Chromatin, the complex of DNA and histone proteins, serves as a main integrator of cellular signals. Increasing evidence links cellular functional to chromatin state. Indeed, different metabolites are emerging as modulators of chromatin function and structure. Alterations in chromatin state are decisive for regulating all aspects of genome function and ultimately have the potential to produce phenotypic changes. Several metabolites such as acetyl-CoA, S-adenosylmethionine (SAM) or adenosine triphosphate (ATP) have now been well characterized as main substrates or cofactors of chromatin-modifying enzymes. However, there are other metabolites that can directly interact with chromatin influencing its state or that modulate the properties of chromatin regulatory factors. Also, there is a growing list of atypical enzymatic and nonenzymatic chromatin modifications that originate from different cellular pathways that have not been in the limelight of chromatin research. Here, we summarize different properties and functions of uncommon regulatory molecules originating from intermediate metabolism of lipids, carbohydrates and amino acids. Based on the various modes of action on chromatin and the plethora of putative, so far not described chromatin-regulating metabolites, we propose that there are more links between cellular functional state and chromatin regulation to be discovered. We hypothesize that these connections could provide interesting starting points for interfering with cellular epigenetic states at a molecular level. Chromatin is a macromolecular complex composed of distinct molecules. The fundamental, repeating unit of chromatin, the nucleosome, is composed of 146 base pairs (bp) of double-stranded DNA wrapped around an octamer made up of two copies each of the core histones H2A, H2B, H3 and H4 [The regulation of chromatin organization is crucial for all aspects of DNA biology including genome replication, gene expression, repair of DNA damage and meiotic recombination. Chromatin organization is directed by a plethora of chemical modifications on any of the different histone proteins or via methylation and its oxidation products of DNA. The modification reactions occur either nonenzymatically, or via the action of specialized chromatin-modifying enzymes  . Chromat+), ATP, S-adenosylmethionine (SAM), α-ketoglutarate, acetyl-CoA and different acyl-CoA moieties. Their chromatin biology has been extensively reviewed in the recent literature [The physiological state of a cell feeds into the regulation of chromatin not only via the supply of the building blocks of chromatin (i.e. nucleotides for DNA and amino acids for histone proteins) and energy status (i.e. driving chromatin remodeling processes which are dependent on adenosine triphosphate (ATP) hydrolysis) but also via a rapidly growing range of metabolites. The molecules that serve as substrates for enzymatic and nonenzymatic histone modifications and cofactors of chromatin-modifying enzymes have been of particular interest. Well-described metabolites in this context include nicotinamide adenine dinucleotide  containing histone lysine demethylases (KDM) and the ten eleven translocation (TET) methylcytosine DNA demethylases . Modin vitro. And it has been speculated that such effect might also be exerted by succinylated histones [Histone lysine succinylation is a nonenzymatic modification originating from succinyl-CoA . It can histones .Fumarate and succinate inhibit the α-ketoglutarate-dependent dioxygenases, thereby affecting DNA replication and stability . Fumaratin vitro and in cellulo, possibly by attaching to critical residues that are serving as intranucleosomal anchors and as binding sites for chromatin readers [Glycation of proteins is a common nonenzymatic covalent modification in diabetes and other hyperglycemic states. Glycation results from the condensation of the aldehyde form of monosaccharides or glycolytic by-products ) with reactive amino acid residues (mainly lysines and arginines) via a nonenzymatic reaction . This re readers . It is n readers .In vitro studies of 3-DG treatment of H1, H2A and H3 have shown that their secondary structures were altered upon glycation [3-DG is an α-oxoaldehyde, a reactive compound involved in vascular damage in diabetes. As with methylglyoxal, 3-DG reacts with histones, and glycation leads to the generation of advanced glycation end-products (AGEs). lycation . Histonelycation .O-acetyl-ADP-ribose (AAR) [+ and act as secondary messengers in chromatin organization processes.ADP-ribosylation reactions can be divided into four groups: mono-ADP-ribosylation, poly-ADP-ribosylation, ADP-ribose cyclization, and formation of se (AAR) . Here, w+ hydrolysis, catalyzed by Sirtuin proteins in a wide range of model organisms. It can act as a signaling molecule and substrate for chromatin-modifying enzymes [nucleoside diphosphate linked to another moiety X) hydrolases [AAR is a by-product of histone deacetylation and NAD enzymes , and itsdrolases .AAR regulates Silent Information Regulator (SIR) complex function in yeast . SIR proIn vitro, AAR is specifically recognized by the histone variant macroH2A1.1 via its macrodomain [In higher eukaryotes, SIRT1, a deacetylase associated with heterochromatin  also prorodomain ,43. Histrodomain ,45. But rodomain , and tharodomain  suggestsrodomain , the exirodomain . AAR hasrodomain ,50.+ [PAR is a negatively charged polymer with variable structure of two or more ADP-ribose units that are derived from NAD+ . PAR is + ,53. The + . The levPAR has many divergent roles in cellular signaling, chromatin maintenance, and stress response . PARP1, the most studied PARP protein, is recruited to DNA strand-break sites and initiates local PAR synthesis. PAR is either covalently attached to histones H1 and H2A  and to oUridine diphosphate N-acetylglucosamine (UDP-GlcNAc) is among the most abundant high-energy metabolites and it is the final product of the hexosamine biosynthetic pathway—a branch of glycolysis responsible for the production of a key substrate for protein glycosylation. O-GlcNAcylation is a PTM of proteins catalyzed by the O-Linked N-acetylglucosamine (O-GlcNAc) transferase (OGT) and reversed by O-GlcNAcase (OGA) .O-GlcNAcylation of all core histones has been described , with O-The OGT enzyme forms many functionally distinct complexes with other nuclear proteins, which determine the target location and the outcome of O-GlcNAcylation. For example, OGT is directed to chromatin by the TET DNA demethylase proteins, and many other OGT complexes have been described . Recent With only single copies of OGT and OGA enzymes in humans, and many roles of O-GlcNAcylation in the cell nucleus, UDP-GlcNAc metabolism is both an attractive target for modulation of its downstream effect and a vulnerable point involved in several pathologies .de novo myo-inositol biosynthesis pathway converts glucose-6-phosphate into inositol 1,4,5-trisphosphate (IP3). IP3 can be subsequently phosphorylated by inositol kinases yielding several phosphorylated species , inositol 1,3,4,5,6 pentakisphosphate (IP5), inositol 1,2,3,4,5,6 hexakisphosphate (IP6), and 5-diphosphoinositol pentakisphosphate (IP7)) [The e (IP7)) ,79. The IPs are highly negatively charged and they exert chromatin effects by interacting with specific protein targets. For example, IP6 inhibits nucleosome mobilization by the chromatin remodeling NURF, ISW2, and INO80 complexes. In contrast, IP4 and IP5 stimulate the chromatin remodeling (nucleosome-sliding) activity of SWI/SNF .in vitro [The enzymatic activity of class I HDAC1, 2 and 3, which form the catalytic subunits of several large transcriptional repression complexes, has been shown to be regulated by IPs. Initially, IP4 was found to be an intrinsic scaffolding part located at a binding pocket formed at the interface between the HDACs and their cognate corepressors NuRD, CoREST, SMRT, Sin3L/Rpd3L and others . It was in vitro . It is aIP7 functions to regulate its target proteins through allosteric interactions or protein pyrophosphorylation. Inositol hexakisphosphate kinase 1 (IP6K1), the enzyme responsible for the synthesis of IP7, associates with chromatin and interacts with the histone lysine demethylase, Jumonji domain containing 2C (JMJD2C). IP6K1 induces JMJD2C dissociation from chromatin and increases H3K9me3 levels, which depend on IP6K1 catalytic activity. Reducing IP6K1 expression levels or IP7 concentration causes a reduction in the levels of the H3K9me3 modification concomitant with an increase in the H3K9ac mark. The molecular mechanisms of this effect are not yet known but this phenomenon shows that there is localized production of IP to control histone methylation .Cholesterol possesses a tetracyclic steroid ring. It is synthesized via the mevalonate pathway, in which acetyl-CoA is converted by subsequent reactions into several fundamental end-products, including cholesterol and isoprenoids .Cholesterol influences chromatin structure and stability by directly binding to nucleosomes inducing compaction of long chromatin fibers . DockingFurther on the notion that cholesterol drives the compaction of nucleosomes, cholesterol was shown to be required for the transcriptional repressor brain acid soluble protein 1 (BASP1)-dependent histone modification and transcriptional repression. As such, BASP1-mediated recruitment of cholesterol might elicit direct effects on the nucleosome that contribute to establishing transcriptionally repressive condensed chromatin at specific genes; yet, further studies are needed to elucidate the exact mechanisms of action .2, PI3,5P2 and PI4,5P2 , and PI3,4,5P3  is the trisphosphate. Overall, PIPs occur in low to very low abundance when compared with other lipids. The different PIPs are well known for their biology at the cell membrane and in intracellular vesicle trafficking. In cell signaling PI4,5P2 is hydrolyzed to form the second messengers, diacylglycerol (DAG) and IP3. But a separate biology of PIP has been emerging in the nucleus with their concentrations varying in dependence of cellular state  [Phospholipids are a class of lipids that consists of fatty acids modified with phosphate groups, and in the case of the glycerophospholipids species, they contain an additional glycerol that bridges multiple fatty acid chains. A prominent example of glycerophospholipids are phosphoinositides . Multiple PIPs that differ in the phosphorylation positions and levels of the inositol polyalcohol are derived from phosphoinositol. The monophosphates are PI3P, PI4P  and PI5P; the bisphosphates are PI3,4Pte etc.) ,100.in vitro [Early studies of lipid analysis of isolated animal chromatin revealed different amounts of (phospho-)lipids associated with hetero- and euchromatin. In addition, higher turnover rates of (phospho-)lipids were found associated with euchromatin . Later iin vitro —an effecin vitro . Over th2. The interaction modulates interaction of the BAF complex with actin and tethering to the nuclear matrix [2 causes BAF to translocate from the soluble to the nuclear-insoluble protein fraction which allows it to participate in nuclear architecture formation [2 bound BRG-1 is not recruited to active promoters [The ATPase subunit, named BRG-1, of the ATP-dependent chromatin remodeling complex, BAF  binds PI4,5Pr matrix . In T ceormation . BAF comromoters .2 has further been found to directly bind to histones and in particular linker histone. This interaction is blocked after phosphorylation of H1 by protein kinase C [Drosophila transcription system, PI4,5P2 binding to H1 counteracted repression of RNA polymerase II basal transcription [2 has been shown to regulate the levels of H3K9me2 at the rDNA promoter by binding to and inhibiting the activity of the histone lysine demethylase PHD finger protein 8 (PHF8), likely resulting in a reduction in rRNA gene transcription [PI4,5Pkinase C . In a Drcription . More recription .The ING2 (inhibitor of growth protein-2), a component of the transcriptional corepressor Sin3a–HDAC1 complex binds PI5P through a plant homeodomain (PHD) finger domain ,112. OveAnother chromatin protein that has been shown to be functionally regulated by PI5P is UHRF1 (ubiquitin-like with PHD and RING finger domains 1) that plays an important role in reading and maintaining epigenetic states . UHRF1 c3 that was linked to apoptosis [3 was also found at sites of DNA damage and required for local recruitment of the ATR kinase [2, PI4,5P2, and/or PI3,4,5P3 [2 and PI5P albeit with so far unclear functional implications [Further illustrating the diverse chromatin biology of nuclear PIPs is the association of nucleophosmin, a factor that besides other functions, serves as a histone chaperone, with PI3,4,5Ppoptosis . AccumulR kinase . The HDAR kinase . DiverseI3,4,5P3 . Differeications ,124.Sphingolipids are a class of lipids containing a single fatty acid chain and amino alcohol group. An example of a nuclear sphingolipid that is involved in chromatin modification is sphingosine. Sphingosine-1-phosphate (S1P) has been found to regulate histone acetylation. Sphingosine kinase 2 (SphK2) which generates S1P, was found to be associated with histone H3 . SphK2 ePalmitic acid is a major saturated fatty acid of 16 carbons (C16:0). S-palmitoylation and O-palmitoylation describe the addition of palmitic acid to protein cysteine and serine residues, respectively . Thiol sO-palmitoylation occurs at histone serine residues whereby the serine hydroxyl group forms an oxygen ester bond with palmitate, that is a more stable bond compared with the cysteine thioester . O-palmiin vitro, attaching to lysine residues via ketoamide adduction or attaching to histidines residues via Michael addition. Under oxidative stress, the adduct on histones disrupts the interaction with DNA, thereby preventing the assembly of nucleosomes [Lipids can be peroxidized to yield highly reactive, electrophilic α,β-unsaturated aldehydes, such as 4-oxo-2-nonenal (4-ONE) that are found to chemically modify a number of proteins and DNA . Lipid pleosomes .Because the 4-ONyl modification is similar to fatty acyl groups, sirtuins and particular SIRT2 were suggested to hydrolyze histone 4-ONyl lysine adducts. This action mitigates the negative impact of this modification, namely the inhibition of nucleosome assembly, caused by oxidative stress .in vitro [Glutarate can be derived from the metabolism of the essential amino acids lysine and tryptophan. It can form reactive glutaryl-CoA, and become a donor for glutarylation of proteins . GCN5  is the reaction product of SAM after donating an activated methyl-group for methylation reactions. Homocysteine is not proteinogenic, however it can be recognized by methionyl-tRNA and transformed into a cyclic thioester homocysteine thiolactone . In one  closure .Glutathione (GSH) is a tripeptide that consists of cysteine, glutamate, and glycine. This molecule is mainly known as a redox agent and buffer that prevents damage to cellular components caused by reactive oxygen species. However, there are also links between GSH metabolism and chromatin function on different levels ,144. BioIn vitro studies showed that addition of GSH destabilizes the nucleosome and leads to an open chromatin state, but the exact functional implications of this modification are not known. The spread of this modification increases during proliferation and decreases with age [Most nuclear functions of GSH are associated with its free form , howeverwith age .Dopamine is a product of the phenylalanine and tyrosine metabolism, whereas serotonin is derived from tryptophan. In addition to their well-known physiological roles as neurotransmitters and neuromodulators, monoamines have also been described in direct chromatin regulation. The extravesicular monoamines can be covalently attached to histones by the transglutaminase (Tgm2) enzyme. Serotonin was found on the H3Q5 residue , and thiDopamine is another monoamine that can be used to modify histones, it is attached to H3Q5 by the same mechanism as serotonin . H3Q5 doin vitro chromatin condensation at the nucleosome level, and modulating of expression and activity of chromatin-modifying enzymes  upon polyamines administration or depletion. Interestingly, polyamines are themselves subject to modification by acetylation. This regulates their chromatin association [Polyamines are formed by enzymatic decarboxylation of the amino acids ornithine or arginine. The most common polyamines are spermidine, spermine, and their biosynthetic precursor putrescine. Though the primary role of polyamines seems to be in RNA translation in the cytoplasm , they arociation  and alsoociation . To thisDespite the absence of a clear understanding of polyamines function on chromatin, polyamine-based anticancer drugs are being developed. Polyamine analogs, such as polyaminohydroxamic acids (PAHAs) and polyaminobenzamides (PABAs), demonstrated potent inhibition of HDACs, re-expression of p21 and significant inhibition of tumor growth .With the small size, the metabolites we have discussed can likely freely diffuse in and out of the nucleus though its pores. While this has been postulated to establish a connection between the cytoplasmic and nuclear state of a cell ,158, it + are also compartmentalizing to the cell nucleus [+ itself is consumed in poly-ADP-ribosylation or production of acetyl-ADP-ribose instead of changing its oxidation state as in cytoplasmic energy conversion. Also, several enzymes of inositolphosphate metabolisms such as phospholipase C and different inositol phosphate kinases are suggested to have a nuclear life based on screens conducted in yeast [This phenomenon is now well documented for enzymes metabolizing the common and well-studied substrates and cofactors of chromatin modification. For example, the acetyl-CoA synthesizing enzymes acyl-CoA synthetase short-chain family member 2 (ACSS2), ATP-citrate lyase (ACLY), and the pyruvate dehydrogenase complex (PDC) are all found nuclear besides their cytoplasmic or mitochondrial primary localization ,160. Sim nucleus ,167. Thiin yeast , and sevin yeast .For the chromatin-regulating metabolites linked to lipids, it is interesting to notice that two main enzymes involved in lipid biosynthesis, acetyl-CoA carboxylase 1 (ACC1) that catalyzes the rate-limiting step of lipid biosynthesis, the conversion of acetyl-CoA into malonyl-CoA and fatty acid synthase (FASN) have been described to show oscillations between cytoplasmic and nuclear localization . Very weBesides a general nuclear presence, it is more and more emerging that different metabolic enzymes might be recruited to specific chromatin regions, for example via interaction with transcription factors . This haClear linkage among cellular state, metabolism, chromatin status, and epigenetics has been established over the last years . While sFirst, there is a plethora of options where metabolites that might affect chromatin could originate see . Besidesin vitro characterization of chromatin effects and modulation of binding or enzymatic activities. The in cellulo and in vivo analysis of the biology of chromatin-regulating metabolites requires approaches that enable the perturbation, control, and quantification of cellular (better nuclear) levels of the molecules in question. Currently, this is limited to exogenous administration of metabolites or their precursors and/or manipulation of the enzyme system controlling their (sub-)cellular levels. Both approaches are, however, fully indirect which makes drawing solid conclusions challenging.Second, the interfaces metabolites can have with chromatin systems are plenty and there are multiple levels where these might exert chromatin regulatory function see . Our disThird, we project that different metabolites and their multiple modes of chromatin function will open future avenues for intervention with important pathways of genome regulation. On one hand, the levels of chromatin-regulating metabolites might be changed in different disease states. Determining and controlling their levels could provide interesting avenues for diagnosis and therapy. On the other hand, the regulatory mode metabolites exert on chromatin modifiers and in particular via allosteric regulation might enable novel approaches for specifically interfering with histone-modifying enzymes, chromatin remodelers, and histone modification-binding proteins.There are clearly many things to be discovered about the interplay of metabolites and chromatin, and we are looking forward to new findings in this exciting research area in the near future.


Bcl-2 and Cycs, in yellow catfish Pelteobagrus fulvidraco. We obtained a 1989 bp Bcl-2 promoter and an 1830 bp Cycs promoter and predicted several key transcription factor binding sites (TFBSs) on the promoters, such as Kruppel-like factor 4 (KLF4), signal transducer and activator of transcription factor 3 (STAT3), forkhead box O (FOXO), metal-responsive element (MRE) and hepatocyte nuclear factor 1α (HNF-1α). Zinc (Zn) increased the activities of the Bcl-2 promoter but decreased the activities of the Cycs promoter. Metal-responsive transcription factor 1 (MTF-1) and HNF-1α directly bound with Bcl-2 and Cycs promoters, and they positively regulated the activity of the Bcl-2 promoter but negatively regulated the activity of the Cycs promoter. Zn promoted the binding ability of HNF-1α to the Bcl-2 promoter but decreased its binding ability to the Cycs promoter. However, Zn had no significant effect on the binding capability of MTF-1 to the regions of Bcl-2 and Cycs promoters. Zn upregulated the mRNA and total protein expression of Bcl-2 but downregulated the mRNA and total protein expression of Cycs. At the same time, Annexin V–FITC/PI staining showed that Zn significantly reduced the apoptosis of primary hepatocytes. For the first time, our study provides evidence for the MRE and HNF-1α response elements on the Bcl-2 and Cycs promoters, offering new insight into the mechanism by which Zn affects apoptosis in vertebrates.B-cell lymphoma 2 (Bcl-2) and cytochrome c (Cycs) are two important proteins relevant to cellular apoptosis. In this study, we characterized the functions of the promoter regions of two apoptosis-related genes,  Bcl-2 and Cycs promoters have been studied extensively in mammals but have rarely been reported in fish [2+/Mg2+-dependent endonuclease responsible for nuclear DNA fragmentation, inhibiting the activity of effector caspase-3 and increasing the Bcl-2/Bax ratio [Bcl-2 and Cycs in Pelteobagrus fulvidraco [Bcl-2 and Cycs in response to Zn remains to be explored.As a naturally occurring and evolutionarily conserved physiological process, apoptosis plays an important role in tissue development, physiology and homeostasis . Cytochr in fish ,6. Zn is in fish ,8,9. How in fish . At pres in fish ,12. Prevax ratio ,14,15. Sax ratio ,17. Recelvidraco . HoweverMTF-1 is an important Zn finger transcription factor that regulates cell adaptation to Zn by binding with the MRE of its target gene promoter . ResearcPelteobagrus fulvidraco is of high market value due to its delicious meat quality and is widely cultured in China and other Asian countries [Bcl-2 and Cycs have been cloned in yellow catfish [Bcl-2 and Cycs promoters were identified, and the binding sites of MTF-1 and HNF-1α in their promoter regions were deciphered in yellow catfish. Furthermore, the transcriptional regulation of Bcl-2 and Cycs by Zn was determined. In addition, the apoptosis of hepatocytes in yellow catfish treated with Zn was detected by flow cytometry. Our study offers a novel insight into the molecular mechanism by which Zn regulates apoptosis in vertebrates.As a kind of freshwater fish, yellow catfish ountries . In our  catfish ,26. HoweBcl-2 promoter  (−221/−232 bp), sterol regulatory element-binding proteins (SREBP) (−1780/−1789 bp), KLF4 (−1646/−1655 bp), ras-responsive element-binding protein 1 (RREB1) (−1551/−1570 bp), FOXO1 (−819/−829 bp) and FOXO4 (−470/−476 bp), specificity protein 1 (SP1) (−1371/−1381 bp) and STAT3 (−1760/−1770 bp). The putative binding sites of MRE (−559/−574 bp) and HNF−1α (−1910/−1924 bp) were also found in the Bcl-2 promoter.We cloned the 1989 bp promoter  and 1830promoter . The traCycs promoter, several important TFBSs, such as FOXO1 (−341/−351 bp), KLF1 (−641/−651 bp), SREBP (−755/−764 bp), FOXO4 (−875/−881 bp), STAT2 (−1023/−1036 bp), CCAAT/enhancer-binding protein (C/EBP) (−1321/−1331 bp), peroxisome proliferator-activated receptor alpha (PPARα) (−1361/−1378 bp) and STAT3 (−1425/−1435 bp) were discovered on its promoter region. Meantime, MRE (−1181 bp/−1196 bp) and HNF−1α (−963/−977 bp) binding sites were also found in the Cycs promoter.On the 4, followed by 5’-sequence deletion analysis. For Bcl-2 promoter, compared to the control, Zn treatment decreased the luciferase activity within the deletion plasmids of −533/+97 bp but increased the activity within −1989/+97 bp, indicating that Zn had a regulatory effect on Bcl-2 promoter. Moreover, for different fragments of plasmids treated with Zn, the luciferase activity of the −905/+97 bp region was significantly higher than that of −533/+97 bp, and the luciferase activity of −1989/+97 bp was also significantly higher than that of −1409/+97 bp, indicating that the two regions from −533 bp to −905 bp and from −1409 bp to −1989 bp have positive regulatory elements  and −1910/−1924 bp HNF−1α site (Mut-Bcl-2-HNF-1α) alleviated the increase of luciferase activity induced by Zn, indicating these two sites are involved in the transcriptional response of Bcl-2 gene to Zn. Zn reduced the luciferase activity of −559/−574 bp site mutant plasmid (Mut-Bcl-2-MRE) compared to the control, indicating that the MRE site plays a positive regulatory role in Bcl-2 transcription. Similarly, −1910/−1924 bp site mutant plasmid (Mut-Bcl-2-HNF-1α) also alleviated the Zn-induced increase in luciferase activity. Thus, these results reflect that −559/−574 bp MRE and −1910/−1924 bp HNF-1α sites play positive regulatory roles in Zn-induced Bcl-2 transcription  pGl3 −1830/+101 bp plasmid, the mutation of −1181/−1196 bp MRE site (Mut-Cycs-MRE) and −963/−977 bp HNF-1α site (Mut-Cycs-HNF-1α) significantly increased the luciferase activities after Zn incubation, indicating that these two sites are important for Zn-induced Cycs transcription. Compared with the WT pGl3−1830/+101 bp vector, the mutation of −1181/−1196 bp MRE site (Mut-Cycs-MRE) and −963/−977 bp HNF-1α site (Mut-Cycs-HNF-1α) increased the luciferase activity in the Zn-treated group, suggesting that these two sites negatively regulate Zn-induced Cycs transcription  competed for the binding, but the 100-fold unlabeled Mut-Bcl-2-MRE sequence did not compete for the protein binding, suggesting that MTF-1 could bind with this region  competed for the binding; however, the 100-fold unlabeled Mut-Bcl-2-HNF-1α sequence did not compete with the labeled probe for the nuclear protein  and one SP1 in the  reports ,36. In apoptosis ,38,39,40poptosis ,42,43. Tpression . HNF1, apression ,24. HoweBcl-2 expression in yellow catfish. Other studies indicate that Zn inhibits cytochrome c release and Cycs expression [In our study, Zn promoted the expression of Bcl-2 protein and gene but inhibited the expression of Cycs. Bcl-2 is an important antiapoptotic factor, and the cytochrome c release is an important marker of cell apoptosis. Cycs could induce apoptosis when it accumulates in the cytoplasm in response to various stress inducers ,46,47. Tpression ,27. TherBcl-2 and Cycs in yellow catfish and identified the MRE and HNF-1α binding sites in these promoters. Zn mediated the regulation of the transcriptional activity of Bcl-2 and Cycs through MTF-1 and HNF-1α. Zn treatment upregulated Bcl-2 mRNA and total protein levels but downregulated Cycs expression and significantly inhibited the cell apoptosis of yellow catfish. Our study offers direct evidence for elucidating the mechanism by which Zn mediates apoptosis via the MRE and HNF-1α binding sites on the Bcl-2 and Cycs promoters. For the first time, our study clarifies the underlying mechanism by which Zn regulates the transcriptional activities of Bcl-2 and Cycs, thus revealing a new mechanism of Zn-induced apoptosis.In conclusion, we identified the promotor regions of apoptosis-related genes The experiments with yellow catfish followed the Institutional Ethical Guidelines of Huazhong Agricultural University (HZAU) and were approved by the Ethics Committee of HZAU (identification code: Fish-2019-12-21).For promoter cloning and functional analysis, yellow catfish were purchased from a local commercial farm , and HEK293T cells were obtained from the Cell Resource Center in Fishery College of Huazhong Agricultural University. Lipofectamine 2000 and LightShift Chemiluminescent EMSA Kit were purchased from Invitrogen . Passive Lysis Buffer and Dual-Luciferase were obtained from Promega . Dulbecco’s Modified Eagle’s Medium (DMEM), 0.25% trypsin-EDTA and fetal bovine serum (FBS) were obtained from Gibco/Invitrogen . Nuclear proteins for EMSA were determined using Pierce BCA protein assay kit . Total RNA was isolated with Trizol reagent  and transcribed into the cDNA with a Reverse Transcription Kit .Bcl-2 and Cycs promoters of yellow catfish according to our publications [Bcl-2 and Cycs promoters based on the open-access genome of yellow catfish [Bcl-2 and Cycs promoters into the pGl3-Basic vectors . Using the purified PCR product and pGl3-Basic vectors , we generated the luciferase reporter constructs, and we ligated the products by using the ClonExpress II One Step Cloning Kit . Based on the distance from the TSS, we defined these plasmids as pGl3−1989/+97 of Bcl-2 and pGl3−1830/+101 of Cycs. Using templates of pGl3−1989/+97 of Bcl-2 vector, we generated the plasmids pGl3−533/+97, pGl3−905/+97, pGl3−1409/+97 and pGl3−1989/+97 of Bcl-2 containing the unidirectional deletions of the promoters with the Erase-a-Base system . Similarly, the plasmids pGl3−424/+101, pGl3−969/+101 and pGl3−1409/+101 were produced by using pGl3−1830/+101 of Cycs vector as the template. We identified the TSSs of ications ,50. Then catfish  and on t catfish . Using Shttp://www.genomatix.de/) and the JASPAR database on 15 November 2020 (http://jaspar.genereg.net/), we predicted the putative transcriptional factor binding sites (TFBSs) of Bcl-2 and Cycs promoters online, and we used the Clustal-W multiple alignment algorithm for the sequence alignment.Using MatInspector on 10 September 2020 (v/v) heat-inactivated FBS  in a SANYO incubator at 37 °C with 5% CO2. Lipofectamine 2000 was used as the transfection reagent. The reporter plasmids were cotransfected with the 20 ng pRL TK as the control. After 4 h transfection, the medium was replaced by the DMEM with 10% FBS. Based on our recent studies [4. After the incubation for 24 h, the cells were lysed and collected. We analyzed the relative luciferase activities by the Dual-Luciferase Reporter Assay System .We transfected the plasmid into HEK293T cells and detected the luciferase activities according to the method of Xu et al. . The pro studies , two Zn Bcl-2 and Cycs promoters, we conducted the site-mutation analysis via the QuickChange II Site-Directed Mutagenesis Kit  according to the manufacturer’s instructions. The site-mutation primers are listed in To identify the MRE and HNF-1α binding sites on yellow catfish Analysis of the functional binding sites of MRE and HNF-1α on the Bcl-2 and Cycs promoters followed the method of Xu et al. . After tThe primary hepatocytes were isolated from yellow catfish and cultured as previously described . Based oβ-actin, hypoxanthine-guanine phosphoribosyltransferase (hprt), beta-2-microglobulin (b2m), ribosomal protein L7 (rpl7), ubiquitin-conjugating enzyme (ubce) and translation elongation factor (elfa), to determine their transcriptional stability. We used geNorm software to analyze the most stable two genes as the endogenous regulation, normalized their geometric mean and then calculated the relative expression of genes by 2−△△ct method .The protocols for the qPCR assay were in agreement with our recent publication . Table SThe protein expression was determined via Western blot according to our recent methods ,52. The Apoptosis was determined using the Annexin V–FITC/PI double staining method according to the method of Hsu and Yen . In briet-test where appropriate. p < 0.05 was considered to be statistically significant.SPSS 19.0 software was used to perform the statistical analysis. The final results are shown as means ± standard error of mean (SEM). Before the present statistical analysis, we evaluated all data for normality via the Shapiro–Wilk test and for homogeneity of variance via Bartlett’s test. Finally, all these data were analyzed via the one-way ANOVA or Student’s


Drosophila spermatogenesis – in Δ86bam mutant testes which is the commonly used model of spermatogonia (SpG) and in larval testes mainly filled with spermatocytes (SpCs). We found that initiation of SpC-specific transcription correlates with promoters’ detachment from the NL and with local spatial insulation of adjacent regions. However, this insulation does not result in the partitioning of inactive TADs into sub-TADs. We also revealed an increased contact frequency between SpC-specific genes in SpCs implying their de novo gathering into transcription factories. In addition, we uncovered the specific X chromosome organization in the male germline. In SpG and SpCs, a single X chromosome is stronger associated with the NL than autosomes. Nevertheless, active chromatin regions in the X chromosome interact with each other more frequently than in autosomes. Moreover, despite the absence of dosage compensation complex in the male germline, randomly inserted SpG-specific reporter is expressed higher in the X chromosome than in autosomes, thus evidencing that non-canonical dosage compensation operates in SpG.Eukaryotic chromosomes are spatially segregated into topologically associating domains (TADs). Some TADs are attached to the nuclear lamina (NL) through lamina-associated domains (LADs). Here, we identified LADs and TADs at two stages of   Drosophila cells  technique , topologla cells . Recent osophila . Furthersolution , appearesolution ,14.et al.  and inactive (B) chromatin compartments ,12. The s system .Phase separation was implicated to explain the segregation of active and inactive chromatin ). The baDrosophila highly correlate with the presence of housekeeping genes  , the layer beneath the inner nuclear membrane formed by lamins (which will be further referred to as Lam) and Lam-associated proteins (reviewed in ). Bindinosophila ,37. LADsnematode , containnematode ,42–45. Tiewed in ). HoweveDrosophila spermatogenesis (reviewed in (de novo expression of testis-specific meiotic arrest complex (tMAC) as well as by testis-specific TBP-associated factors (TAFs) (iewed in ) is a usiewed in . During s (TAFs) ). TranscDrosophila tissues, two mechanisms likely mediate the equalization of expression of a single dose of X-linked genes in males with the double dose of X-linked genes in females or autosomal genes in males and females. The first mechanism relies on the activity of the canonical Male-Specific Lethal (MSL) dosage compensation complex which introduces H4K16 hyperacetylation in the male X chromosome thus making chromatin context more favorable for transcription . Howevepression ,56. Moret active . Thus, at active . In malet active . Based one cells . While D w67c23(2), y embryos, performed by the standard protocol , we first genetically combined UAS-LamDm0-RNAi  and UAS-LamC-RNAi  hairpins in the common genotype. To obtain efficient knockdown of both Lam genes in SpCs, we crossed the resulting line with the bam-Gal4-VP16 germline specific driver    using prectively ,69. The P{CaSpeR-stil-βgal} construct, the ∼0.6-kb fragment containing stil gene promoter was PCR-amplified from DNA of (1)w67c23(2), yDf strain using primers 5′-GACTCTGCAGGCTGCCACCATGTTTCAGA and 5′-GCTCGGATCCATTTTTCAGTTTCATAAATGGG. The PCR product was first cloned into the pGEM-T easy vector (Promega), and then recloned by BamHI and PstI upstream from lacZ ORF into P{CaSpeR-βgal} vector  promoter is active only in the germline cells, elimination of the stop-cassette by Cre recombinase and subsequent genomic Dam or Dam-Lam methylation proceeds only in the germline cells of Δ86bam testes (i.e. mainly in SpG). The germline specificity of this procedure was validated by PCR-amplification of methylated fragments in the absence of nos-Cre  was isolated by DNeasy Blood and Tissue kit (Qiagen). The protocol for PCR-amplification of methylated genomic fragments (www.evrogen.com) resulting in 41–52 million 100-nt single-end reads per sample  resulting in 23–144 million 100-nt or 150-nt paired-end reads per sample .Contact frequencies between several NcoI fragments and the anchor regions were analyzed by qPCR with TaqMan-probes  and bam-Gal4-VP16 (control) males. RNA was isolated using Trizol (Invitrogen). RNA samples were subjected to poly(A)+ selection, cDNA synthesis and deep sequencing on Illumina NovaSeq in the GENEWIZ , resulting in 33–40 million 150-nt paired-end reads per sample , was isolated from third instar larvae of r sample .Drosophila SuUR mutant strain, in which heterochromatin was mostly not underreplicated  at 42°C for 1 h in the buffer containing 50% formamide, 10% dextran sulfate, 0.2× SSC, 0.5 μg/μl salmon sperm DNA. Then, DNA was denatured by incubation at 85°C for 15 min, and slides were hybridized with the probes at 37°C for 3 days in a wet chamber. After hybridization, slides were consequently washed with 2× SSC for 30 min at room temperature, with 2× SSC for 30 min at 37°C, with 0.2× SSC for 30 min at 37°C, with 0.2× SSC for 30 min at 42°C and with 0.2× SSC for 10 min at room temperature. Then, slides were washed in PBST (0.1% Tween-20 in PBS) for 5 min at room temperature and incubated in the blocking solution (1% BSA in PBS) for 1 h at room temperature. After that, slides were incubated for 14 h at 4°C with ADL84 mouse monoclonal anti-Lam Dm0 antibody  with a blind experimental setup. Distances between the NL and the centers of masses (CMs) of chromosome territories (CTs) of X and 2L chromosomes were estimated as previously described  for X and 2L chromosomes in the corresponding nucleus was determined and recorded. In parallel, volumes of nuclei were retrieved and recorded, and the radii of nuclei were calculated assuming that nuclei are spherical. Finally, CM-NL distances were normalized to the nuclear radii. Data were obtained separately for SpG (i.e. germline cells of Δ86bam testes) and SpCs  in two independent experiments with 50–75 nuclei per experiment . Optical sections with 0.4-μm intervals along the Z-axis were captured. Images were processed and analyzed by using escribed . Brieflyperiment .UAS-LamDm0-RNAi/+; UAS-LamC-RNAi/bam-Gal4-VP16 males and from bam-Gal4-VP16 control males were manually isolated in PBT (PBS containing 0.01% Tween-20) on ice and then rinsed in PBS. Testes were fixed in 4% formaldehyde (in PBT) for 30 min at room temperature. Then, testes were washed in PBT three times for 10 min at room temperature. Prior to staining, testes were incubated in PBTX (PBT containing 0.3% Triton X-100) with 0.3% sodium deoxycholate for 1 h, then in PBTX with 5% CELLect goat serum  for 1 h. Testes were incubated with primary antibodies in PBTX containing 3% normal goat serum  overnight at 4°C, washed in PBTX three times for 10 min at room temperature, incubated with secondary antibodies in PBTX containing 3% NGS overnight at 4°C, and then washed in PBTX three times for 10 min at room temperature in a dark chamber. Testes were stained with 4′,6-diamidino-2-phenylindole  in PBT for 10 min, and incubated in SlowFade® Gold antifade reagent (Invitrogen) overnight at 4°C. As the primary, a mixture of murine monoclonal ADL67 and ADL84 anti-Lam Dm0 antibodies  and different cell types were counted in several images corresponding to the medial confocal slices of the whole testes.Immunostaining was performed as previously described  with somtibodies   at 37°C for 3 h in the buffer containing 0.15 M NaCl, 10 mM NaH2PO4 (pH 7.5), 1 mM MgCl2, 3.1 mM K3[FeII(CN)6] and 3.1 mM K4[FeIII(CN)6], as it was previously described . Expression levels of β-galactosidase-encoding transgenes were determined using primers represented in rp49 gene expression.The real-time RT-qPCR assay for transgene expression was performed on cDNAs synthesized with random primers on total RNA isolated  from testes of hemizygous for Drosophila tissues/organs . As a result, 272 testis-specific genes, expressed in SpG, as well as 1014 SpC-specific genes were found  A gene should be represented by at least three (out of four) calls  in the FlyAtlas RNA-chip database from each of 15 adult and larval (GSE7763 ); (ii) aGSE97182 . A gene GSE97182 ); (iii) GSE97182 . Testis-re found . A SpC-sre found . We notebowtie2 (HTSeq-count software (2(Dam-Lam/Dam) profiles were generated. LADs (2(Dam-Lam/Dam) profiles around transcription start sites (TSSs) were generated using in-house scripts (available at GitHub https://github.com/foriin/Lam_Sperm) written in RStudio IDE using the R package as well as the ggplot2, dplyr, GenomicRanges, and reshape2 packages. Briefly, log2(Dam-Lam/Dam) values were extracted and averaged for the 0.3-kb bins containing TSSs (i.e. for zero bins), as well as for four upstream and four downstream bins of the analyzed gene subsets, and then the resulting profiles were plotted.Sequencing reads from two biological replicates of Dam and Dam-Lam samples were adapter clipped and uniquely mapped to the dm3/R5 genomic assembly by the bowtie2 . Reads wsoftware  and mappsoftware . Dam-Lamed. LADs  were caled. LADs . Averagedistiller-nf pipeline (version 0.3.3) (https://doi.org/10.5281/zenodo.3350926). Mapping was done on the chromosomes 2L, 2R, 3L, 3R, 4, X and M of the dm3/R5 Drosophila genome assembly with the default settings and with an option no MAPQ filter. For the analysis of the interaction matrix, the main diagonal and the second adjacent diagonal were removed. As two libraries had the read length of 150 nt, while the others had the read length of 100 nt, the 150-nt reads were trimmed to become uniform across all libraries. The trimming was done at the 3'-ends with cutadapt . Re-mapping of the trimmed libraries with the same approach did not result in substantial changes of mapping statistics, distributions of contact probabilities, and replicates similarities. Thus, the non-trimmed libraries were retained for further analysis. After that, only 2L, 2R, 3L, 3R and X chromosomes were considered. Hi-C maps were downsampled with the cooltools package (https://cooltools.readthedocs.io/en/latest/) to nearly the same total number of contacts. Then, the iterative correction (IC) (HiCRep (cooler package (https://cooler.readthedocs.io/en/latest/), downsampled to nearly the same total counts, and corrected by IC, resulting in the final Hi-C maps for SpG and SpCs. TAD boundaries were called with the Armatus (version 2.2) software (https://github.com/kingsfordgroup/armatus) on the log2-transformed IC Hi-C maps of the 5-kb resolution. To avoid boundary misidentification, regions of poor mappability were filled in with the mean contact frequency values of the nearest informative bins. Then Armatus was run with the parameter γ = 1.9 for SpG, or γ = 2.0 for SpCs   was appl  values corresponding to A compartment were selected by the correlation with gene expression. Next, for each chromosome, bins were ranked according to their PC1 values, and 1% of bins with the highest and lowest PC1 values were filtered out. Then, normalized (observed/expected) contact frequencies were averaged within quantiles of the PC1, and the resulting values were used for saddle plot generation. We defined the same number of PC1 quantiles for both A and B compartments across all chromosomes and stages to analyze changes of compartmentalization in SpCs relative to SpG, averaged by chromosomes.Active (A) and inactive (B) compartments  were ideThe compartment strength, displaying the ratio of contact frequencies in the active or inactive compartments to that in the whole chromosome, was determined for 2-kb resolution IC Hi-C maps as described previously . Brieflycooltools.insulation module with 12-kb window size. Orientation of IS-curves around TSSs of 3′→5′ oriented genes was reversed, and then all IS-curves were smoothed using linear interpolation. To generate heat maps demonstrating the increased contact frequencies between TSSs of different classes of genes, mean contact frequencies between 2-kb bins containing TSSs (±10 bins around) and belonging to different TADs were averaged across the whole genome. The frequency of contacts between TSSs divided into transcription quantiles was calculated within the A compartment at 5-kb resolution.The insulation score (IS)  was calcPc(s) curves for autosomes and the X chromosome was estimated as previously described (10) coordinates. To determine the significance of differences in Hi-C signal decay, we approximated a part of Pc(s) curve using linear model fitting (lm function from R) for values ranging from 4.6 to 5.7 in log-log scale. Then the difference of slope coefficient obtained on the previous step was calculated either between pairs of autosomes or between X chromosome and autosomes, and Wilcoxon rank-sum test was applied to these two groups.The significance of differences between escribed . The anaSalmon tool  Drosophila spermatogenesis, we employed Δbam86 , we ides Figure . The actll types ,42.Δ86bamtestes  profiles around TSSs of various groups of genes. As expected, in both SpG and SpCs, 93% of promoters of ubiquitous genes are localized in inter-LADs  profile around promoters of ubiquitous genes has the saddle-like form with the minimum at the TSSs , and their averaged log2(Dam-Lam/Dam) profile around TSSs has negative values in both SpG and SpCs with a minimum at the TSSs in SpCs  profile with a prominent dip at the TSSs are also typical for Aly-independent , whereas the majority of Aly-independent promoters . Finally, averaged Lam-DamID profiles around promoters of Aly-dependent and Aly-independent SpC-specific genes have positive values at the TSSs in neurons and embryonic Kc167 cells, where these genes are not expressed . AltΔ86bammutant testes mostly containing SpG, and in the larval testes mostly containing SpCs. We identified 3206 TADs with a median length of 25 kb in SpG and 3274 TADs with a median length of 20 kb in SpCs  curves representing the decay of contact frequency as a function of genomic distance s, also demonstrate that distant interactions become weaker in SpCs  at the current Hi-C resolution. However, active chromatin of SpC-specific genes is expected to suppress contacts between the nearby inactive chromatin regions. In agreement with numerous published observations . Consistent with these findings, the averaged IS profile , and only 5.2% of SpC-specific gene promoters are positioned in close vicinity to the newly arisen TAD boundaries in SpCs . Therefore, our analysis indicates an accidental colocalization of SpC-specific gene promoters with both types of TAD boundaries. Based on these results, we conclude that although initiation of SpC-specific transcription within inactive TADs may lead to local spatial insulation and chromatin transition from inactive to active compartment, it does not result in the partitioning of these TADs into sub-TADs.If the activity of SpC-specific gene promoters results in the partitioning of TADs into sub-TADs, one would expect the non-random colocalization of the majority of these promoters with the newly arisen TAD boundaries in SpCs. However, only 10% of SpC-specific gene promoters in SpCs are positioned within 2.5-kb distance from the TAD boundaries, not associated with the ubiquitous genes  at the 2(Dam-Lam/Dam) values for the whole X chromosome are 1.4-fold and 1.7-fold higher than for autosomes, respectively. This effect is even more pronounced in LADs  to the NL in both SpG (Δ86bam testes) and SpCs (WT testes) . NL was visualized with anti-Lam immunostaining. We found that, in the majority of nuclei, 2L homologues were paired, thus forming common CTs. Unpaired homologues with separate CTs were detected in only ∼7% of nuclei. Using  testes)  and norm testes) . In agre testes) , we also testes) . This pe testes) . Importa testes) , D. The Lam Dm0 in S2 cells resulted in the removal of total chromatin from the NE  values > 1 in control larval testes, is slightly enhanced Figure . ImportaDrosophila males  is 0.75 . MedianP = 10–17 and P = 10–15 in SpG and SpCs, respectively, M–W U-test; Figure P = 0.02, M–W U-test; Figure We corroborated these results by analyzing chromatin accessibility to Dam-methylation ,110 of tDrosophila . Such short segments of active chromatin may be insufficient to notably perturb 3D organization of long regions of inactive chromatin within which they emerged. In support of this idea, our polymer modeling has shown that short active regions are looped out from the inactive TAD globule, whereas long active regions result in the partitioning of this globule into different sub-TADs . Spatiaon level .Drosophila male germline cells . The link for UCSC Browser (https://genome.ucsc.edu/s/Shevelyov/LaminDamID%20in%20SpG%20and%20SpCs).All raw and processed Lam-DamID-seq, RNA-seq and Hi-C data were deposited in the NCBI Gene Expression Omnibus (GEO) under the accession numbers GSE157565 (for Lam-DamID-seq and RNA-seq) and GSE156567 (for Hi-C). In-house scripts are available at GitHub (gkac109_Supplemental_FilesClick here for additional data file.


Gonocobiodon) , 4. Sexurone (T) –7. Gonad1a1 gene . Pregnenn testes –8.2 or T for two months respectively  shows such sexual shift in the circulatory pattern  Resting Phase ii) Preparatory/Recrudescence Phase iii) Pre-Spawning/Developing Phase iv) Spawning Active/Capable Phase v) Post-Spawning/Regressing/Spent Phase ] ] , 8, 18. via glucocorticoid receptor (GR) and critically regulates the promoter function of Cyp19a1a and Cyp11c1/Cyp11b or Hsd11b2 genes thereby fixing the E2: 11-KT concentration , member of the winged helix/fork-head group of proteins is known for ovarian differentiation  have been shown to determine the ovarian function  or Fushi Tarazu factor 1 (FTZ-F1) is an orphan nuclear receptor under subfamily 5 group A member 1 (NR5A1) that gets co-localized in the interstitial cells of pre-vitellogenic ovary and granulosa cells of the vitellogenic follicles along with FOXLn medaka , 35 and n medaka . Althoug93 cells . Howevercription . On the bra-fish .Dax1 is expressed in adrenal cortex, gonads, ventromedial hypothalamus and pituitary gonadotrophs potentially crucial for testis differentiation , an orphan receptor is a member of the nuclear receptor superfamily (NR0B1). ntiation –40. The ansition . In medaollicles . In medaollicles .Dmrt1) is the critical inducer of testicular differentiation  acts as a master of male sex in medaka  gene(s) encode variety of transcription factor(s) critical for gonadal morphogenesis   . The exph testis . Precisein males . During cription  whereas s akaara .Clarias batrachus, WT1 has been shown to upregulate the promoter activity of Hsd11b2 (2 bound Estrogen Receptor (coded by Esr1 gene) acts as a potential transcriptional inducer for Cyp19a1a in zebra-fish  critical for the meiotic progression of developing oocytes (Wilm’s tumor 1 (WT1) is a key transcription factor having critical role in mammalian gonadal morphogenesis . In Indi Hsd11b2 . E2 bounbra-fish , 58. Finatfishes . In pre- oocytes .Cyp19a1a, Cyp11b and Hsd11b2 by various transcription factors to regulate the turnover rate of T for fixing the E2: 11-KT concentration in fish gonads  or Chromatin immuno-precipitation (ChIP) assay investigating DNA-protein interactions have revealed the differential promoter activities of the key steroidogenic genes like h gonads . For exaNotably there is a potential scope for the commercial application of genetically engineered fishes having mutant transcription factors by advanced genome editing technologies like TALEN/CRISPER-CAS9 Transgenesis/RNAi mediated silencing etc. These gain in function (by transgenic over-expression/integration) or loss of function (by knock-out/down) models can be successfully implemented in aquaculture or fishery industry to increase the productivity/yield by manipulating fertility of fishes. However more studies are required in non-model teleost species by employing advanced high throughput next generation sequencing with multi-omics approach to generate gonadal transcriptomic resource datasets. This may further help identify the involvement of new putative factor(s) like chromatin remodelling complexes, DNA-methyl-transferases (DNMTs) and novel micro-RNAs or long-non-coding RNAs which can potentially regulate the sex specific transcriptional switch in gonadal steroidogenesis .IB conceived the idea. PS wrote the first draft of the Text, Figure, and Table with support from SP, SK and SN. IB generated the final form of the manuscript. All authors contributed to the article and approved the submitted version.IB acknowledges the financial support from University Grants Commission (F.30104/2015BSR) and Department of Science and Technology (ECR/2018/000868). The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful experimental approach to study cellular heterogeneity. One of the challenges in scRNA-seq data analysis is integrating different types of biological data to consistently recognize discrete biological functions and regulatory mechanisms of cells, such as transcription factor activities and gene regulatory networks in distinct cell populations. We have developed an approach to infer transcription factor activities from scRNA-seq data that leverages existing biological data on transcription factor binding sites. The Bayesian inference transcription factor activity model (BITFAM) integrates ChIP-seq transcription factor binding information into scRNA-seq data analysis. We show that the inferred transcription factor activities for key cell types identify regulatory transcription factors that are known to mechanistically control cell function and cell fate. The BITFAM approach not only identifies biologically meaningful transcription factor activities, but also provides valuable insights into underlying transcription factor regulatory mechanisms. Single-cell RNA sequencing (scRNA-seq) is a powerful experimental technique to investigate the transcriptomic heterogeneity of individual cells within a tissue and uncover novel subpopulations of cells with distinct biological functions. It has successfully identified subtypes of mature differentiated cells in distinct tissues . The chaWe surmised that integrating the vast amount of known biological data on transcription factor binding sites could be leveraged to analyze scRNA-seq data. We therefore introduced a Bayesian hierarchical model that uses existing transcription factor ChIP-seq data for the inference of transcription factor activities in scRNA-seq data, which in turn can be used for downstream analysis such as identifying cell clusters based on distinct inferred transcription factor activities as well as generating a weighted hierarchy of target genes.Y) into a product of two matrices, W and Z. The rows of Y correspond to genes (N) and columns correspond to cells (M). The matrix Z is a scheme to generate alternative profiles of single-cell data. The matrix W represents factor loadings. Φ is the unobserved stochastic noise term with zero mean and a finite variance.Our Bayesian inference transcription factor activity model (BITFAM) is based on a fundamental biological principle that the differences in scRNA-seq profiles of individual cells reflect distinct underlying transcription factor activity states . SpecifiW and Z, we embedded a structure in W by using the consensus transcription factor ChIP-seq data. Specifically, each column of W represents a transcription factor, and each row represents its potential targets, which are determined by the binding sites of the transcription factors in the ChIP-seq data. Because the existing ChIP-seq data are not specific to the cellar context where the single-cell profiles of interest are generated, we incorporated this information as prior probabilities on the elements in W and let the Bayesian inference procedure learn the posterior distributions of W and Z from the observed single-cell profiles. By doing so, the inferred matrix W determines the transcription factor targets according to the data of context.To guide the construction of biologically meaningful matrices Z can be interpreted as the inferred transcription factor regulatory activities in the corresponding cell. As the number of transcription factors is much smaller than the number of the genes profiled, this model achieves dimension reduction of single-cell RNA-seq data. The matrix W can be interpreted as the regulatory weights between the transcription factors and genes. The values in a column of matrix W can be used to determine the most probable target genes of one transcription factor in the specific data set, a valuable insight because aggregate information on all potential transcription factor target genes does not provide a ranking of target genes. The matrices Z and W could be applied to several downstream analyses, including (1) the decomposition of the single-cell transcriptomic profile into transcription factor activities, (2) identifying a ranking of transcription factor target genes for each scRNA-seq data set, and (3) performing downstream analyses, such as clustering of cell subpopulations.A column of the matrix Tabula Muris data sets that provide scRNA-seq data on all major organs of the mouse during adult homeostasis . Cells in the first two data sets have been experimentally labeled by cell type using either antibodies or gene expression profiles and therefore can be used for evaluating the accuracy and functional relevance of BITFAM for assessing distinct cell population phenotypes. The CRISPRi data set can be used as a biological validation or ground truth of our inferred transcription factor activities because CRISPR-targeted deletion or knockdown of a transcription factor should reduce the corresponding transcription factor activity, even though the transcription factor activity will not be reduced to zero because CRISPR does not have complete deletion efficiency. The transcription factor ChIP-seq data were chosen from GTRD, a comprehensive database of transcription factor binding sites (TFBSs) identified from ChIP-seq experiments for human and mouse (http://gtrd.biouml.org)  .Tabula Muris lung data set (We investigated whether the transcription factor (TF) activities inferred by BITFAM for each cell are biologically meaningful. We show the results in two data sets as examples: The data set . They reTabula Muris lung data set has 16 distinct cell types including epithelial cells, endothelial cells, lymphocytes, and macrophages visualized in a t-SNE plot with gene expression profiles . These default criteria for BITFAM can be modified to include any transcription factor for which ChIP-seq data is available and users can thus easily add additional transcription factors that they are interested in into the learning list even if these additional transcription factors are not among the most variably expressed. The number of transcription factors and the redundancy of transcription factors did not significantly impact the inferred transcription factor activities or downstream analyses such as visualization of clusters, thus suggesting robustness of the model in the setting of multiple transcription factors that may have significant proportions of overlapping target genes .The profiles A. The ceZ matrix. By applying the same model to each cell type, we generated a landscape of inferred TF activities across all cell types. The heatmap of the inferred TF activities in the Tabula Muris lung data set . However, Tal1 mRNA in the scRNA-seq data was only detected in 25% of endothelial cells (Pax5 and Mafb was even lower in the B cells and macrophages (Tabula Muris Consortium). For example, BITFAM inferred PAX5 activity for each cell in the data set. For this analysis, the ground truth was the identity of the cells as categorized by the Tabula Muris Consortium using cell-specific markers. B cells were assigned to the positive class, whereas all other cells were assigned to the negative class. We then associated the PAX5 inferred activities with the positive and negative classes, thus allowing us to generate an AUROC for each inferred transcription factor activity in each cell population. We found that the inferred activities for TAL1, PAX5, and MAFB had markedly higher AUROC values than those which would have solely relied on measures transcription factor mRNA levels . By comparing t-SNE visualization of the BITFAM-inferred transcription factor activities with those of transcription factor mRNA expression , we observed that inferred transcription factor activities can segregate biologically defined cell subsets, whereas mRNA levels do not.One of the utilities of inferring TF activities for each cell is the potential to generate profiles of cell type–specific TF activities. For this purpose, we used a random forest model to identify the inferred TF activities that were associated with specific cell types. For example, we labeled cells of a given cell type  as 1 and all other cells as 0. The random forest model is built using this cell label and the inferred transcription factor activities from the BITFAM data set B showed data set C. PAX5 aal cells F, and throphages G,H, thusGata1 mRNA expression , the AUROC values for the inferred activities were substantially higher for CEBPA and STAT5A . These results indicate that the inferred activities derived from BITFAM do not necessarily correspond to the mRNA expression levels of the transcription factors. This is especially important because limited sequencing coverage in scRNA-seq may inadequately capture the mRNA levels of key regulatory genes in individual cells.For the blood cell development data set I, GATA1-pression M and Cebpression N corresp to GMPs O. AlthouW in the BITFAM model. The GTRD database integrates the ChIP-seq transcription factor binding data obtained from distinct cell types and biological conditions. In such a comprehensive transcription factor target gene list, many transcription factors have thousands of potential target genes; however, it is very likely that in any given cell type only a small fraction of these potential target genes is truly being targeted by a transcription factor. BITFAM learns the weights for potential transcription factor target gene pairs in every data set. This allows BITFAM to generate a ranking of target genes for any given transcription factor based on the mean of the posterior distribution of the weights . We next investigated whether the negatively weighted genes identified by BITFAM were indeed ChIP-seq target genes and what their functional relevance could be for PAX5 and MAFB as illustrative examples. As shown in Supplemental Figure S9, the negatively weighted genes for the B cell development TF PAX5 are enriched in genes from the GO term related to T cell activation, consistent with the notion that PAX5 could suppress T cell activation and differentiation while promoting B cell development. Genes with the most negative weights for PAX5 and MAFB were indeed ChIP-seq target genes.We selected the transcription factors that were inferred to be specifically activated in alveolar macrophages, B cells, and endothelial cells, and examined the top-weighted target genes learned by BITFAM in the data set B. We perdata set C. On thedata set D. The todata set E includefunction , consistfunction . The trafunction , but thifunction F. Howevefunction G, thus p studies . The topThese results showed BITFAM's ability to learn target genes of the transcription factors specific to their biological function and would allow for the inference of preferred target genes and functions of transcription factors.Tabula Muris heart data, we identified six cell clusters , whereas IRF8 was one of the most active transcription factors specific to cluster 6 . These results were consistent with the biological cell labels , indicating that cells in cluster 3 were endothelial cells, cells in cluster 5 were endocardial cells, and cells in cluster 6 were leukocytes.We next explored how inferred transcription factor activities could be used for downstream analyses and whether activity patterns would reflect distinct functions of cell subpopulations. We applied Louvain's algorithm on the inferred transcription factor activities (Methods) to assess cell–cell inferred transcription factor activity distances and identify cell clusters, which were visualized by t-SNE. In the clusters A and comclusters B. We fouTabula Muris brain data set. We identified eight clusters , whereas ASCL1 was one of the most active transcription factors specific to cluster 3 . These results corresponded nicely to the biological cell labels, indicating that cells in cluster 2 were neurons and cells in cluster 3 were astrocytes  toward either megakaryocyte–erythroid progenitors (MEPs) or granulocyte-macrophage progenitors (GMPs) became apparent E. We alsTabula Muris lung data sets. We found that in the CRISPRi data, the AUROC for randomly selected transcription factor target genes drops down from 0.575 to 0.482 .We then examined the impact of how transcription factor target genes are predicted when using BITFAM. In addition to the ChIP-seq derived transcription factor target gene prior data, one could also use alternate approaches to predicting transcription factor target genes such as coexpression of TFs and genes, detecting transcription factor binding motifs in the promoter of genes, or combining ChIP-seq data with motif information such as ChIP-eat  to generSupplemental Fig. S12). We next asked the question whether the cell type from which the ChIP-seq data were generated was relevant for accurately inferring TF activities in various cell types. We therefore selectively removed ChIP-seq targets derived from certain cell types and assessed how the inferred TF activities were impacted. We specifically focused on selectively removing ChIP-seq data sets that may be of relevance to a given cell type . We found that for a transcription factor such as CEBPA, for which a large number of ChIP-seq data sets exist, BITFAM inference results of CEBPA activity match known biological functions of CEBPA in a hematopoietic development data set even when we used nonhematopoietic ChIP-seq data sets as prior knowledge . However, when we removed non-B cell ChIP-seq data sets for the transcription factor PAX5, which is known to be highly active in B cells, we found that BITFAM was no longer able to accurately assign high PAX5 activity in B cells . For the TF NEUROD1, there are currently 19 ChIP-seq data sets available, which have been derived from embryonic stem cells, pancreas beta cell, and the pituitary AtT-20 tumor cell line. None of these data sets include primary brain neurons; nevertheless, BITFAM inferred increased activity of NEUROD1 in brain neurons. However, when we removed the ChIP-seq data from the pituitary tumor line, which is arguably the closest to primary brain neurons, the BITFAM inference of increased NEUROD1 activity in selected populations was no longer associated with these cell types . These data suggest that for certain TFs with increased activity in specific cell types, ChIP-seq data derived from those or related cell types may improve the inference of the TF activities.We then evaluated how the source of the ChIP-seq data could influence the results. BITFAM uses GTRD as the default database but there are also other ChIP-seq databases available such as ReMAP . Our evaWhen identifying the transcription factor targets from ChIP-seq data, the range of a gene promoter region could also conceivably influence the BITFAM output. The default approach for BITFAM is setting the promotor region as 2 kb upstream and 200 bp downstream from the transcription start site (TSS) of each gene. We then varied the promoter region length by comparing the BITFAM performance with the default setting to changing the regions as follows: (1) 500 bp upstream of TSS to 50 bp downstream from TSS , (2) 1000 bp upstream of TSS to 100 bp downstream from TSS , (3) 5000 bp upstream of TSS to 5000 bp downstream from TSS , and (4) 10 kb upstream of TSS to 10 kb downstream from TSS . We identified the ChIP-seq target genes on each of these different defined promoter regions and applied BITFAM using the CRISPRi data set. We found that setting the promoter region to  and  had the best performance  E.Tabula Muris lung, heart, and brain data sets, the BITFAM-based clustering approach displayed improvements in terms of ARI, NMI, and RI when compared to other methods , whereas SCENIC and BITFAM showed some overlap in the inferred transcription factor activities . We next also performed a GO enrichment analysis on the top 100 genes with the highest positive weights and compared this to the GO analysis on target genes identified from SCENIC. We found that BITFAM predicted target genes were more relevant to the known biological function of transcription factors. For example, RELB is an important transcription factor mediating inflammatory responses and cell survival . On the other hand, the top-weighted target genes for RELB identified by BITFAM were enriched for processes such as immune cell activation, apoptosis, and proliferation . There is a limited overlap of the transcription factor targets and pathways identified between the two methods, but the two methods also identify distinct target genes and pathways. The RELB ChIP-seq target genes were enriched for blood cell differentiation .We next compared the overlap between transcription factor target genes identified by SCENIC and by BITFAM by focusing on the top-weighted genes using the same number of genes for a particular transcription factor. We found limited overlap between these two methods using the Jaccard index (Jaccard index > 0.1) . Even though the AUROC is widely used for the purpose of benchmarking, we also used a Precision-Recall AUC (PRAUC), which is more appropriate for unbalanced data sets such as the CRISPRi data in which only a small fraction of cells undergo TF deletion. Using the PRAUC, we again found that BITFAM performed slightly better than SCENIC when using the optimized regions for both methods . The run time of BITFAM on the Tabula Muris lung data set and CRISPRi data set was 2.81 h and 2.54 h, respectively, which was significantly lower than the corresponding run times of SCENIC using the same data sets (10.23 h and 11.78 h) and the same computer (AMD 3900XT 12-core CPU)  was 0.537. The AUROC of SCENIC using motifs and ChIP-seq data with 10 kb centered on TSS  was 0.557. These AUROCs were lower than the AUROCs of BITFAM using the optimal promoter regions we established for BITFAM . SCENIC-ChIP-seq performs better than BITFAM in the 5–10 kb regions (ore CPU) C.Supplemental Fig. S20), consistent with what is known about the mechanistic roles of these TFs in the respective cell types  for the inference of transcription factor activities in individual cells by analyzing scRNA-seq data and leveraging known transcription factor ChIP-seq data based on a Bayesian factor analysis. In contrast to many current methods developed for scRNA-seq data analysis that are primarily by the observed data itself, our approach of dimension reduction of individual cell gene expression profiles is guided by the integration of prior biological knowledge (ChIP-seq) with the observed data to infer transcription factor activities of the most variably expressed genes.Tabula Muris lung data  and high MAFB activity in macrophages , which is a specific regulator of the B lymphoid lineage . This could be because transcription factor activity is often regulated at the posttranslational level and does not require continuous de novo synthesis of mRNA  from scRNA-seq data. The coexpression network is commonly used among these methods. The relationships of a transcription factor and its target genes are established by Pearson's correlation , mutual Supplemental Fig. S15), but BITFAM outperforms SCENIC slightly in the CRISPRi validation data set when assessing transcription factors for which ChIP-seq data is available in the optimal searching regions . For our current performance comparisons, we used the AUROC approach that is commonly used in similar studies   C.One possible application of BITFAM would be the discovery of novel heterogeneous subpopulations that cannot be identified using standard clustering approaches because inferred transcription factor activities may identify more subtle phenotype differences owing to the regulatory function of transcription factors than only clustering on global gene expression. Our goal in the current study was to establish the value of combining ChIP-seq data and Bayesian analysis for inferring transcription factor activities using known transcription factors in known cell types. We used clustering and comparisons to the identity of biologically established cell subpopulations (which are likely distinct in their transcription factor activity profiles) merely as an approach to validate BITFAM. Establishing additional novel downstream approaches for transcription factor–based trajectory building or identifying transcription factor activity–based subclusters would be an exciting future application of BITFAM. BITFAM can be readily combined with multiple clustering or trajectory building approaches. The CSHMM-TF  combinesSupplemental Fig. S12). We acknowledge that limiting the promoter region to  bp around the TSS may exclude important distal enhancer and regulatory regions. A recent study with scATAC-seq data has shown that a large proportion of accessible chromatin regions can be more than 10 kb distant from the nearest TSS . Furthermore, existing ChIP-seq data are generated from bulk cells or tissues, and it is unclear whether the ChIP-seq data derived from one cell type is as relevant for a scRNA-seq experiment as one that is derived from another cell or tissue type. We found that for certain transcription factors such as NEUROD1 and PAX5 for which BITFAM inferred increased activities in neurons (NEUROD1) and B Cells (PAX5), the presence of ChIP-seq data sets derived from related cell types was required to allow for these specific inferences in the analyzed scRNA-seq data. However, for transcription factors such as CEBPA, removal of selected ChIP-seq data sets did not significantly impact the inferred activities. This could indicate the importance of having a larger number of input data sets (such as the case for CEBPA) to create a robust prior knowledge required for a successful Bayesian prediction model or it could also reflect that some transcription factors such as CEBPA may have similar ChIP-seq targets across tissues and cells, whereas other transcription factors such as NEUROD1 and PAX5 may be associated with cell type–specific targets. These limitations of applying bulk ChIP-seq data derived from several cell types to a given scRNA-seq data set derived from a different group of cell types may in part be overcome in the future with the emerging availability of context-specific single-cell chromatin accessibility data as seen in studies of the transcription factor targets and regulatory mechanism by scATAC-seq . In the The idea of integrating prior biological knowledge with RNA-seq data to infer gene regulatory network has also been explored in several previous approaches . These mZ-values, which would only provide a univariate analysis. However, users retain the flexibility to choose the downstream analysis method that may be best suited for establishing cell type–specific TFs based on BITFAM-inferred activities.TF activities inferred by BITFAM can be used to identify putative cell type–specific TFs. We chose a random forest model to generate such cell type–specific TF profiles but other methods can also be applied for such an analysis. For example, a linear model could be used to identify differentally activated transcription factors across cell types. We posited that the ranked feature list in the random forest model allows for the evaluation of the activity of each TF in the presence of other TFs and their potential nonlinear relationships, whereas ranking based on the scale of mean In summary, we have developed a Bayesian factor analysis model to infer transcription factor activity in individual cells, and we use this approach to develop key biological hypotheses regarding the regulatory transcription factors in each cell as well as derive insights into the biological functions of cell subpopulations.Y: Y = WZ + Φ. BITFAM assumes a linear relationship between the scRNA-seq data and the inferred transcription factor activities specified by Z and the transcription factor–specific gene regulation patterns specified by W. Here, we describe the structure and relation of Y, W, and Z, and we define the probability distributions for the matrix elements.BITFAM is a model of Bayesian factor analysis  that aimW is the weight matrix that represents the evidence of transcription factors and their target genes obtained from the transcription factor ChIP-seq data. We assign normal prior distributions on elements of matrix W with two different variances as follows.Matrix k on gene n follows normal distribution that is modeled with the prior distribution for Wnk:n = 1, …, N and transcription factor k = 1, …, K. We use automatic relevance determination (ARD) to model the parameter δk, i.e.,We assume that the weight of a transcription factor on (ARD)  is used Z is facilitated as the transcription factor activities for each cell in our model. We assign a Beta prior to the activity of transcription factor k in mth cell , that is,Matrix ϕnm is modeled by a normal distribution with variance ε. The residual noise σk and weights can be found in Supplemental Figure S21.The likelihood of our BITFAM isW and the inferred transcription factor activities matrix Z are constructed by taking the means of the samples from the posterior distributions. The sample size is 300.To achieve scalability to large numbers of cells and genes, we use approximate Bayesian inference based on variational methods . The varWe implemented the inference of BITFAM with R  package Tabula Muris lung, heart, and brain data were generated from Smart-seq2  and processed. The Seurat R objects with raw counts are downloaded from figshare (https://figshare.com/projects/Tabula_Muris_Transcriptomic_characterization_of_20_organs_and_tissues_from_Mus_musculus_at_single_cell_resolution/27733). The cell types were labeled in each organ by the expression of well-known marker genes (https://www.ncbi.nlm.nih.gov/geo/) (accession GSE72857). Using FACS, 2729 myeloid progenitor cells  were identified.The We used the NormalizeData function from R package Seurat  to normaZ-score for dispersion within each bin .We identified the most variably expressed genes using the function FindVariableGenes in Seurat. It calculates the average expression and dispersion for each gene, places these genes into bins, and then calculates a each bin . We defiThe ChIP-seq data were obtained from the Gene Transcription Regulation Database (GTRD v19.04) , which iFor each data set, we selected the expressed transcription factors as input to our model. The transcription factor prior target genes are the intersection between ChIP-seq-based regulatory target genes and the most variably expressed genes in each scRNA-seq data set. We excluded transcription factors with less than 10 (prior) target genes.Z matrix) are the features as input. We train the random forest model with the R package randomForest (version 4.6). Then for each type, we identify the most significant transcription factors by the feature importance score.For each cell type in a data set, we label the cells for a given cell type as 1 and the other cells as 0. The inferred transcription factors activities  is defined asH(.) is the entropy of class labels; pi is the probability of an element belonging to cluster i; and I is the mutual information defined as I = H(X) − H(X|Y).Given a set of A and B are sets.Jaccard index is a statistic used for quantifying the similarity between two sets. It is defined as Diffusion pseudo time (DPT) was calculated using diffusion distances between each cell based on the profiles of reduced dimensions (PCA or BITFAM). One CMP cell served as the starting point, whereas GMP and MEP cells were the end points of each branch. Pseudo time toward MEP and GMP branches was scaled between  and  to show the branching more distinctly. The R package destiny (Version 2.14.0) was used to compute the Diffusion pseudo time (DPT) .Seurat (Version 3.0), SIMLR (Version 1.10.0), and SC3 (Version 1.12.0) were downloaded from Bioconductor (Version 3.9). Seurat was run with default parameters and set 20 as the number of principal components used for clustering. SIMLR was run with default parameters and the number of clusters was detected by the function SMLR::SIMLR_Estimate_Number_of_Clusters. SC3 was run with default parameters and the number of clusters was detected by the function SC3::sc3_estimate_k.P-value < 0.005). We used the level 5 Biological Process (BP) as the annotation of sets to annotate the function of the selected genes.The Gene Ontology enrichment analysis of the top inferred weighted genes for transcription factors was conducted using The Database for Annotation, Visualization and Integrated Discovery (DAVID) v6.8   and negative class (nonperturbed cells) represent the biological ground truth that is determined by the CRISPRi experiment. So, a thresholding of logFC of inferred X activity from BITFAM in all cells will determine whether a cell will be inferred as X deletion (lower logFC). When this threshold varies, then an AUROC can be computed. For every TF, we used the same approach, thus allowing us to calculate an overall AUROC for all TFs. This AUROC indicates how accurately inferred TF activities were associated with the positive and negative classes.The CRISPRi data include 141 perturbation experiments with CRISPR-targeting of 50 transcription factors. It is available on NCBI GEO (accession GSE127202). For each CRISPRi perturbation experiment, we calculated the logFC of all transcription factor inferred activities comparing perturbed cells and control cells. For example, for CRISPR-targeting of TF We performed the AUROC analysis with the R package yardstick (version 0.0.3).P-value < 0.05) were defined as coexpressed genes of the transcription factor. For transcription factor binding motifs on promoters, we chose the region from 2000 bp upstream of the transcription starting site (TSS) to 200 bp downstream from TSS as the promoter. Then we used a motif scanning tool, FIMO  were identified as the targets of TFs. For the ChIP-eat method, it combines computational transcription factor binding models and ChIP-seq peaks to automatically predict direct TF–DNA interactions. We downloaded the Individual BED files for specific TFs on the UniBind website (https://unibind.uio.no).The genes with the top 500 highest  correlations  can be freely downloaded from GitHub .The BITFAM (version 1.2.0) is implemented in R , and the


Pseudokeronopsis ciliates to better understand transcription regulation on gene-sized chromosomes. We searched within the short subtelomeric regions for potential cis-regulatory elements and identified distinct AT-rich sequences conserved in both species, at both the 5’ and 3’ end of each gene. We further acquired transcriptomic data for these species, which showed the 5’ cis-regulatory element is associated with active gene expression. Gene family evolution analysis suggests nanochromosomes in spirotrichs may originated approximately 900 million years ago. Together our comparative genomic analyses reveal novel insights into the biological roles of cis-regulatory elements on gene-sized chromosomes.Gene-sized chromosomes are a distinct feature of the macronuclear genome in ciliated protists known as spirotrichs. These nanochromosomes are often only several kilobase pairs long and contain a coding region for a single gene. However, the ways in which transcription is regulated on nanochromosomes is still largely unknown. Here, we generated macronuclear genome assemblies for two species of  Oxytricha, Stylonychia, Uroleptopsis, Euplotes and Strombidium, this fragmentation is extensive, resulting in a MAC genome composed of more than 10,000 telomere-capped chromosomes (Gene-sized chromosomes (nanochromosomes) are an intriguing genetic architecture found in a subset of ciliates, one of the most diverse clades of unicellular eukaryotes. Ciliates contain two distinct types of nuclei, called the micro- and macronucleus, which are both present in the cell throughout its vegetative life cycle . During omosomes . These lomosomes . These gomosomes .cis-regulatory elements  needed for transcription regulation in eukaryotes are often located in intergenic regions , Qingdao, China. Species were initially determined by morphological features and later confirmed by sequencing their SSU-rRNA genes. A single cell was picked, washed, and cultured in flasks using filtered and autoclaved pond water. Cells were incubated with rice grains at 23°C for 21 days, then collected using a glass micropipette under a stereomicroscope. Genomic DNA was extracted using the MagAttract HMW DNA kit . A DNA library was constructed with NEBNext DNA Library Prep Master Mix Set for Illumina  following the manufacturer’s instructions. RNA extraction was performed with the RNeasy Plus Mini Kit  following the manufacturer’s instructions. The RNA libraries were generated using NEBNext Ultra RNA Library Prep Kit for Illumina  following the manufacturer’s instructions.cis-regulatory sequence motifs were searched within subtelomeric regions using MEME v5.3.3  on at least one end . The average size of contigs capped with telomeric repeats on both ends is 1.7 kb (P. carnea) and 1.1 kb (P. flava), and the vast majority  appear to be gene-sized chromosomes , a feature also observed in several other ciliate genomes  and Bayesian inference (BI) methods generally agrees with previous studies . This opens the possibility that the assignment of litostomateans could require further review in the future by expanding sampling.To perform the phylogenomic analysis, we collected public genomic/transcriptomic datasets available for 31 ciliates , and ide studies . Phyloge species . The anano acids . Similarop codon , reflectincluded , we findPseudokeronopsis nanochromosomes have limited space, we found an AT-rich region exists in the 5′ subtelomeric regions of most complete nanochromosomes that carry single genes  (nAWTWn which is positioned between the transcription start site (TSS) and translation start codon in both species  (Although ctively) . Further species . Similarombidium  and Nycttotherus . We idente (TES) , and is P. carnea and P. flava, respectively), indicating that nucleotides in the CRE are under stronger selection. The substitution pattern also shows a distinct difference between the 5′ CRE and surrounding nucleotides . These observations suggest that transcription initiation on Pseudokeronopsis nanochromosomes depends on this CRE near the transcription start site, though the nature of this CRE is not clear. The sequence may act as a promoter by binding directly to a transcription factor, or it may contribute a necessary structural feature to the DNA in this region. Future studies should further test whether chromatin accessibility is greater at this location, and if active chromatin marks are enriched  in P. carnea and P. flava, respectively. These expanded gene families are enriched in a variety of pathways , suggesting multiple origins of extensive fragmentation within ciliates . The spivolution , we idenpathways . Comparepathways , the expPseudokeronopsis ciliates, which consist of compact, gene-sized nanochromosomes.In summary, we report the first macronuclear genome assemblies of two Pseudokeronopsis nanochromosomes have tiny introns and small subtelomeric regions. We identified AT-rich sequences conserved within the 5′ and 3′ subtelomeric regions in both species and observed that these potential CREs are associated with active gene expression, suggesting a role in transcription regulation. Both P. carnea and P. flava have expanded their complement of genes related to nucleotide binding and gene expression regulation since the origin of gene-sized chromosomes in spirotrichs approximately 900 million years ago. Together, these findings suggest that ciliates may have developed a unique mechanism to regulate transcription from gene-sized chromosomes during evolution.Similar to other spirotrichs, https://www.ncbi.nlm.nih.gov/, PRJNA507672, PRJNA534036, https://pse.ciliate.org, Pseudokeronopsis DB.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 below: WZ, CL, XC, and LL conceived the study. WZ provided the biological materials. WZ, WS, and XC designed the experiments. WZ and HD performed the experiments. WZ, CL, HD, and XC performed computational and experimental analysis for all figures and tables. WZ, CL, SAA, WS, and XC interpreted the data. NAS and AB constructed the genome database website. WZ, CL, XC, and LL wrote the manuscript with contribution from all authors. All authors read and approved the final manuscript.The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.