Feline infectious peritonitis (FIP) is considered highly fatal in its naturally occurring form, although up to 36% of cats resist disease after experimental infection, suggesting that cats in nature may also resist development of FIP in the face of infection with FIP virus (FIPV). Previous experimental FIPV infection studies suggested a role for cell-mediated immunity in resistance to development of FIP. This experimental FIPV infection study in specific pathogen free (SPF) kittens describes longitudinal antiviral T cell responses and clinical outcomes ranging from rapid progression, slow progression, and resistance to disease.Differences in disease outcome provided an opportunity to investigate the role of T cell immunity to FIP determined by T cell subset proliferation after stimulation with different viral antigens. Reduced total white blood cell (WBC), lymphocyte and T cell counts in blood were observed during primary acute infection for all experimental groups including cats that survived without clinical FIP. Antiviral T cell responses during early primary infection were also similar between cats that developed FIP and cats remaining healthy. Recovery of antiviral T cell responses during the later phase of acute infection was observed in a subset of cats that survived longer or resisted disease compared to cats showing rapid disease progression. More robust T cell responses at terminal time points were observed in lymph nodes compared to blood in cats that developed FIP. Cats that survived primary infection were challenged a second time to pathogenic FIPV and tested for antiviral T cell responses over a four week period. Nine of ten rechallenged cats did not develop FIP or T cell depletion and all cats demonstrated antiviral T cell responses at multiple time points after rechallenge.In summary, definitive adaptive T cell responses predictive of disease outcome were not detected during the early phase of primary FIPV infection. However emergence of antiviral T cell responses after a second exposure to FIPV, implicated cellular immunity in the control of FIPV infection and disease progression. Virus host interactions during very early stages of FIPV infection warrant further investigation to elucidate host resistance to FIP. Naturally occurring feline infectious peritonitis (FIP) is invariably fatal once clinical signs appear . HoweverThe first study reporting virus-specific immune responses in cats with recurrent FIPV infection revealed that multiple epitopes within the spike (S) protein encoded by FIPV, were targeted by both CD4 and CD8 T cells . HoweverThis report describes the characterization of antiviral T cell responses of multiple cohorts of cats involved in experimental FIPV infection studies. One group of cats developed FIP following experimental primary FIPV infection with disease distinguished by two different outcomes including rapid or slow progression. A small proportion of cats within the same experimental infection study, proved resistant to disease demonstrating a third possible outcome during primary experimental FIPV infection. A cohort of cats resistant to development of FIP during primary infection were re-inoculated with FIPV to examine resistance and cellular immune responses associated with a secondary virus challenge. Antiviral T cell responses observed for these different infection outcomes were compared to characterize possible virus-specific T cell immune correlates for resistance to the development of FIP. Although a unique antiviral T cell response during early acute infection could not be correlated with resistance or susceptibility to FIP, differences in antiviral T cell responses were observed for primary versus rechallenge exposures to FIPV.Specific pathogen-free (SPF) cats were obtained from the breeding colony of the Feline Nutrition and Pet Care Center, School of Veterinary Medicine, University of California, Davis, CA. Animals were housed and maintained according to regulations and guidelines of the University of California Davis Institutional Animal Care and Use Committee .n = 19) aged five to six months and designated as a primary infection group were inoculated by the oro-nasal route with FIPV-i3c2, a tissue-derived isolate from experimental cats infected with field virus as previously described [Virus naive SPF cats . Mononuclear cells were isolated from lymph node tissues (LNMC) harvested post mortem from cats that developed FIP as previously described . PBMC anCD3+ lymphocyte (T cell) counts were based on T cell frequencies within PBMC preparations determined by multicolor flow cytometry and total lymphocyte counts derived from CBCs. PBMC were assayed for T cell frequency by staining with either a biotinylated or Alexa Fluor (AF)647-conjugated anti-human CD3 monoclonal antibody (CD3–12)  specific for a conserved intracellular epitope shown to be cross-reactive for multiple species including feline CD3 , 19 and Serum samples collected from FIPV-inoculated cats and uninfected controls were tested for viral antibody by an indirect immunofluorescence antibody (IFA) assay as previously described  with comTwo different extraction methods were used for nucleic acid extraction from cells and tissues in this study. One method for viral nucleic acid extraction from PBMC used RLT lysis buffer  (with beta mercaptoethanol as per manufacturer’s instructions). The resulting lysate was protein-digested at 56 °C for 30 min and extracted for RNA and DNA utilizing a commercial kit  and a BioSprint 96 magnetic bead extraction (Qiagen) according to the manufacturer’s protocol. Viral RNA extraction from plasma (140 μl) also utilized RLT lysis buffer, but RNA was extracted using the QIAamp Viral RNA Mini kit (Qiagen). LNMC from mesenteric lymph nodes were lysed in AB lysis buffer  and RNA extraction was performed using the ABI Prism 6100  based on protocols previously described . The proFelis catus whole fetus-4 (fcwf-4) cell (ATCC) cultures. Virus was precipitated from culture supernatants using polyethylene glycol (PEG) and high speed centrifugation, and inactivated by ultraviolet (UV) irradiation for 15 min. Western blot and infectivity assays using fcwf-4 cells were performed to confirm the presence of virus particles and virus inactivation for WKV preparations respectively.A panel of 54 peptides derived from the heptad region (HR) 1 and inter-helical (IH) region of spike 2 (S2) domain of the type I FIPV-UCD11 spike protein (accession number: FJ917519)  were use6 cells per well in a 24 well plate in standard PBMC media without IL-2 and cultured (rested) overnight at 37 °C. The next day cells were stimulated with 2 μg WKV or an overlapping peptide pool derived from spike HR1 and IH regions with a working concentration of 2 μg/ml. Unstimulated cells cultured in PBMC media without IL-2 served as a negative control. Cells were incubated with 10 μM BrdU for 24 h prior to harvesting and then stained for BrdU incorporation using a commercial anti-BrdU antibody labeled with allophycocyanin (APC) (BD Biosciences) according to manufacturer’s instructions. For cell surface markers, cells were stained with anti-feline CD4 (clone 34F4)  conjugated to Pacific Blue using a commercial kit  and anti-feline CD8- PE (clone vpg9) (AbD Serotec) and assessed for viability with Live/Dead® Fixable Aqua stain (Invitrogen). Cells were fixed and permeabilized according to manufacturer’s instructions and next stained for intracellular CD3 expression using anti-human CD3- fluorescein isothiocyanate (FITC) (clone CD3–12) (AbD Serotec) and BrdU incorporation with an APC-labeled anti-BrdU antibody (BD Biosciences). Data were acquired using an LSRII flow cytometer (BD Biosciences) and 100,000 events were collected and analyzed for each sample using FlowJo software . Frequencies for viral immunogen-stimulated proliferation were adjusted by subtraction of frequencies measured for unstimulated control PBMC preparations for each sample to yield a final value for frequencies of antigen-specific T cells.Virus-specific T cell proliferation responses in PBMC and LNMC isolated from FIPV-inoculated cats and uninfected controls were assessed using a commercial kit based on bromodeoxyuridine (BrdU) incorporation  and a previously described protocol  with modP values < 0.05 were considered significant.Statistical analysis comparing trends for differential blood and T cell counts determined for cats during primary and rechallenge infection over time was based on linear mixed effects regression models using random effects for subject to account for repeated measures . A Kruskal Wallis test was used to compare lymphocyte and T cell counts between three different disease outcomes during primary FIPV infection . Pair-wise analysis was performed to compare median frequencies of proliferating CD4 and CD8 T cells after stimulation with viral immunogens using the Mann-Whitney U test (one-tailed) also using GraphPad Prism. Nineteen naive SPF cats were inoculated oronasally with the FIPV-i3c2 isolate and monitored for illness up to 106 days post-infection. Fifteen cats (79%) succumbed to FIP during primary infection while the remaining four cats (21%) were still healthy without fever or clinical signs of FIP until the end of the study (106 days PI) and designated FIP resistant or survivors. The median survival for those cats that developed FIP during primary FIPV-i3c2 infection was 43.5 days. Eleven of the 15 diseased cats (73%) manifested the effusive form (wet) of FIP characterized by ascites and inflammation of intestinal serosa and 4/15 (27%) developed the non-effusive (dry or wet-dry) form characterized by granulomatous lesions in abdominal organs, central nervous system, or both tissues. Eight of 11 cats with effusive FIP died within 30 days and were deemed rapid progressors (Table P < 0.001) when compared to healthy age-matched controls  and (P = 0.01) respectively  counts decreased significantly following primary infection  values ranging from 7.44 (lower loads) to − 4.2 (highest virus load) (data not charted). Virus loads for MLN showed significant variability but no correlation with rapid or slow disease progression. Virus was also detected in MLN (3.7 × 104 copies million cells) and PBMC (3.9 × 103 copies million cells) drawn at the terminal time point from the single cat that developed FIP upon a secondary exposure to FIPV-i3c2. Otherwise, virus was not detected in PBMC or plasma from the nine cats that remained healthy after secondary challenge with FIPV-i3c2.Virus detection or virus loads in blood did not correlate with development of FIP based on viral RNA concentrations in either plasma or PBMC for 4/8 (50%) rapid progressors, 3/7 (43%) slow progressors, and 2/4 (50%) survivors (Table n = 4) and the number of surviving cats (2–3) assayed at each time point. Similarly, only three rapid progressors were tested at four weeks PI due to deaths of rapid progressors at earlier time points. Despite these limitations, certain trends in antiviral immune responses were demonstrated between the three groups of cats. The emergence of CD8 T cell responses to WKV was detected more frequently in slow progressors (4/5 tested) and survivors (2/3 tested) at one week PI compared to rapid progressors (2/7 tested) , 3 (P = 0.015) and 4 (P = 0.037) where higher CD4 T cell responses were observed for rechallenged survivor cats. CD8 T cell responses to WKV and viral peptides were also higher for rechallenge infections for week 3 (P = 0.032 and P = 0.011 respectively). As mentioned above, significant declines in circulating lymphocyte and T cell counts were observed during primary infection likely impacting antiviral T cell responses in blood. In contrast, similar declines in lymphocyte and T cell counts were not associated with rechallenge infection. It is noteworthy that cats within the primary infection group showing responses similar to the highest responders in the rechallenge group at the week 4 time point tended to be slow progressor or survivor cats . T cell responses were comparable between both groups of cats at weeks 2–4 during rechallenge infection, regardless of time duration between primary and rechallenge infections. Importantly, all cats within the rechallenge infection group demonstrated antiviral T cell responses for multiple time points after a secondary FIPV exposure.Based on the wide range of T cell responses observed for rechallenge infection, this group was further stratified into two cohorts based on timing of the previous exposure to FIPV-i3c2 Fig. B. Cats tn = 5) at terminal time points compared to PLN responses observed for slow progressors (n = 3) .The final analysis of this study compared antiviral T cell responses between LNMC harvested from MLN and PLN with PBMC collected at the time of euthanasia due to onset of FIP. PBMC and LNMC were similarly assayed for virus-specific CD4 and CD8 T cell proliferation responses to both viral immunogens, although data for PLN were not available for all cats undergoing post mortem exam. A longitudinal assessment of lymph node T cell responses over time after infection was possible due to temporal differences in the disease course for different cats with FIP. Assay of lymph node T cell responses from two different cats at the same time point of primary infection  were also available. Responses from both rapid and slow progressors were compared with each other with LNMC harvested from age-matched healthy control cats serving as negative controls. CD4 and CD8 antiviral T cell proliferative responses varied between different tissues within individual cats, between different cats, and between different time points (Fig. Although multiple reports have described immune responses associated with FIPV infection, immune correlates that protect infected animals from this fatal disease remain poorly understood. Previous studies investigating antiviral T cell responses induced by experimental infection with either FIPV79–1146 or FIPV KU-2, were limited to a single time-point after infection and provided evidence supporting the role of T cell immunity in resistance to FIP . This reBoth WKV and S2 peptides proved capable of evoking CD4 and CD8 T cell proliferative responses in cats experimentally infected with FIPV-i3c2, and implemented a valuable tool for a longitudinal investigation of antiviral T cell responses during primary and rechallenge infections. Limitations to these studies included the low number of survivor cats during primary infection and assessment of T cell responses restricted to blood only, for early time points of primary infection. In contrast, lymph node and circulating T cell responses were compared at terminal time points in cats that progressed to disease. A comparison of responses in abdominal lymphoid tissues at earlier time points of infection for both progressors and survivors would be more informative based on the typical localization of FIPV replication to the abdominal cavity. Despite these limitations, examination of antiviral T cell responses in blood during primary infection revealed certain trends and critical observations. An earlier emergence of both CD4 and CD8 T cell responses observed for slow progressors and survivors at one week after virus inoculation contrasted to a delay of one week in responses emerging in some cats with rapidly progressive disease. Regardless of these temporal differences in early responses, the magnitude of T cell responses did not differ significantly between cats with different disease outcomes during the first three weeks of early infection and almost all cats showed negligible responses by three weeks into infection. However, by four weeks after infection, a proportion of slow progressors and survivors were distinguished from rapid progressors in their recovery of antiviral T cells responses. Collectively, these findings suggested that circulating antiviral T cell responses during very early time points of acute infection do not predict disease outcome. Yet, recovery of circulating antiviral T cell responses later in acute infection may be associated with a delay in disease or absence of disease. These findings lend some support to the widely-held hypothesis that FIP results from a failure of T cell immunity , but als. These findings suggest that MLN may represent a major site for T cell-mediated control of virus replication and disease progression and FIPV responses in both disease and antiviral immunity. This finding may not be surprising based on previous reports showing virus replication in systemic lymphoid tissues including MLN [Robust CD4 and CD8 T cell responses were more frequently demonstrated in MLN harvested from more cats surviving 29 days or later. In contrast, T cell responses in PLN harvested at earlier time points of primary infection tended to be of higher magnitude compared to PLN responses observed for slow progressors. Also, PLN-associated T cell responses frequently did not match MLN responses from the same host, particularly for cats surviving 29 days or laterding MLN –30 and ading MLN . Furtherding MLN , 32.The most striking abnormalities observed during primary FIPV infection were a progressive decline in WBC, lymphopenia, and T cell depletion, which most likely impacted host responses to the virus infection during the early phases of acute primary infection. This is not a new observation and was described in other reports of natural and experimental FIPV infections in cats developing FIP , 33, 34.A recent experimental FIPV infection study by our group revealed that sustainable immunity is not always maintained upon multiple exposures of the virus suggesting a distinct antiviral T cell response may confer resistance to FIP among survivor cats . RechallViremia was not an accurate predictor of disease outcome and was observed in either plasma or PBMC in approximately 50% of cats that either died or survived primary infection, with circulating virus most frequently observed at two weeks PI. This time point coincides with the emerging lymphocyte and T cell depletion and compares well to results of experimental studies with pathogenic Type 2 FIPV 79–1146 where viremia was typically restricted to 4–12 days PI, when detected . Viral RTaken together, findings from these infection studies did not reveal definitive immune correlates for resistance, or a delay in progression to FIP during acute primary infection with a pathogenic type 1 FIPV, except for a recovery of antiviral T cell responses at a later time point of infection for some but not all of the slow progressors and survivors tested. Intriguing findings were that antiviral T cell responses, circulating virus loads, or the magnitude of T cell depletion immediately after a primary exposure to pathogenic FIPV, did not appear to predict disease resistance in this experimental cohort. However, those cats that recovered FIPV-specific T cells responses without developing FIP during a primary infection, proved capable of resisting a second challenge with FIPV that was associated with emergence of recall T cell responses.Critical virus-host interactions very early in infection, which are responsible for resistance or slow progression to disease, remain unclear from our experimental primary infection studies. Possible explanations include differences in the magnitude of disruption of innate responses or specific alterations of innate factors that have been reported for both SARS CoV and FIPV. Virus-induced inhibitory macrophages reported for the mouse model for the SARS-CoV  were shoKey findings from these studies indicate that circulating antiviral T cell responses within the first four weeks of primary infection with pathogenic FIPV isolate, were not predictive of disease outcome. Furthermore, all cats developed lymphopenia and T cell depletion during early FIPV infection regardless of progression or resistance to FIP. Findings that suggest a role for cellular immunity in FIPV pathogenesis were a recovery of antiviral T cell responses at a later time point of primary infection for a subset of cats showing slow progression or resistance to disease. T cell responses measured in MLN harvested post mortem were more robust and more frequent for cats showing slower progression to FIP. Lastly, secondary or recall antiviral T cell responses observed in FIP-resistant cats after a second exposure to FIPV were associated with resistance to disease progression as well as an absence of lymphopenia and T cell depletion. Overall these findings suggest that antiviral T cell immunity may contribute to FIPV pathogenesis and resistance to disease, but also provoke questions regarding the role of other host factors including innate response factors, immunoregulatory responses, and B cell alterations that may also determine disease outcome.


Myalgic encephalomyelitis / chronic fatigue syndrome (ME/CFS) is a syndrome of unknown etiology characterized by profound fatigue exacerbated by physical activity, also known as post-exertional malaise (PEM). Previously, we did not detect evidence of immune dysregulation or virus reactivation outside of PEM periods. Here we sought to determine whether cardiopulmonary exercise stress testing of ME/CFS patients could trigger such changes. ME/CFS patients (n = 14) and matched sedentary controls (n = 11) were subjected to cardiopulmonary exercise on 2 consecutive days and followed up to 7 days post-exercise, and longitudinal whole blood samples analyzed by RNA-seq. Although ME/CFS patients showed significant worsening of symptoms following exercise versus controls, with 8 of 14 ME/CFS patients showing reduced oxygen consumption ( Myalgic encephalomyelitis/ chronic fatigue syndrome (ME/CFS) is characterized by long-term, debilitating fatigue that is characteristically exacerbated by physical and mental exertion ,2. PatieNo specific cause for ME/CFS has been identified. Diagnosis of the syndrome based on symptoms and exclusion of other diseases in the absence of established biomarkers contributes to inconsistent clinical case definitions . WorkingRNA-seq can be leveraged to identify and count at once all transcripts and RNA genomes within a sample. This method has been used to discover host biomarkers and study molecular pathways involved in disease response , as wellOur study protocol was approved by the Institutional Review Board at the University of British Columbia (Certificate # H14-01149). Potential subjects were recruited through postings to relevant newsletters and websites and information made available by participating physicians. Informed consent was obtained from all participants and/or their legal surrogates. Written informed consent included a full explanation of how the CPET regimen had been designed to provide an objectively measurable physiological stimulus to induce fatigue. Healthy people were matched with ME/CFS subjects by 5-year age stratum, ethnicity and gender. Patients were excluded from any group if they did not provide consent, were <18 years of age, were unable to understand English, or if they had another medical condition that accounted for their main symptoms. Any specific diagnosis that would exclude a person from a diagnosis of ME/CFS also excluded a healthy person from being recruited as a control. Because our sample size in this small pilot study was too small to address gender related differences, we recruited female cases and controls only.2); urea; creatinine; total bilirubin; uric acid; alanine transaminase (ALT); aspartate transaminase (AST); creatine kinase (CK); gamma-glutamyl transferase (GGT); albumin; rheumatoid factor; thyroid stimulating hormone (TSH); ferritin; anti-nuclear antibody (ANA), total protein with protein electrophoresis; human immunodeficiency virus (HIV); hepatitis B virus (HBV); hepatitis C virus (HCV); syphilis; and two-tiered Lyme serology. A complete physical examination was conducted with a strong focus on musculoskeletal and neurological findings. All baseline data were reviewed to assure that case definitions were met.We recruited female patients with ME/CFS (Canadian 2003 criteria)  and age-Before enrollment, cases and controls wore a wGT3X-BT accelerometer  on an elasticized belt around their waist for a period of five days, including one weekend. The device records human activity in three axes. Participants were asked to remove the device in order to shower or bathe but to wear it at all other times, including sleep periods. Parameters provided by the accelerometer report include: mean percent of time it was worn, total steps, percent of time spent in sedentary, light, moderate, vigorous and very vigorous activity, time in bed, time sleeping and number of awakenings.The protocol called for each subject to undergo two maximal cardiopulmonary exercise tests (CPET) approximately 24h apart. Subjects were fitted with electrocardiogram (ECG) electrodes for monitoring of heart rhythm, a mouthpiece and headgear for collection of expired air, and a pulse oximeter for monitoring arterial oxygen saturation. Subjects were allowed to pedal for a short period (less than one minute) prior to an incremental exercise test performed on an electronically braked bicycle ergometer . Workload was increased 15 watts per minute until volitional fatigue. ECG was monitored continuously for signs of cardiac arrhythmia or ischemia, and pulse oximetry was monitored to ensure safe levels of arterial oxygenation. Blood pressure was regularly monitored for patient safety. Expired respiratory gases were collected using a metabolic cart  and minute ventilation  from cases and controls was drawn into a PAXgene Blood RNA Tube  to stabilize RNA prior to extraction and stored at -20°C. Total RNA was extracted at University of British Columbia using the PAXgene Blood RNA Kit  and all samples were lyophilized in RNAstable reagent  for shipment at room temperature to University of California, San Francisco (UCSF) for further processing and long-term storage. The Ovation Human blood RNA-seq kit  was used to generate strand-specific RNA-seq libraries depleted for reads derived from rRNA  and globin  according to the manufacturer’s protocol. The Ovation RNA-seq kit employs depletion of human RNA and globin transcripts, which can constitute up to 76% of mRNA in whole blood ; in our p-value < 0.05, and adjusted p-value  < 0.1%.Gene expression analysis was conducted using Partek Flow software (version 5.0). Paired-end reads were mapped to the human genome (hg38), annotated to exons, and normalized to FPKM (fragments per kilobase of exon per million fragments mapped) values for all 25,278 human RNA reference sequences in the National Center for Biotechnology Information (NCBI) RefSeq database (August 2016 build) using STAR v2.4.1  and CuffSequencing data from whole transcriptome libraries were analyzed for the presence of RNA sequences corresponding to known human viral pathogens using the sequence-based ultra-rapid pathogen identification (SURPI) computational pipeline . After cAll clinical, laboratory and exercise data were anonymized to study number and stored securely in a common linked database at the University of British Columbia. Descriptive statistical tests were performed in R. To avoid the assumption that the data fit a fixed probability distribution, univariate analyses comparing groups were performed using non-parametric methods; a Fisher’s Exact Test was used for categorical variables and a Mann-Whitley rank sum test for continuous variables.ME/CFS patients were classified according to whether or not they had a significant decline in performance on the second exercise test defined as ≥ 7% decrease in The CPET study screened 136 potential participants and enrolled a total of 25 subjects . The potential participants who were not enrolled included those who were lost to follow-up (n = 2), withdrew from the study prior to exercise testing (n = 2), were unable to participate due to the lack of eligible age-matched controls or cases (n = 26), declined to participate , or were ineligible according to Canadian Consensus Criteria because of the presence of an underlying illness precluding a diagnosis of ME/CFS (n = 41). Seven out of the fifteen ME/CFS subjects had also participated in a previous published study examining gene expression profiles in comparison to a matched control group ; howeverp > 0.62). Cases had more defining symptoms and scored lower on disability scales than did controls (p < 0.01). Nine cases described a history of an infectious prodrome at disease onset, manifesting as an acute “flu-like illness” or gastroenteritis, while five described a sudden onset of ME/CFS. Baseline accelerometry revealed that sedentary controls trended toward logging more steps over a five-day period than did ME/CFS subject  successfully completed only one exercise test because systolic blood pressure was found to be high after testing. These subjects were retained for the gene expression analysis because follow-up confirmed the development of PEM for the patient with ME/CFS.We identified eight ME/CFS subjects with a significant decline in performance from day 1 to day 2 based on a decrease in Participants with ME/CFS but not controls reported a worsening of symptoms following exercise that became maximal by day 3 or day 7 on most scales . This waFig A inFig B inAn average of 15.4 million reads per sample were generated (IQR: 13.4–19.4 million reads per sample), with an average of 78% uniquely mapping reads and average transcriptome coverage of 68%. Two samples from a single subject (day 3 and 7 samples) had unusually low number of expressed genes detected (< 40%) and thus were removed from the analysis Fig A in. PrincipFigs C-F inNo DEGs were detected when comparing pre- to post-exercise samples in ME/CFS as compared to controls, both overall  or at any single time point . Six DEGWe detected sequences from a small number of viruses in the RNA-seq data, including enterovirus A, influenza A virus, anelloviruses/torque teno viruses (TTVs), and human herpesviruses (HHVs) . Only onThis study builds on previous observations we have made in people with ME/CFS outside of PEM episodes . While wOverall, we did not find significant alterations in gene expression in patients with PEM relative to controls. Fifteen out of 19 DEGs identified in this study were likely false positives due to low gene coverage; the other four DEGs  were of unclear function and/or showed borderline differences in gene expression. Patients with ME/CFS did also not show significant or relevant exercise-induced after exercise in ME/CFS using RT-PCR of targeted genes ,41 and iReasons for the absence of differential gene expression between ME/CFS patients and controls include (1) the lack of objective diagnostic testing for ME/CFS and reliance on subjective definitions, resulting in heterogeneity in the ME/CFS study cohort, (2) the lack of an immunological signature in ME/CFS that is detectable by transcriptomics, (3) localization of ME/CFS pathogenicity to a specific tissue  rather than blood, (4) heterogeneity of whole blood components with respect to the transcriptional signature, (5) multiple mechanisms underlying ME/CFS in different patients, complicating the identification of a unique signature. Dynamic shifts in viral abundance have been associated with immune cell dysregulation in setting of immunosuppression and obesity ,13; hereA limitation of the current study is the small size of the study cohort. However, a power analysis study reviewing 6 RNA-seq datasets measuring differential gene expression in human and mouse showed a study power >0.9 when comparing more than 10 samples per condition at a sequencing depth of 10–25 million reads per sample, 8–36% transcriptome mapping rate and comparing 5 differential expression calculation methods . Our preIn conclusion, we observed no important differences in gene expression in ME/CFS patients over time and relative to controls in association with experimentally induced PEM. Previously reported differences in immunologic and metabolic function were derived from cross-sectional studies for which causation may not be inferred. Further progress in understanding ME/CFS may be dependent on nested analyses within prospective cohort studies and larger multi-center randomized clinical trials.S1 Appendix Next-generation sequencing total read counts (grey bar), percentage of reads uniquely mapping to the human transcriptome (black diamond), and transcriptome coverage as the percentage of genes detected (red diamond). Two outlier samples with transcriptome coverage <40% were removed from subsequent human transcriptome analysis.  Principal component analysis of the global gene expression shows no batch effect among the different sets of whole blood samples processed by RNA-seq analysis. . Expression level dot plot and box plot of 6 differentially expressed genes  found when comparing CFS patients to controls at all time points. . Expression level dot plot of LINC01158 found when comparing CFS patients to controls at day 7 only. . Expression level dot plots of 6 differentially expressed genes found at day 1 (LOC105372441), day 3 , and day 7  when comparing a subset of CFS patients with reduced peak Fig F). Expression level dot plots of 9 differentially expressed genes found at day 2 , day 3 , and day 7  when comparing a subset of CFS patients with test-retest effect compared to CFS and controls without test-retest effect.Contains Figures A-F. (DOCX)Click here for additional data file.


Continuous storage root formation and bulking (CSRFAB) in sweetpotato is an important trait from agronomic and biological perspectives. Information about the molecular mechanisms underlying CSRFAB traits is lacking.p-value < 5 × 10− 7), we identified 34 unique SNPs that were significantly associated with the complex trait of CSRFAB at 150 days after planting (DAP) and seven unique SNPs associated with discontinuous storage root formation and bulking (DCSRFAB) at 90 DAP. Importantly, most of the loci associated with these identified SNPs were located within genomic regions (using Ipomoea trifida reference genome) previously reported for quantitative trait loci (QTL) controlling similar traits. Based on these trait-associated SNPs, 12 and seven candidate genes were respectively annotated for CSRFAB and DCSRFAB traits. Congruent with the contrasting and inverse relationship between discontinuous and continuous storage root formation and bulking, a DCSRFAB-associated candidate gene regulates redox signaling, involved in auxin-mediated lateral root formation, while CSRFAB is enriched for genes controlling growth and senescence.Here, as a first step toward understanding the genetic basis of CSRFAB in sweetpotato, we performed a genome-wide association study (GWAS) using phenotypic data from four distinct developmental stages and 33,068 single nucleotide polymorphism (SNP) and insertion-deletion (indel) markers. Based on Bonferroni threshold (Candidate genes identified in this study have potential roles in cell wall remodeling, plant growth, senescence, stress, root development and redox signaling. These findings provide valuable insights into understanding the functional networks to develop strategies for sweetpotato yield improvement. The markers as well as candidate genes identified in this pioneering research for CSRFAB provide important genomic resources for sweetpotato and other root crops. Perennial plants, species that live for more than 2 years, account for 13% of food crops and provide advantages over annual crops because they increase the carbon of storage organs . CSRFAB Sweetpotato is an important food crop in sub-Saharan Africa (SSA). Its production continues to increase in east and central Africa due to recent interests in the crop for its unsurpassed healthy promoting values and being staple food crop in the region. High production in the region is recorded in Uganda and Tanzania , howeverThrough various research networks, sweetpotato breeders across countries and continents have shown outstanding consistency in the general areas of priority for breeding, most of which point to the need for increased yield potential and resistance to biotic and abiotic stresses. Frequently mentioned breeding objectives, targeting increased sweetpotato adoption and wide utilization, revolve around improvement of fresh storage root yield, storage root dry matter content (DMC), resistance to principal local pests and diseases, tolerance to adverse soil and climatic conditions, good plant habit, ornamental quality and other quality traits . ConsideSeveral reports have described the anatomy and physiological processes of storage root formation and development in sweetpotato under controlled conditions –15 and fIpomoea trifida and I. triloba [DNA-based genetic markers can provide great potential to assist plant breeders in the identification of genes of interest and detecting markers tightly linked to traits for the development of new cultivars. Tanaka et al. 2016)  reviewed16  revie triloba  and the In this study, we evaluated a diverse set of 358 sweetpotato breeding accessions for CSRFAB and DCSRFAB under field conditions. We genotyped this diversity panel and analyzed it for quantitative trait nucleotides (QTNs) using SNP and indel markers to identify genomic regions and polymorphisms associated with CSRFAB and DCSRFAB traits.Pair-wise correlations provide information on whether two traits are related. This information about the magnitude and direction (negative or positive) of the relationship can assist in breeding selection decisions. Thus, CSRFAB was highly and positively correlated with storage root number (SRN), storage root yield (SRY) and harvest index (HI) Fig. . CSRFAB The distribution of genotypic performance across the four-harvest times (HT) was visualized using a boxplot Fig. . On averWe previously  reportedI. trifida and I. triloba) in order to determine sequences with 2x, 4x, and 6x doses that correspond to diploid, tetraploid, and hexaploid genotypes in the hexaploid sweetpotato genome. Based on the distribution of read depth for each individual in the population  values were calculated using the diploidized genotypic data. Overall, based on the I. trifida genome assembly size of 526.4 Mb [The pattern and extent of genome-wide linkage disequilibrium (LD), which is the non-random association of alleles at different loci, is presented in a boxplot that shows the distribution of LD within a range of distances between marker pairs Fig. . All pai526.4 Mb  and a to526.4 Mb . Accordi526.4 Mb  rapid LDOut of 33,068 SNPs analyzed in this GWAS, based on Bonferroni correction-based threshold, 34 and seven SNPs were significantly associated with CSRFAB 150 DAP) and DCSRFAB (90 DAP), respectively. The number of significant SNPs associated with the other CSRFAB-related traits  are shown in Table 50 DAP anAmong the 57 SNPs associated with CSRFAB and DCSRFAB at different storage root development stages, some SNPs were shared between 150 DAP, AUGPC and 180 DAP, while other SNPs were simultaneously detected between 90 DAP, 120 DAP and slope. The largest number of associated SNP markers was detected on chromosome 1 and the largest number of significant associations was recorded for 150 DAP followed by the slope  ranging from 8.11E-43 to 1.79E-03, implying that the type I error with false positive SNPs was minimized. The top four most confident SNPs discoveries were Chr04_6057850, Chr12_903336, Chr01_30468732, and Chr09_6258404. Most significant SNPs that passed the stringent significance threshold of 5% Bonferroni were also associated with CSRFAB variations at 150 DAP and R2 were greater than 9% and the majority explained more than 15% of the variation. In the previous study, 150 DAP and 90 DAP were proposed to predict CSRFAB and DCSRFAB traits and further investigation in this study will be only focused on these two times [Thirty-four SNPs (around 60% of the significant SNPs) were associated with CSRFAB scores collected at 150 DAP and were concentrated on chromosome 1, 6 and 8. All the minor allele frequencies (MAFs) of the significant SNPs at Bonferroni correction rate of 5% were above 10%, equivalent to recommended strict threshold for MAFs. All the significant SNPs passing Bonferroni threshold of 5% scored low adjusted P value for each of the SNPs in the genome , when tested for differences in frequency between trait and markers. The line shows the threshold for genome-wide significance . Each dot is a SNP laid out across the sweetpotato chromosomes from left to right, and the heights correspond to the strength of the association to traits under study . Chromosomes 1, 3, 4, 6, 8 and 12 had a high number of the associations over the four harvest times forming clusters where multiple traits were associated due to probably a set of linked genes. These regions include: chromosome 1 (six traits), chromosome 3 (4 traits), chromosome 4 (4 traits), and chromosome 12 (four traits). On a multiple plot, we placed vertical lines at specific positions along the x-axis to identify consistently associated QTNs  plots . The ManI. trifida reference genome (http://sweetpotato.plantbiology.msu.edu/). Candidate genes that are co-located with the top significant SNPs are listed in Table GWAS are useful for identifying genomic locations related to a trait of interest, however, the information provided does not capture the function of genes under the region of interest. It is therefore of great importance that putative causal gene functions be determined and investigated through different functional genomic approaches. It is well recognized that few of associated markers involve genes previously reported to be related to the trait of interest, and some are found in genomic locations harboring no known genes and sometimes regulatory elements . TherefoCandidate genes that might underlie variation in CSRFAB were identified based on proximity of the associated SNPs with predicted genes Table . The strThe associated Chr01_30468732 SNP is co-localized with P-LNPHP gene encoding a P-loop containing nucleoside triphosphate hydrolases super family protein involved in regulation of growth and hormonal signaling in plants. The gene HVA22 was found to be associated with Chr02_16003392 locus encoding basic helix-loop-helix bHLH-DNA-binding superfamily protein and was previously reported to regulate leaf senescence, cell death and abscisic acid (ABA) biosynthesis . AnnotatCandidate gene TRO-Z was associated with Chr03_18599676 SNP marker and this gene encodes thioredoxin z previously reported as a facilitator of plants to cope with fluctuating environments by integrating energy transduction, metabolism, gene expression, growth and development . The genP-values for the slope, indicating that slope is not a good predictor of responses at 90 DAP. Five genome wide SNPs hit common chromosome region at 150 DAP and AUGPC . This indicates that AUGPC and 150 DAP tightly affect the same phenotype. In a previous study [In this study, we showed that CSRFAB, SRN, SRY and HI were highly correlated, suggesting that their genetic basis is controlled, in part, pleiotropically . This implies that we can expect positive changes in the SRN and SRY for varieties whose CSRFAB expression is increased. In breeding, this means that when we select for CSRFAB we can get a correlated response in the SRN and SRY. Further detailed analyses on the CSRFAB trait responses over the four harvest times and their respective predictor (AUGPC and slope) revealed that AUGPC and 150 DAP were highly and positively correlated . In a previous study  these haP-values  suggesting the presence of some major to moderate QTL particularly underlying CSRFAB (the wild type or predominant habit of sweetpotato), while DCSRFAB (the habit bred for in cultivated sweetpotato) seems to be controlled by minor alleles. The rapid decay of LD in the diversity panel breaks the genome in small LD blocks and provides us with the ability to fine map QTL, oftentimes to the gene level [We used genome-wide association (GWA) analysis to identify SNPs associated with CSRFAB and DCSRFAB in sweetpotato. GWA analysis provides much more precise physical positioning than the method of QTL mapping in biparental mapping populations that has been previously used for complex growth traits in plants. We observed particularly high ne level . In thisne level . We deteI. trifida genome sequence to identify candidate genes encompassing or adjacent to these SNPs. Several of the candidate genes that we identified play a role in the plant growth pathways. Candidate genes can also be identified based on their positions on quantitative trait locus (QTL) maps or patterns of gene expression [2+ dependent kinase) and stress signaling pathways whereas the cluster of genes associated with CSRFAB involved mostly growth hormone signaling such as auxin, ABA, gibberellins . The former modulate growth that is declining due to senescence, aging, and drought stress. ABA is reported to be involved in the repression of germination, drought response, and promotion of leaf senescence [We used the publicly available pression . Identifpression . Suzuki pression  argued tpression  classifipression . In thispression  increasepression  by maintpression . The clunescence .This situation makes continuous storage root growth regulation a highly complicated process and it seems to be controlled at many different levels by complex actions of gene networks in both time and space. For instance, SNPs as well as genes involved in storage root formation and bulking at 90 DAP were completely different from genes found at 150 DAP implying that the regulation mechanism of root formation and bulking is time dependent.A key contribution of this study is the multipurpose characteristic of the discovered SNPs that can be validated for early maturing and continuous storage root formation for commercial storage root production, piecemeal harvesting and biomass production for animal feed. At the top of significant and upregulated genes, P-loop containing nucleoside triphosphate hydrolases super family protein and DHHC-type zinc finger family protein were associated with CSRFAB in this study and these genes generally have a meristem-specific mRNA expression pattern and KNOX proteins playing central roles in shoot development by maintaining the apical meristem activity . CelluloWe genotyped 358 sweetpotato genotypes and run the first GWAS in sweetpotato for CSRFAB. The study identified 40 unique SNPs significantly associated with CSRFAB traits and 12 unique SNPs significantly associated with DCSRFAB. Novel genes including 12 promising genes for CSRFAB and seven genes for DCSRFAB were identified in this study. These genes can be used for sweetpotato genetic improvement of CSRFAB using maker assisted breeding approaches after marker validation. To validate these associations and candidate genes, additional studies will be required . The discovery of the candidate genes has increased our understanding of the molecular causal mechanisms of CSRFAB in sweetpotato and may contribute to the basic knowledge in breeding for CSRFAB in sweetpotato. The validation of genotypes with excellent haplotypes will provide valuable breeding materials to improve sweetpotato for CSRFAB through marker-assisted selection in future breeding efforts.Parental genotypes were obtained from the National Crops Resources Research institute (NaCRRI) in Uganda. The study involved a collection of 358 sweetpotato genotypes including; a set of 130 genetically diverse genotypes from two contrasting genepools previously characterized with 31 simple sequence repeat (SSR) markers  and a seThe experimental design was based on a randomized complete block design, with a two-row experimental plot containing 20 plants. A total of 2 replications were sampled even though 4 replicates were planted. Measurements were conducted at one-month intervals starting from 3 months after planting (MAP).To identify the patterns of storage root formation and bulking, storage roots for each plant genotype were destructively sampled at 3, 4, 5, and 6 MAP. At each sampling point, data were collected from four plant stands using above- and below-ground parts. The following sweetpotato storage root traits were measured for each of the four harvest times to allow correlation analysis of CSRFAB and yield component traits and these included: (i) number of harvested plants (NPH), allowing calculation of average values, (ii) total storage root number (SRN), (iii) total root weight (TRW), and (v) vine weight (VW). CSRFAB was estimated using a previously developed scale of 1 to 9, where 1 = no visible storage root (SR) initiation and no visible bulking; 2 = No visible SR initiation but bulking is detectable; 3 = No visible SR initiation; 4 = Distinct SR initiation, with 2 bulking roots; 5 = Distinct SR initiation and 3 bulking roots; 6 = Distinct SR initiation and 4 bulking roots; 7 = Distinct SR initiation and 5 bulking roots; 8 = Distinct SR initiation and 6 bulking roots; 9 = Distinct SR initiation and 7 bulking roots [Most growth curves in living organisms follow a logarithmic or exponential growth trend. The logarithmic growth curves increase quickly in the beginning, but the gains decrease and become slower as time goes by, while the exponential growth curves increase slowly in the beginning, but the gains increase rapidly and become larger as time goes . The incg time i .Slope: m = f ‘(x) = constant .Yijk = μ + Gi + Tj + Ek + GTij + GEik + GTEijk + Rlk + eijkl; where μ is the total mean, Gi is the effect of the ith genotype, Tj is the effect of the jth harvesting time, GTij is the effect of the interaction between the ith genotype and the jth harvesting time, GTEijk is the effect of the interaction between the ith genotype and the jth harvesting time in the kth environment, Ek is the effect of the kth environment, GEik is the interaction effect between the ith genotype and the kth environment, Rik/ is the effect of the Lth block within the kth environment, and eijk is a random error .The analysis of variance was performed for CSRFAB scores, slope and AUGCP predictors that take into account all the time harvests at once. R package lme4  was usedCorrelation analyses between CSRFAB traits were performed using the cor function and Pearson method in R software.Boxplots  were useA total of 5 mg of young leaf tissue was sampled for each genotype using a polythene sampling bag and then kept cool under ice. Extraction buffer was prepared  and placed in a water bath at 65 °C. Using a FastPrep-24™ 5G tissue homogenizer, leaf tissue samples in 2 ml tubes were ground for 5 min in liquid nitrogen using sterile 4 mm stainless steel ball bearings. To obtain high molecular weight DNA, 1 ml of prewarmed (65 °C) CTAB buffer  was added to the ground samples and vortexed at 3000 rpm for 30 s. Tubes were heated in a water bath at 65 °C for 30 min and mixed gently at 10 min interval during incubation. Samples were cooled on ice, 500 μl chloroform:isoamyl alcohol (24:1) was added, and mixed by inverting the tubes 20 to 30 times. Samples were then centrifuged at 15,000 round per minute (rpm) for 15 min and the top layer recovered into a new tube. This step  was repeated to ensure purity of extracted DNA. DNA was precipitated with 1/5 volume of 5 M NaOAC and 2.5 volume of cold absolute ethanol (stored at − 20 °C). The samples were gently inverted to mix and incubated at − 20 °C for 60 min. Samples were centrifuged and DNA pellet recovered by decanting the supernatant. The DNA pellet was washed twice with 500 μl of cold (− 20 °C) 70% (v/v) ethanol and air dried. The DNA pellet was resuspended in 100 μl low-EDAT TE buffer  containing 400 μg RNase-A. The DNA concentration and purity were determined using a NanoDrop spectrophotometer.I. trifida and I. triloba [DNA samples (40 μl each) were sent to the integrated genotyping services and support (IGSS) at the Biosciences eastern and central Africa - International Livestock Research Institute (BecA-ILRI) Hub for sequencing based on the DArTseq technology. The raw Fastq files were processed within the GBSapp pipeline for pre-processing fastq files, variant and dosage calling, and variant filtering. The pipeline integrates various software, including GATK v3.7 , optimiz triloba  were use2), and only P-values < = 0.01 for each pair of loci were considered significant. The LD decays were computed for LD-based genome-wide association analysis.Linkage disequilibrium analysis was performed using GAPIT  and implN = 358). The first three principal components of the SNP data were included in the GWAS model. The Bonferroni threshold for P values was calculated based on the number of markers  according to the method described in Li et al., (2013) [To minimize false positive rates and increase statistical power, the population structure Q and kinship (K) matrix were estimated. A compressed mixed linear model (CMLM) was used, with the kinship or relatedness (K) matrix as a random effect to account for population structure and reduce spurious associations. The analysis was performed using the R package for Genome Association Prediction Tool (GAPIT) version 3 . Varianc, (2013) .I. trifida (http://sweetpotato.plantbiology.msu.edu/) was used as the reference genome for identifying candidate genes. The putative function candidate genes that co-localized with associated SNPs were annotated based on similarity to known annotated genes in other species, particularly, Arabidopsis thaliana. Independent analyses were performed using the Basic Local Alignment Search Tool from the National Center for Biotechnology Information Basic Local Alignment Search Tool (NCBI BLAST) and the conserved domains database (CDD) resources [Based on the significant trait-associated SNPs, the physical genome assembly of the diploid esources  for anno


CDK4 & 6 inhibitors have enhanced the effectiveness of endocrine therapy (ET) in patients with advanced breast cancer (ABC). This paper presents exploratory analyses examining patient and disease characteristics that may inform in whom and when abemaciclib should be initiated. MONARCH 2 and 3 enrolled women with HR+, HER2- ABC. In MONARCH 2, patients whose disease had progressed while receiving ET were administered fulvestrant+abemaciclib/placebo. In MONARCH 3, patients received a nonsteroidal aromatase inhibitor+abemaciclib/placebo as initial therapy for advanced disease. A combined analysis of the two studies was performed to determine significant prognostic factors. Efficacy results (PFS and ORR in patients with measurable disease) were examined for patient subgroups corresponding to each significant prognostic factor. Analysis of clinical factors confirmed the following to have prognostic value: bone-only disease, liver metastases, tumor grade, progesterone receptor status, performance status, treatment-free interval (TFI) from the end of adjuvant ET, and time from diagnosis to recurrence. Prognosis was poorer in patients with liver metastases, progesterone receptor-negative tumors, high grade tumors, or short TFI (<36 months). Benefit  from abemaciclib was observed in all patient subgroups. Patients with indicators of poor prognosis had the largest benefit from the addition of abemaciclib. However, in MONARCH 3, for patients with certain good prognostic factors  ET achieved a median PFS of >20 months. These analyses identified prognostic factors and demonstrated that patients with poor prognostic factors derived the largest benefit from the addition of abemaciclib. A series of clinical factors may help identify those patients with hormone receptor-positive breast cancer who are most likely to benefit from a newly approved cell cycle–blocking drug. Angelo Di Leo from the Hospital of Prato, Italy, and colleagues retrospectively analyzed data from two phase III trials that evaluated the CDK4 & 6 inhibitor abemaciclib against a placebo administered in combination with either fulvestrant endocrine therapy or an aromatase inhibitor. Pooling the results, the researchers found that patients with certain indicators of poor prognosis—including liver metastases, progesterone receptor negativity, high-grade tumors, or short treatment-free intervals following the completion of adjuvant endocrine therapy—benefited most from abemaciclib, as measured by progression-free survival. The findings, if confirmed in prospective trials, could help inform personalized drug choices for patients. However, for the majority, ET will eventually become ineffective.2Over 70% of metastatic breast cancers are hormone receptor-positive (HR+) and are treated with sequential endocrine-based therapies.12 One of the most successful approaches is the combination of cyclin-dependent kinase 4 & 6 (CDK4 & 6) inhibitors with ET.12 These combinations have improved progression-free survival (PFS) and objective response rates (ORR) in patients with HR+ advanced breast cancer (ABC), both as initial therapy and after progression on ET. Since none of the Phase III studies reported thus far permitted crossover between treatment arms upon progressive disease, the relative value of upfront CDK4 & 6 therapy versus therapy on progression is unknown.12 Furthermore, no predictive markers for HR+ breast cancer have been identified for this class of medicines.14 Prior studies have described potential prognostic factors for patients with HR+ ABC, including metastatic site  and prior sensitivity to ET .18 In addition, tumor-specific prognostic factors in the adjuvant setting include progesterone receptor (PgR) expression and tumor grade.19 However, the implications of these factors in guiding treatment decisions for the use of ET alone versus in combination with CDK4 & 6 inhibitors need further exploration.Efforts to improve the effectiveness of ET by adding medicines that target potential mechanisms of resistance are ongoing.21 CDK4 & 6 inhibitor trials published to date have demonstrated treatment benefit for the addition of a CDK4 & 6 inhibitor to ET across all patient subgroups.22 The present analyses of abemaciclib aim to determine independently prognostic subgroups, characterize the benefit of the addition of abemaciclib to endocrine therapy in these subgroups, and then determine those which derived the largest benefit from abemaciclib and those for which endocrine monotherapy may be an appropriate initial treatment. This approach may inform tailoring of treatment choices to individual patients.Given the complexity of these treatments, the identification of patient and tumor characteristics that can help inform when to use CDK4 & 6 inhibitors in the treatment paradigm and in which patients is a subject of considerable interest.12 A two-step approach was employed that first identified independent prognostic characteristics in the MONARCH 2 and 3 studies  and in disease that progressed while receiving ET (MONARCH 2).12Patients in MONARCH 2 were enrolled from August 7, 2014 to December 29, 2015 and in MONARCH 3 from November 18, 2014 to November 11, 2015. This subgroup analysis uses data from the final PFS analyses of MONARCH 2 and MONARCH 3. Both studies are still blinded for overall survival. Patient disposition for MONARCH 2 and MONARCH 3 is described in the CONSORT diagram . To identify prognostic factors related to endocrine sensitivity, a univariate analysis was performed to examine the significance of TFI (<36 or ≥36 months), time from diagnosis to recurrence (TTR) (≤10 or >10 years), and de novo metastatic disease (yes or no). The first two analyses excluded patients with de novo metastatic disease, and TFI included only patients who had received adjuvant ET. Univariate analysis indicated TFI and TTR as significant prognostic factors , last line of ET (neoadjuvant/adjuvant or metastatic), and type of ET resistance (primary or secondary). or 2, laThe MONARCH 3 analysis of TFI dichotomized at a 36-month cut-off indicated that patients with a longer TFI had a longer PFS Fig. . SubgrouTo further examine the prognostic value of TFI and the association of the effect of abemaciclib and TFI, independently of the 36-month cut-off, sliding window subpopulation treatment effect pattern plot (STEPP) analyses of the 18-month PFS rate for both arms and the PFS HR were performed. These variables were evaluated across eight overlapping groups of ~80 patients, with patients in each group having similar TFIs and adjacent groups sharing 60 common patients. As TFI increased, the 18-month PFS rate increased in both arms Fig. , supportSimilar to TFI, the dichotomized analysis of TTR using a 10-year cut-off showed TTR to be significantly prognostic. However, STEPP analyses of the 18-month PFS rate and PFS HR showed no interpretable pattern, indicating that TTR may not be relevant information in the treatment choice  of adding abemaciclib to ET in a prognostic subgroup may be interpreted in the context of the median PFS and ORR of endocrine monotherapy in that population.Previously reported subgroup analyses of studies combining CDK4 & 6 inhibitors with ET have concluded that all subgroups benefit from the addition of CDK4 & 6 inhibitors.A caveat of this approach is that it was limited to the possible prognostic factors that were identifiable within the MONARCH 2 and 3 databases. A two-step approach was used: first, identifying independent prognostic variables derived from the entire population regardless of treatment assignment, and second, describing the treatment effects of endocrine monotherapy and ET combined with abemaciclib in each of the identified prognostic subgroups.The first step of the exploratory analysis identified two groups of clinical characteristics of independent prognostic relevance for patients receiving endocrine-based therapy in MONARCH 2 and 3: those characteristics that existed in both populations and those that existed in only one. The former consisted of histological grade (low/intermediate grade conferring better prognosis than high), PgR status (positive conferring a better prognosis than negative), liver metastases (absence conferring a better prognosis than presence), bone-only metastases (presence conferring a better prognosis than absence), and ECOG status (0 conferring a better prognosis than 1). The only prognostic characteristics not common to both studies identified as being independent prognostic variables were TFI and TTR (longer conferring a better prognosis than shorter for both variables) in MONARCH 3. Of note, visceral disease was significantly prognostic in the univariate analysis but not in the multivariate analysis, indicating that presence of liver metastases drove the perceived prognostic value of visceral disease Table .The second step of the analysis described the outcomes of patients who received ET alone or ET plus abemaciclib in each of the prognostic subgroups identified within the first step. Abemaciclib conferred substantial benefit in patients regardless of prognosis. However, patients with poor prognostic factors  consistently derived the largest benefit from the addition of abemaciclib to ET. Furthermore, in MONARCH 3, patients who had received adjuvant ET were evaluated using a STEPP analysis of TFI, demonstrating that those with a shorter TFI derived the largest benefit from the addition of abemaciclib to ET.The interaction tests between these prognostic factors and treatment effect were not significant. Given the fact that the studies were not designed or powered to detect interactions, this lack of significance should be balanced against the available evidence. Specifically, the differential treatment effect observed in patients with poor prognostic factors  was consistently better across both MONARCH 2 and 3, lending credibility to the hypothesis that these patients may benefit more from the addition of abemaciclib to ET.In summary, the results of these exploratory analyses have identified certain key prognostic factors, which can be used to identify groups of patients that derived large and clinically significant benefits from treatment with abemaciclib in both the first and second line settings, regardless of the endocrine partner. Data from patients receiving initial therapy for advanced disease, have led us to hypothesize that there may be a group of patients with clinically indolent disease that can be treated sequentially with endocrine monotherapy (e.g. aromatase inhibitor) followed by the introduction of a CDK4 & 6 inhibitor at progression. It should be noted that in a population with indolent disease detecting a difference in PFS between treatment groups will take an appropriately powered study with much longer follow up. However, groups of patients with more aggressive disease and thus concerning clinical characteristics gained considerable benefit from the addition of abemaciclib to ET. These data are hypothesis generating and will need to be evaluated along with other known pathological and molecular determinants of abemaciclib response in the context of prospective clinical trials. Importantly, these data can provide the groundwork to develop individualized therapy for women with HR+, HER2− breast cancer.12 MONARCH 2 investigated abemaciclib or placebo plus fulvestrant in patients whose disease had progressed while receiving ET. MONARCH 3 investigated abemaciclib or placebo plus a nonsteroidal aromatase inhibitor (NSAI) as initial therapy.MONARCH 2 and 3  are Phase III, randomized, double-blind trials for women with locally tested HR+, HER2− ABC.23 In MONARCH 2, patients had disease that progressed while receiving adjuvant ET, ≤12 months from the end of adjuvant ET, or while receiving ET for ABC. Patients were permitted one prior ET and no prior chemotherapy for ABC. MONARCH 2 allowed any menopausal status ; MONARCH 3 enrolled postmenopausal patients. In MONARCH 3, patients were ET naïve or could have received neoadjuvant or adjuvant ET if the disease-free interval was >12 months from the end of ET. Other inclusion and exclusion criteria were previously described.12In both studies, eligible women were aged ≥ 18 years with a 0 or 1 ECOG PS. Patients had measurable disease or non-measurable bone-only disease  as defined by the Response Evaluation Criteria in Solid Tumors (RECIST) v1.1.www.clinicaltrials.gov.The MONARCH 2 and MONARCH 3 studies were approved by the local IRBs for the sites participating in the clinical trials. The data in this manuscript are retrospective analyses of the data from these studies. All patients provided written informed consent. The trials were conducted in compliance with the Declaration of Helsinki. Trial protocols were previously disclosed on 10 Patients were stratified by metastatic site  and ET resistance (primary or secondary), as defined by European Society for Medical Oncology guidelines.10 In MONARCH 3, patients were randomized (2:1) to receive abemaciclib (150 mg twice-daily continuous schedule) or matching placebo plus NSAI .12 Patients were stratified by metastatic site  and prior neoadjuvant or adjuvant ET .12 Dose reductions and interruptions were previously described.12In MONARCH 2, patients were randomized (2:1) to receive abemaciclib (150 mg twice-daily continuous schedule [or 200 mg prior to protocol amendment]) or matching placebo plus fulvestrant (500 mg per label).12Using RECIST v1.1, tumors were evaluated by computed tomography or magnetic resonance imaging at baseline and post-baseline. Scanning frequencies were previously described.12The endpoints of both studies, including investigator-assessed PFS (the primary endpoint of both studies), ORR, and safety results, were previously described.12This exploratory post hoc analysis compared the investigator-assessed PFS between abemaciclib plus ET and placebo plus ET among subgroups in the MONARCH 2 and 3 studies. ORRs in patients with measurable disease are provided as supportive data but no formal comparisons between subgroups were made. The sample size, randomization methods, and statistical methods for the primary and secondary endpoints for the original trials were previously described.p-value was < 0.05. Subsequently, a multivariate Cox model analysis, was performed including variables found to be potentially prognostic by univariate analysis. Variables were selected in a stepwise fashion, with an entry p-value = 0.05 and a retaining p-value = 0.05. Only patients with a complete record of included baseline variables were included. For subgroup variables identified as prognostic through multivariate analysis, treatment effects for the addition of abemaciclib to ET were reported for each subgroup within each study. Treatment effects were summarized by study and treatment arm.To identify prognostic variables potentially associated with the performance of endocrine monotherapy or combination therapy, cross-study subgroup analyses were performed for key demographic and clinical variables and those identified in the literature as associated with prognosis: age, race, baseline ECOG PS, bone-only disease, visceral disease, liver metastases, lung metastases, pleural metastases, PgR status , tumor grade , number of involved organs at baseline, and prior neoadjuvant or adjuvant chemotherapy Fig.. UnivariDue to differences in entry criteria, and thus study populations, variables associated with sensitivity to ET were evaluated at a study level in a separate analysis from above Fig. . For MON24 TFI was performed on the subset of patients from MONARCH 3 who previously received adjuvant ET (n = 211). TTR was performed on the subset of patients from MONARCH 3 who had recurrent disease (n = 292). The analysis was performed using the 18-month PFS rate in each arm as the response. Patients were grouped using r1 = 60 and r2 = 80. No inferential statistics were calculated due to difficulties in controlling Type I error with such a sample size using this analysis.25 To describe the effect of adding abemaciclib to ET in the same manner, the analysis was repeated using the stratified HR as the response. SAS v9.2 or later (SAS Institute) was used for statistical analyses.To further explore the prognostic value of TFI and TTR, STEPP analyses were performed.Supplemental Material_Di Leo_M2 and 3 Subgroup


Mycobacterium tuberculosis. Rv1258c is implicated in drug resistance to numerous antimycobacterials including first line drugs rifampicin and isoniazid as well as fluoroquinolone and aminoglycoside antibiotic classes. To date, compounds like verapamil and piperine have been shown to inhibit Rv1258c but no direct evidence for binding or mode of action exist. Therefore in the present study we generated an accurate 3D model of Rv1258c using MODELLER and validated its structure using molecular dynamic simulation studies with GROMACS software. The 3D-structures of Rv1258c and the homologous template 1pw4 were simulated within a POPE/POPG lipid bilayer and found to behave similar. Another important finding was the identification of one local energy minima state of the apo protein, which speaks to the flexibility of the protein and will be investigated further. Extraction of one of the open channel conformations of Rv1258c and blind docking of various structurally diverse putative inhibitors and substrates, allowed for the identification of a probable binding site. Spectinamide was found to bind to a different location on the outside surface of the protein suggesting its ability to avoid the efflux channel. We further identified 246 putative compounds that showed higher binding affinity values to Rv1258c compared to piperine and verapamil. Interaction analysis of the top 20 purchasable compounds identified crucial hydrogen bond interactions with Ser26, Ser45 and Glu243 as well as a pi-pi stacking interaction with Trp32 that accounted for the strong affinity of these compounds for Rv1258c. Future studies will entail purchasing a number of compounds for in vitro activity testing against Mycobacterium tuberculosis.Mycobacterial efflux pumps play a major role in the emergence of antimycobacterial drug resistance. Of particular interest is the proteinaceous multi-drug efflux pump protein Rv1258c that encodes a tetracycline/aminoglycoside resistance (TAP-2)-like efflux pump which is active in susceptible and drug resistant  Mycobacterium tuberculosis is an obligate parasite and the causative agent of tuberculosis. It is an infectious disease that accounts for approximately 1.4 million deaths and 10.4 million new cases per year [M. tuberculosis develops resistance to tuberculosis drugs has been the focus of intensive research but it is recognised that efflux pumps play an important role in intrinsic as well as acquired genetic resistance [sistance –4.M. tuberculosis isolates has led to the identification of a number of open reading frames that encode putative efflux pumps. However, the exact mechanism of these pumps associated with intrinsic and acquired drug resistance has not been elucidated [Mycobacterium bovis BCG increases the susceptibility of the organism to isoniazid and rifampicin [Sequencing of numerous ucidated , 6. One ucidated , 8. Prevfampicin . Additiofampicin . This effampicin .Previous experimental studies showed that compounds like piperine, verapamil and chlorpromazine, inhibit the functioning of Rv1258c however no direct evidence have been provided for the binding and mode of action of these small molecules , 14. Spein silico pharmacophore modelling.In the present study, we remodelled the structure of Rv1258c using a homologous protein that shares a similar fold and performed molecular dynamic simulation studies of the protein in a POPE-POPG lipid bilayer. We also investigated the possible binding of piperine, verapamil, chlorpromazine, spectinamide and spectinamycin to Rv1258c and describe their interactions with the efflux pump. In addition to this we identified novel compounds that could be used to inhibit Rv1258c utilising Eschericia Coli based on an alignment confidence score of 331 (p = 4 x 10−8) which represents the most closely related structure to Rv1258c. The routinely used python scripts in MODELLER were used to perform an alignment prediction between the template (1pw4) and sequence Rv1258c using align2d.py [http://services.mbi.ucla.edu/SAVES/). Superimposing all atoms of the protein model onto the template provided an RMSD value, with lower values suggesting that there is very little deviation in main chain atoms and indicates that the correct fold was assigned to protein Rv1258c.The PSIPRED web server was used to search for distantly related template structures for Rv1258c . The amiign2d.py , 19. Theign2d.py . The Ramign2d.py . The Proign2d.py . The ERRThe CHARMM-GUI interface was used to prepare protein-membrane systems for Rv1258c and template 1pw4 . BrieflyThe analysis of the trajectory files was done using GROMACS utilities. The root mean square deviation (RMSD) was calculated using g_rmsd and RMSD per-residue analysis using g_rms. The change in the solvent accessibility surface area (SASA) was calculated using g_sas to determine if the system reached convergence over the 200 ns simulation. Clustering analysis was performed using the GROMACS tool g_cluster to identify different conformational states of the protein. The Visual Molecular Dynamics package (VMD) was used to visually inspect motions along the trajectory . Each 20Principal component analysis (PCA) is a statistical technique that reduces the complexity of a data set in order to extract biologically relevant movements of protein domains from irrelevant localized motions of atoms. For PCA analysis the translational and rotational movements were removed from the system using g_covar from GROMACS to construct a covariance matrix. Next the eigenvectors and eigenvalues were calculated by diagonalising the matrix. The eigenvectors that correspond to the largest eigenvalues are called "principal components", as they represent the largest-amplitude collective motions. We filtered the original trajectory and project out the part along the most important eigenvectors namely: vector 1 and 2 using g_anaeig from GROMACS utilities. Furthermore, we visualized the sampled conformations in the subspace along the first two eigenvectors using g_anaeig in a two-dimensional projection. Afterwards we oversimplified the calculation of the free energy landscape (FEL) using the GROMACS utility g_sham only along the first two eigenvectors. The FEL represents all the possible different conformations a protein can adopt during a simulation and are typically reported as Gibbs free energy. The molecules free energy was calculated with the formula ΔG(r) = − kBT ln P/Pmin, where P is the probability distribution of the two Cartesian coordinates namely eigenvector 1 and 2, Pmin is the maximum probability density function, Kb is the Boltzmann constant and T is the temperature of the simulation. Conformations sampled during the simulation are projected on a two dimensional plane and the number of points in each cell was counted. The clustering of points in a specific cell represented a possible metastable conformation. All the simulations were carried out using the GROMACS 4.6.5 package  along wi2O, more than one molecule chain break and alternate locations. The various parameters for the docking process was stored in a configuration file named: conf.txt  essential for binding of a ligand to a receptor or enzyme, which accounts for its effect on the protein’s activity . The sinThe 548 compounds identified for the Rv1258c docked complexes from the ZINC database were prepared with automated bash and python scripts namely, split_multi_mol2_file.py and prepare_ligand4.py as mentioned previously. The same input parameters from the previous docking were used and the output of the docking run was filtered based on the docking score measured as kcal/mol. Subsequently, the interactions of the top binders that showed higher binding affinity than the five putative inhibitors was analysed using Poseview . PosevieThe amino acid sequence of Rv1258c shared very low sequence identity of approximately 24% and an overall sequence coverage of 96% to that of the template 1pw4 . The ove2) in comparison to Rv1258c (248 ±4.71 nm2). Principal component analysis of the first two eigenvectors for the two trajectories indicated smaller randomised movements for 1pw4 compared to Rv1258c after diagonalisation of the covariance matrix  with values 51.07 nm and 60.81 nm, respectively. Calculating the contribution of each PCA component for each system indicated for 1pw4 PCA component 1 and 2 each contributed 63 and 11% of the overall motion while for Rv1258c PCA components 1, 2, 3 and 4 each contributed 37, 20, 15 and 9%, respectively. Plotting the movement of the first two principal components throughout the free energy landscape indicated two metastable clusters for 1pw4, while Rv1258c showed only one conformational cluster  inhibition of efflux function through direct binding to the protein. Particularly in the case of verapamil, a number of studies propose conflicting mechanisms for observed augmented antimycobacterial efficacy in f action , 35, 36.f action . Piperinf action , 38. Howin vitro to determine their ability to restore sensitivity of H37rV to rifampicin by inhibiting the efflux pump Rv1258c. These compounds could serve as alternatives to piperine and verapamil in the combination treatment of multi-drug resistant tuberculosis strains and could provide lead structures for further drug development.The 3D structure of Rv1258c was predicted through homology modelling and validated using molecular dynamic simulation studies in a lipid bilayer consisting of POPE/POPG lipids. Pharmacophore modelling of Rv1258c-drug complexes was performed to search for new putative compounds and these were then validated by performing blind docking studies to Rv1258c. The Lowest Dope score protein model predicted for Rv1258c showed good consensus with our homologous template protein based on the structural comparison and a very low root mean square deviation between atoms suggesting that a correct fold was assigned to Rv1258c. Furthermore, the predicted model of Rv1258c passed all the quality evaluation tests suggesting that an reliable model was predicted. Additional validation of the model using molecular dynamics simulation indicated similar trajectory behaviours based on RMSD, RMSD per-residue and SASA analysis between Rv1258c and its homologous template 1pw4, providing support for template selection. The homologous template however showed lower RMSD, RMSD per-residue and SASA suggesting that it is less flexible and more compact than the predicted 3D model of Rv1258c. The PCA analysis affirmed previous statistics that Rv1258c explored a larger area of the phase space suggesting that Rv1258c is more flexible and dynamic in comparison to the template 1pw4 as was seen by analysis of the variance data. Interestingly, the first four PCA's contributed significantly to the mobility of Rv1258c. Another important finding of our study is the identification of one local energy minima state of the apo protein Rv1258c, which suggests that the structure of Rv1258c is highly flexible and dynamic. However, a limitation of this study is the use of only two principal components (Cartesian coordinates) to estimate the correct rugged free energy landscape that may result in artifacts due to the mixing of internal coordinates and overall motion of the protein . Also, uin vitro as potential modulators of antimycobacterial treatment. The implications of this study will provide alternatives to piperine and verapamil for Tuberculosis treatment to improve the bacterium’s susceptibility to various antimicrobials by inhibiting the efflux pump Rv1258c.This study predicted the 3D-structure of the multi-drug efflux pump protein Rv1258c that is important to model protein-drug interactions. The 3D-structures of Rv1258c and the homologous template 1pw4 were also further utilized in molecular dynamic simulation studies within a POPE/POPG lipid bilayer and showed similar kinetic behaviour based on dynamics. Extraction of one cluster that represented a metastable conformation of Rv1258c allowed the identification of a putative binding site within a binding cleft when the protein channel has an open conformation. We identified 246 putative ligands using docking studies and also validated residues Ser26, Ser45, Glu243, Trp32 as crucial interaction partners responsible for the higher affinity. A selection of the top compounds will be purchased to validate them S1 FigHistidine residues shown as sticks.(TIFF)Click here for additional data file.S2 Fig(TIFF)Click here for additional data file.S3 Fig(TIFF)Click here for additional data file.S4 Fig(TIFF)Click here for additional data file.S5 FigPiperine is shown as sticks and residues of Rv1258c are coloured by type using the python script “resicolor.py”. Colour schemes: acidic residues are red, basic in blue, nonpolar in orange, polar in green, cysteine residues in yellow and backbone atoms white.(PNG)Click here for additional data file.S6 FigVerapamil is shown as sticks and residues of Rv1258c are coloured by type using the python script “resicolor.py”. Colour schemes: acidic residues are red, basic in blue, nonpolar in orange, polar in green, cysteine residues in yellow and backbone atoms white.(PNG)Click here for additional data file.S7 FigPiperine is shown as sticks and residues of Rv1258c are coloured by type using the python script “resicolor.py”. Colour schemes: acidic residues are red, basic in blue, nonpolar in orange, polar in green, cysteine residues in yellow and backbone atoms white.(PNG)Click here for additional data file.S8 FigVerapamil is shown as sticks and residues of Rv1258c are coloured by type using the python script “resicolor.py”. Colour schemes: acidic residues are red, basic in blue, nonpolar in orange, polar in green, cysteine residues in yellow and backbone atoms white.(PNG)Click here for additional data file.S9 FigPiperine is shown as sticks and residues of Rv1258c are coloured by type using the python script “resicolor.py”. Colour schemes: acidic residues are red, basic in blue, nonpolar in orange, polar in green, cysteine residues in yellow and backbone atoms white.(PNG)Click here for additional data file.S10 FigVerapamil is shown as sticks and residues of Rv1258c are coloured by type using the python script “resicolor.py”. Colour schemes: acidic residues are red, basic in blue, nonpolar in orange, polar in green, cysteine residues in yellow and backbone atoms white.(PNG)Click here for additional data file.S11 FigPiperine is shown as sticks and residues of Rv1258c are coloured by type using the python script “resicolor.py”. Colour schemes: acidic residues are red, basic in blue, nonpolar in orange, polar in green, cysteine residues in yellow and backbone atoms white.(PNG)Click here for additional data file.S12 FigVerapamil is shown as sticks and residues of Rv1258c are coloured by type using the python script “resicolor.py”. Colour schemes: acidic residues are red, basic in blue, nonpolar in orange, polar in green, cysteine residues in yellow and backbone atoms white.(PNG)Click here for additional data file.S13 FigHydrophobic interacting residues are labelled and coloured in magenta. Interactions calculated with binana.py python script .(TIFF)Click here for additional data file.S14 FigHydrophobic interacting residues are labelled and coloured in cyan/blue. Interactions calculated with binana.py python script .(TIFF)Click here for additional data file.S15 Fig(TIFF)Click here for additional data file.S16 FigThe dashed lines represent hydrogen bonds and the green spline segments illustrate hydrophobic contacts.(TIFF)Click here for additional data file.S17 FigThe dashed lines represent hydrogen bonds.(TIFF)Click here for additional data file.S18 FigThe dashed lines represent hydrogen bonds and the green spline segments illustrate hydrophobic contacts.(TIFF)Click here for additional data file.S19 FigThe dashed lines represent hydrogen bonds and the green spline segments illustrate hydrophobic contacts and green dots represent pi-pi stacking interaction.(PDF)Click here for additional data file.S20 FigThe dashed lines represent hydrogen bonds and the green spline segments illustrate hydrophobic contacts and green dots represent pi-pi stacking interaction.(PDF)Click here for additional data file.S21 FigThe dashed lines represent hydrogen bonds and the green spline segments illustrate hydrophobic contacts and green dots represent pi-pi stacking interaction.(PDF)Click here for additional data file.S22 FigThe dashed lines represent hydrogen bonds and the green spline segments illustrate hydrophobic contacts and green dots represent pi-pi stacking interaction.(PDF)Click here for additional data file.S23 FigThe dashed lines represent hydrogen bonds and the green spline segments illustrate hydrophobic contacts and green dots represent pi-pi stacking interaction.(PDF)Click here for additional data file.S24 FigThe dashed lines represent hydrogen bonds and the green spline segments illustrate hydrophobic contacts and green dots represent pi-pi stacking interaction.(PDF)Click here for additional data file.S25 FigThe dashed lines represent hydrogen bonds and the green spline segments illustrate hydrophobic contacts and green dots represent pi-pi stacking interaction.(PDF)Click here for additional data file.S26 FigThe dashed lines represent hydrogen bonds and the green spline segments illustrate hydrophobic contacts and green dots represent pi-pi stacking interaction.(PDF)Click here for additional data file.S27 FigThe dashed lines represent hydrogen bonds and the green spline segments illustrate hydrophobic contacts and green dots represent pi-pi stacking interaction.(PDF)Click here for additional data file.S28 FigThe dashed lines represent hydrogen bonds and the green spline segments illustrate hydrophobic contacts and green dots represent pi-pi stacking interaction.(PDF)Click here for additional data file.S29 FigThe dashed lines represent hydrogen bonds and the green spline segments illustrate hydrophobic contacts and green dots represent pi-pi stacking interaction.(PDF)Click here for additional data file.S30 FigThe dashed lines represent hydrogen bonds and the green spline segments illustrate hydrophobic contacts and green dots represent pi-pi stacking interaction.(PDF)Click here for additional data file.S31 FigThe dashed lines represent hydrogen bonds and the green spline segments illustrate hydrophobic contacts and green dots represent pi-pi stacking interaction.(PDF)Click here for additional data file.S32 FigThe dashed lines represent hydrogen bonds and the green spline segments illustrate hydrophobic contacts and green dots represent pi-pi stacking interaction.(PDF)Click here for additional data file.S33 FigThe dashed lines represent hydrogen bonds and the green spline segments illustrate hydrophobic contacts and green dots represent pi-pi stacking interaction.(PDF)Click here for additional data file.S34 FigThe dashed lines represent hydrogen bonds and the green spline segments illustrate hydrophobic contacts and green dots represent pi-pi stacking interaction.(PDF)Click here for additional data file.S35 FigThe dashed lines represent hydrogen bonds and the green spline segments illustrate hydrophobic contacts and green dots represent pi-pi stacking interaction.(PDF)Click here for additional data file.S36 FigThe dashed lines represent hydrogen bonds and the green spline segments illustrate hydrophobic contacts and green dots represent pi-pi stacking interaction.(PDF)Click here for additional data file.S37 FigThe dashed lines represent hydrogen bonds and the green spline segments illustrate hydrophobic contacts and green dots represent pi-pi stacking interaction.(PDF)Click here for additional data file.S38 FigThe dashed lines represent hydrogen bonds and the green spline segments illustrate hydrophobic contacts and green dots represent pi-pi stacking interaction.(PDF)Click here for additional data file.S39 FigCS (purple), PASS (cyan), SURFNET (brown), Q-SiteFinder (blue), Fpocket (pink), ConCavity (orange), GHECOM (yellow) and POCASA (wheat) are all from their top 1 prediction where ligands piperine (red) and verapamil (blue) bind. The MetaPocket site for Rv1258c is shown as a red sphere.The different coloured spheres represent the different methods employed by Metapocket: LIGSITE(PNG)Click here for additional data file.S40 Fig(PDB)Click here for additional data file.S1 Text(TXT)Click here for additional data file.S1 Python Script(PY)Click here for additional data file.S1 Moviehttps://drive.google.com/open?id=1YJuVMEH3xOVFBmCc_wzhlHrr-ul37KXJ.Surface representation of 1pw4. (MPG)Click here for additional data file.S2 Moviehttps://drive.google.com/open?id=1Zpy4GV6WIIVnmTw0oXWUiSSHEUtyJR32.Surface representation of Rv1258c. (MPG)Click here for additional data file.