS participated in some of data analysis and provided editing assistance. NS, YJ and XQ provided intellectual and data analysis support. QW and XY provided strong intellectual and data analysis support. HW designed the study, supervised the project and prepared the manuscript. and All authors contributed to the article and approved the submitted version.

FUNDING
This work was supported in part by the National Institutes of Health (NIH) grants HL82774, HL-110764, HL130233, HL131460, DK104114, DK113775, and HL131460 to HW.