Hematopoietic stem cells (HSCs) have the properties to self-renew and/or differentiate into any blood cell lineages. In order to balance the maintenance of the stem cell pool with supporting mature blood cell production, the fate decisions to self-renew or to commit to differentiation must be tightly controlled, as dysregulation of this process can lead to bone marrow failure or leukemogenesis. The contribution of the cell cycle to cell fate decisions has been well established in numerous types of stem cells, including pluripotent stem cells. Cell cycle length is an integral component of hematopoietic stem cell fate. Hematopoietic stem cells must remain quiescent to prevent premature replicative exhaustion. Yet, hematopoietic stem cells must be activated into cycle in order to produce daughter cells that will either retain stem cell properties or commit to differentiation. How the cell cycle contributes to hematopoietic stem cell fate decisions is emerging from recent studies. Hematopoietic stem cell functions can be stratified based on cell cycle kinetics and divisional history, suggesting a link between Hematopoietic stem cells activity and cell cycle length. Hematopoietic stem cell fate decisions are also regulated by asymmetric cell divisions and recent studies have implicated metabolic and organelle activity in regulating hematopoietic stem cell fate. In this review, we discuss the current understanding of the mechanisms underlying hematopoietic stem cell fate decisions and how they are linked to the cell cycle. Hematopoietic stem cells (HSCs) are defined by their multipotency or ability to differentiate into all types of blood cells, as well as their ability to self-renew and maintain the stem cell pool to support mature blood cell production. They reside at the top of the hierarchy as they differentiate into increasingly restricted progenitors to produce mature blood cells of all lineages . The HSCin vivo (0). Cells that will divide enter a growth or gap phase (G1), followed by replication of DNA in S phase, and a second growth phase (G2) in preparation for mitosis    which coSK-SLAM) , as wellSK-SLAM) . HSCs de1 phase, where cells are primed to activate developmental genes to initiate fate decisions  at developmental genes can be observed in G1, suggesting a permissive chromatin state for cells to respond to differentiation cues in G1  and erythroid progenitors (ERPs), but MKPs more prominently express the cell cycle machinery genes CDCs and CDKs, whereas ERPs more so express TP53 (p53), MYC, and CDKN1C (p57). This differential gene expression is functionally important. Short-hairpin RNA-mediated knockdown of MYC in MEPs decreased proliferation and increased MK lineage colonies at the expense of E colonies. On the other hand, knockdown of p53 in MEPs increased cell proliferation and E specification, demonstrating that p53 and MYC are effectors in MEP fate. It was further shown that most fast-cycling MEP cells were E-specified, most slow-cycling cells were bipotent MEPs, and most cells with a medium proliferation speed were MK-specified. Further genetic and pharmacologic studies demonstrated that acceleration of cell cycle promoted E specification, while inhibition of cell cycling promotes MK specification, showing that cell cycling itself affects MEP fate decisions  Figure .KIP2 and PU.1, and reconfiguration of chromatin at the erythroid-specific β-globin gene locus allow for the transition from self-renewal to erythroid terminal differentiation, and this fate switch is dependent on S-phase progression. They also demonstrated that the transition to erythroid terminal differentiation coincides with S-phase shortening due to downregulation of p57KIP2-mediated CDK inhibition and increase in replication fork speed , are equally quiescent as LT-HSCs (Lin- CD34+ CD38− CD45RA− CD90+ CD49f+), but contain higher CDK6 protein levels that permit faster cell cycle entry upon mitogenic stimulation, providing a molecular link between HSC activity and cell cycle length  with a latent phenotype  undergo four asynchronous divisions prior to becoming dormant (−CD150+CD135−CD34−), there is a decreased proportion of G0 cells and differential expression of cell cycle pathway genes including genes encoding Cyclins and Cdks  that maintained quiescence under repeated polyIC challenges maintained their functions. Exposure to the pro-inflammatory cytokine interleukin (IL)-1 can repress cell cycle and protein synthesis genes to maintain HSC functions  dye exclusion and EPCR expression to varying doses of Stem Cell Factor (SCF) in vitro. They show that the cell cycle kinetic of HSCs was unchanged by exposure to different SCF concentrations, but that HSC integrity was sustained only in the high SCF concentration when cultured for more than 8 h. SCF signaling was shown to control expression of transcription factors in HSCs while traversing the cell cycle, demonstrating that external cues control HSC fate during cell cycle progression  pathway, an important regulator of HSC functions. Inhibition of mitochondrial FAO induces HSC exhaustion by reducing LSK CD150+ CD48− CD41− Flt3- CD34− HSC asymmetric division and driving HSC symmetric commitment  cells in the G2 phase, unable to proliferate and differentiate (https://doi.org/10.1101/2023.01.23.525185)  . Sin3B whromatin .+/CD38−/CD45RA− cells)  occurs in LSK-SLAM HSCs during the activation phase . TEs can− cells) . The autex vivo with limited results. Failure of numerous HSC expansion protocols arises from gaps in knowledge of the fundamental mechanisms linking HSC fate decisions to cell division. A recent study identified a culture system that allowed for the long-term ex vivo expansion of functional HSCs, utilizing polyvinyl alcohol (PVA) in place of serum albumin, and found that high levels of thrombopoietin (TPO) synergized with low levels of stem-cell factor (SCF) and fibronectin to promote LSK CD150+ CD34− HSC self-renewal  for decades. Yet, the success of HSCT depends on HSC numbers and their regenerative capacity, which has been a great limitation. Many studies have attempted to expand fully functioning HSCs -renewal . These c-renewal . More res ST-HSC . This st1 phase among old compared with young HSCs  from cord blood, adult bone marrow, and mobilized peripheral blood revealed that increasing donor age was associated with progressively delayed cell cycle progression and prolonged G1 phase, as well as desensitization to mitogenic stimulation by extrinsic growth factors (In humans, aging is also accompanied by an accumulation of phenotypic HSCs that have decreased repopulation activity . A recen factors  (Figure  factors . Interes−Flk2− HSCs (In elegant and difficult studies, the Geiger group used paired-daughter cell assays in which the daughter cells of a single cell division are physically separated and individually transplanted into lethally irradiated mice to show that old HSCs execute symmetric self-renewing divisions more frequently than young HSCs. This symmetric mode of division is associated with symmetric distribution of the RhoGTPase Cdc42 and tubulin in old LSK CD34k2− HSCs . Interesk2− HSCs  (Figure k2− HSCs . Pharmack2− HSCs . These dSubstantial progress has been made in our understanding of HSC fate. Accumulating evidence indicates that HSC fate and cell cycle progression are coupled in several ways . Cell cy


MET exon skipping drive oncogenesis in 8–9% and 3% of non-small cell lung cancers (NSCLC) respectively. Their detection are essential for the management of patients since they confer sensitivity to specific targeted therapies with significant clinical benefit over conventional chemotherapy. Immunohistochemistry (IHC) and fluorescent in situ hybridization (FISH) account for historical reference techniques however molecular-based technologies (RNA-based sequencing and RT-PCR) are emerging as alternative or complementary methods. Here, we evaluated the analytical performance of the fully-automated RT-PCR Idylla GeneFusion assay compared to reference methods using 35 fixed NSCLC samples. Idylla demonstrated overall agreement, sensitivity and specificity of 100% compared to RNASeq. Interestingly, it succeeded in retrieving 10 out of 11 samples with inconclusive results due to insufficient RNA quality for sequencing. Idylla showed an overall agreement, sensitivity and specificity of 90.32%, 91.67% and 89.47% compared to IHC/FISH respectively. Using commercial standards, the limit of detection of the Idylla system for the most frequent fusions and exon skipping ranges between 5 and 10 ng RNA input. These results support that the Idylla assay is a reliable and rapid option for the detection of these alterations, however a particular attention is needed for the interpretation of the expression imbalance.Gene fusions and  Despite improvements in diagnostic procedures, about 49% of patients are diagnosed with distant stage lung malignancies, associated with a limited number of therapeutic strategies and a poor prognosis . Non-small cell lung cancers (NSCLC) represent almost 85% of all lung cancer cases and comprise different histological subtypes, including adenocarcinomas, squamous cell lung carcinomas and large cell carcinomas4. Major efforts have been made in the last decades to better decipher NSCLC carcinogenesis and develop molecular-specific therapeutic options. The identification of gene amplifications, mutations and gene fusions as major driver alterations in NSCLC has led to the development of specific inhibitors that have revolutionized the clinical outcomes of patients. In 2019, more than 30% of NSCLC displayed oncogenic alterations that make patients eligible to targeted therapies5.Lung cancer represents the second most diagnosed cancer and the first cause of death worldwide, contributing for 11.4% of all new diagnosed cancers and 18% of all cancer-related deathsEGFR, KRAS and BRAF genes for all patients with advanced non-squamous cell carcinoma considering their major implications to identify patients eligible to targeted treatment (ESCAT scores IA-IB)6. Gene fusion assessment of ALK, ROS1, RET and NTRK gene fusions (ESCAT scores IA-IB-IC) and MET exon 14 skipping (ESCAT score IB) is also recommended6.Besides the determination of the PDL1 status, experts from the European Society for Medical Oncology (ESMO) currently recommend the molecular genotyping of 11. Gene fusions predominantly occur in adenocarcinoma subtype (12% of cases) and in metastatic stage disease9. The number of gene partners implied in fusions are highly variable with up to 24 fusion partners identified in ROS-rearranged NSCLC, 48 in RET-rearranged NSCLC and 92 in ALK-rearranged NSCLC14. All these gene fusions give rise to cancer susceptibility to different specific targeted therapies6.Gene fusions result from chromosomal rearrangements  that lead to the fusion of 2 normally independent genes. Gene fusions can be found in 8–9% of NSCLC, including 3–7% of cases with ALK fusions, 0.9–2.6% with ROS1 fusions, 1–2% with RET fusions and 0.1–0.17% with NTRK fusionsMET exon 14 skipping occurs in almost 3% of NSCLC15, particularly in patients over 70 years with a smoking history and/or pleomorphic carcinoma or adenosquamous cell carcinoma subtypes15. These events can originate from more than 500 different molecular alterations located at branch sites, poly-pyrimidine tract, splice acceptor or donor sites of the MET gene that impacts RNA splicing, induces loss of the MET protein juxtamembrane domain and enhances MET oncogenic signalling pathways15. Clinical data demonstrated substantial benefit to use crizotinib multi-kinase inhibitor in advanced NSCLC patients with MET exon 14 skipping16. More recently, two MET kinase-specific inhibitors (capmatinib and tepotinib) have received US Food and Drug Administration (FDA) approval based on overall response rate and duration of response in patients with metastatic NSCLC harbouring MET exon 14 skipping in GEOMETRY mono-1 and VISION trials respectively18.MET exon skipping or RET fusions by IHC22. FISH commonly accounts for a complementary approach to confirm doubtful IHC results or a screening tool when no specific and reliable IHC assay is available. FISH is commonly performed using break-apart probes that allow the detection of rearrangements whatever the fusion partner. Nevertheless, FISH suffers from prolonged technical and reading times and requires specific infrastructure and expertise. FISH can also miss rare cases of cryptic or complex chromosomal rearrangements and the quality of the slides strongly depends on the technical procedures used. Conventional RT-PCR assays look for fusion transcript variants at the RNA level with high sensitivity but these targeted approaches require previous diagnosis hypotheses about the 2 fusion partners and the exact breakpoints to select the appropriate probes and primers to use and offer only limited multiplexing possibilities. Sequencing methods represents broader approaches that cover a large number of gene fusions in one assay or can even detect all fusions without previous knowledge of the fusion partners depending on the panel design. RNA-based sequencing methods notably display better sensitivities and specificities than IHC and FISH, however they are time-consuming and cost-consuming.Standard reference methods for molecular status determination in NSCLC include immunohistochemistry (IHC), Fluorescent In-Situ Hybridization (FISH) and molecular-based approaches such as RT-PCR or RNA sequencing (RNASeq). These methods are complementary options with distinct benefits and limitations. IHC can reveal aberrant protein expression caused by rearrangements. IHC has the advantage to be implemented and automated in most clinical laboratories and give rapid results with minimal costs that makes it the screening method of choice for the detection of major gene fusions. Moreover, a wide panel of antibodies covering molecular targets are now available for IHC testing. However, IHC is submitted to subjective interpretations, it can lead to inconclusive results due to preanalytical factors and its limit of sensitivity is quite high (15%). Moreover, fusion partners and precise breakpoints cannot be determined and several studies reported a lack of reliability to identify ALK fusions, 13 ROS1 fusions, 7 RET fusions and MET exon 14 skipping) as well as expression imbalance  without pre-analytical RNA extraction23. In this retrospective study, we performed the evaluation of the Idylla GeneFusion system for the multiplex detection of ALK, ROS1, RET, NTRK1, NTRK2, NTRK3 fusions and MET exon 14 skipping in 35 clinical formalin-fixed paraffin-embedded (FFPE) specimens from NSCLC patients using IHC/FISH and RNASeq as the gold standards. This included the analysis of 11 clinical samples that did not reach RNA quality requirements for RNASeq. We also determined the limit of detection of the cartridges for the detection of the most frequent gene fusions and exon skipping events using two different commercial controls.The Idylla system  is a novel fully automated RT-qPCR-based molecular diagnostics system that can identify a panel of specific gene fusions . The specific alterations covered by the Idylla GeneFusion assay are detailed in Supplementary Table We evaluated the analytical performance of the Idylla GeneFusion assay by considering their ability to detect either specific alterations in The Idylla GeneFusion assay gave 94.29% of valid results for the detection of specific alterations. Only 2 samples (#5 and #34) out of the 35 analysed were found inconclusive by Idylla while results for 2 and 11 samples were previously found inconclusive by IHC/FISH and RNASeq respectively  and gave only partial results in 7 out of the 35 samples analysed . In the samples analysed, all specific ALK, ROS1, RET, NTRK1, NTRK2, NTRK3 fusions and MET exon 14 skipping using different inputs of two commercial standards directly loaded into the Idylla GeneFusion cartridges. Using the SeraSeq commercial standard, the LOD was 5 ng for the detection ALK and ROS1 specific gene fusions and MET exon 14 skipping and 7.5 ng for the detection of RET specific gene fusion  of the Idylla GeneFusion assay for the detection of 29. This could be of particular interest for samples rejected for NGS analysis due to low RNA quality, as archival extracted RNA could be reused for Idylla assay instead of collecting supplementary FFPE sections.Here, we performed the validation of the Idylla GeneFusion assay for the detection of the most frequent gene fusions and exon skipping observed in NSCLC. Using 35 fixed samples previously characterized by IHC/FISH and RNASeq, we showed that the Idylla results were comparable with those obtained from reference testing methods. Notably, a 100% overall agreement was observed between RNASeq and Idylla for the detection of specific gene fusions. A lower overall agreement was observed between Idylla and IHC/FISH methods, albeit comparable to that reported between RNASeq and IHC/FISH. Interestingly, Idylla showed ability to analyze problematic samples that were previously rejected for RNASeq due to low RNA quality. Thus, the Idylla system meets the clinical need to rescue samples that appeared suboptimal for other molecular analyses and avoid rebiopsy , thus appearing as a suitable molecular-based option for clinical cases with urgent treatment decision making.6. As proposed by other research groups, the Idylla GeneFusion assay and other PCR-based assays could be integrated into a 1-day laboratory workflow for the detection of hotspot KRAS, EGFR and BRAF mutations as well as major ALK, ROS1, RET and NTRK gene fusions and MET exon 14 skipping events30. Given that these alterations of high clinical impact are theoretically mutually exclusive, sequential analyses could be proposed based on the indications of the different targeted therapies and the frequencies of the alterations observed in NSCLC  7, our study lacks from the analysis of clinical NSCLC samples with characterized NTRK fusions. Despite the use of commercial RNA reference standards with known NTRK fusions, we were not able to evaluate the Idylla cartridges for the detection of NTRK gene fusions due to partial results obtained. Other research groups recently succeeded in estimating the performance of the Idylla GeneFusion assay for the detection of NTRK fusions using a limited number of extrapulmonary tumour samples or through multicenter studies36. They notably reported a limited sensitivity of the cartridges for NTRK fusions compared to other gene fusions, probably due to the analysis by expression imbalance only without fusion specific detection method. Another study evaluating the interest of the Idylla GeneFusion cartridges to detect NTRK fusions in pan-cancer samples highlighted some false positive and false negative results37. However, these incorrect results were more frequently observed in extrathoracic tumours, for which the cartridges were not designed for. Indeed, NTRK higher expression levels were reported in some cancer types  compared to pulmonary tumours that can affect the analysis by expression imbalance.Due to the very low frequency of To conclude, the Idylla GeneFusion assay appears as a fast and reliable alternative to reference methods to detect gene fusions. Due to its minimal hands-on-time and low turnaround time, it could be easily integrated into laboratory workflows in order to give more rapid tumour molecular profiles or retrieve samples rejected by other molecular techniques. A careful interpretation is however needed for the determination of gene fusions using the expression imbalance module.in-situ hybridization (FISH) and RNA-based sequencing (RNASeq) according to current expert recommendations. A written informed consent was obtained from all patients enrolled in this study for the use of their biological specimens for research purposes. Data collected were anonymised at the time of inclusion. The clinical characteristics of the patients are detailed in Table The research was conducted on 35 formalin-fixed paraffin-embedded (FFPE) tumour samples from NSCLC patients that were retrospectively selected among the biological collection of Lorraine Cancer Institute  and Nancy University Hospital Center . All samples were obtained during standard clinical practice based on the research of clinically actionable mutations and fusions in NSCLC to determine appropriate treatment. Notably, samples were analysed using immunohistochemistry (IHC), fluorescent 38. Regarding ROS1 IHC assays, cases scored as 0 were considered ROS1-negative while cases with positive scores should to be confirmed by FISH or molecular-based techniques according to expert consensus opinion39. Given the poor value of MET and RET IHC to screen MET exon 14 skipping and RET fusions, RNASeq was used as the standard reference method to detect these alterations22.IHC assays were performed according to the routine practices of the ICL-CHRUN pathology lab. Briefly, immunohistochemistry staining of ALK and ROS1 proteins was performed on 5 µm-thick unstained FFPE sections using the automated Ventana BenchMark ULTRA IHC/ISH system . Anti-ALK antibody  and anti-ROS1 antibody  were used. Detection was performed using the OptiView DAB IHC Detection Kit (Roche Diagnostics). IHC staining patterns were checked for each antibody using positive and negative internal or external controls and slides were examined by a senior pathologist. A score was established  depending on the percentage of stained cells and the staining intensity. Regarding ALK IHC assays, cases scored as 0 were considered negative, those with score 3+ were designed as ALK-positive and those with score 1+ or 2+ were defined equivocal and required a complementary FISH techniqueALK and ROS1 rearrangements were investigated by FISH in case of equivocal IHC results. Unstained FFPE tumour tissue sections were incubated with dual-colour ALK and ROS1 IQFISH Break-Apart Probes  using Dako Omnis instrument (Dako Omnis) following the manufacturer’s instructions. The slides were screened using Olympus BX51 epifluorescence microscope . For interpretation of FISH status, at least 100 intact tumour nuclei cells were needed. Tumour samples were considered ALK- or ROS1-rearranged if more than 15% of tumour cells harboured split red and green signals and/or single fluorescent red signals41. Otherwise, the samples were interpreted as FISH negative.ALK, BRAF, KRAS, EGFR, RET and ROS1 genes, the panel is designed to detect fusions in 13 genes including ALK, BRAF, EGFR, FGFR1, FGFR2, FGFR3, MET, NRG1, NTRK1, NTRK2, NTRK3, RET and ROS1 (see details in Suppl. Table S2). The enrichment method is based on anchored Multiplex PCR (AMP) technology that allows the detection of known and novel gene fusions without any prior knowledge of the fusion partners. Briefly, FFPE tumour tissue samples were macrodissected to obtain 5-µm thick sections with at least 10% tumour content. Total RNA was extracted using the RNeasy FFPE kit  without adding DNAse during the process . Extracted RNA was quantified using the Qubit 3 fluorometer  and the Qubit RNA HS Assay kit (Thermo Fisher Scientific). A total of 250 ng total RNA input was used for the preparation of the libraries. RNA was converted into cDNA by reverse transcription using random primers. After first strand cDNA synthesis, a real-time PCR-based assay was performed using the Archer PreSeq RNA quality control (QC) Assay (Pre-Seq Ct score) (Invitae Corporation) to assess RNA quality. Cases with Pre-Seq Ct score > 28 were qualified as having poor RNA quality. Double-stranded cDNA undergoes end repair, dA tailing and ligation using partially-functional molecular barcode (MBC) adapters that contain an universal primer binding site. The generated fragments were amplified by two-step PCR using gene-specific primers (GSPs) targeting the genes of interest and housekeeping genes, and universal primers complementary to MBC adapters. Libraries were quantified using the KAPA Universal Library Quantification kit , pooled at equimolar concentrations, and sequenced at 2× 150 base pairs using the MiSeq instrument . Raw data were processed using the Archer Analysis Software and version 6.2.7 pipeline (Integrated DNA technologies).Targeted RNA-based sequencing was performed using the commercial FusionPlex Lung Panel . Besides the research of hotspot single nucleotide variations (SNVs) and small insertions-deletions (indels) in ® FFPE Tumor Fusion RNA v4 Reference Material  was used as a quality internal control for each run . The whole process lasts 180-min and comprises sample liquefaction, total nucleic acid extraction, reverse transcription of mRNA, real-time PCR amplification, fluorophore-based detection, automated data analysis and final report generation.The fully-automated Idylla GeneFusion assay  allows the detection of multiple gene fusions and exon-skipped transcripts in a single assay with less than 5 min hands-on-time. The single-use Idylla cartridges include all reagents on board to cover all steps from sample-to-result starting from FFPE tumour sections. The number of FFPE sections depends on the tissue area: one 5 µm-thick section is sufficient for tissue surface over 20 mmALK, ROS1 and RET gene fusions and MET exon 14 skipping  is evaluated in each sample to testify the correct execution of the whole process. The Cq of the RNA controls reflects the amount of amplifiable RNA in the sample and allows the analysis of expression imbalance when 5′ kinase expression is insufficient. An internal DNA control (KIF11) is also included in the cartridge and serves as an indicator of RNA degradation in cases of invalid results. The MET wild-type control helps to prevent incorrect calling due to MET overexpression.The Idylla GeneFusion assay is designed for the detection of specific ALK gene fusion (#7 to #16), 2 had a ROS1 gene fusion (#17 to #18), 1 had a RET fusion (#19), 5 had a MET exon 14 skipping (#20 to #24) and 6 had a negative status for ALK, ROS1, RET, NTRK and MET genes (#1 to #6). Eleven out of the 35 samples (#25 to #35) were found inconclusive by RNASeq due to the absence of alterations detected and the presence of non-satisfied quality criteria: Pre-Seq Ct score > 28, low percentage of total RNA reads compared to the percentage of total DNA reads, < 20.000 RNA unique fragments and < 30% RNA unique fragments, and/or Fusion QC score < 10 (Suppl. Table The 35 tumour specimens were collected from NSCLC patients between September 2017 (#30) and May 2022 (#16). The delay between Idylla analysis and tumour sampling ranged between 0  and 48 months (#30) with a median of 2 months. The specimens consist in surgical resections , biopsies  or cytology samples  and tumour content varied from 10 to 85% with a median of 45%  is calculated as the proportion of true positive results among all positive results obtained by the tested assay. Negative predicted value (NPV) is calculated as the proportion of true negative results among all negative results obtained by the tested assay. A 95% confidence interval (95% CI) was calculated for sensitivity and specificity using Wilson's method43.The performance of the Idylla GeneFusion assay was evaluated based on the determination of ® FFPE Tumor Fusion RNA v4 Reference Material (LGC Seracare) and the Horizon ALK-RET-ROS1 Fusion FFPE RNA Reference Standard . The specific gene fusions covered by the two standards are detailed in Suppl. Tables Two different commercial fusion RNA standards were used to determine the limit of detection of the Idylla GeneFusion assay: the SeraseqSupplementary Tables.


MC1R and TBX3. The mtDNA hypervariable regions analysis in 23 Przewalski horses allowed assigning them to three distinctly different haplotypes, showing the greatest similarity to the Equus caballus reference, the Equus przewalskii reference, and to extinct species—Haringtonhippus. The Y chromosome analysis using fluorescently labelled assays differentiated horses in terms of polymorphism (g731821T>C) characteristic of Equus przewalskii. All male individuals presented genotype C characteristics for Przewalski horses. The polymorphisms within the coat color genes indicated only native, wild genotypes. The Y chromosome and coat color analysis denied admixtures of the tested horses with other Equidae.Przewalski horses are considered the last living population of wild horses, however, they are secondarily feral offspring of herds domesticated ~ 5000 years ago by the Botai culture. After Przewalski horses were almost extinct at the beginning of the twentieth century, their population is about 2500 individuals worldwide, with one of the largest breeding centers in Askania-Nova Biosphere Reserve (Ukraine). The research aimed to establish the maternal variation of Przewalski horses population maintained in Askania-Nova Reserve based on mitochondrial DNA hypervariable 1 and hypervariable 2 regions profiling, as well as, analysis of Y chromosome single nucleotide polymorphism unique for Przewalski horses, and coat color markers:  Przewalski’s horse genome falls outside a domestic horse’s group, approximately 38,000–72,000 year BP . PrzewalOne of the largest breeding centers for Przewalski horses is Askania-Nova Biosphere Reserve (Ukraine). The first 10 Przewalski horses were imported to Askania-Nova from Mongolia in 1899–1903 and by 1945, 37 live foals were born but a decline in interest in horses, inadequate breeding conditions, extremely difficult exchange of producers and, as a result, breeding in strict inbreeding caused the degeneration of the entire population and almost extinction in the 1940s. Purposeful breeding work on this species, which led to its preservation, began after the publication in 1959 of the first Przewalski horse studbook [Previous studies on mitochondrial DNA (mtDNA) identified three unique haplotypes in Przewalski horses, none of which were found in modern horses—two similar ones and one substantially divergent from them . Howeverloci, namely Extension (E) and Agouti (A) which are responsible for the three basic colors occurrence—black, bay, and chestnut [ASIP gene, which is possibly related to their living in the Asian steppe’s adaptation [MC1R recessive alleles ‘ee’ should not be present in Przewalski horses, however, the previous research showed the ‘fox’ color alleles presence in 15% of the tested Przewalski horses [TBX3 gene for Dun-related trait; there is only one characteristic genotype observed in all Przewalski horses. Horses with this genotype show a diluted body color and have dark points called primitive markings including dorsal stripe and leg barring [The basic coat colors genetic control in horses resides at two genetic chestnut . The mosaptation , 14. Theaptation . It was aptation , and allaptation . The ‘foMC1R (‘fox’) and TBX3 (Dun).The research purpose was to establish the maternal variation of the Przewalski horses population maintained in Askania-Nova Reserve based on whole mtDNA hypervariable regions profiling, as well as, analysis of Y chromosome SNP unique for Przewalski horses, and coat color markers: The analyses were carried out on hair follicle samples derived from 23 Przewalski horses population. The analysed individuals belonged to three Przewalski horses lines  living in the Askania-Nova reserve (Ukraine). The tested horses  represented all the lines of the Askania-Nova reserve population. The 21 animals were sampled after death and the other two when disturbed according to the classic procedure for zoos including branding animals and assigning them an international breeding number. During the veterinarian procedure, the hair follicle samples were taken from the mane or tail of the horses.Equus caballus (X79547.1), Equus asinus (MK896302.1), Equus asinus somalicus (MG885769.1), Haringtonhippus francisci (KT168329.2), Equus grevyi (NC020432.2), Equus kiang (NC020433.1), Equus hemionus (NC018782.1), and Equus przewalskii (KT221845.1).The DNA was isolated using Sherlock AX kit (A&A Biotechnology) according to the manufacturer’s protocol and stored at −20 °C. For the mitochondrial DNA hypervariable regions amplification, three PCR products were designed covering a total region of 1062 bp  and the TaqMan™ Gene Expression Master Mix kit (Thermo Fisher Scientific). The X chromosome were used as an internal control. Next, all of the male samples were genotyped using the allelic discrimination method with assay complementary to the Y chromosome (designed using Primer Express™) and TaqPath™ ProAmp™ Master Mix (Thermo Fisher Scientific). The SNP localized on the Y chromosome and distinguishing Equus caballus from Equus przewalskii was investigated , [To analyse Y chromosome SNP, first, the amplification of X chromosome part was performed for all samples using fluorescently labeled probe unique complementary to the X chromosome 821T>C), . SamplesT>C, . SaTBX3 and MC1R. All PCRs were carried out with Phanta Ready Mix (Vazyme Biotech). Two polymorphisms at the TBX3 gene  were detected by Sanger sequencing and the heterozygosity state of deletion  was investigated by the amplification length polymorphism method using method described by Stefaniuk-Szmukier et al. [MC1R) gene polymorphism responsible for ‘fox’ color  was detected by Sanger sequencing.Moreover, two coat color genes were investigated: r et al. . MelanocEquus caballus reference, while the haplotype 2 is separated from the genus Equus, showing almost the same value of evolutionary distance with Equus przewalskii reference  and is probably low admixtured with other Equidae. The haplotype 1 presented the evolutionary distance value similarity to the Haringtonhippus —extinct species, that lived in North America during the Pleistocene.The sequencing of mitochondrial DNA hypervariable region 1 and hypervariable region 2 assigned Przewalski horses to the three distinctly different haplotypes  is located within the region deleted in non-dun2, 1067 bp downstream of the deletion breakpoint at chr8:18,227,267. SNP2 , is located 362 bp upstream of the deleted region in non-dun2 at chr8:18,226,905. The results assigned all horses to the genotype with G/G alleles in both SNP loci, and deletions were not observed. MC1R variants were also identified; none of Przewalski horses presented C to T missense mutation associated with an unfavorable ‘fox’ color variant for this species. The ability to produce phaeomelanin is attributed to the recessive alleles ‘ee’ and is not in concordance with the recommendations for the Przewalski horses [The coat color genes analyses indicated only native, wild genotypes. The i horses .MC1R and TBX3 indicated only native, wild genotypes. Unexpected polymorphisms’ appearance in genes determining the coat color and in Y chromosome could indicate population crossbreeding, therefore the homogeneity in these genes and the Y chromosome in the Askania-Nova population needed to be confirmed.The presented genetic analysis of Przewalski horses population living in the Askania-Nova reserve adds useful information for the conservation of the breed. The mtDNA results, according to the literature, confirm three pure and fixed remaining maternal lineages in Przewalski horses, assigning the individuals to three distinctly different haplotypes. The Y chromosome polymorphism analysis confirmed the presence of genotype C characteristics for Przewalski horses in all 13 male individuals, moreover, the polymorphisms within the coat color genes


Bacterial cell wall peptidoglycan is composed of innate immune ligands and, due to its important structural role, also regulates access to many other innate immune ligands contained within the bacteria. There is a growing body of literature demonstrating how innate immune recognition impacts the metabolic functions of immune cells and how metabolic changes are not only important to inflammatory responses but are often essential. Peptidoglycan is primarily sensed in the context of the whole bacteria during lysosomal degradation; consequently, the innate immune receptors for peptidoglycan are primarily intracellular cytosolic innate immune sensors. However, during bacterial growth, peptidoglycan fragments are shed and can be found in the bloodstream of humans and mice, not only during infection but also derived from the abundant bacterial component of the gut microbiota. These peptidoglycan fragments influence cells throughout the body and are important for regulating inflammation and whole-body metabolic function. Therefore, it is important to understand how peptidoglycan-induced signals in innate immune cells and cells throughout the body interact to regulate how the body responds to both pathogenic and nonpathogenic bacteria. This mini-review will highlight key research regarding how cellular metabolism shifts in response to peptidoglycan and how systemic peptidoglycan sensing impacts whole-body metabolic function. More recently, there has been a growing body of research exploring the intricate and essential shifts in cellular metabolism that accompany and shape the innate immune responses to microbes. In addition, there is a growing appreciation that microbial ligands derived from the gut microbiota impact whole-body metabolic regulation contributing to metabolic diseases. This mini-review will focus on how recognition of bacterial cell wall peptidoglycan fragments impacts not only cellular but also whole-body metabolic systems and associated inflammation .Crosstalk between the innate immune system and the metabolic systems of both immune and nonimmune cells is an expanding area of research. Even during the early years of innate immune research, it was clear that in response to inflammatory stimuli, myeloid cells shifted their metabolism to rely predominantly on glycolysis for energy production n-acetylglucosamine and n-acetylmuramic acid that are crosslinked by amino acid sidechains –4. Peptidoglycan is the dominant component of the cell wall of Gram-positive bacteria but is also a component of the cell wall of Gram-negative bacteria, though less abundant, making it an important innate immune ligand in the body’s recognition of all bacteria. Peptidoglycan degradation products themselves are innate immune ligands –7, but peptidoglycan, based on its degradation sensitivity, also regulates access to additional innate immune ligands making it a vital factor influencing innate immune recognition of bacteria ,9. A comparison of wild-type Staphylococcus aureus, with degradation-insensitive peptidoglycan, and a degradation-sensitive mutant found a significant elevation in the quantity and duration of the tumor necrosis factor (TNFα), interleukin-6 (IL-6), and nitric oxide (NO) produced by macrophages when the bacteria’s peptidoglycan is degradation sensitive . This highlights the inflammatory and structural importance of peptidoglycan. However, peptidoglycan’s sugar backbone and amino acid sidechain composition varies significantly between different strains of bacteria and often undergo significant modifications ,10, consequently, many of the observed impacts of peptidoglycan on the innate immune system and the metabolic system may not always be broadly applicable. Direct examination of the inflammatory and metabolic differences driven by peptidoglycan modifications in different bacterial strains is a significantly understudied area of research.Peptidoglycan is a polymeric structure composed of a sugar backbone of repeating subunits of –13. NOD1 and NOD2 are cytosolic receptors that directly detect fragments of peptidoglycan shed from bacteria or generated during the degradation of bacteria in the phagolysosome. NOD1 primarily detects peptidoglycan motifs found in Gram-negative bacteria ,13, whereas NOD2 primarily detects peptidoglycan motifs found in Gram-positive bacteria . Experimentally, synthetic fragments of peptidoglycan specific for NOD1 such as γ-d-glutamyl-meso-diaminopimelic acid (iE-DAP) and l-Ala-γ-d-Glu-meso-diaminopimelic acid (Tri-DAP), or specific for NOD2, such as muramyl dipeptide (MDP), are the minimal motifs able to activate receptors for peptidoglycan and are often used to mimic peptidoglycan ,7. Recent work by Stafford et al  showed that the metabolite recycling enzyme, n-acetylglucosamine kinase (NagK), is necessary for NOD2 sensing of synthetic peptidoglycan ligand MDP. The authors showed that both MDP and peptidoglycan fragments need to be phosphorylated by NagK to be recognized by NOD2. Further work is necessary to determine the importance of NagK to the recognition of whole bacteria in vivo. However, this is an interesting example of metabolic pathways playing a role in sensing peptidoglycan.Peptidoglycan fragments are recognized by both immune and nonimmune cells throughout the body by numerous receptors. The first identified peptidoglycan receptors were the nucleotide-binding oligomerization domain 1 and 2 (NOD1 and NOD2) . Degradation of peptidoglycan in the macrophage lysosome produces fragments that trigger activation of the NOD-like receptor family pyrin domain containing 3 (NLRP3) inflammasome . Subsequent research found that the n-acetylglucosamine subunit of the peptidoglycan sugar backbone was sufficient to activate the NLRP3 inflammasome leading to IL-1β production by macrophages . This activation was linked to n-acetylglucosamine inhibition of the glycolytic enzyme hexokinase  illustrating an additional level of crosstalk between cellular metabolism and innate immune sensing. The synthetic peptidoglycan ligand MDP actives the NOD-like receptor family pyrin domain containing 1 (NLRP1) and NLPR3 inflammasome ,19 indicating that multiple peptidoglycan ligands may collaborate to induce inflammasome activation.Another place where metabolic pathways play a role in peptidoglycan sensing is in the activation of the inflammasome by peptidoglycan. The inflammasome is a multiprotein signaling complex also found in the cytosol that regulates the activation of the proteolytic enzyme caspase-1 which plays a vital role in controlling the production of potent inflammatory cytokines such as IL-1β and IL-18 –22. Meanwhile, other studies have modeled TLR2 binding to possible peptidoglycan fragments , have measured the binding of peptidoglycan fragments to TLR2 by surface plasmon resonance , or used bacterial mutants to try to eliminate lipoproteins from peptidoglycan preparations and still found some level of TLR2 activity in reporter cells . The physiological importance of possible peptidoglycan binding to TLR2 is unclear given that peptidoglycan is often encountered in the presence of other stronger TLR2 agonists. What is clear is that peptidoglycan synergizes with other TLR ligands from the bacteria to influence the overall inflammatory response. To focus this mini-review on the metabolic impacts of peptidoglycan, this introduction is necessarily limited to a brief description of the relevant receptors. For a more in-depth examination of the signaling pathways for each of these innate immune receptors, the reader is encouraged to read one of the many comprehensive reviews that can provide that additional signaling details ,16,26.Often peptidoglycan is listed as a ligand for toll-like receptor 2 (TLR2), however, this is a controversial topic. Due to the complex crosslinked polymeric structure of peptidoglycan, several studies have concluded that commercial peptidoglycan preparations have significant lipoprotein contamination leading to strong TLR2 stimulating capacity ,28. However, a comprehensive understanding of how NOD and TLR receptors synergize and regulate glycolysis in response to particulate peptidoglycan has yet to be undertaken. Nonetheless enhanced glycolysis seen in response to synthetic peptidoglycan ligands is the same response observed during classic activation of TLR4 in macrophages by lipopolysaccharide (LPS), which is used extensively to model the metabolic shifts in macrophages in response to proinflammatory stimuli . Like LPS, the NOD1 and NOD2 ligands, Tri-DAP and MDP, lead to an acute increase in glycolysis during the first 2 hours as measured by extracellular acidification (ECAR) in human monocyte macrophages, but this glycolytic change is absent by 24 hours . Using the glycolytic inhibitor, 2-deoxyglucose (2-DG), research has generally shown that glycolysis is necessary for at least some inflammatory cytokine production in myeloid cells in response to numerous innate inflammatory ligands –32 though the opposite effect has also been observed . In human monocytes, 2-DG inhibition of glycolysis during stimulation with the NOD1 ligand Tri-DAP did not impact cytokine production under normoxic conditions but did impair cytokine production under hypoxic conditions . However, 2-DG treatment was also found to induce endoplasmic reticulum (ER) stress and an unfolded protein response, which affected responses independent of an impact on glucose utilization . Due to the nonspecific impacts of 2-DG, it is not an ideal tool to examine the importance of increased glycolysis during peptidoglycan stimulation. As an alternative approach for assessing the importance of glycolysis during inflammation, macrophages deficient for the glucose transporter 1 (Slc2a1), which is important for supporting glycolysis, showed reduced inflammatory responses to LPS . Unfortunately, LPS was the only innate immune ligand tested in these experiments, so further work will need to be carried out using similar genetic models to fully untangle the importance of glycolytic changes to peptidoglycan-induced inflammatory responses.Peptidoglycan is primarily recognized by phagocytic cells of the immune system, which include macrophages, dendritic cells, and neutrophils. Peptidoglycan’s polymeric structure requires phagocytosis and enzymatic degradation in lysosomes to liberate the ligands contained within the structure, and phagocytic cells are uniquely equipped for this process. Most of the research characterizing peptidoglycan signaling pathways and metabolic changes has been performed in primary macrophages and macrophage cell lines. Stimulation of myeloid cells with peptidoglycan, iE-DAP, or MDP leads to an increase in glycolytic gene expression and an acute transient increase in glycolysis ,35,36. Akt phosphorylation results in increased mitochondrial localization of the glycolytic enzyme hexokinase 2 , which is believed to enhance the glycolytic rate of cells . Treating macrophages with an Akt inhibitor blocks NOD1-induced acute increases in ECAR, a measure of lactate production and cellular glycolysis, however, inhibition of Akt only affects NOD1-induced cytokine responses, not LPS responses . Though there are some common metabolic changes in response to LPS and peptidoglycan ligands, the impact on inflammation may vary based on the receptor/s that are activated.Treatment of macrophages with whole particulate peptidoglycan or soluble NOD1 or NOD2 ligands  leads to an increase in protein kinase B (a.k.a Akt) and mammalian target of rapamycin (mTOR) activation, both of which are known to modulate metabolic systems in cells  suggesting maybe peptidoglycan signaling through NOD2 does not impact glycolysis as much as other studies suggest. Mouse bone-marrow macrophages from NOD1 and NOD2 single- or double-knockout mice also show altered cytokines production in response to mycobacteria but no reduction in lactate production . But lactate production from mycobacteria-treated TLR2−/− cells was reduced, indicating an essential role for TLR2 in glycolytic regulation but not NOD sensing in response to whole mycobacteria . Therefore, in the context of whole bacteria, the metabolic changes that occur in macrophages may be indirectly regulated by peptidoglycan based on the degradation sensitivity of the peptidoglycan being studied, which has been shown to control the availability of TLR2 and TLR9 ligands in Gram-positive bacteria ,9. Unfortunately, because each bacteria modifies its peptidoglycan uniquely, impacting the exact ligand mixture, the role of peptidoglycan in bacterial sensing and metabolic changes will likely require experimentation using the relevant factors for each unique microbe. Another factor to consider is that in response to LPS, human and mouse macrophages demonstrate divergent changes in metabolism . Thus far a side-by-side comparison of human and mouse cells stimulated with peptidoglycan or synthetic ligands has not been undertaken, though mouse vs human differences could account for some of the variability in the reported metabolic changes induced by peptidoglycan and will have to be considered in future studies.Although increased glycolysis does seem to be a common response to peptidoglycan ligands, when peripheral blood mononuclear cells from NOD2 deficient humans were stimulated with mycobacteria there was a decrease in the production of certain cytokines but no decrease in lactate production . When macrophages were stimulated with MDP together with the TLR2 ligand Pam3CSK4 they synergized leading to the phosphorylation of ataxin-3 in a NOD2/Rip2k/TBK1-dependent manner . Ataxin-3 localizes to mitochondria cristae protein MIC60 and is important for the regulation of oxidative phosphorylation complex I proteins expression . Knocking down ataxin-3 reduces basal mitochondrial oxygen consumption and prevents MDP/Pam3CSK4-induced mitochondrial reactive oxygen species (ROS), impairing the ability of the cells to kill Salmonella typhimurium, but whether it was the mitochondrial oxygen consumption, ROS, or both are important for bacterial killing is unclear. Conversely, MDP can also induce NOD2-dependent IL-10 , which has been shown to counter mTOR activation and dampen glycolysis, and NO production , which can inhibit mitochondrial oxygen consumption –43. With all these counterbalancing metabolic signals, the exact shift in mitochondrial function in response to peptidoglycan and its ligands will be dependent on the exact mix of ligands, cytokines, and signaling intermediates that are produced in response to specific bacteria. What happens to glycolysis and oxygen consumption in response to particulate peptidoglycan is less clear and will require study in NOD and TLR2 deficient cells to untangle the crosstalk between the two pathways to fully understand the complete metabolic impact . But consistent with other innate immune ligands peptidoglycan signaling seems to generally increase glycolysis but the impact on mitochondrial oxygen consumption seems more mixed.Although there is a lot of evidence that peptidoglycan increases myeloid cell glycolysis the impact on mitochondrial function is much less clear. In human monocytes, when NOD1 and NOD2 ligands induced an acute increase in glycolysis there was no concurrent change in mitochondrial oxygen consumption rate (OCR) in response to soluble NOD1 or NOD2 ligands N-terminal kinases (JNK) signaling pathways are activated by both TLR and NOD ligands . IKKβ and JNK activation leads to serine phosphorylation of insulin receptor substrate 1 and reduction in insulin sensitivity in monocyte, hepatocyte, and adipocyte ,45–47. In macrophages reduced insulin sensitivity shifts the cells toward a less inflammatory or more trained immunity phenotype ,49, which may have longer-term impacts on macrophage responsiveness.The metabolic shifts that occur in response to peptidoglycan are impacted by several signaling pathways. For example, the inhibitor of nuclear factor-kβ kinase (IKKβ) and c-Jun −/− macrophages supporting the conclusion this is a peptidoglycan-driven response . Type I interferons induced specifically by live mycobacteria dampen the rate of glycolysis, which is normally enhanced by innate immune ligands, and increased the mitochondrial damage in macrophages in an interferon alpha receptor (IFNAR)-dependent manner . Although these studies hint at a role for peptidoglycan-induced type I interferon responses unlike live mycobacteria, live S. aureus and S. aureus peptidoglycan do not induce type I interferon production from dendritic cells . Therefore, the induction and subsequent metabolic modulation by type I interferons in response to peptidoglycan is likely to vary significantly between bacterial strains as are the type I interferon-induced metabolic changes.Mycobacteria infection also strongly activates the NOD2/Rip2k/interferon-regulatory factor-5 signaling pathway in macrophages resulting in the production of type I interferons, which is reduced in NOD2 and inducible nitrogen oxide synthase (iNOS) expression ,55. PGE2 treatment of alternatively activated macrophages reduced mitochondrial function and glycolysis . The impact of PGE2 on mitochondrial function may be a result of increased iNOS expression induced by PGE2 leading to high levels of NO production, which has been shown to inhibit mitochondrial complex IV function . But while PGE2 may be an important peptidoglycan-induced inflammatory response, the importance of PGE2 in peptidoglycan-induced metabolic changes needs more research to be fully understood.Peptidoglycan also leads to the production of prostaglandin E2 (PGE2) from macrophages starting at 2 hours and peaking at 24 hours. Blocking PGE2 production with cyclooxygenase inhibitor blocks peptidoglycan-induced IL-6 production . Itaconate is an important antimicrobial metabolite and inhibits succinate dehydrogenase disrupting the TCA cycle and oxidative phosphorylation ,59. However, stimulation with the NOD1 ligand, Tri-DAP, only slightly increases ACOD1 compared to LPS  which may explain the lack of an impact on macrophage oxygen consumption (OCR) in response to NOD1 ligands alone , but when NOD signals synergize with TLR signals during bacterial infection this important antimicrobial pathway is likely enhanced.Finally, a hallmark of the LPS-induced metabolic shift in macrophages is increased expression of aconitate decarboxylase 1 , which shifts citrate from the TCA cycle into the itaconate production pathway n-acetylglucosamine which was found to induce NLRP3 inflammasome activation through inhibition of the glycolytic enzyme hexokinase . Hexokinase inhibition leads to its dissociation from mitochondria and triggers NLRP3 activation . This is an interesting observation where a metabolic protein is contributing to the sensing of microbial ligands. It also begs the question do the sugars generated during bacterial degradation directly alter myeloid cellular metabolism? This possibility has yet to be addressed. But the NLRP3 inflammasome activating capacity of peptidoglycan from different strains of bacteria was linked to the differential n-acetylglucosamine content of each strain’s peptidoglycan  reinforcing the need for the study of strain-specific peptidoglycan-induced responses.There are a few other pathways that are involved in peptidoglycan recognition that are linked to metabolic changes that are worth mentioning and considering further in the future. First, peptidoglycan degradation also generates  but so far PGRP’s impact on metabolism has only been examined in limited studies. THP-1 macrophage cell line stimulation with PGRP-peptidoglycan complexes resulted in increased mitochondrial metabolic activity as well measured by resazurin incorporation and enhance inflammatory responses . Future studies focusing on the metabolic impacts of PGRP recognition of circulating peptidoglycan fragments are vital and will likely shed light on additional mechanisms of metabolic regulation.Second, peptidoglycan recognition proteins (PGRP) are the important receptors for peptidoglycan that impact inflammation and cellular function throughout the body . Coggeshall et al demonstrated that opsonization and FcγR engagement are important for recognition and inflammation induced by macrophages in response to Bacillus anthracis peptidoglycan . Although metabolic shifts were not directly measured in these studies, FcγR crosslinking leads to increased transcription of glycolytic genes, increased glycolysis, and decrease oxygen consumption in both mouse and human macrophages ,64. In a mouse model of lupus-like immune complex-mediated nephritis, the macrophage FcγR-induced glycolytic switch was found to be a good target for modulating the inflammatory responses . Whether circulating peptidoglycan fragments can form immune complexes and induce similar inflammatory and metabolic changes is an underexplored possibility.Third, peptidoglycan can also be opsonized by serum antibodies that are found almost ubiquitously in the human bloodstream . In addition, peptidoglycan fragments have been found in the circulation of mice with no obvious infection ,66. Germ-free mice have significantly lower levels of circulating NOD1 and NOD2-specific peptidoglycan fragments , indicating that the microbiome is a significant source of circulating peptidoglycan fragments. Under certain conditions, such as high-fat diet feeding, where intestinal permeability is increased, there are elevated levels of peptidoglycan fragments in the circulation . Circulating peptidoglycan fragments can be a significant source of inflammatory signals. For example, systemic administration of peptidoglycan fragments can lead to arthritis, and during a B. burgdorferi infection model of Lyme disease, peptidoglycan fragments were detected in circulation . The implication is that cells throughout the body without strong phagocytic function, but that express NOD1 and/or NOD2, are also likely to encounter peptidoglycan ligands. Several nonimmune cell subsets express NOD1 and/or NOD2 such as adipocytes, which express NOD1 . White adipose tissue (WAT) explants stimulated with the NOD1 ligand KF565 leads to increased lipolysis that was not observed in NOD1−/− WAT. The adipocyte cell line 3T3-L1 increases lipolysis in response to peptidoglycan-derived NOD1 ligands ,71. However, stimulation with the NOD2 ligand MDP does not increase adipocyte lipolysis , either due to lower NOD2 expression or differential signaling by the two receptors in adipocytes. Another possibility given recent research is that MDP phosphorylation by NagK to generate the proper NOD2 ligand  is impaired in adipocytes leading to diminished NOD2 ligand sensing. The lipolysis induced by KF565 activation of NOD1 is a result of the extracellular signal-regulated kinase and protein kinase A signaling , which should also be activated in response to NOD2 signaling. Unlike in adipocyte cell lines, MDP may impact human primary adipocyte differentiation, so one explanation for the discordant results is differential NOD2 expression in primary cells compared to cell lines . The field would benefit from a more comprehensive examination of NOD1 and 2 expressions and signaling in primary adipocytes to better understand how circulating peptidoglycan fragments may impact adipocytes, especially in models of metabolic stress.Even though peptidoglycan is generally a polymeric structure that is tightly associated with whole bacteria, peptidoglycan fragments are shed from bacteria during cell wall restructuring associated with processes such as bacterial division . NOD2 polymorphisms are some of the strongest genetic associations found in Chron’s disease ,73. Tonic signaling through NOD2 from microbiota-derived NOD2 ligands leads to increased production of defensins by Paneth cells, which is important for maintaining the proper balance of the gut microbiota . NOD2 signaling also increases mucus production by goblet cells  and counterbalances the TLR4 signals leading to excessive TNF-α production that can damage epithelial cells and increase intestinal permeability –78. This is an example of NOD2 ligand recognition in nonimmune cells serving an essential homeostatic function in the body the impact of this explored further in the next section.NOD2 also plays an important role in the maintenance of intestinal epithelium . Due to the presence of peptidoglycan fragments in the circulation ,66, peptidoglycan-induced NOD1 and NOD2-driven changes in whole-body metabolic function are important areas of study. Within 1 week of being on a high-fat diet, there are increased bacteria in adipose tissue, and this is reduced in NOD1−/− mice . NOD1 detection of Gram-negative bacterial ligands is necessary for the shift in glucose and insulin responsiveness in mice, the translocation of bacteria across the intestinal epithelium, and the inflammation associated with the presence of bacteria in adipose tissue ,47,80,81. However, the NOD1-expressing cell type that is responsible for each of these outcomes is unclear. One of the key tissues that respond to NOD1 ligands is adipose tissue. Diet but not genetic-induced obesity leads to increased NOD1 expression in adipose tissue . However the fold increase in NOD1 gene expression is more than NOD2, it is unclear if the overall expression of NOD2 is low or just not increased. NOD1 knockdown decreased adipocyte responses to the NOD1 ligand Tri-DAP . NOD1 activation in adipocytes can lead to inflammatory responses and reduced insulin sensing and glucose uptake . Pancreatic islet cells are another metabolically important cell type impacted by peptidoglycan ligands. NOD1 ligands from gut microbiota can stimulate insulin trafficking in islet cells . However, tissue-specific deletion of NOD1 has yet to be undertaken to determine if peptidoglycan sensing in either adipocytes or islet cells is regulating systemic metabolic changes.In healthy volunteers, antibodies generated against MDP can detect stable levels of peptidoglycan fragments in the serum at 0.16–1 mg/mL in 75% of subjects and 4–5 mg/mL in 22% of subjects −/− mice exhibit exacerbated insulin resistance, impaired glucose tolerance, and increased adipose tissue inflammation, but there is no difference in overall body mass compared to controls ,84. Using bone-marrow chimeric mice, the difference was attributed to a role for NOD2 in nonhematopoietic cells. NOD2−/− mice had more bacteria in adipose tissue following oral gavage of Escherichia coli. Bacterial translocation may be due to increased bacterial adherence to the mucosa in NOD2−/− mice and in NOD1−/− mice . Because only the translocation of orally gavaged E. coli, not endogenous gut microbes, was measured it is unclear if the increased translocation is specific to E. coli strains or a result of the levels of bacteria that were gavaged. A comparison of the translocation of orally gavaged Gram-positive and Gram-negative bacteria in high-fat diet-fed mice would be interesting and would help pars out the importance of different types of bacteria during metabolic disease. NOD2−/− also leads to changes in liver metabolism and inflammation . In addition, NOD2−/− mice may have gut microbiome dysbiosis in both chow and high-fat diet-fed mice. Colonization of germ-free mice with the microbiota from high-fat diet-fed NOD2−/− mice transferred many, but not all metabolic and inflammatory changes to germ-free mice ,84. Based on the impaired metabolic function and more severe inflammation in the NOD2−/− mice the conclusion is that NOD2 signaling is protective against metabolic disease ,85 and this protection requires Rip2k signaling . In contrast, NOD1 signaling negatively influences metabolic disease. The protective role for NOD2 and recognition of peptidoglycan fragments in metabolic disease is also supported by experiments in a mouse model of malnutrition that found that oral gavage with live Lactiplantibacillus plantarum or cell wall isolates enhanced the weight gain and growth of the mice in a NOD2-dependent manner despite the nutrient restriction . Using tissue-specific knockout mice, the impact of L. plantarum was determined to be mediated by NOD2 expression in intestinal epithelial cells (IEC), and IEC-specific MyD88−/− mice still showed increased growth and weight gain in response to L. plantarum ruling out a role of TLR signaling . Although the exact link between NOD2 signaling in IEC and weight gain are not fully elucidated, the study found an increase in type I interferon production and increased responsiveness of the liver to growth hormone leading to increased insulin-like growth factor 1 production . Weight gain is not only impacted by NOD2 signaling in the intestine but studies looking at a lack of NOD2 expression in inhibitory γ-aminobutyric acid transporter–positive (GABAergic) neurons found increased weight gain and poor temperature regulation in older female mice . These are clear examples that the expression of NOD2 and recognition of microbiota-derived peptidoglycan fragments in circulation impact numerous cell types throughout the body to positively impact the overall balance of the body’s metabolic systems. However, when NOD1/2 double-knockout mice were studied they were protected from diet-induced metabolic disease including inflammation and insulin resistance, and showed lower lipid accumulation in WAT . This suggests that on balance the impact of NOD1 activation is a stronger influence on metabolic disease than the protective signals from NOD2.The importance of NOD2 to whole-body metabolism is illustrated by the fact that high-fat diet-fed NOD2 and bacterial peptidoglycan is a known activator of NLRP3 ,17, but experiments directly testing the impact of peptidoglycan on NLRP3-induced metabolic changes have not been reported and will require further study.In addition to NOD1 and NOD2, NLRP3 activation is linked to more severe metabolic disease Figure 1). Difficulties can arise in assembling a clear picture of all these different signaling pathways activated in response to peptidoglycan and how they impact metabolism because of the diverse reagents utilized throughout the studies. For example, some of the studies use crude peptidoglycan which is particulate and a mixture of NOD and TLR ligands, others only examine the response to soluble synthetic ligands such as MDP, and others combine MDP with synthetic TLR2 ligands. Because peptidoglycan is experienced by the body as both free NOD1 and NOD2 ligands and in the context of the whole microbes where TLR ligands can synergize to enhance responses ,90, it is extremely important to use genetic knockouts to understand what pathways are being activated by peptidoglycan in each context and that data is not always available. With the ever-expanding repertoire of genetic tools available for the manipulation of mice and cells hopefully some of these pathways will be reexamined more comprehensively.All of the research highlighted here paints a picture of the importance of peptidoglycan during infection and homoeostatic interactions with the gut microbiota. Peptidoglycan strongly influences cellular and whole-body metabolism and inflammation (,81 and there may be endogenous ligands, such as sphingosine-1 phosphate, that can activate NOD1 and NOD2 . Some studies of metabolic dysfunction using NOD1 and NOD2 deficient mice were conducted in germ-free mice, devoid of circulating peptidoglycan fragments, strongly supporting a role for circulating peptidoglycan in metabolic disease. But additional studies will need to be performed to determine if NOD1 and NOD2 nonmicrobial ligand sensing are contributing to associated metabolic dysfunction. As mentioned throughout this review, each strain of bacteria uniquely modifications its peptidoglycan, therefore understanding the peptidoglycan from different bacterial strains with unique modifications impacts the inflammation and metabolic response is an area that will require additional research to fully understand. There is no double that future studies will clarify and solidify the important role that peptidoglycan sensing plays in regulating metabolism in response to both pathogenic and nonpathogenic microbes.Another complication that has recently become apparent is that many of the studies use NOD1 and NOD2 activation in vivo as a surrogate for peptidoglycan sensing, but NOD1 and NOD2 can also be activated by certain lipids The author declares that he has no conflicts of interest.This work was supported by grant R01AI148465 “Mechanisms of peptidoglycan-induced modulation of metabolic and inflammatory responses to bacteria”.


Addressing clinician documentation burden through “targeted solutions” is a growing priority for many organizations ranging from government and academia to industry. Between January and February 2021, the 25 by 5: Symposium to Reduce Documentation Burden on US Clinicians by 75% (25X5 Symposium) convened across 2 weekly 2-hour sessions among experts and stakeholders to generate actionable goals for reducing clinician documentation over the next 5 years. Throughout this web-based symposium, we passively collected attendees’ contributions to a chat functionality—with their knowledge that the content would be deidentified and made publicly available. This presented a novel opportunity to synthesize and understand participants’ perceptions and interests from chat messages. We performed a content analysis of 25X5 Symposium chat logs to identify themes about reducing clinician documentation burden.The objective of this study was to explore unstructured chat log content from the web-based 25X5 Symposium to elicit latent insights on clinician documentation burden among clinicians, health care leaders, and other stakeholders using topic modeling.Across the 6 sessions, we captured 1787 messages among 167 unique chat participants cumulatively; 14 were private messages not included in the analysis. We implemented a latent Dirichlet allocation (LDA) topic model on the aggregated dataset to identify clinician documentation burden topics mentioned in the chat logs. Coherence scores and manual examination informed optimal model selection. Next, 5 domain experts independently and qualitatively assigned descriptive labels to model-identified topics and classified them into higher-level categories, which were finalized through a panel consensus.determining data and documentation needs ; (2) collectively reassessing documentation requirements in electronic health records (EHRs) ; (3) focusing documentation on patient narrative ; (4) documentation that adds value ; (5) regulatory impact on clinician burden ; (6) improved EHR user interface and design ; (7) addressing poor usability ; (8) sharing 25X5 Symposium resources ; (9) capturing data related to clinician practice ; and (10) the role of quality measures and technology in burnout . Among these 10 topics, 5 high-level categories emerged: consensus building , burden sources , EHR design , patient-centered care , and symposium comments .We uncovered ten topics using the LDA model: (1) We conducted a topic modeling analysis on 25X5 Symposium multiparticipant chat logs to explore the feasibility of this novel application and elicit additional insights on clinician documentation burden among attendees. Based on the results of our LDA analysis, consensus building, burden sources, EHR design, and patient-centered care may be important themes to consider when addressing clinician documentation burden. Our findings demonstrate the value of topic modeling in discovering topics associated with clinician documentation burden using unstructured textual content. Topic modeling may be a suitable approach to examine latent themes presented in web-based symposium chat logs. Developing actionable strategies to reduce clinician documentation burden is a growing priority for researchers, thought leaders, and policy makers from many organizations ranging from government and academia to industry -7. DocumThe rapid and widespread transition to EHRs has made available a plethora of quantitative data for research. These data have been applied to a number of study contexts, including those examining clinician EHR actions and EHR work. However, limited qualitative research has been dedicated to understanding critical areas of interest and current trends regarding clinician documentation practices as it directly or indirectly relates to burden among those who practice in the United States .As with clinical encounters, meetings and other forms of social interaction instantaneously pivoted to web-based platforms due to the onset of the COVID-19 pandemic in March 2020. The 25X5 Symposium was initially planned as an in-person event, and planners adapted to a web-based symposium platform given modifications to institutional participation guidelines. At the time, the concept of web-based symposiums remained comparatively novel . The litPrior studies have demonstrated the value of analyzing chat logs to understand participants’ perceptions and interests . HistoriThe objective of this study was to explore unstructured chat log content from the web-based 25X5 Symposium using topic modeling and elicit additional insights and contextual information on reducing clinician documentation burden.The 25X5 Symposium targeted representatives from clinical settings, academia, industry, government, professional organizations, payers, and patients. The symposium was promoted through a panel presentation on documentation burden and professional networking sessions at the 2020 American Medical Information Association (AMIA) Annual Symposium. Additionally, email invitations to participate were sent to a list of key clinicians and other health care leaders identified by the 25X5 Steering Committee, which comprised clinicians, informatics experts, and health care leaders . Over a We concatenated Zoom chat messages that were shared publicly  among alWe normalized the chat messages by removing all person names from the corpus using pattern matching with regular expressions supplemented by manual human annotation (due to Zoom user handles) and applied gensim  and nltktopic coherence [C_v]), a measure that scores the degree of semantic similarity between co-occurring terms within a single topic, where higher topic coherence signifies the higher quality of learned topics ). Using bar graphs, we examined the distributions of the per-topic terms that appeared in the chat log as well as the distribution of topics stratified by session number. Lastly, we generated a t-distributed Stochastic Neighbor Embedding (t-SNE) plot   collectively reassessing documentation requirements in EHRs; (3) focusing documentation on patient narrative; and (4) documentation that adds value  and patient-centered care , respectively. Another 2 topics, collectively reassessing documentation requirements in EHRs and determining data and documentation needs, co-occurred at the highest volume in all sessions with the exception of Session 4 (Emerging and Future Innovations and Solutions) and Session 5 (Reactor and Prioritization Session for Actions). Interestingly, focusing documentation on patient narrative and determining data and documentation needs emerged in Sessions 4 and 5 as the top 2 steps to address clinician documentation burden; this finding suggests that clinician documentation focusing on the patient’s story may be perceived as less burdensome—a view that may be supported by numerous stakeholders in addition to clinicians.We conducted an exploratory analysis using 25X5 Symposium chat logs to elicit additional insights and context on documentation burden. Not surprisingly, 6 terms were particularly prominent based on the distribution of terms used among 25X5 Symposium participants who engaged in the chat: “need” (q=0.61)] . Among tds value . Notablyregulatory impact on clinician burden, capturing data related to clinician practice, role of quality measures and technology on burnout, improved EHR user interface and design, and addressing poor usability appeared less frequently, it does not reflect each topic’s overall importance. For example, these topics were consistent with 5 of 6 documentation burden domains that the American Nursing Informatics Association (ANIA) identified . This was a key conceptual framework applied in the 25X5 Symposium [rel_freq=0.16) and “billing” (rel_freq=0.15) were among the top 100 most frequently used terms throughout the 25X5 Symposium chats. Thus, these latent topics identified among symposium participants may additionally represent salient future directions that should be further assessed and prioritized for policy and practice.It is worthwhile to note that while topics that characterized the causes of burden such as ymposium , and it sharing the 25X5 Symposium resources  may be an approach to improve topic model analyses of documents  with rapidly evolving, fragmented topics [Our topic modeling analysis uncovered themes associated with two parallel processes that emerged over the web-based symposium platform: (1) unstructured conversations regarding the clinician documentation burden previously described above, and (2) discussions focused on the content and format of the presentations, such as esources . Becaused topics . Finallyd topics .Our initial aim was to investigate the distribution of topics over time in parallel with transcripts generated from the presentation content. Although 1000 documents were identified as adequate for conducting topic modeling , we did collectively reassessing documentation requirements in EHRs and determining data and documentation needs , while chat content from Sessions 4 and 5 (which centered around future directions) underscored topics including focusing documentation on patient narrative and determining data and documentation needs . Therefore, chat logs may provide additional valuable contextual information on the receptiveness and priorities of attendees on the content presented in web-based formats in an unobtrusive and fluid fashion—and in this scenario, clinician documentation burden.Furthermore, prior topic modeling applications suggest documents should be, at minimum, 3 sentences long . While hIn this analysis, we note that we iterated over the chat logs to remove unique identifiers including person names and Zoom user handles using a rule-based approach  supplemented with manual human annotation. Among the raw corpus, approximately 6.5% (n=270) of the terms represented person names; this was anticipated as attendee chat interactions were impersonal and fluid and frequently referred to fellow attendee messages. Chat log content is distinctive because Zoom user handles are theoretically and infinitely unique. Programming techniques such as employing “chunking” to extract terms that are part-of-speech tagged as proper nouns  are imperfect and indiscriminate. For example, “chunking” was unable to detect Zoom user handles satisfactorily and resulted in data loss among important proper nouns  that we sought to retain in the analysis such as “the joint commission,” “tjc” , and “cms” to uncover more meaningful topics. Thus, trade-offs exist in how researchers opt to preprocess chat log data. While our application of topic modeling on chat logs is novel, this challenge has been documented and similarly discussed among well-known, large clinical datasets such as the Beth Israel Deaconess Medical Center data via the Medical Information Mart for Intensive Care III (MIMIC-III), which employ manual, rule-based methods  to deidentify textual data . As mentAs with all secondary analyses, this study has several limitations. As attendees who participated in the 25X5 Symposium, and those who engaged with the synchronous chat functionality may not be representative of all clinicians and health care leaders, selection bias may be present in the data. Furthermore, it is possible that some attendees experienced reluctance in sharing their comments and opinions in a public forum  , which wAdditionally, there was marginal attendee attrition from Session 1 (n=203) to Session 4 (n=173); however, new participants joined as previous participants exited, and we were unable to verify which participants attended Sessions 5 and 6. Overall, over 70% (n=209) of attendees joined more than 1 session between Sessions 1 and 4. Fewer attendees participated in the chat functionality in Sessions 5 and 6 as compared to Session 1. However, this is fitting, as Sessions 5 and 6 were reactor sessions and participants were more engaged in the private breakout discussion sections that did not include access to the chat functionality. Alternatively, Session 1 was the first instance many attendees had convened in such a large group during the COVID-19 pandemic to interactively discuss clinician documentation burden. These factors may have stimulated discussion early on. Nevertheless, as we did not disseminate an exit survey, we were unable to ascertain why some participants “dropped out.” Finally, between-participant chat messages were not captured in Zoom. As a result, the subsequent topics related to documentation burden identified in this analysis may not be exhaustive of all themes that concern the greater population. Therefore, our results may not be generalizable to settings outside of the 25X5 Symposium.As we continue to refine our models and explore network analyses, additional results will be forthcoming. We have already generated short-, medium- and long-term goals to reduce documentation burden immediately following the 25X5 Symposium ,55. FutuGiven the ongoing challenges of recruiting clinicians for qualitative studies post pandemic, topic modeling may offer an alternative and less intrusive approach to investigating clinician documentation burden and burnout using their own words. For instance, topic modeling analysis of high-volume textual data sources not unconventionally applied in this domain, such as Twitter data, may provide timely and relevant representations of documentation burden themes among clinicians, health care leaders, and other stakeholders at any given point in time or facilitate the monitoring of clinician sentiment and areas of interest over time. Alternatively, there is a potential for topic modeling to be applied to longitudinally investigate documentation burden by examining the evolution of clinical note content. Consequently, topic modeling may complement existing mixed methods research.In this study, we employed topic modeling on unstructured textual content from the 25X5 Symposium to explore the feasibility of this novel application to multiparticipant chat logs and elicit additional insights on clinician documentation burden from the web-based symposium. Our findings uncovered 4 critical high-level areas to consider when resolving clinician documentation burden: achieving consensus on existing and future interventions; identifying specific causes of burden; refining EHR design, user interface, and usability; and improving the synthesis of the patient narrative. Topic modeling may be a valuable method to rapidly examine latent themes presented in chat logs as well as unobtrusively investigate topics associated with clinician documentation burden using unstructured textual content.


Correction: BMC Palliat Care 22, 69 (2023)https://doi.org/10.1186/s12904-023-01150-3Following the publication of the original article , it has fromHowever, the reality of the many barriers to release near the end of life [6–8] necessitates the provision of high quality primary palliative care within prisons….…While the basic palliative care needs of many of these people are managed internally by correctional healthcare providers, [9] providing care to those with complex or escalating palliative care needs is more challenging in the prison environment [10].intoHowever, many barriers prevent this from often occuring, [6–8] necessitating the provision of high quality primary palliative care within prisons….…While the basic palliative care needs of many of these people are managed internally by correctional healthcare providers, [9] providing care to those with more complex or escalating palliative care needs is challenging in the prison environment [10].The original article has been updated.