Starch and cellulose are the principal components in diets for dairy cows worldwide, providing the primary energy to the rumen microorganisms as well as the host. Starch and cellulose degradation in the rumen have always been of key importance for dairy cows to obtain high production performance. To improve the starch- and cellulose-degrading activities in the rumen, the amylolytic and cellulolytic microbes and the related enzymes need to be well understood. As the rapid development of sequencing technologies, bioinformatic tools and reference databases, the rumen metagenomics have made great progress in mining the rumen microbial community for novel enzymes, such as the carbohydrate active enzymes (CAZymes). This review will summarize the ruminal microbes and enzymes involved in starch and cellulose degradation. Recent studies with metagenomics techniques on CAZymes related to starch and cellulose degradation will be discussed.Carbohydrates  are the main energy source in the diets of dairy cows. The ruminal digestion of starch and cellulose is achieved by microorganisms and digestive enzymes. In order to improve their digestibility, the microbes and enzymes involved in starch and cellulose degradation should be identified and their role(s) and activity known. As existing and new analytical techniques are continuously being developed, our knowledge of the amylolytic and cellulolytic microbial community in the rumen of dairy cows has been evolving rapidly. Using traditional culture-based methods, the main amylolytic and cellulolytic bacteria, fungi and protozoa in the rumen of dairy cows have been isolated. These culturable microbes have been found to only account for a small fraction of the total population of microorganisms present in the rumen. A more recent application of the culture-independent approach of metagenomics has acquired a more complete genetic structure and functional composition of the rumen microbial community. Metagenomics can be divided into functional metagenomics and sequencing-based computational metagenomics. Both approaches have been applied in determining the microbial composition and function in the rumen. With these approaches, novel microbial species as well as enzymes, especially glycosyl hydrolases, have been discovered. This review summarizes the current state of knowledge regarding the major amylolytic and cellulolytic microorganisms present in the rumen of dairy cows. The ruminal amylases and cellulases are briefly discussed. The application of metagenomics technology in investigating glycosyl hydrolases is provided and the novel enzymes are compared in terms of glycosyl hydrolase families related to amylolytic and cellulolytic activities. The rumen ecosystem harbours a vast number of microorganisms fermenting the ingested feedstuffs and producing various metabolites to meet the host’s nutritional requirement . NutritiUnlike ruminants in the wild, starch and cellulose are the principal components in diets for commercial dairy cows worldwide, providing the primary energy to the rumen microorganisms as well as the host . Starch The exploration of the species and enzyme activities involved in ruminal cellulose and starch digestion has been hampered by the limited number of rumen bacteria that can be cultured . MetagenStarch-rich grain is the primary energy component used in the modern diet for dairy cows, accounting for 20–40% of the ration of high-yielding cows. Due to the relatively high price of starch-containing ingredients, dietary starch should be used wisely to achieve cost-effectiveness and efficient production. Starch is a heterogeneous polysaccharide containing two structurally distinct α-linked polymers of glucose: amylose and amylopectin. The former is a linear D-glucose polymer containing ~99% α-1,4 links and the latter is the most abundant component of starch with 95% α-1,4 links and 5% α-1,6 links .Unlike non-ruminants, starch degradation mainly occurs in the rumen, partly in the small intestine with the remainder fermented in the hindgut of ruminants. Starch degradation in each segment of the gastrointestinal tract is influenced by starch sources  and processing  of the grain . Data frStreptococcus bovis, Ruminobacter amylophilus, Succinimonas amylolytica, Selenomonas ruminantium and Bifidobacterium spp. , amylopectin  and pullulan  [Succinimonas amylolytica is an anaerobic, Gram-negative and straight rod with rounded ends which can be motile with polar flagella. This species is less abundant among the ruminal bacteria when cattle are fed forage rations but is among the predominant bacteria when dietary starch is offered in the form of a grain mixture [Selenomonas ruminantium is anaerobic and Gram-negative, and it consists of motile rods of 0.8–1.0 μm in width and 2–7 μm in length. This species was found to be more abundant in the rumen when animals were fed cereal grains compared to that fed roughage [Fibrobacter succinogenes, Butyrivibrio fibrisolvens and Clostridium spp. are also capable of unitizing starch under certain conditions [he rumen . Streptod is low . When co6 bonds) , mainly 6 bonds) . Succini mixture . This sproughage . Most stroughage . Besidesnditions ,18.2, CO2, acetate, butyrate and glycerol as products. However, the rate of uptake of starch grains varies greatly between species. The protozoa with high amylolytic activities include Eremoplastron bovis, Diploplastron affine, Ophryoscolex caudatus and Polyplastron multiesiculatum. The breakdown rate of starch by protozoa is by approximation determined by the initial starch or amylopectin concentration inside the protozoa [The protozoa are also involved in degrading starch in the rumen. Between 20–45% of the amylolytic activities in the rumen have been attributed to protozoa . The amyprotozoa . Protozoprotozoa .Neocallimastix frontalis was reported to hydrolyze starch by generating an endo-hydrolytic α-amylase from which maltose, maltotriose and maltotetraose were the major products [Orpinomyces joyonii, Neocallimastix patriciarum and Piromyces communis, were also observed to be capable of digesting cereal grains [Fungi account for a small proportion (~8%) of the rumen biomass where they are involved in degrading structural carbohydrates by producing a wide range of enzymes . Neocallproducts . Anotherl grains .Due to their small size, bacteria cannot directly ingest starch granules or high-molecular-weight starch , but generate enzymes which specifically cleave the α-1,4 or α-1,6 bonds of amylose and amylopectin. These amylases can be typically classified into three main categories of hydrolytic activity: endoamylases, exoamylases and debranching enzymes .Endoamylases cleave the α-1,4 glucosidic linkages in the interior of the starch polymer or oligosaccharides in a random manner leading to the production of linear and branched oligosaccharides . α-AmylaThe rate and content of ruminal starch degradation vary with the type of cereal grains. Usually, wheat and barley starch are degraded more rapidly in the rumen than corn or sorghum starch . RuminalPhysical processing is another factor influencing ruminal starch degradation. Generally, processed grains are more digestible in the rumen . With thMoreover, starch degradation in the rumen is influenced by intricate interrelations of multiple factors including diet composition, amount of feed consumed per unit time, mechanical alterations, chemical alterations and adapting degree of ruminal microbiota to the starch ratios in diet .The rations for dairy cows are predominantly plant-based. The plant cell walls are primarily composed of cellulose which accounts for 20–30% of the dry weight of the primary cell wall. Cellulose is a homopolymer of glucose linked by linear 1,4-β-glycosidic bonds. Cellulose molecules associate with each other to form microfibrils in the form of crystalline formulations.A large number of anaerobic bacteria, protozoa and fungi possess very efficient cellulolytic machinery which enables them to improve the feed conversion efficiency of cellulose. Cellulolytic organisms are those microbes predominantly digesting cellulose present in the diet, which were dominated mainly by bacteria, fungi and to a lesser extent the protozoa .Ruminococcus flavefaciens, Ruminococcus albus and Fibrobacter succinogenes are the major cellulolytic bacteria [Fibrobacter succinogenes is one of the most widespread cellulolytic bacteria in the rumen, which contributes 5–6% of the total prokaryotic 16S rRNA in the rumen contents of cattle [Fibrobacter succinogenes strains have been reported to degrade cellulose, glucose and cellobiose mainly producing acetate and succinate [Ruminococcus flavefaciens. Ruminococcus flavefaciens are usually Gram-positive or Gram-variable and often generate a characteristic yellow pigment, particularly when grown on cellulose. Most Ruminococcus flavefaciens strains are able to degrade quite recalcitrant forms of cellulose which is difficult to digest by other species [Ruminococcus flavefaciens mainly attach to the cut edges of the epidermis, sclerenchyma and phloem cells when incubated with ryegrass leaves [Ruminococcus flavefaciens mostly degrade cellulose and cellobiose, while some strains can also utilize glucose and other carbon compounds including maltose, lactose, xylose and starch. The main end products include acetate, succinate, formate and lactate, together with traces of hydrogen and CO2. Ruminococcus albus cells are usually single or diplococcic, 0.8–2.0 μm in diameter and Gram-negative to Gram-variable. Generally, in the rumen, Ruminococcus albus is more abundant than Ruminococcus flavefaciens [Ruminococcus albus strains are able to degrade cellulose and cellobiose but cannot utilize glucose or other sugars. The main end-products of this degradation include acetate, ethanol, formate, lactate, hydrogen and CO2 with different combinations and proportions as the major products. Ruminococcus albus can produce ethanol, while the Ruminococcus flavefaciens produce succinate instead. The abovementioned three cellulolytic bacteria share some common features: (1) their growth needs a strict pH range from 6 to 7, (2) they are all strictly anaerobic and cannot survive when exposed to oxygen, (3) they digest cellulose by attachment to the cell surface through an extracellular glycocalyx and (4) these bacteria are majorly restricted to cellulose or the hydrolyzed products of cellulose.The bacteria   endoglucanases , (2) exoglucanases  and (3) β-glucosidases (β-D-glucosidases)  Table 4Table 4. All these abovementioned cellulases have been isolated from the ruminal cellulolytic microbes and classified into specific GH families, for example, the endoglucanases mainly belong to the GH family 5 and 9, whereas, exoglucanases are mostly present in the GH family 6, with the β-glucosidases mainly classified into GH family 3 .To date, only a relatively small fraction of rumen microorganisms has been successfully isolated and cultured. Therefore, the largely unexplored microorganisms represent a significant untapped source of novel enzymes, especially those with multiple functions. Thanks to the development of next-generation sequencing technologies and bioinformatics tools together with the rapid progress in reference databases, metagenomics has become a powerful tool to study the rumen microbiome.With metagenomics technologies, we can acquire the collective genetic structure and functional composition of rumen microorganisms without culturing their inhabitants. According to amino acid sequence similarity, GH and related enzymes are classified into specific families with all members in one family possessing the conserved catalytic mechanism. The public database of Carbohydrate Active enZyme (CAZy) describes the present knowledge on the enzyme families which build and breakdown complex carbohydrates and glycoconjugates. These families are classified based on experimentally characterized proteins and populated by sequences from public databases with significant similarity ,56,57. TRumen metagenomics analysis comprises two areas, including (1) functional metagenomics, in which the high-throughput screening technique is used for investigating gene products out of cloned expression libraries established by rumen metagenome DNA and (2) sequencing-based metagenomics in which the genomes and genes present in rumen microbes are explored through high-throughput next-generation sequencing.Ferrer et al.  first apFibrobacter succinogenes S85 and Ruminococcus albus. The publications since 2015 on metagenomics application related to ruminal starch- and cellulose-degrading enzymes of cows are summarized in Sequencing-based metagenomics provides the collective genetic composition and functional activities of a microbial community at the DNA level. The first publication using next-generation sequencing-based rumen computational metagenomics for cataloguing the genes and activities involved in ruminal fibre degradation was reported in 2009 . Later, In total, with the assistance of metagenomics tools, comprehensive studies as illustrated above will broaden our knowledge of the ruminal microbial structure and enzymatic activities, which in turn would allow for rumen manipulations to achieve a more efficient fibre and starch degradation. For instance, (1) as more microbial amylases and cellulases are identified out of the ruminal microbiome, it will be foreseeable to regulate the ruminal microbial amylolytic and cellulolytic activities through supplementing exogenous enzymes in the form of feed additive, (2) newly identified species will promote the process of isolating microbes out of the rumen and improve the development of microbe-culture techniques and (3) it will facilitate the commercial applications of rumen enzymes in various industries including feed additives and biofuel production.This review has summarized the microbes and enzymes involved in starch and cellulose degradation and discussed the state of metagenomics technology in mining novel cellulase and amylase GH families in the rumen of dairy cows. To date, a number of amylolytic and cellulolytic microorganisms, their characteristics and their metabolic mechanisms in the rumen of dairy cows have been described. Still, uncharacterized microbes and enzymes need to be identified. The recently emerging technologies such as metagenomics have become more efficient in exploring new microbial species and strains, mining novel enzymes and monitoring microbial and enzymatic activities. This will improve the development of new culturing techniques. In turn, the advancement of our knowledge into the functioning of the microbiota of the rumen can facilitate the directed regulation of specific microbial activities or supplementation of exogenous enzymes.


Mucormycosis is a group of infections, caused by multiple fungal species, which affect many human organs and is lethal in immunocompromised patients. During the COVID-19 pandemic, the current wave of mucormycosis is a challenge to medical professionals as its effects are multiplied because of the severity of COVID-19 infection. The variant of concern, Omicron, has been linked to fatal mucormycosis infections in the US and Asia. Consequently, current postdiagnostic treatments of mucormycosis have been rendered unsatisfactory. In this hour of need, a preinfection cure is needed that may prevent lethal infections in immunocompromised individuals. This study proposes a potential vaccine construct targeting mucor and rhizopus species responsible for mucormycosis infections, providing immunoprotection to immunocompromised patients. The vaccine construct, with an antigenicity score of 0.75 covering, on average, 92–98% of the world population, was designed using an immunoinformatics approach. Molecular interactions with major histocompatibility complex-1 (MHC-I), Toll-like receptors-2 (TLR2), and glucose-regulated protein 78 (GRP78), with scores of −896.0, −948.4, and −925.0, respectively, demonstrated its potential to bind with the human immune receptors. It elicited a strong predicted innate and adaptive immune response in the form of helper T (Th) cells, cytotoxic T (TC) cells, B cells, natural killer (NK) cells, and macrophages. The vaccine cloned in the pBR322 vector showed positive amplification, further solidifying its stability and potential. The proposed construct holds a promising approach as the first step towards an antimucormycosis vaccine and may contribute to minimizing postdiagnostic burdens and failures. Mucormycosis, caused by fungal infiltration of blood vessels, is linked to a high mortality rate. This angioinvasive infection is acquired predominantly by sporangiospore inhalation, but the chances of infection through traumatic, nonsterile inoculation and contaminated food are equally high ,2. HematAs expected, the risk factors for mucormycosis also differ. Diabetes mellitus (DM) has been reported as the most prevalent risk factor for mucormycosis in Asian countries, whereas transplantation and malignancies occupy this spot in the European world ,7. ApartThe most prevalent type of mucormycosis is rhino-orbito-cerebral mucormycosis (ROCM), which is frequently observed in individuals with ketoacidosis or uncontrolled diabetes mellitus. The second most obvious and currently the most predominant clinical form of mucormycosis is the pulmonary (PM) type. It is most often seen in patients with transplants and hematological issues. COVID-19 is another major contributor to the escalated development of PM . Among iRhizopus organisms are said to be behind ROCM, Saksenaea and Apophysomyces are usually identified in cutaneous mucormycosis, and Cunninghamella organisms are usually associated with disseminated and pulmonary forms of the infection [Mucormycosis has a wide range of clinical manifestations, based on the host’s pre-existing immunosuppression. Even though there is substantial diversity, the clinical manifestation may be generally classified into six categories, ROCM , pulmonanfection .Despite a redundant and fatal attack of mucormycosis every now and then, therapeutics are focused on postdiagnosis solutions. In most cases, it gets too late to apply that solution. Ibrahim et al. (2010) worked on developing an Ftr1 vaccine to counter mucormycosis; however, that project was limited to the demonstration of a potential antibody vaccine . Today, Mucor and Rhizopus species are known to be at the heart of a majority of mucormycosis infections, so it targets one transmembrane protein from each species and combines their antigenic epitopes to construct a multipotent vaccine that can protect the human host from both species’ infections. It will consider the global coverage of epitopes selected, immune stimulation, and response to the vaccine, and will provide a cloning insert to help future researchers with the in vitro vaccine development. Although performing a rigorous, extensive, and thorough analysis, we have amalgamated epitopes from proteins of two species to construct the vaccine, which can be improved since mucormycosis is a broad-spectrum disease and epitopes from other species could also be of significance. Furthermore, it is solely a computational approach and lacks the sensitivity of dry lab assessments. For an efficient vaccine, molecular and immunological assays can contribute a great deal.This study proposes a multiepitope vaccine against mucormycosis. R. oryzae and M. circinelloides from CELLO2GO, 18 proteins for both organisms were short-listed as they were present in the transmembrane region. Out of these 18, phytoene dehydrogenase from M. circinelloides (UniProt ID: tr|Q9Y798) and a hypothetical protein from R. oryzae (accession number: KAG1630349.1) were selected for vaccine design. Both proteins showed a hundred percent conservation among various rhizopus and mucor species. The selected proteins were found to be antigenic and nonallergenic, which made them fit for further analyses. Upon analyzing 6000 proteins for The BepiPred 2.0 tool predicted B-cell-specific epitopes for both the proteins. On average, the predicted score for the epitopes of phytoene dehydrogenase fitness was 0.450 with a minimum of 0.218 and a maximum score of 0.663. For the hypothetical protein, the average score was 0.444 with a minimum of 0.213 and a maximum score of 0.690. According to preferential surface accessibility, 13 peptides for phytoene dehydrogenase and 6 peptides for the hypothetical protein were selected. The average score of accessibility for both the phytoene dehydrogenase and the hypothetical protein residues was 1.000; the graphs for surface accessibility and antigenicity of both the proteins are provided in The analysis provided 20 peptides with antigenicity above the average and a threshold value of 1.027 with the minimum score being 0.889 for the residues of phytoene dehydrogenase A. This ptrRosetta predicted the tertiary protein structure for phytoene dehydrogenase with a Tm score of 0.846 and the structure for hypothetical protein with a Tm score of 0.742. The models are demonstrated in PROCHECK predicted that 89.8% of the hypothetical protein’s residues fall in the core category with 9.0% in the allowed category. Only 1 bad contact was found in the protein, and 99% of planar groups were within limits. According to the RC plot, 95.053% residues were found to be in the highly preferable regions, and 4.240% residues were shown in additionally allowed regions, whereas 0.7% residues were present in the questionable category. Verify3D scores predicted that 97.75% phytoene dehydrogenase residues averaged >0.2 scores. Additionally, a minimum of 80% of AAs passed the 3D/1D profile complementing the overall score. Similarly, for the hypothetical protein, 90.53% scored >0.2, and more than 80% of residues passed the 3D/1D scores as well.ElliPro predicted 15 linear epitopes and 8 discontinuous epitopes for phytoene dehydrogenase along with 11 linear and 3 discontinuous epitopes for hypothetical protein. The discontinuous epitopes for phytoene dehydrogenase and hypothetical protein shown in A,B are pOut of the 20 epitopes retrieved from the antigenicity analysis and 13 epitopes retrieved from the surface accessibility analysis for phytoene dehydrogenase, 3 epitopes  were selected for the final construct on the basis of their antigenicity, accessibility, and allergenicity values. Similarly, out of the 6 surface accessibility epitopes and 10 antigenicity-based epitopes for the hypothetical protein, 3  were finalized for the final vaccine construct. A total of 6 B-cell epitopes were selected. The antigenicity and accessibility scores for these residues are provided in For phytoene dehydrogenase, 15,738 interactions with all the HLA molecules were predicted, out of which 50 epitopes’ IC50 values were below the threshold, and hence, the remaining results were discarded. Out of these 50, 10 were further processed for population coverage analysis. For the hypothetical protein, 12,584 interactions were predicted, out of which 23 epitopes’ IC50 values were below the threshold value of 500, and 6 were further run for population coverage.Among phytoene dehydrogenase’s epitopes, MAFTFQTMY was a dominant binder to 10 HLA alleles , WVMFMFFYF and LTSSISFY were dominant binders of 9 alleles , respectively. Among hypothetical protein’s six epitopes, QMFNPPFVY and RLMNGHNSM were dominant binders of 10 HLA alleles, STIDPAQSY bound 9 HLA alleles, RYCCRRMVL and KVYEWDFSR bound 6 HLA alleles dominantly, and YLSLIQAEY bound to 5 HLA alleles.MHC-II analyses provided quite an extensive result wherein out of 44,523 allelic interactions, 10 core peptides of the phytoene dehydrogenase were chosen with all the epitopes having an IC50 value of <100. Hypothetical protein’s results were surprisingly elaborative with one of the core peptides (FVYSLAIST) binding dominantly to 177 HLA-DR alleles with IC50 values of <100. A total of 14 core peptides were selected.Selected MHC-I epitopes showed phenomenal accumulative world coverage of 97.15% with a PC90 value of 3.08, whereas the MHC-II epitopes displayed a coverage of 77.71% coverage, with a PC90 average of 0.45, which suggests the minimum epitopes hit per HLA combination acknowledged by 90% of the population. This indicated the potential of the selected epitopes for the final vaccine construct. The coverage maps of phytoene dehydrogenase’s MHC-I and MHC-II epitopes are shown in The combined world coverage displayed extraordinary results with a 98.26% coverage of the selected epitopes of phytoene dehydrogenase C. The avOut of 10 MHC-I and MHC-II epitopes of phytoene dehydrogenase, 6 core epitopes were nonallergenic as per AllerTOP v.2.0. Out of 5 MHC-I and MHC-II epitopes of the hypothetical protein, 4 epitopes and 3 core peptides were considered nonallergenic. Among the selected 6 core epitopes of phytoene dehydrogenase and 3 core epitopes of the hypothetical protein, 2 for each satisfied the VaxiJen v.20 analysis at a threshold of 0.5, and 6 epitopes from phytoene dehydrogenase and 8 from the hypothetical protein were finalized for vaccine construct based on their antigenicity scores, as shown in The software indicated the vaccine to be a transmembrane protein, validating its origin from two transmembrane proteins. PSIPRED predicted that most of the vaccine was involved in a transmembrane helix and only 31 residues (7%) were disordered. The MEMSTAT analysis predicted that residues from 314 to 458 are extracellular, exposed to the host proteins. trRosetta predicted the tertiary structure of the vaccine with a TM score of 0.237. Such a low score indicates the uniqueness of the protein structure. The contact maps of the predicted 2D structure indicate the credibility of the protein structure. ERRAT predicted that the overall protein quality was 88.452, as shown in The RC plot suggested that right-handed helices and β-sheets are the most dominant secondary structures in the protein corresponding to good hydrophilicity. Only 0.5% of residues were in the disallowed region, which proved the protein quality. Verify3D scores predicted that 67.90% of the construct’s residues averaged >0.2 scores. Additionally, a minimum of 80% of AAs passed the 3D/1D profile, complementing the overall score. The overall quality of the predicted 2D structure was satisfactory with most of the residues concentrated in the lower left of the graph, indicating the presence of α-helices.The number of amino acids submitted to ProtParam was 458. The output molecular weight was 49051.19 g/mol. The adjuvant part was considered as the N-terminal of the vaccine, and the estimated half-life in reticulocytes according to in vitro analysis was 30 h, which is a quality attribute. The instability index was calculated to be 34.96, whereby the threshold value is 40. This prediction indicated that the vaccine construct is stable. The aliphatic index predicted by the server was 78.52. The GRAVY index was calculated to be −0.064, indicating the hydrophilicity of the vaccine construct. Solubility analysis predicted by the Protein-Sol also predicted the same about vaccine hydrophilicity or solubility. The index is given in These analyses showed that majority of the epitopes, including MAKLSTDEL, STIDPAQSY, SIDLNES, YSLAIST, and DGNWIAAYDKIY, were conserved after fusing them into a new construct. Docking analyses of vaccines with MHC-I gave 25 clusters with varying energies, out of which model 0 was selected for simulations. It had the lowest energy, −987.5, and its center was estimated to be −896.0. The docking complex is provided in A total of 48, 55, and 26 interactions between the ligand and the receptors, MHC-I, TLR2, and GRP78, respectively, were computed from Discovery Studio. MD simulations helped visualize the molecular interactions, as shown in The reverse-translated sequence processed by EMBOSS, when run on JCAT, provided sublime optimization with a CAI value of 1.0, indicating that the refined codons will show a maximum expression in the host, and GC contents reduced from 61% to 50%. Cloning was performed by replacing the EcoRV and Nru1 restriction sites with our vaccine construct C using tC-ImmSim provided an excellent immune response to the designed vaccine. Th0 activity decreases as Th1 activity increases in response to the vaccine. The memory TC cells are produced constantly during the 1st month of injection, while the concentration of active TC cells decreases after the first 2 weeks. The NK cell population showed a pulsating increase and decrease; however, during the first 10 days of injection, their population was quite extraordinary. The MA graph per state depicts both the active and resting populations increasing in the first 30 days of injection. Antigen presentation increases in the first 3 days and then decreases spontaneously.C. jejuni [Candida glabrata [With the increasing threat of mucormycosis adjoined with COVID-19 variants and underfacilitated hospital systems, a permanent intervention is required. As mentioned by Imran et al. 2021), mucormycosis, although dangerous and lethal for immunocompromised people, is equally infectious to immunocompetent people, and no vaccine has yet been developed to minimize its chances of infection . This st21, mucorThese, along with other innovations, support the potential use of bioinformatics, computational vaccinology, and immunoinformatics in the fields of health and medicine. Chaudhuri and Ramachandran (2017) consider immunoinformatics as a golden weapon in applying techniques such as reverse vaccinology . The effA recently published study identified 80 COVID-19-associated mucormycosis . Among tRhizopus species, found in 47% of the cases, followed by Mucor, found in 18% of the cases, in a survey of over 900 documented human reports [Mucor, Rhizopus, and Lichtheimia are most reported, among which Rhizopus arrhizus is the most dominant organism [Cunninghamella are said to be the most fatal, with only 7% pathogenesis recorded [Apophysomyces, Absidia species, Saksenaea species, and Rhizomucor pusillus with 5%, 5%, 5%, and 4% documentation, respectively.Roden et al. (2005) reported that the majority of mucormycosis episodes were associated with  reports . The genorganism . Althougrecorded . Other sAn important aspect of designing any vaccine construct is to ensure its antigenicity, nonallergenicity, and nontoxicity. Results for all three aspects were consistent with that of Naveed et al. (2021), who worked on antigenicity analyses throughout . This stFurthermore, the use of C-ImmSim eased the process of immune simulation. As Abdelmoneim et al. (2020) reported, C-ImmSim plays with the immune response and provided extraordinary analyses, and our results demonstrated the same observations . DespiteMucor circinelloides and Rhizopus oryzae, downloaded in FastA format. Gene ontology analysis was run on both the species’ proteins separately using CELLO2GO [http://cello.life.nctu.edu.tw/cello2go/ (accessed on 23 July 2021). Conservation and homology analyses using Blastp [https://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE=Proteins (accessed on 23 July 2021) were performed on both selected proteins individually.The NCBI protein database was to retrieve 6000 proteins of both CELLO2GO , availabg Blastp  availablhttp://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html (accessed on 23 July 2021), and AllerTOP v.2.0 [https://ddg-pharmfac.net/AllerTOP/ (accessed on 23 July 2021) were utilized for antigenicity and allergenicity analyses of the selected fungal proteins, respectively.VaxiJen v2.0 , availabOP v.2.0  (https:/http://tools.iedb.org/bcell/ (accessed on 23 July 2021) with plain sequence (no headers) input. BepiPred 2.0 was used to depict linear epitopes based on the random forest algorithm. The surface antigenicity scale put forward by Parker et al. (1986) was dedicated to calculating the surface epitopes of the target proteins [B-cell epitopes were predicted using different IEDB tools (proteins . The Kolproteins . The β-thttps://yanglab.nankai.edu.cn/trRosetta/ (accessed on 23 July 2021). These structures were further utilized to identify the discontinuous B-cell epitopes of the protein discussed in the next section. The models retrieved from trRosetta were refined on GalaxyRefine [http://galaxy.seoklab.org/cgi-bin/submit.cgi?type=REFINE (accessed on 24 July 2021), which were further run on three tools of the UCLA-DOE lab server (https://servicesn.mbi.ucla.edu/PROCHECK/ (accessed on 24 July 2021), PROCHECK [Three-dimensional structures of both selected proteins were modeled using the trRosetta server   analyzed the 3D structure of preferable epitopes in both the target proteins. The 3D models of the target proteins (refined PDB formats) were provided to the server, and the parameters were set to default values.ElliPro  (http://https://ddg-pharmfac.net/AllerTOP/ (accessed on 26 July 2021) analysis, and the ones with the prediction of being nonallergens were selected for the final vaccine construct.The most probable B-cell epitopes present on the target antigen’s surface were selected for the vaccine construct. Their antigenicity and surface accessibility scores were given prime importance. Furthermore, the final selection of epitopes was carried out by analyzing the allergenicity of all preliminarily selected epitopes through AllerTOP v.2.0  (https:/http://tools.iedb.org/main/tcell/ (accessed on 26 July 2021) were performed for potential T-cell epitopes. All the alleles with a length of 9 residues were selected for analysis. Artificial neural network version 4.0 [50 values below or equal to 500 were short-listed. Among these, epitopes with acceptable antigenicity and allergenicity were finalized for the construct.MHC-I  and MHC-sion 4.0  was sele50 values less than or equal to 100 were selected for further analysis. These were then screened based on their antigenicity and allergenicity predictions.For MHC-II analyses, the prediction method was selected as NN-align-2.2 , which ahttp://tools.iedb.org/population/ (accessed on 27 July 2021) before the allergenicity and antigenicity analyses. The ones with higher coverage were further run for these tests.All the short-listed epitopes for MHC-I and core epitopes for MHC-II alleles were evaluated for population coverage  (http://https://ddg-pharmfac.net/AllerTOP/ (accessed on 27 July 2021), and the ones with the prediction of being nonallergens were selected for the final vaccine construct. For MHC-I, the selected peptides were run on VaxiJen v.2.0 (http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html (accessed on 27 July 2021). For MHC-II, however, this process was further subdivided into two parts. First, the core epitopes were analyzed in a FastA format, and the threshold value was kept at 0.5. All core epitopes having a greater value were subjected to AllerTOP v.2.0 [The most probable T-cell epitopes, present on the target antigen’s surface, were run on AllerTOP v.2.0  , was used as an adjuvant at the N-terminal of the vaccine construct as it significantly improves the recognition by Toll-like receptors and can polarize CD4 T cells . Three l T cells  and Elam21, was uhttp://ddg-pharmfac.net/AllergenFP/data.html (accessed on 28 July 2021) for antigenicity analyses by the first two tools and allergenicity analyses by the last two tools, respectively. Furthermore, since the vaccine construct must not be toxic, the sequence was subjected to ToxinPred analysis [https://webs.iiitd.edu.in/raghava/toxinpred/algo.php (accessed on 28 July 2021).The vaccine sequence was subjected to VaxiJen v2.0, Scratch , AllerTOanalysis  availablhttp://bioinf.cs.ucl.ac.uk/psipred/ (accessed on 28 July 2021) and Scratch [http://scratch.proteomics.ics.uci.edu (accessed on 28 July 2021). Sequence data of the vaccine in FastA format were inputted in the required box. The prediction of secondary structure by PSIPRED 4.0, prediction of disordered regions by DISOPRED3 [The primary sequence of the vaccine construct was subjected to secondary structure analysis on PSIPRED  . The model retrieved from trRosetta was refined on GalaxyRefine [http://galaxy.seoklab.org/cgi-bin/submit.cgi?type=REFINE (accessed on 29 July 2021) and was subjected to three verification tools of the UCLA-DOE lab server (https://servicesn.mbi.ucla.edu/PROCHECK/ (accessed on 29 July 2021), PROCHECK [https://zlab.umassmed.edu/bu/rama/ (accessed on 29 July 2021) was also utilized for an additional confirmatory RC analysis.The 3D structure of the vaccine was modeled using the trRosetta server   of the Expasy server [https://protein-sol.manchester.ac.uk (accessed on 29 July 2021) was used to analyze the solubility index of the protein compared with a standard E. coli protein.The ProtParam tool (y server  was utily server  (https:/http://tools.iedb.org/mhcnp/ (accessed on 30 July 2021) to check for the conservation of discontinuous B-cell epitopes and T-cell epitopes. The MHC-NP tool analyzed the epitopes processed by the MHC molecules upon interaction. All alleles of the human species were selected to run the analysis. The protein sequence was provided in FastA format, and the default values were not changed.The vaccine construct was subjected to ElliPro  and MHC-https://www.rcsb.org (accessed on 30 July 2021). MHC-1 interaction with the vaccine is crucial as it represents the antigen to the cytotoxic T cells. TLRs further play a role in recognizing specific PRRs that initiate an innate immune response. GRP78 plays a role as a general attachment point for mucormycosis agents, and interaction with GRP78 confirms the conservation of these epitopes and their recognition as fungal entrants.To analyze whether the designed construct binds with the general mucormycosis and immune receptors, crystal structures of three receptors, MHC-I (PDB ID: 2XPG), TLR2 (PDB ID: 6NIG), and GRP78 (PDB ID: 5F1X), were retrieved from the Protein Data Bank  (https:/https://cluspro.bu.edu/home.php (accessed on 30 July 2021). The best model clusters were analyzed based on model scores and binding affinities (negative energies were the standard) and were further processed for binding affinity analyses. PyMOL2 [https://pymol.org/2/ (accessed on 30 July 2021) and Discovery Studio Visualizer downloaded from https://discover.3ds.com/discovery-studio-visualizer-download (accessed on 30 July 2021) were utilized to visualize the binding residues between the vaccine and the receptors. The MDWeb tool [https://mmb.irbbarcelona.org/MDWeb (accessed on 30 July 2021) was utilized for the simulations of the docked complexes.The docking analyses of the vaccine along with three individual receptors were run on ClusPro supercomputers  (https:/. PyMOL2  downloadWeb tool  (https:/https://www.ebi.ac.uk/Tools/st/emboss_backtranseq/ (accessed on 31 July 2021) using an E. coli reverse translation codon table as the vaccine was expressed in its vector. The input sequence was provided in plain one-letter-residue format. After reverse translation, the nucleotide sequence was pasted on the JAVA codon adaptation tool [http://www.jcat.de/ (accessed on 31 July 2021), and codon optimization was performed to adapt to most sequenced eukaryotic and prokaryotic organisms. The E. coli strain K-12 was utilized for the analysis.Reverse translation of our vaccine construct was retrieved from EMBOSS Backtranseq (ion tool  (http://E. coli pBR322 expression vector was utilized from https://www.snapgene.com/resources/plasmidfiles/?set=basic_cloning_vectorsandplasmid=pBR322 (accessed on 31 July 2021), as it is a standard in cloning procedures and is optimum for quality results. SnapGene software downloaded from https://www/snapgene.com/ (accessed on 31 July 2021) was used to perform the cloning and expression. The insert method was used for this purpose.An http://kraken.iac.rm.cnr.it/C-IMMSIM/ (accessed on 1 August 2021). The server provides immune activity against vaccines or drugs based on particulars, such as injection time. It shows B and T lymphocytes’ response to the vaccine along with the prediction of immunoglobulins’ and immunocomplexes’ response.To verify the immune response generated by the vaccine construct, the sequence was run on the C-ImmSim server  (http://This study proposed a potential vaccine against mucormycosis, one of the daunting challenges faced by medical professionals due to immunosuppression of COVID-19-affected patients. Using the immunoinformatics approach and immune simulations, epitopes from two proteins of different species were selected. It was found that several epitopes of both proteins fit well in the preferred regions, and after analyzing the population coverage and immune response simulations, it was concluded that the designed construct, if manufactured industrially, holds great promise. The vaccine structure was found to be stable with only 7% disordered regions. Docking analyses with two immune receptors and one natural fungal receptor demonstrated great potential with a variety of interactions. Lastly, this study eliminates the economic and emotional burden of physical screening pre- and, to some extent, postmanufacture, easing and enhancing the whole vaccine design process.


Epitopes are short amino acid sequences that define the antigen signature to which an antibody or T cell receptor binds. In light of the current pandemic, epitope analysis and prediction are paramount to improving serological testing and developing vaccines. In this paper, known epitope sequences from SARS-CoV, SARS-CoV-2, and other Coronaviridae were leveraged to identify additional antigen regions in 62K SARS-CoV-2 genomes. Additionally, we present epitope distribution across SARS-CoV-2 genomes, locate the most commonly found epitopes, and discuss where epitopes are located on proteins and how epitopes can be grouped into classes. The mutation density of different protein regions is presented using a big data approach. It was observed that there are 112 B cell and 279 T cell conserved epitopes between SARS-CoV-2 and SARS-CoV, with more diverse sequences found in Nucleoprotein and Spike glycoprotein. Since the first case of COVID-19 was reported in December 2019, the prevalence of SARS-CoV-2 has grown at an incredible pace, resulting in a worldwide pandemic. The scale and rate of spread of the disease continue to grow. As a consequence, humans have had to drastically change their lifestyle, and scientists and clinicians have been presented with great challenges to address such a paramount public health issue. The research needs primarily include expansion of our fundamental knowledge about SARS-CoV-2 in the context of other genomically similar Coronaviridae, analyzing individual and population level immune responses, to model the disease spread and to improve and support vaccine development.Adaptive immunity comprises humoral and cell-mediated immunity and provides the major mechanisms for individuals to recognize and mitigate foreign agents or antigens. Humoral immunity is driven by B cell lymphocytes and occurs when foreign materials, i.e., antigens from an extratracellular agent such as bacteria or virus, occurs in the body. After introduction to an antigen, B cells differentiate, leading to the creation of memory and effector B cells. Cell-mediated immunity, however, does not depend on antibodies for its adaptive immunity and is instead controlled by mature T cells, macrophages, and cytokines functioning in response to an antigen.An epitope is a small site on an antigen where a complementary antibody or T cell receptor can specifically bind. The corresponding antigens vary between 8 and 17 amino acids in length, and each epitope is defined by a unique sequence of amino acids. Epitopes are broadly classified into two structural categories: linear epitopes that are continuous amino acid sequences, and non-linear or conformational epitopes that are discontinuous peptides on the unfolded sequence. Non-linear epitopes may only be recognized by the immune system depending on neighboring loci. In this investigation, we focus on linear epitopes. There are two types of epitopes: T cell epitopes and B cell epitopes. B cell epitopes are solvent-exposed portions of peptides on the surface of an antigen that bind to secreted and cell-bound immunoglobulins, i.e., the ones to which antibodies bind [T cell epitopes are further separated into Class I  and Class II based on their presentation by the major histocompatibility complex (MHC) molecule for recognition by two distinct subsets of T cells: cytotoxic CD8+ and helper CD4+ T cells, respectively . There aWith regard to linear epitopes, there are several tools that aim to predict epitope sequence and structure using a multitude of machine learning (ML) methods, which achieve varying levels of accuracy. EpitopeVec  predictsIn our work, we focus on Nucleoprotein and Spike glycoprotein, as they have been identified as the main proteins of interest in SARS-CoV-2 ,14,15,16The Spike glycoprotein (S) is important, as it plays a vital role in binding, fusion, and entry into the host cell  ,35,36,37There were two primary motivations for studying and predicting epitopes in SARS-CoV-2 by leveraging known epitopes of other Coronaviridae. First, understanding the homology of epitope sequences between different related viruses could provide valuable insight for developing better epidemiological models that could account for adaptive immune response developed from exposure to similar pathogens. Second, the adaptive immune system of two individuals infected by the same organism may learn different peptide sequences and, therefore, produce antibodies for different epitopes. Conversely, as some protein domains are common between organisms, an adaptive immune response developed on the basis of exposure to one organism may sometimes increase risk against a different infectious pathogen . IdentifTo further the understanding of the adaptive immune response to SARS-CoV-2 and to increase our understanding of its genotypic and phenotypic homology with SARS, we aimed to identify and investigate the presence and evolution of SARS-CoV-2 epitopes, specifically Nucleoprotein (N) and Spike glycoprotein (S). We present our analysis of 11 different SARS-CoV-2 proteins amounting to 28 K unique protein sequences using in silico methods for epitope prediction based on sequence homology. We used data from ancestral lineages as well as eight variants of concern and of interest to show the relevance of results specifically for the Delta variant, thus underscoring the stability of top epitopes. Beyond this, we analyze how the occurrence of different groups of epitopes vary by host. Specific emphasis is placed on detailing Nucleoprotein and Spike glycoprotein findings with structural depictions, as these proteins are of particular medical interest in human health . We beliData for this study were obtained primarily from two sources. Coronaviridae epitope data were retrieved from the Immune Epitope Database and Analysis Resource (IEDB) on 20 July 2020 and again in April 2021 . The IBMpositive assays only, i.e., epitopes known through confirmed laboratory experiments. No filters for host, MHC restrictions, or diseases were applied. For epitope sequences in IEDB that were found originally in other Coronaviridae, the identification of those sequences in SARS-CoV-2 was based on observation of the exact sequence in SARS-CoV-2 proteins in FGP .At the time of download in 2020, the data contained 601 linear T cell epitopes with positive assays across 10 proteins and 23 organisms, and 586 linear B cell epitopes with positive assays across 12 proteins and 30 organisms. There were 28 column descriptors, of which the following were extracted for this study:From IEDB, we retrieved all distinct (non-redundant) epitope sequences, the name of the protein from which they were found, and the antigen on which it was identified. This was used as ground truth data to locate conserved regions across SARS-CoV-2 proteins and to identify new epitopes not originally in IEDB.https://ibm.biz/functional-genomics) is a comprehensive database and analytics platform that provides (at the time of this work) ∼300,000 pre-annotated bacterial and viral genomes, with over 75 million unique gene sequences, over 57 million protein sequences, and over 263 million functional domains. For SARS-CoV-2, it contains high quality, curated genomic data defined as genomic sequences with <1% Ns (unknown bases), over 29,000 bp, and <0.05% unique amino acid mutations , along For this study, we utilized ∼28 K unique SARS-CoV-2 protein sequences and ∼49 K related protein domains and annotations identified from a collection of ∼62 K high quality SARS-CoV-2 genomes from NCBI GenBank  and GISAThe host metadata used in this work were downloaded from NCBI GenBank  and GISAIn the reference genomes for SARS-CoV-2, the copy number for each protein is 1 ,50. AfteIdentification of whether an epitope occurs on a protein sequence and at what locus (amino acid position) using exact string matching functions provided in base Python. For this, the epitope’s parent protein was matched to the SARS-CoV-2 protein, e.g., epitopeA, whose parent protein indicated in IEDB is Nucleoprotein, would be checked for presence on SARS-CoV-2 Nucleoprotein sequences only;Calculation of the number of times an epitope occurs on a protein sequence.First, known epitope sequences that are present in our protein sequence corpus were identified. In this section, the set of 28K SARS-CoV-2 protein sequences from FGP was utilized to determine conserved epitope sequences from two subsets within IEDB data: a limited set of ground truth SARS-CoV-2 epitopes and epitopes observed in other Coronaviridae. To compare these sequences, the following steps were completed:The above analysis was performed separately for B cell and T cell epitopes and for all protein names in the set of parent protein names retrieved from IEDB.Next, identification of candidate epitopes was performed, i.e., potential peptides with high sequence homology to the laboratory confirmed epitopes. This would enable accounting for slight changes in known epitope sequences due to evolving protein regions. CANDIDATE epitopes are denoted as NEW in the To this end, we completed a sequence search using BLAST  to compaThe steps outlined in We relied on sequence homology approach and BLAST to identify CANDIDATE epitopes because epitope prediction tools can sometimes be unreliable ,7.Because T cell MHC Class I epitopes are known to be important in the process of vaccine design ,53, predThe entire set of CANDIDATE and ORIGINAL T cell epitope sequences from S and N proteins was passed as input to netMHC4.0.The output from netMHC4.0 was parsed to identify peptides with strong and weak affinities to HLA Class I alleles based on the above threshold. Additionally, overlap of these with our top ten T cell epitopes from N and S proteins was calculated as well as their location in structural representations.Furthermore, for our top ten T cell N and S epitopes, IEDB was manually searched to record how many epitopes have been verified by assays to be MHC Class I.The presence of epitopes on proteins and genomes was studied from the compiled list of ORIGINAL and CANDIDATE epitopes identified in our set of 28 K protein sequences specifically to answer questions such as: What are the most abundant epitopes? Is an epitope found multiple times in a protein sequence? How many epitopes are present on average in a genome?To explore how different epitopes relate to one another based on sequence homology and similarity of their loci on the protein sequence, both sequence-based clustering and position-based clustering of epitopes were performed.1.Epitopes (both ORIGINAL and CANDIDATE) were grouped based on functional type (T or B) and parent protein;2.For each group above, the Levenshtein edit distance measure was calculated (using Python) for every epitope–epitope pair. This yields a square matrix with axes labeled by epitope and cell values as the distances between all pairs;3.Linkage (Python scipy package) was run on the edit distance matrix using ’Euclidean’ metric and ’Single’ method;4.The resulting linkage matrix was used to compute and plot a dendrogram;5.A cluster threshold was defined where cluster members had a length normalized edit distance less than one. We color any linkage lineage lines not clustering together in blue.For sequence-based clustering, the following steps were performed:After clustering epitopes by sequence and extracting the relevant clusters for each protein and epitope type (B or T cell), the occurrence of these clusters on SARS-CoV-2 genomes was investigated. To this end, the dendrogram was combined with a bar plot indicating frequency of occurrence of all epitopes in the cluster combined to which the epitope belongs.A stacked bar plot was generated to present the distribution of genomes over three categories of original data source (hosts/samples): humans, animals, and environmental. Source data were retrieved from genome metadata files from NCBI and GISAID. These data have also been made available as a table in 1.B cell and T cell epitopes were divided into separate sets based on parent protein as described above;2.To remove spurious sequences or assembly errors, only sequences with length within  UniProt  referenc3.Furthermore, multi-sequence alignments (MSA) were run for all sequences per protein name relative to one another using MAFFT (v7.431) with the reorder option ;4.For each protein, a matrix was then constructed, with the x-axis indicating the amino acid position on the protein and the y-axis indicating the epitopes where the length of the x-axis was the maximum length of the protein sequence;5.Each cell was then assigned a binary value, i.e., 0 or 1. The cell is assigned 1 if the epitope on that row is found on the position on the protein corresponding with the column index of the cell, e.g., if ‘epitopeA’ is on row 0 and is found on a Nucleoprotein sequence between indices 7 and 15, then index 7 would be filled with 1;6.After sequences have been aligned in the MSA, the positional information was padded so that all pairwise comparisons are represented with identical start and stop coordinates, and gaps are filled in with dashes. To accommodate this relative positional information generated by an MSA, regular expressions were used to identify the start index of epitope sequences with the allowance for epitopes that span “gapped regions” due to the filling in of sequences in the MSA;7.Epitope sequences spanning regions of insertion described in step 6 were investigated further for key non-synonymous mutations and their prevalence in this corpus of ORIGINAL and CANDIDATE epitopes;8.After alignment, the epitopes were identified on the same position within the protein sequences. The bars representing epitopes were colored by their presence across the genomes;9.After constructing the matrix in steps 2 and 3, ‘single’ linkage was run using method ‘Euclidean’ metric;10.Finally, the clustermap and dendrogram were plotted using Python’s seaborn library.To obtain position-based clustering of the epitopes, the following steps were completed:Certain epitopes were not found as exact subsequences on MSA proteins due to filling in of gap regions. In such cases, a regular expression (regex) match was used to find the start position for the epitope. Additionally, these regions identified locations where protein mutations were occurring and were thus studied in greater detail by visualizing MSA results.Mutations at any given position on the Spike glycoprotein transcript were logged using the MSA results as percentages. The proportion of amino acids matching the reference were measured at every position on the sequence to estimate ‘mutation density’. These were plotted to examine regions of elevated mutagenesis on the protein sequence with respect to the MSA-derived consensus sequence. Mutation density plots were constructed for both Spike glycoprotein and Nucleoprotein transcripts.The clustermap generated in Methods To identify epitope relationships within protein functional domains, the domains for each protein were also checked against epitopes for complete sequence homology, i.e., if an epitope sequence exactly matched or was found as a sub-sequence within a domain sequence for the corresponding protein.In order to visualize the position of the epitopes on the quaternary structure of the Nucleoprotein (N) and the Spike glycoprotein (S) of SARS-CoV-2, the VMD software  was usedFurthermore, MHC Class I epitopes of N and S T cell epitopes were visualized separately on the 3D protein structure.To understand the correctness of our results, a later set of lab-confirmed epitopes (IEDB retrieved 04-2021) was used. We did not rely on comparing results with other epitope prediction tools since this has been benchmarked comprehensively elsewhere , and theThe predicted set of epitopes were analyzed against the latest set of epitopes from IEDB. BLAST  was usedAdditionally, even though a complete analysis with the latest variants of concern was out of the scope for this paper, in light of the outbreak and seriousness of the Delta variant of SARS-CoV-2, the two most frequently seen sequences of Spike glycoprotein for Delta variant were retrieved and assessed to identify if our top Spike T cell and Spike B cell epitopes are present on this Spike sequence. The sequences were obtained from  and are In this work, a corpus of 61,850 SARS-CoV-2 genomes from NCBI GenBank and GISAID was analyzed. Protein annotations for each were downloaded from FGP. After performing the data sanitation steps outlined in Methods Additionally, 492 candidate linear B cell (304) and T cell (188) epitopes were identified in SARS-CoV-2 proteins with high sequence similarity to epitopes from IEDB . Our resThe top ten (by occurrence) B and T cell epitopes for Spike glycoprotein and Nucleoprotein are listed in In order to test the predicted epitopes and to measure the sequence diversity, the predicted epitope sequences were compared to the known epitopes for each cell type, and the percent match was calculated. We find the average percent identity for Nucleoprotein B cell is 95.05%, Nucleoprotein T cell is 97.75%, Spike B cell is 94.64%, and Spike T cell is 97.40%. Furthermore, range of percent identities for all predicted epitopes within each cell type was analyzed. Additionally, to verify the validity of the top Spike epitopes in the Delta variant, the presence of the top ten B and T cell Spike epitopes was checked on 2 SARS-CoV-2 Delta spike sequences. We found that all epitopes are indeed present on the Delta Spike as well.In In all figures noted previously, a significant number of CANDIDATE epitopes are observed. When we compare the presence of clusters against each other, it is observed that there are certain clusters of epitopes that have a much higher weight than other clusters.T cell MHC Class I epitopes bind to HLA Class I and are known to be important in the process of vaccine design ,53; thusAs described in The alleles from SD5 have been grouped and ordered by their frequency of occurrence and listed in For epitope comparisons with netMHC, we consider matching epitopes as those only with 100% sequence identity. Out of 143 T cell S epitopes, thirteen matched strong binding S peptides and 37 with weak binding S peptides (SD5). Out of 119 T cell N epitopes, only three exhibited strong binding with N peptides and seven with weak binding N peptides (SD5). All of the above matches were found only in ORIGINAL S epitopes and not with any of the CANDIDATE epitopes for both weak and strong. For N epitopes, there was one CANDIDATE epitope, with all others being ORIGINAL for both weak and strong.From manual inspection of IEDB at the time of writing, all of the top ten T cell epitopes are known epitopes for SARS-CoV-2. In addition, eight of the top ten T cell S epitopes and seven of the top ten T cell N epitopes were identified as MHC Class I through biological assays referenced on IEDB. These have been indicated in Understanding the rate of mutation across epitope sequences can provide insights into waning host immunity and the average period of host reinfection. These insights can also shape our understanding of vaccine efficacy over time. To evaluate this, we computed a multiple sequence alignment (MSA) of unique Spike glycoprotein and Nucleoprotein sequences and evaluated the mutational density across the B cell and T cell epitopes.In Spike glycoprotein ,it is obFor Nucleoprotein, there were no epitopes that overlapped with mutations or indels (insertions or deletions). However, for the Spike glycoprotein consensus sequence logo, the most prevalent amino acid by position was observed to be present with a median value of 99.84% (range 0.023%–100%), suggesting high sequence conservation across the protein. However, there was a small amount of mutations or indels in epitope sequences. For example, for epitope WTAGAAAYYVGY at amino acid position 272–288, we observed several key differences from the wild type epitope sequence. We observed a three amino acid insertion in one of our Spike glycoprotein sequences. Additionally, at amino acid position 278, we observed higher variability where the dominant amino acid glycine (G) is present in only 91.41% of sequences, with substitutions to serine (S) or aspartic acid (D) being the most common. This shifts from an aliphatic amino acid to a polar, hydroxylated amino acid or negatively charged acid, respectively, which can then change binding affinity to host proteins.The immunodominance In For Spike, as shown in Immunodominance figure of Nucleoprotein B cell  reveals To better understand the relationship between epitopes and protein functional domains, evaluation of the occurrence of complete sequence identity between epitopes and protein domains was performed. Although epitopes are observed to span the length of Nucleoprotein and Spike glycoprotein, no exact matches were observed between epitopes and full length domain sequence or sub-sequence of any domain sequence on SARS-CoV-2 proteins. All matches contained at minimum a single amino acid mutation or indel. Domains and epitopes were only compared with respect to the same parent protein.We analyzed the position of the ten B and T cell epitopes that were most frequently identified in all genomes investigated and observed that 9/10 high frequency B cell and 7/10 high frequency T cell epitopes were localized in the CTD of the N protein. Therefore, in 10 helix . In B ceinal IDR A,B. Compinal IDR A,C. The Because the S protein undergoes major conformational changes allowing membrane fusion between the SARS-CoV-2 viral membrane and the host cell, the top ten B cell and T cell epitopes identified after analyzing all genomes have been marked on a 3D representation of the protein. Both prefusion and postfusion states are shown in We observe that the top ten B and T cell epitopes are localized in the S2 subunit of the S protein and, specifically, they are found between residue 902 and 1065, except the B cell epitope GSFCTQLN (violet) located between residue 757 and 764 before the fusion peptide (FP). The B cell epitope MAYRFNGIGVTQNLVYE (green), partially located in the heptad repeat 1 (HR1), is also recognized by T cells, although the two epitope sequences differ for the glutamic acid E, which is absent in the T cell epitope sequence  and VLNDILSRL* (green) are located in the HR1 domain and partially overlap with the other two epitopes AQALNTLVKQL* (bright blue) and SVLNDILSRL* (red). The epitope LITGRLQSL* (pink) is localized entirely in the CH domain. Consecutively located on the CD domain are the following epitopes: RVDFCGKGY* (orange), YHLMSFPQSA* (purple), FPQSAPHGVVF* (yellow), and SFPQSAPHGVVFLHV (cyan).Additionally, MHC Class I epitopes from the top ten list have been presented on the 3D structure of the S and N proteins in During our study, it was observed that 77% of IEDB ground truth data were from SARS-CoV or SARS-CoV-2 (or their parent organisms in taxonomic tree), and 74% of the total epitopes were for Spike glycoprotein or Nucleoprotein . This isIn addition, complete sequence homology was found only with respect to identified SARS-CoV-2 and SARS epitopes, whereas no epitopes from other Coronaviridae were found to be present in SARS-CoV-2 genomes when performing an exact sequence match. This result is scientifically interesting because it underscores the similarity between SARS and SARS-CoV-2 genomes and highlights the lack of similarity of SARS and SARS-CoV-2 to other Coronaviridae. This observation has been confirmed in another study of COVID-19 . Our resAlthough no exact matches of epitopes were observed with Coronaviridae other than SARS, when considering fuzzy matches or candidate epitopes, we see that fourteen B cell epitopes had parent epitopes found in organisms other than SARS and SARS-CoV-2. These organisms are: Murine hepatitis virus strain JHM, Feline infectious peritonitis virus (strain KU-2), Infectious bronchitis virus, Porcine epidemic diarrhea virus, Murine hepatitis virus strain A59, Avian infectious bronchitis virus (strain M41), Avian infectious bronchitis virus (strain Vic S), and Porcine transmissible gastroenteritis coronavirus strain Purdue. Additionally, 26 candidate T cell epitopes were also found to have parent epitopes belonging to organisms other than SARS and SARS-CoV-2. These organisms are: Feline infectious peritonitis virus (strain KU-2), Human betacoronavirus 2c EMC/2012, Feline infectious peritonitis virus (strain 79-1146), Murine hepatitis virus, and Avian infectious bronchitis virus (strain Vic S). We think that the similarity between regions of SARS-CoV-2 proteins and immune targets of other Coronaviridae might be relevant for investigating viable therapeutics and refining epidemiological models to better estimate effective rate of transmission by accounting for possibly resistant populations. The results shown in We reported in From epitope distributions and quantities in the analyzed genome cohort (1.From sequence based clustering , it is p2.By analyzing mutation density regions and immunodominance regions, it is possible to evaluate which segments of proteins may be undergoing the fewest amino acid changes and thus would advise the most stable regions on the protein or those that may be evolving under selective pressure. This type of analysis combined with studying position-based clustering could provide more insight for selection of epitopes, as it would highlight most prevalent epitopes with consideration for their neighbors.In our work, we provide two approaches for selection of epitopes to better understand inter-species homology and relevance for fundamental immunological understanding affecting vaccine design, spread of virus, and development of more accurate epidemiological models:Finally, we analyzed the epitope location on the 3D structure of the N and S proteins. There are important synergies between the work of Grifoni et al.  and our 1.It is possible to study the proximity of epitopes to more mutation prone regions to eliminate immune targets that otherwise may seem promising.2.It is possible to also combine the epitope evaluation approaches discussed in 3.It would be interesting scientifically to study the presence of the top ten epitopes in vaccine sequences.


Starch–CNC blends were processed by rapid visco-analysis (RVA) and cooled to 70 °C. Pasting parameters such as pasting temperature, peak, hold and breakdown viscosity were assessed. After RVA testing, starch–CNC blends were immediately analyzed by rotational and dynamic rheology at 70 °C. Gelatinization temperature and enthalpy were assessed by differential scanning calorimetry. Our results suggest that CNCs modify the starch gelatinization but that this behavior depends on the starch origin. In potato starch, CNCs promoted a less organized structure after gelatinization which would allow a higher interaction amylose–CNC. However, this behavior was not observed in wheat and waxy maize starch. Insights focusing on the role of CNC on gelatinization yielded relevant information for better understanding the structural changes that take place on starch during storage, which are closely related with starch retrogradation. This insight can be used as an input for the tailored design of novel materials oriented towards different technological applications.The goal of this work was to analyze the effect of CNCs on the gelatinization of different starches  through the characterization of the rheological and thermal properties of starch–CNC blends. CNCs were blended with different starches, adding CNCs at concentrations of 0, 2, 6 and 10%  Starch and cellulose are the most widely distributed polymers in nature. Starch is found in the form of granules which are energy reservoirs for plants . CelluloLikewise, more recently, interest in exploring the use of nanocellulose in the design of starch-based composites has also increased. Among the more interesting choices are cellulose nanocrystals (CNCs). CNCs correspond to the crystalline fractions presented in the elemental fiber of a cellulose microfibril ,13. CNCsDespite the effect of CNCs on the physical properties and functionality of different starches being described in the literature, studies describing the role of CNCs during the gelatinization of starch are scarce. The gelatinization of starch has been well-described in terms of the mechanism of granule swelling, granule disruption, the loss of birefringence and amylose and amylopectin leaching due to the effect of temperature and stirring ,17,18. HTherefore, this work aimed to study the gelatinization of starches from different sources  in the presence of CNCs produced from cotton cellulose pulp. Pasting parameters closely related to starch gelatinization  were assessed in starch–CNC blends by rapid visco-analysis (RVA). The viscoelasticity of these blends were evaluated by rheology test at 70 °C, whereas the gelatinization temperature and enthalpy were assessed by differential scanning calorimetry (DSC). Native potato , wheat  and waxy maize  starches were purchased in powder form. Cellulose nanocrystals (CNCs) produced from cotton fibers were purchased in freeze-dried form from The Process Development Center of University of Maine . Starch and CNCs were used as received without further purification and stored at room temperature until further use.w/w dry cellulose (Equation (1)). For this purpose, well-defined amounts of CNCs were suspended in distilled water (conductivity < 10 mS) only by stirring. Before being mixed with starch, CNCs were sonicated  at ambient temperature (20 ± 2 °C) to promote the complete dispersion of nanocrystals:CNC suspensions were prepared for blending with native starch to have a starch suspension with an CNC concentration of 0, 2, 6 and 10% The gelatinization of starch–CNC blends was carried out by rapid visco-analysis  in accordance with the methodology proposed by Diaz-Calderon et al.  with minThe changes in viscoelasticity of starch–CNC blends were carried out by rheology . Samples analyzed by rheology were prepared using the RVA machine and following the same protocol explained in Apparent viscosity (Pa·s) and shear stress (Pa) were assessed from the rotational flow curve obtained at 70 °C using cone geometry  in the shear rate range between 1 and 1000 1/s.Dynamic changes in storage modulus  and loss modulus  were obtained through a frequency sweep carried out at 70 °C to analyze the behavior of G′, G″, and the loss factor (G″/G′) as a function of angular frequency from 1 to 648 rad/s at 0.5% strain which was within the linear viscoelastic range (LVR) previously defined by an amplitude sweep (0.1–1000%) at 70 °C. A flat plate geometry (5 cm diameter) was used for analysis and a 300-micron gap was selected for testing. The analysis considered at least five replicates per experimental condition. w/w). The starch concentration was 20% w/v. Approximately 60 µL of starch–CNC blend suspensions were loaded into 100 µL aluminum pans and then hermetically sealed. The DSC was calibrated using indium , and an empty pan was used as a reference. Thermal properties of the suspensions were measured as follows: holding temperature at 5 °C during 3 min, heating from 5 °C to 85 °C at 10 °C/min, and holding at 85 °C during 3 min. The gelatinization temperature (°C) was recorded from the onset of the endothermic peak associated with the starch granule swelling, while gelatinization enthalpy was considered as the area under the endothermic peak. Gelatinization enthalpy was normalized in terms of starch dry mass and was expressed in J/g starch. All measurements were performed at least in triplicate.Gelatinization temperature (°C) and enthalpy (J/g starch) were measured by differential scanning calorimetry  following the protocol reported by Díaz-Calderón et al. . In ordet-test (same variances) and ANOVA using the Solver tool in Excel . Where appropriate, the statistical significance was assessed by a paired Viscosity patterns of starch samples resulted from RVA are presented in p-value < 0.05) in the pasting parameters were observed among the pure starch samples (0%CNC). Regarding the pasting parameters of starch–CNC blends, our results show that the presence of CNC did not produce a significant difference  on the pasting temperature. However, CNC produced a significant decrease  in peak viscosity which was proportional to the increase in CNC concentration only in potato starch. In wheat and waxy starch, a decrease in peak viscosity was only detected at the highest CNC concentration tested. On the other hand, hold viscosity  only at the highest CNC concentration tested (10%) in potato and waxy maize starch. This parameter was not significant modified  in the wheat starch blend. Likewise, the breakdown viscosity was significantly reduced  by CNC in potato starch, and only at the highest CNC level tested in wheat and waxy starch. Interestingly, the assessment of the relative decrease only showed a marked decrease in potato starch, whereas in wheat and waxy starch, this ratio was lower only at the highest CNC level tested. Significant differences (iscosity ) was sigRheology analysis by rotational and dynamic tests were carried out at 70 °C on samples completely gelatinized by RVA. Apparent viscosity and shear stress as a function of shear rate recorded in pure starch samples and starch–CNC blends are presented in p-value < 0.05) the apparent viscosity of waxy starch at 6% and 10%, tested at 70 °C. Likewise, the non-Newtonian condition of gelatinized starches tested at 70 °C was not modified by the presence of CNC, independently of the starch source. As expected, and following the same trend observed in p-value > 0.05) in G′ and both showed a slight slope over the angular frequency range tested at 70 °C  but a significant decrease in wheat starch. However, this effect was not proportional to the CNC concentration  among CNC concentrations. In both wheat and waxy starch, the presence of CNC did not change the value of energy necessary to trigger the granule swelling and disruption. Both the gelatinization temperature (°C) and gelatinization enthalpy (J/g starch) of starch–CNC blends are shown in Differences in the viscosity patterns among starches showed in On the other hand, the pasting temperature was not changed by CNCs, suggesting that the process by which the starch granules begin to swell is not modified by the presence of CNCs, regardless of the starch origin. These results agree with the study of Cui et al.  who repoThe hold viscosity was shown to be significantly lower at the highest CNC concentration tested in potato and waxy starch, which agree with the data of apparent viscosity . The litRegarding the apparent viscosity behavior of starch at 70 °C, our results show a significant higher viscosity in potato starch . This beChanges in pasting properties can be better understood through the viscoelastic characterization of starch pastes. The results shown in Regarding the effect of CNCs on the viscoelasticity of gelatinized starches assessed at 70 °C, our results showed to be strongly dependent on the starch origin. Only a few studies reporting viscoelastic data in starch–CNC blends are available in the literature. For instance, Cui et al.  suggestep > 0.05) suggests that temperature related to the starting point of starch gelatinization is not modified by CNCs. Similar behavior was reported by Cui et al. [Thermal characterization by DSC showed a good correlation with rheology data. The fact that the gelatinization temperature a and pasi et al.  and Ji ei et al. . Differei et al. . On the i et al. . Howeveri et al. ,33, can i et al.  explainei et al. ,26. HoweOur results suggest that the presence of CNC produces changes in the mechanism of granule swelling and disruption during gelatinization, along with promoting a certain amylose–CNC interaction, although this behavior would be dependent of the starch origin. Thus, in potato starch, there was a marked decrease in peak viscosity and breakdown viscosity over CNC concentration, both values being consistent with the assessment of relative decrease in viscosity. This behavior was not observed in wheat starch and waxy starch. Hold viscosity measured by RVA agreed with apparent viscosity measured by rotative test, whereas both the peak temperature and gelatinization temperature were not changed by the presence of CNCs in all the starches studied. However, viscoelastic characterization at 70 °C showed higher values of loss factor in potato starch–CNC, suggesting a less organized structure after gelatinization, which in turn showed good correlation with the increase in G″ at different CNC concentration. Our results are presumably influenced by how potato starch gelatinizes and would not be affected by the presence/absence of amylose, as data from wheat and waxy starch suggest. The amylose–CNC interaction in potato starch could explain the higher values of enthalpy detected by calorimetry. However, complementary studies are needed to define the characteristics of these interactions. These results can be useful in different technological applications based on the use of starch–nanocellulose composites. For instance, for the design of novel composite materials for packaging, bioplastics, bioprinting and food ingredients, to name a few. The potential impact of these biomaterials on sustainable processes opens interesting perspectives in terms of industrial application.


Homozygous loss of SAMD1 was embryonic lethal, with no living animals seen after embryonic day 18.5. At embryonic day 14.5, organs were degrading and/or incompletely developed, and no functional blood vessels were observed, suggesting failed blood vessel maturation. Sparse red blood cells were scattered and pooled, primarily near the embryo surface. Some embryos had malformed heads and brains at embryonic day 15.5. In vitro, SAMD1 absence impaired neuronal differentiation processes. Heterozygous SAMD1 knockout mice underwent normal embryogenesis and were born alive. Postnatal genotyping showed a reduced ability of these mice to thrive, possibly due to altered steroidogenesis. In summary, the characterization of SAMD1 knockout mice suggests a critical role of SAMD1 during developmental processes in multiple organs and tissues.SAM domain-containing protein 1 (SAMD1) has been implicated in atherosclerosis, as well as in chromatin and transcriptional regulation, suggesting a versatile and complex biological function. However, its role at an organismal level is currently unknown. Here, we generated SAMD1 SAMD1 may participate in LDL retention and uptake, and its knockdown suppresses vascular smooth muscle cell (VSMC) differentiation and proliferation, which affects foam cell development3. However, SAMD1 is widely expressed in most organs and tissues, both in mice and humans . The extensiveness of expression suggests roles in adult homeostasis, but the molecular function of SAMD1 has been minimally studied so far. SAMD1 was found to directly associate with CpG islands at the chromatin, acting as a transcriptional regulator, and be required for proper embryonic stem (ES) cell differentiation4. Earlier reports further support epigenetic functions for SAMD1. SAMD1 was enriched at nucleosomes and H3K4me3-possessing chromatin regions, as revealed by mass spectrometry6. It was also among the highest 5% of proteins that associate with both H3K4me3-modified and bivalently H3K4me3/H3K27me3-modified chromatin7.Human SAMD1 (SAM domain-containing protein 1) is a 538 amino acid protein that has orthologs in most vertebrates, including zebrafish. Originally, SAMD1 was identified as a protein involved in atherosclerotic low-density lipoprotein (LDL) binding in humans4. The N-terminal domain belongs to a group of related winged helix domains that bind to unmethylated CpG motifs8, explaining the enrichment of SAMD1 at CpG islands on the genome9. SAMD1 associates with repressive chromatin regulatory complexes that contain the histone demethylase KDM1A and several other SAM domain proteins, including the Polycomb group-related proteins L3MBTL3 and SFMBT112. Consequently, the absence of SAMD1 typically leads to the derepression of its target genes9. SAMD1 is often upregulated in cancer tissues and correlates with worse prognosis in some cancer types, such as adenoid cystic carcinoma (ACC) and liver cancer13. A CRISPR screen in K562 cells suggested that SAMD1 is required for the efficient proliferation of these cells14. Further work demonstrated that SAMD1 deletion in HepG2 hepatocellular carcinoma cells leads to a global readjustment of the active H3K4me2 chromatin mark and a more favorable gene signature, supporting an oncogenic role in this context9. SAMD1 has also been described to play a role in muscle adaptation15. Taken together, these findings suggest that SAMD1 likely has a function in multiple biological processes and diseases, but the biological role of SAMD1 at the organismal level has yet to be investigated.The SAMD1 protein has two globular domains. At the C-terminus, it possesses a SAM domain that can interact with other SAM domain-containing proteins, such as L3MBTL3, and allows multimerization−/−) embryos were observed at embryonic day (E) 14.5, and none were observed after E18.5. Development appeared grossly normal at E12.5, but at E14.5 the investigated embryos exhibited numerous defects, including degrading endothelial cell (EC) tubes, degrading inner organs and an almost complete absence of red blood cells (RBCs). The heterozygous (SAMD1+/−) mice had a less severe phenotype and approximately 70% of the mice survived past week 3 postnatally but they exhibited reduced weight gain and hormonal changes.Here, we report the first knockout of SAMD1 in mice. Fewer homozygous (SAMD1+/+), heterozygous , and knockout  mice were close to the expected Mendelian ratios through E12.5. All embryos removed at E12.5 had heartbeats. The first dead SAMD1 KO embryos and abnormal phenotypes were observed at E14.5  also showed no KO (p = 5.1 × 10–13)  (Supplementary Table +/+ mice were crossed with SAMD1 ± mice (p = 0.003)  embryos were not noticeably different from that of the WT embryos, and HET mice were born alive  tubes, as well as complete regional network regression, leaving behind "sleeves” of collagen and apoptotic ECs21. During maturation, progenitor/pericyte/smooth muscle cells connect to EC tubes, providing stability needed to control regression20. The presence of RBCs in the KO skin implies that a functional circulatory system existed prior to an apparent abnormal regression of the EC tube vascular plexus in the KO embryo.When the yolk sac was removed, the KO embryo within the amnion appeared developmentally delayed and visually smaller than the WT embryo Fig. e. BloodySimilar to the embryo, the KO placenta was pale compared to the WT Fig. e, probab22. The E15.5 embryo was less pale than the E14.5 embryo, had longer and larger red lines, and had a few apparently intact blood vessels. Some areas of the embryo surface in and beneath the skin, and above the brain, had small hemorrhages and lines of RBCs that may have once been contained in vessels, suggesting a failing circulatory system. Since the E14.5 KO had a skull vault and was paler, the investigated E15.5 KO embryo is likely an example of a different SAMD1 KO phenotype, but the surface RBC patterns appear similar to the E14.5 KO. Failed skull vault development may be caused by failure of neural tube closure. Clear fluid leakage due to failing lymphatic and blood vessels likely produced edema  staining on sagittal slices of an E14.5 embryo Fig. . Hematoxans Fig. . AlthougThe KO lung also appeared to be fragmenting, and RBCs were not apparent, in contrast to the WT lung where RBCs filled large vessels Fig. b. BronchThe KO liver had lobes and was large enough to have developed at least until E12.5 , which line all blood and lymph vessels, from primitive endothelial tubes to fully mature vessels. We used H&E, CD31 or VEGFR2 staining (with hematoxylin counterstain) to mark ECs and provide morphological identification of other cells. Cluster of differentiation 31  is widely used as a marker for ECs, staining the cell surface. Vascular endothelial growth factor 2  stains the cytoplasm of ECs. Using these different markers, we investigated the organs and tissues of the SAMD1 KO embryos.Looking first at the embryo surface, H&E staining of the WT embryo showed groups of RBCs in the skin and RBCs in a larger blood vessel beneath the subcutaneous tissue. Here, CD31 stained ECs of capillaries and larger vessels  cells have nuclei, and begin to mix with definitive RBCs from approximately E12.5, and enucleating EryPs at E12.531. A mix of nucleated and definitive RBCs remains in circulation until gestation. Obviously, nucleated and enucleated RBCs could not have arrived at these locations unless a functional circulatory system existed later than 12.5. This observation suggests circulation failure trapped EryPs and definitive RBCs in a nonfunctioning vessel around E13.5, when approximately 50% of RBCs would not have nuclei31. Some KO RBCs appeared faded  in a blood vessel above a WT rib do not have nuclei, and few if any lymphocytes are seen Fig. d. RBCs aded Fig. d, implyiTaken together, our work so far suggests that the absence of SAMD1 leads to improper development of all investigated organs and tissues in the embryo. We have summarized our findings in Table 4. Although SAMD1 functions mainly as a transcriptional repressor, we found both up and downregulated pathways upon SAMD1 deletion, suggesting direct and indirect consequences of SAMD1 deletion. Thus, it appears that SAMD1 plays a rather pleiotropic role during differentiation processes, consistent with the multifaceted mouse phenotype.To better understand the molecular source of the many defects upon SAMD1 deletion, we investigated our previously published RNA-Seq data upon undirected ES cell differentiation in the absence of SAMD14 and several genes related to brain development, such as Cbln1, Ntrk2, and Plxna4, Eph4, become derepressed upon SAMD1 deletion during differentiation , Pecam1 (CD31), Thy1 and Tie1, which are critical factors for angiogenesis34, were strongly downregulated in SAMD1 KO cells, both in differentiated and undifferentiated cells  and Mef2c35  and Nestin (Nes), were dysregulated upon SAMD1 KO  embryo was not noticeably different from that in the WT embryo  and LDL (low-density lipoprotein) were higher in the HETs, while HDL (high-density lipoprotein) was lower in the HETs Fig. h. ProperCollectively, these data suggest that although the heterozygous deletion of SAMD1 has no substantial consequences on embryogenesis, it still has an impact on the ability of the mice to develop normally after birth.9. Upon mouse ES cell differentiation, the absence of SAMD1 enhances the expression of genes involved in neuronal pathways, while factors important for metabolism and angiogenesis are downregulated  FS-23]. The Pfizer Institutional Animal Care and Use Committee (IACUC) approved all of the animal procedures and protocols according to the criteria stated by the National Academy of Sciences National Research Council (NRC) publication 86-23, 1985. All methods were performed in accordance with the relevant guidelines and regulations, as well as in accordance with the ARRIVE guidelines.51 was used to replace 2396 bp of the mouse SAMD1 (Accession ID D3YXK1) gene encompassing the 3′ 615 bp of exon 1, exons 2–4 and all of exon 5 except for the 3′ 83 bp with a neomycin phosphotransferase cassette in a 9433 bp genomic subclone obtained from a C57BL/6J BAC . The vector was introduced into Bruce4 (mixed C57Bl/6N:C57BL/6J) mouse embryonic stem cells52 using a standard homologous recombination technique. Southern blot analysis was used to identify correctly targeted mouse embryonic stem cell clones. Chimeric mice were generated by injection of the targeted ES cells into Balb/c blastocysts. Chimeric mice were bred with C57Bl/6J mice to produce F1 heterozygotes. Germline transmission was confirmed by PCR analysis . F1 heterozygous males and females were mated to produce F2 mice. The colony was maintained by intercrossing under specific pathogen-free conditions with unrestricted access to food and water.SAMD1 knockout mice were genetically engineered by Pfizer . To generate the SAMD1 targeting vector, recombineeringRNA and cDNA measurements were performed at the Brigham & Women’s Hospital . Synthesis of cDNA (ThermoScript RT-PCR System (Invitrogen Cat 11146-016)). RT-PCR was performed in a MyiQ single-color real-time PCR system . Total ribonucleic acid (RNA) from 250,000 cells was reverse transcribed by Superscript II (Invitrogen) according to the manufacturer's instructions. Quantitative PCR was performed with SYBR green PCR mix (primers for SAMD1 from QIAGEN N.V.) and analysis performed with StepOne Software . Levels of mRNA were normalized to glyceraldehyde 3-phosphate dehydrogenase (GAPDH) mRNA levels. cDNAs were synthesized using a High-Capacity RNA-to-cDNA™ Kit . Total 2 µg RNA/100 µl cDNA synthesis reaction and 1 µl cDNA per TaqMan reaction. GAPDH was used as an endogenous control .53. In short, embryos from WT, SAMD1+/−, and SAMD1−/− mice were isolated between E12.5 and E18.5. Internal organs, heads, tails, and limbs were removed from embryos prior to treatment with trypsin to dissociate the cells. Cells were seeded into T-75 cell culture dishes in 15 ml of MEF media: KO DMEM , 15% FBS (Life Technologies), nonessential amino acids (Life Technologies 111140-050), 2 mM l-glutamine , 0.1 mM β-mercaptoethanol (Sigma M7522), and 0.25 mg/ml gentamicin .Mouse embryonic fibroblasts (MEFs) were isolated as described+/+ to SAMD1+/− were performed by Caliper Life Sciences  based on the Phenotype Pfinder platform54.All behavioral, physical, hormonal, and response phenotype comparisons of SAMD118. In short, tissues were collected immediately after E14.5 embryo collection, fixed in 10% buffered formalin and embedded in paraffin. Four micrometer serial sections were cut and mounted on glass slides. One section from each lesion was stained with hematoxylin and eosin (H&E). Other slices were stained for CD31 (ab28364) or VEGFR2 (ab2349) with hematoxylin counterstaining following Abcam’s protocols.Antibodies against CD31 and VEGFR2 were purchased from Abcam . Immunohistochemical staining procedures were performed by Pfizer  and at the Brigham & Women’s Hospital . E14.5 has been established as the optimal time point for mouse developmental disorder phenotype analyses4. The WT and SAMD1 KO mESCs were cultured in Dulbecco’s modified Eagle’s medium (DMEM) and GlutaMAX , 15% fetal calf serum (FCS) , 1 × nonessential amino acids , 1 × sodium pyruvate , 1 × penicillin/streptomycin , 10 µM β-mercaptoethanol , and LIF  on 0.2% gelatin-coated plates.E14 mESCs (E14TG2a) were provided from the lab of Jacqueline Mermoud . SAMD1 KO E14 mESCs were established previously6 on 0.1% gelatin-coated and dried 100 mm cell culture dishes for 3 days in mESC medium, and the medium was changed every 24 h. To start neurodifferentiation, 1 × 106 dissociated ES cells were incubated on nonadhesive bacterial dishes (100 mm) for 6–8 days in mESC medium without LIF, and the medium was changed every 2 d. Embroid bodies (EBs) were collected and redistributed to 4 wells of a 6-well plate containing 2 ml differentiation medium containing DMEM , 20% FCS, 2 mM l-glutamine , 1 × penicillin/streptomycin, 1 × nonessential amino acids, 50 µM β-mercaptoethanol, and 0.5 µM trans-retinoic acid  and incubated for 2 days. EBs from two wells each were collected and redistributed to a 100 mm cell culture dish, coated with 20 μg/ml poly-l-ornithine hydrobromide in PBS  and incubated for 7 days in neurobasal medium  with 1 × L-glutamine, 1 × penicillin/streptomycin, and 1 × B-27 supplement 55.mESCs were plated at a density of 1 × 10l-glutamine, 1 × penicillin/streptomycin, 50 µg/ml BSA , 20 µg/ml insulin , 10 ng/ml EGF , and 10 ng/ml FGF  on 0.1% gelatin-coated and subsequently 5 μg/ml laminin-coated  six-well tissue culture plates and passaged with Accutase  (adapted from56).The established neural precursor cells (NPCs) were cultured in Euromed-N medium  supplemented with 1 × N-2 , 1 × 5) into gelatinized (0.1% in H2O) and poly-l-ornithine hydrobromide-coated (20 μg/ml in PBS) six-well tissue culture plates for differentiation with N2B27 medium, consisting of DMEM/F12  and neurobasal medium (1:1), with 1 × N-2, 1 × B-27, 100 µM β-mercaptoethanol and the addition of 10 ng/ml FGF  (adapted from57). Analysis of the cells was performed with immunofluorescence staining and RT-qPCR at chosen time points.To promote the neurogenic capacity of NPCs and allow the formation of mature neurons, WT and SAMD1 KO NPCs were seeded at high density  according to the manufacturer’s manual, including an on-column DNA digest . The PrimeScript RT Reagent Kit  was used to transcribe mRNA into cDNA according to the manufacturer’s manual. Samples were incubated for 30 min at 37 °C followed by 5 min at 85 °C to inactivate PrimeScript RT enzymes. Subsequently, cDNA was diluted 1:20 to be used in RT-qPCR. For analysis by real-time quantitative PCR, MyTaq Mix  was used. For gene expression analysis, values were normalized to mActb and mGapdh expression. The qPCR primers used are presented in Supplementary Table 4.The following commercial antibodies were used: H3K4me2 , TUJ1/TUBB3 , and GFAP . The antibody for SAMD1 is custom-made. It is directed against the SAM domain of human SAMD1 (amino acids 452 to 538) and was produced as described previously5 cells per well. Cell viability was determined at 1, 3, and 6 days after seeding using the MTT assay by adding 90 µl of 5 mg/ml thiazolyl blue ≥ 98%  to each well. The medium was aspirated after 1 h, and stained cells were dissolved in 400 µl of lysis buffer . Absorption was measured at 595 nm using a plate reader. All values were normalized to day 1 to compensate for variations in seeding density. The mean value of three biological replicates was determined.To determine proliferation rates, cells were seeded on 6-well plates at a density of 1 × 10l-ornithine hydrobromide-coated coverslips Cells were fixed with 4% formaldehyde (w/v), methanol-free , and subsequently permeabilized with wash buffer (0.5% Triton X-100 in PBS). Blocking was performed with 10% FCS in wash buffer. Primary antibodies were diluted 1:500 in blocking solution and incubated in a wet chamber overnight at 4 °C. Three washing steps of the cells were performed before incubation with secondary antibodies, using Alexa Fluor 488 goat anti-rabbit IgG (H + L) and Alexa Fluor 546 goat anti-mouse IgG (H + L) , at a 1:2000 dilution each. Following three washing steps, the coverslips were mounted onto microscopy slides using VECTASHIELD® Antifade Mounting Medium with DAPI  and sealed. Microscopy was performed using a Leica DM5500 microscope, and data were analyzed using ImageJ (Fiji).WT or SAMD1 KO mESCs were seeded on 0.2% gelatin-coated coverslips. WT or SAMD1 KO differentiated neural precursor and neural cells were seeded on 0.1% gelatin-coated and 20 μg/ml poly-To determine the cellular fluorescence in microscopy images using ImageJ, cells of interest were outlined and measured for their area, integrated intensity and mean gray value. Adjacent background regions were measured for the same parameters to calculate the corrected total cell fluorescence (CTCF) according to the following formula: CTCF = integrated density – . Average CTCF values and standard deviations for 5 cells each in 3 different fluorescence microscopy images per sample are presented in a graph.https://www.encodeproject.org/, under “Mouse Development”)24 and from GSE4571923. GSEA was performed with standard settings58 using previously published data (GSE144396)4.Gene expression data from mouse embryo tissues were obtained from ENCODE . Deviation from the mendelian ratio was evaluated using Chi Square tests. The significance of differences between two independently measured groups were evaluated using a two-tailed unpaired t-test. Two-way ANOVA followed by Tukey’s postdoc test was applied for multiple group comparisons. Significance of the Lipoprint analysis was evaluated using a two-tailed Mann–Whitney Supplementary Information.