Streptococcus pneumoniae responds to 20 antibiotics. We build a genome-wide atlas of drug susceptibility determinants and generated a genetic interaction network that connects cellular processes and genes of unknown function, which we show can be used as therapeutic targets. Pathway analysis reveals a genome-wide atlas of cellular processes that can make a bacterium less susceptible, and often tolerant, in an antibiotic specific manner. Importantly, modulation of these processes confers fitness benefits during active infections under antibiotic selection. Moreover, screening of sequenced clinical isolates demonstrates that mutations in genes that decrease antibiotic sensitivity and increase tolerance readily evolve and are frequently associated with resistant strains, indicating such mutations could be harbingers for the emergence of antibiotic resistance.Detailed knowledge on how bacteria evade antibiotics and eventually develop resistance could open avenues for novel therapeutics and diagnostics. It is thereby key to develop a comprehensive genome-wide understanding of how bacteria process antibiotic stress, and how modulation of the involved processes affects their ability to overcome said stress. Here we undertake a comprehensive genetic analysis of how the human pathogen  Streptococcus pneumoniae and build a genome-wide atlas to understand the genes and interactions that contribute to altered drug susceptibility.A lack of understanding in the development and emergence of antimicrobial resistance presents as a problem for accurate infection diagnosis and treatment. Here, authors utilize  However, whether these changes play a role or not is often unclear because the distribution of changes that can affect a bacterium’s drug sensitivity are largely unknown7. Therefore, understanding which genes, pathways and processes can contribute to altered drug susceptibility, could help identify genomic changes that not only sensitize bacteria to certain drugs, but desensitize them and may thereby act as precursors for antibiotic escape and/or resistance development.The emergence of antibiotic resistance in bacterial pathogens is a continuously developing complex problem that is only solvable if besides new drugs we also learn to understand the exact (genetic) processes that enable resistance. For instance, new antibiotics and treatment strategies are key to retain the ability to treat resistant infections. However, a comprehensive understanding of how and under which conditions resistance emerges, which genes and pathways contribute to drug sensitivity, and how resistance may be prevented or even taken advantage of, are equally important, as it could make treatments more focused and possibly less dependent on new drugs. For many antibiotics, we know which genomic changes can cause resistance. However, it is often not clear how we get there with respect to which evolutionary paths are taken and whether for instance tolerance or lowered drug sensitivity precedes resistance. Interestingly, clinical strains isolated during antibiotic treatment failure may lack known resistance markers and instead contain multiple changes that may have no clear or known role in resistance13. Importantly, an antibiotic’s effects go far beyond the interaction with its direct target. We, and others, have shown that when a bacterium is challenged by an antibiotic, the imposing stress can expand throughout the bacterium and affect and demand the involvement of many different processes17. For instance, while fluoroquinolones like ciprofloxacin inhibit DNA replication by targeting gyrase and/or topoisomerase, this also triggers double-stranded breaks requiring the involvement of DNA repair mechanisms, which in turn requires nucleotide and energy metabolism. Antibiotics can thereby trigger a stress cascade, that with mounting stress increasingly reverberates through the organismal network, until the accumulating stress passes a threshold at which point the organism succumbs to the pressure17. This explains why mutations in genes or pathways involved in dealing with the downstream (indirect) effects of antibiotic exposure can often make a bacterium more sensitive to a specific antibiotic. Indeed, we have shown for Streptococcus pneumoniae and Acinetobacter baumannii that, for instance, targeting DNA repair makes bacteria more susceptible to DNA synthesis inhibitors (DSIs)18, or targeting the Rod-system and/or Divisome makes A. baumannii more sensitive to cell-wall synthesis inhibitors (CWSIs)6. This means that downstream genes, pathways and processes can be used as new targets or drug potentiators, either by themselves or in combination with others14. Moreover, in most bacteria, as in any other organism, the majority of genes are of unknown function, it is unclear what role they play in a specific process and/or pathway, or how they are connected within the organismal genomic network. Thus, besides solving gene function, mapping-out which genes, pathways and processes are involved in dealing with and overcoming antibiotic stress, and how they interact with each other, can provide key insights into uncovering new drug targets, or for instance rational combination strategies6.Resistance emerges primarily through drug target mutations blocking antibiotic lethal action, upregulation of efflux pumps, and the acquisition of drug inactivating enzymes19, cell density20, antibiotic stress21 and stress from the immune system22 into a cell state that enables them to tolerate high (transient) concentrations of antibiotics. Cell states associated with tolerance include cell dormancy, slow growth, transient expression of efflux pumps, and induction of stress response pathways26. However, the mechanistic underpinnings of tolerance and decreased antibiotic sensitivity remain largely undefined and possibly differ between bacterial species and vary among antibiotics27. Moreover, specific mutations can  increase the fraction of the surviving population30, indicating these tolerant phenotypes have a genetic basis. Lastly, since clinical isolates often carry mutations located outside well-characterized drug targets32, they could thus be composed of variants with different antibiotic sensitivities. Consequently, such variants with decreased antibiotic sensitivity could enable antibiotic escape, and/or enable multi-step high-level resistance mutations to evolve as they are given an extended opportunity to emerge36. Variants with decreased antibiotic sensitivity may thereby play an important role in antibiotic treatment failure38. However, the breadth of possible genetic alterations that can enable (increased) tolerance and/or decrease antibiotic sensitivity are largely unknown, making it unclear how often and probable it is that such variants arise.While identifying off-target genes and pathways that increase drug sensitivity may thus be useful, it is possible that changes in associated processes could, in contrast, just as well reduce the experienced antibiotic stress. Such changes would thereby decrease antibiotic sensitivity and could possibly function as precursors to the emergence of resistance. A possible example of this is tolerance and/or persistence, where a small proportion of cells in a population can be induced by external conditions including nutrient starvationS. pneumoniae exposed to 20 antibiotics, 17 additional environments, and two in vivo infection conditions, to generate a genome-wide atlas of drug susceptibility determinants and build a genome-wide interaction network that connects cellular processes and genes of unknown function. We explore several interactions as new leads for gene function, while we show that specific interactions can be used to guide the identification of targets for new antimicrobial strategies. We highlight one such novel target in the membrane, by successfully developing a combinatorial antibiotic-antibody strategy that significantly reduces the bacterial load during an acute mouse lung infection. Furthermore, detailed mapping of antibiotic sensitivity data to pathways and genes with known function suggests a multitude of genome-wide genomic changes exist that can make the bacterium less susceptible and often tolerant to specific antibiotics. We untangle some of the underlying genetic mechanisms and show that decreased susceptibility and tolerance can come from a variety of changes including those in (nucleotide) metabolism, (p)ppGpp and ATP synthesis, transcription, and translation, as well as different types of transport. By further combining in vivo-infection- with antibiotic-Tn-Seq data we predict and experimentally validate that many disruptions may retain their decreased antibiotic sensitivity phenotype in vivo, and thereby outcompete the wild type in the presence of antibiotics. Moreover, by screening hundreds of clinical isolates we show that changes in genes that can decrease antibiotic sensitivity readily evolve in human patients and are often associated with antibiotic resistance. Consequently, these data highlight the wide array of possibilities that can lead to lowered antibiotic sensitivity and/or tolerance and underscore the importance of understanding the genetics of variants with altered drug susceptibility.In this study, we use Tn-Seq in S. pneumoniae, Tn-Seq was employed in the presence of 20 antibiotics (ABXs), representing 9 different ABX groups and four classes including cell wall synthesis inhibitors (CWSIs), DNA synthesis inhibitors (DSIs), 30 S and 50 S protein synthesis inhibitors (PSIs) and an RNA synthesis inhibitor (RSI)  is calculated to represent their environment-specific relative growth rate, which means that a mutant with for instance a fitness of 0.5 (WMT = 0.5) grows twice as slow as the wild type (WT)44. Each gene’s antibiotic-specific fitness is statistically compared to baseline fitness without ABXs, and is represented as ΔW (WABX − WnoABX) and categorized as: (1) Neutral, ΔW = 0, a mutant’s relative growth is similar in the absence and presence of an ABX; (2) Negative, ΔW < 0, a mutant’s fitness is significantly lower and thus grows relatively slower in the presence of an ABX; (3) Positive, ΔW > 0, a mutant’s fitness is significantly higher and thus grows relatively faster in the presence of an ABX. All antibiotics trigger both positive and negative fitness effects  Fig. . Six indSI) Fig. . Tn-mutacts Fig. . Importacts Fig. . Additio 42 Fig. .Fig. 1A 18 , which identifies multiple clusters that represent specific pathways and processes including purine metabolism, cell-wall metabolism, cell division and DNA repair  genes indicates the data is rich in known and new information.Screens such as Tn-Seq are geared toward highlighting the genetic regions and/or genes that are important under a specific screening condition. With increasing conditions, genes acquire profiles that reflect their involvement/importance in those conditions, where genes with similar profiles indicate having similar and/or shared tasks. Such profiles can thereby help fill gaps in pathways, and/or identify genes and gene clusters with similar roles. By building a correlation matrix based on each gene’s ABX fitness-profile patterns emerge along a similarity range; from genes with highly similar to contrasting profiles. Moreover, to increase statistical power  the ABX dataset was supplemented with previously collected Tn-Seq data from 17 additional non-antibiotic conditionster Fig. , I–III. gki, glucokinase), an enzyme that converts α-D-Glucose into G-6P, has a positive effect on fitness in all CWSIs and a negative effect in 30S PSIs. In contrast, inhibiting SP_1498 , the major interconversion enzyme of G-6P and G-1P, has a negative effect on fitness with all antibiotics  can decrease sensitivity to CWSIs. Lastly, interfering with protein turnover, for instance through the protease complex ClpCP  and the regulator CtsR (SP_2195), which are generally assumed to be fundamental for responding to stress49, leads to decreased CWSI sensitivity and increased sensitivity to 30S and 50S PSIs sensitize bacteria against existing antibiotics, genome-wide antibiotic susceptibility data have the potential to guide the development of new antimicrobial strategies. One such strategy could be a combined therapeutic antibody-antibiotic approach; the antibody would target a gene product that is important for sensitivity to one or more antibiotics and ideally the product would be easily accessible for the antibody at the bacterial cell surface. To find suitable candidate targets, Tn-Seq data were filtered for gene products that, based on a known function or localization prediction, are likely to be present in the cell wall or membrane, and that when disrupted, increase sensitivity to one or more antibiotics. Moreover, it would likely be ideal if the gene is also important for survival in vivo. A strong candidate is SP_1505, which in the interaction network is most tightly linked to cell wall metabolism and cell-division genes Fig. . After wile Fig. . Importaile Fig. . The proile Fig. , and in ile Fig. . The genile Fig. , which wuct Fig. . Potentiuct Fig. . To asseout Fig. . This shami-operon is among genes with the greatest number of positive fitness effects. The operon forms a tight cluster in the interaction network  and SP_1890 (amiC) confirm that increased fitness results in decreased drug sensitivity in the form of increased relative growth in the presence of ciprofloxacin, vancomycin and gentamicin, and increased sensitivity  to Synercid  affinity for at least two different peptides (P1 and P2)53. These have been theorized to possibly function as signaling molecules and under certain circumstances may be generated by the bacterium itself53. Both peptides were synthesized and while neither peptide affects growth of the WT or knockout mutants in the absence of antibiotics  transporting antibiotics into the cell, which could explain its effect on antibiotic sensitivity. To explore this, bacteria were exposed to ciprofloxacin or kanamycin and the internalized antibiotic concentration was determined through mass spectrometry for WT and both ami knockout mutants. In both mutants the amount of internalized ciprofloxacin was significantly lower , while the kanamycin concentration was found to be significantly lower in ΔamiC 25. At ~10 h the decline ceases and the remaining population (~0.01%) survives at least up to 24 h, which is representative of a persister fraction25. In contrast, the WT and amiE mutant populations decline at similar rates when exposed to vancomycin, showing that inhibition of the ami-transporter can lead to tolerance and persistence in an antibiotic-specific manner while MICs of gentamycin and vancomycin for WT and ΔamiE are similar sensitizing approaches. In contrast, the occurrences of increased fitness in the dataset indicate that a large number of options exist that could lead to reduced antibiotic sensitivity Fig. . With inrk Figs.  and 5a acid Fig. . There ideoB. Their profiles suggest they do not affect growth in the absence of ABXs and have increased sensitivity to Synercid, which was confirmed with individual growth curves  ABXs via induction of the stringent response pathway56, in which relA (SP_1645) is the key player with both synthetase and hydrolase activity57. Additionally, SP_1097 is annotated as a GTP diphosphokinase and may be involved in the synthesis of pppGpp from GTP , also increases tolerance to cefepime by ~1000-fold at 24 h ppGpp, ADP, ATP, GDP, and GTP. Additionally, we included SP_0831 a purine nucleoside phosphorylase involved in nucleotide salvage, which has the same ABX profile as ΔrelA ppGpp overexpression has been associated with decreased sensitivity to linezolid59. Our data suggest that (p)ppGpp and ATP synthesis may be intrinsically linked, i.e., at least in S. pneumoniae the inability to produce the alarmone also results in lowered ATP synthesis, which is associated with a lowered ABX sensitivity to β-lactams and glycopeptides. However, ΔSP_0831 shows that even if (p)ppGpp can be synthesized, modulation of purine metabolism, for instance through the salvage pathway, can result in decreased ATP synthesis, and can lead to lowered ABX sensitivity . Importantly, in many bacterial species, alarmone production is generally assumed to be triggered in response to different types of stress and has been shown to affect a large variety of processes including nucleotide synthesis, lipid metabolism, and translation. (p)ppGpp is thereby a ubiquitous stress-signaling molecule that enables bacteria to generate a response that is geared toward overcoming the encountered stress. However, contradictory results between species indicate a possible non-uniformity across bacteria, leaving much to be learned about how the alarmone and the processes it can control fit into the entire organismal (response) network56. Our data suggest that the inability  to generate the alarmone in S. pneumoniae in response to β-lactams and glycopeptides is linked to reduced ATP, which under specific circumstances may be an optimal response, as it results in decreased ABX sensitivity translating into increased relative growth and tolerance, and thereby a higher probability to survive the insult ppGpp is synthesized from GTP and/or GDP. Like other bacterial species, GTP Fig. . Our datGTP Fig. , that whwth Fig. . Moreovewth Fig. , Fig. 6,th Figs.  and 7a. 4 h Fig. , without relA SP_645 is thelA Fig. , but shonse Fig. . Concern097 Fig. . It has lt Figs. .Fig. 7De62. Moreover, it turns out that in the majority of cases, increased fitness not only results in increased relative growth in the presence of an antibiotic, but also tolerance. Thereby, the part of the atlas that depicts decreased ABX sensitivity  includes a genome-wide ‘tolerome’, composed of a wide variety of pathways and processes that when modulated trigger tolerance in vitro in an ABX dependent manner.For instance, in the glycolysisTo further confirm that antibiotic sensitivity can be decreased by inhibiting a variety of processes, knockouts (KOs) were generated for fourteen mutants from 8 different processes. Moreover, an additional goal was to determine what increased fitness  would look like phenotypically, and thus whether it would translate into increased relative growth and/or tolerance. Of the 14 mutants with a Tn-Seq predicted increased fitness, 13 display an increased ability to grow in the presence of an ABX compared to the WT. Moreover, eight mutants, which inhibit several different processes including different metabolic pathways, transport, and transcription and translation, displayed tolerance, while retaining a similar MIC, and thereby have an increased ability to survive high-level exposure to an ABX (5–10xMIC) for at least 24 h   SP_wth Fig. , it has wth Fig. , and it wth Fig. . Both muCI) Fig. . ImportaCI) Fig. . Combini; (3) have little to no effect on antibiotic sensitivity and in vivo; (4) have no effect or increase antibiotic sensitivity and have a defect in vivo  resistant and a group of β-lactam resistant strains, and each group was matched with an equal number of sensitive strains from the database. In all strains in the SXT and β-lactam groups, irrespective of resistant or sensitive status, the number of stop codons in gene sets 1 and 3 are highest, which reflects the Tn-Seq predicted in vivo effects, i.e., while gene sets 1 and 3 contain mostly genes with potentially neutral effects, gene sets 2 and 4 contain many genes that are suggested to have a defect in vivo when disabled (e.g. with a stop codon)  decrease antibiotic sensitivity in at least 1 antibiotic and have no strong defect in vivo; (2) decrease antibiotic sensitivity in at least 1 antibiotic and have a defect in vivoivo Fig. . Thousanon) Fig. . Moreoveon) Fig. . While tThe emergence and increase in antibiotic resistance among most bacterial pathogens is a continuously developing problem with several important drivers, which include: (1) a lagging development of new drugs and treatment strategies; (2) a lack of (rapid) diagnostics and prognostics; and (3) an incomplete understanding of how antibiotic resistance develops. Moreover, these drivers are inherently connected making it a complex problem to solve. First, the ability of bacteria to evolve resistance elicits an arms-race that requires the development of new drugs and treatment strategies to keep the balance of infection-control tipped in our favor. Thus, while developing new drugs would keep the arms-race in place, the ability to slow or prevent the emergence of resistance could resolve the status quo. Furthermore, even though it is critical to understand how and under which circumstances resistance evolves, the applicability of this knowledge depends on the availability of diagnostics that could inform on the emergence of resistance (precursors) and thereby guide and enable timely, tailored, and targeted treatments. To progress toward a comprehensive understanding of how an infection is developing in the absence or presence of treatment, and how to decide what to do next, we believe that a detailed genetic understanding of how a bacterium deals with and overcomes stress, as well as its genetic potential to achieve this, are key aspects. In this study, we contribute to reaching such an understanding by building and exploring a detailed atlas of ABX sensitivities, which highlights how modulation of specific genes, pathways and processes does not only result (as expected) in increased ABX sensitivity, but surprisingly often in decreased ABX sensitivity. We show that such an atlas can be used to identify leads for gene function, to uncover the genome’s underlying architecture and genetic relationships among genes, for the identification of new drug targets, and the development of new proof-of-principle antimicrobial (ABX sensitizing) strategies. Most importantly, these data identify genome-wide genetic changes that show how modulation of genes, pathways, and processes can lead to reduced antibiotic sensitivity , not only in vitro, but also in vivo. Moreover, we show that mutations that have the potential to trigger the same phenotypes readily occur in patients.68. In E. coli it has been shown that inhibition of specific steps in purine metabolism can lead to decreased sensitivity to ampicillin and ciprofloxacin, and increased sensitivity to gentamycin69. Our data explores a wider set of alterations in purine metabolism in S. pneumoniae, which also leads to a wider distribution of changes. However, the overlap with E. coli includes decreased sensitivity to β-lactams (an ABX class that includes ampicillin) and we show that inhibition of some reactions in purine metabolism can also lead to decreased sensitivity to fluoroquinolones (an ABX class that includes ciprofloxacin). Additionally, it has been shown for E. coli that antibiotic sensitivity to ampicillin and ciprofloxacin can be increased, at least over 4 h, by supplementing with adenine, but not guanine, which is possibly linked to a reduced ATP demand and synthesis69. Our results show that limiting the ability to synthesize adenine, but also guanine, leads to significantly lowered ABX sensitivity to β-lactams and glycopeptides. Moreover, we show that limiting the ability to synthesize adenine, as well as (p)ppGpp, leads to lowered ATP synthesis. The association between decreased ATP synthesis/availability and decreased sensitivity and/or tolerance to antibiotics including β-lactams, glycopeptides and fluoroquinolones has now been shown for a variety of Gram-positive and -negative organisms71. While this suggests that low ATP demand/synthesis/availability may be at the root of a general mechanism that leads to decreased sensitivity to some antibiotics, our genome-wide atlas shows that decreased ABX sensitivity and tolerance to β-lactams, and glycopeptides can be triggered by alterations to pathways and processes other than those directly related to ATP synthesis, which include parts of glycolysis, pyruvate, ascorbate, glucose and purine metabolism, protein turnover and c-di-AMP synthesis  approaches are being explored to build such a comprehensive understanding73, however, unequivocally more genome-wide data from more species remain needed. Thereby, the genome-wide insights we present here are helping build a rationale to measure and model this complexity. One goal of such models would be to obtain a detailed understanding and ability to predict how alterations to specific processes affect responses to ABXs and thereby drive changes in sensitivity69. Lastly, these detailed data on reduced antibiotic sensitivities also suggest that more potential routes to ABX escape, and eventually resistance, may exist than assumed. We believe these data are thereby both an argument and potential starting point for a platform to predict clinically relevant mutations and determinants of antibiotic resistance and/or tolerance. Consequently, they underscore the importance of understanding the genetics of variants with altered drug susceptibility, as their genetics makes them diagnostically identifiable and trackable, while their often-associated collateral sensitivities to other ABXs or drugs could make them targetable.While the primary processes targeted by ABXs are mostly known, this work contributes to the increasing notion that downstream processes, not directly related to the target and which include metabolism, can significantly contribute to antibiotic efficacyThe research presented within complies with all relevant ethical regulations and protocols as approved by the Boston College environmental health and safety board.S. pneumoniae strain TIGR4 (NCBI Reference Sequence: NC_003028.3). TIGR4 is a serotype 4 strain that was originally isolated from a patient from Norway with Invasive Pneumococcal Disease (IPD)75. All ‘SP_’ gene numbers in the tables and figures are according to the TIGR4 genome. Single gene knockouts were constructed by replacing the coding sequence with a chloramphenicol and/or spectinomycin resistance cassette as described previously40. S. pneumoniae was grown on sheep’s blood agar plates or statically in THY, C + Y or semi-defined minimal media at pH 7.3, with 5 µL/mL Oxyrase , at 37 °C in a 5% CO2 atmosphere15. Where appropriate, cultures and blood plates contained 4 µg/mL chloramphenicol (Cm) and/or 200 µg/mL spectinomycin (Spec). Single strain growth assays were performed three times using 96-well plates by taking OD600 measurements on a Tecan Infinite 200 PRO plate reader or BioSpa 8 (BioTek). Growth curves are fitted to an exponential growth equation to calculate their doubling time. WT doubling time is divided by a mutant’s (MT) doubling time to represent each mutant’s fitness , making it directly comparable to Tn-Seq fitness77. Tolerance experiments were performed by exposing exponentially growing bacteria to 5–10xMIC of an antibiotic. Samples were taken at different time points over a 24 h period, washed with PBS, and plated on blood agar for enumeration. The number of surviving bacteria at different time points are divided by the starting population to determine the surviving proportion at each time point. The proportion of surviving MT bacteria are divided by the proportion of surviving WT bacteria to determine the fold survival (MT/WT) at each time point as depicted in Fig. Experiments were performed with 77. Selection experiments were conducted in rich medium with glucose as a carbon source in the presence or absence of 20 different antibiotics at a concentration that slows growth by ~30–50%  up to an OD600 of ~0.3–0.6 (~1.107 CFU/mL), representing ~7–8 generations. Sample preparation, Illumina sequencing and fitness calculations were done as described77. Sequencing analyses and fitness calculations are performed with analysis platform Aerobio v2.378. In short, fitness of a single mutant (Wi) is calculated by comparing the fold expansion of the mutant to the fold expansion of the population and is determined by the following exponential growth equation we previously developed60:Ni(t1) and Ni(t2) are the mutant frequency at the beginning and end of the experiment respectively and d is the population expansion. All of the insertions in a specified region or gene are then used to calculate the average fitness and standard deviation of the gene knockout in question. The advantage of using this approach is that Wi represents the actual growth rate per generation, which makes fitness independent of time and enables comparisons between conditions. To determine whether fitness effects are significantly different between conditions three requirements have to be fulfilled: (1) Wi is calculated from at least three data points, (2) the difference in fitness between the presence and absence of antibiotic has to be larger than 15% (thus Wi − Wj = <−0.15 or >0.15), and (3) the difference in fitness has to be significantly different in a one sample t-test with Bonferroni correction for multiple testing60. Importantly, we have previously validated that a mutant’s Tn-Seq fitness is indeed directly related to its relative growth rate, which means that a mutant with for instance a Tn-Seq fitness of 0.5 (Wi = 0.5) grows twice as slow as the WT. In this study, we show that fitness is also directly related to a mutant’s relative growth rate, where negative fitness indicates increased ABX sensitivity and decreased relative growth, while positive fitness indicates decreased ABX sensitivity and increased relative growth. Moreover, while Tn-Seq selection is performed under ABX pressure during growth for 7–8 generations we find here that positive fitness in many cases also leads to increased survival, i.e., tolerance.Six independent transposon libraries, each containing ~10,000 insertion mutants, were constructed with transposon Magellan6 in WT-T4 as described previouslyΔW (WABX − WnoABX) is displayed, which means that each gene’s antibiotic-specific fitness is statistically compared to baseline fitness without ABXs. ΔW thereby indicates a gene’s antibiotic-specific fitness effect which can be categorized as: (1) Neutral, ΔW = 0, a mutant’s relative growth is similar in the absence and presence of an ABX; (2) Negative, ΔW < 0, a mutant’s fitness is significantly lower and thus grows relatively slower in the presence of an ABX; (3) Positive, ΔW > 0, a mutant’s fitness is significantly higher and thus grows relatively faster in the presence of an ABX. For instance, if a gene knockout’s ΔW = 0.25, it means that its relative growth rate is ~25% higher in the presence of an ABX than in the absence of the ABX.In multiple figures p value and Benjamini–Hochberg adjusted p value were calculated for each process and antibiotic group, where an adjusted p value below 5% is considered to identify statistical enrichment.To determine whether a particular process or pathway is specifically involved in responding to an antibiotic group, a hypergeometric test was performed to test for enrichment. The distribution of significant genes within each process was compared to the distribution of the pathways in the overall genome. A 17. In short, ~1 × 105 CFU of mid-exponential bacteria are cultured in 200 μL in 96-well plates in fresh medium containing a single antibiotic at the following concentration gradients and increments: Ciprofloxacin gradient 0.4–1.8 μg/mL with 0.1 μg/mL increments; Cefepime 0.0175-0.03 μg/mL with 0.0025 μg/mL increments; Gentamicin 17–39.5 μg/mL with 2.5 μg/mL increments; Meropenem 0.006-0.02 μg/mL with 0.002 μg/mL increments; co-trimoxazole 4.5–10 μg/mL with 0.5 μg/mL increments; vancomycin 0.12–0.32 μg/mL with 0.04 μg/mL increments. MICs were determined in triplicate and monitored on a Tecan Infinite 200 PRO plate reader or BioSpa 8 (BioTek) at 37 °C for 12 h. MIC is determined as the lowest concentration that abolishes bacterial growth  analysis46 on the co-fitness network was performed with Cytoscape. A SAFE analysis is geared toward defining local neighborhoods for each node within a network and calculates an enrichment score for every functional attribute. It then highlights the areas that are the most enriched for that attribute. Attributes were assigned by merging KEGG80 pathway annotation and available functional category annotations, which covers 94% of the genes within the network. The distance threshold was set to the 1st percentile of the map-weighted distance, the Jaccard similarity index was set to 0.5, and nodes in different landscapes were retained.A gene x condition matrix was constructed to identify correlating fitness profiles and built a co-fitness network. The matrix is based on 20 antibiotic conditions from experiments performed here, supplemented with 17 conditions from van Opijnen and CamilliE. coli BL21 (DE3) was transformed with recombinant plasmid. A single colony was inoculated into LB medium containing kanamycin; cultures were incubated at 37 °C at 200 rpm and IPTG was introduced for induction. SDS‐PAGE and western blot were used to monitor the expression. Protein was purified from 1 L batch culture in Terrific Broth. Cells were harvested by centrifugation, cell pellets were lysed by sonication, and supernatant after centrifugation was kept for future purifications. SP_1505 protein was obtained by three‐step purification using Ni column, Superdex 200 column, and Q Sepharose. Fractions were pooled and dialyzed followed by 0.22 μm filter sterilization. Protein was initially analyzed by SDS‐PAGE and Western blot by using standard protocols for molecular weight and purity measurements. The concentration was determined by BCA protein assay with BSA as a standard. Final protein product was stored in 50 mM Tris-HCl, 150 mM NaCl, 10% Glycerol, 0.2% DDM, pH 8.0 and stored at −80 °C. Lab confirmation of the expression construct was undertaken using a 1:3000 dilution of mouse-anti-His-antibody, sourced from LIFE Technologies  followed by Goat Anti-Mouse IgG (H + L)-HRP Conjugate , used at 1:5000 dilution.Cloning and expression of SP_1505 was undertaken commercially (Genscript). Codon-optimized SP_1505 was cloned into pET28a with a C-terminal His-tag. A single rabbit was vaccinated by a commercial vendor (Rockland) with recombinant SP_1505 via the following schedule. Rabbit was immunized via intradermal route with 0.1 mgs SP_1505 with Complete Freund’s Adjuvant (CFA) followed by an intradermal 0.1 mg booster injection with Incomplete Freund’s Adjuvant IFA as an adjuvant at day 7, followed by two subcutaneous 0.1 mg booster injections at days 14 and 28 with IFA. Terminal bleed was collected on day 52 following challenge. SP_1505 IgG was purified from immunized rabbit serum using protein G resin and columns (Pierce) according to manufacturer specifications. Following purification, antibody was concentrated using 10,000 MWCO centrifugal filters (Millipore) and was dialyzed three times against PBS in a 3.5 kDa Slide-A-Lyzer dialysis cassette (Thermo Scientific). Antibody specificity was determined by western Blot against the parental wild-type and isogenic SP_1505 mutant, performed in duplicate as indicated in Fig. 81. Briefly, 2 mL of culture was centrifuged at maximum speed. The pellet was resuspended in cell wall digestion buffer  and incubated at 37 °C for 60 min. After centrifugation, the supernatant containing the cell wall was saved. Pelleted protoplasts were snap frozen in a dry ice ethanol bath, then treated with MgCl2, CaCl2, DNase I (Qiagen), and RNAse A (Roche) in 50 mM Tris buffer (pH 7.5) with 20 mM HEPES (pH 8.0), 20 mM NaCl, and 1 mM DTT with protease inhibitors. The pellet was incubated on ice for 1 h, then spun at max speed for 30 min at 4 °C. The supernatant, which contained the cytoplasmic fraction, and the pellet, which contained the membrane fraction, were saved. 100% TCA was added to the samples so that the final concentration of TCA was 20%. Samples were incubated on ice for 30 min, then centrifuged at full speed at 4 °C to pellet precipitated protein. The TCA supernatant was aspirated, and the pellet was washed twice with 100% acetone, then air-dried at 95 °C for 1 min. Pellets were resuspended in NuPage LDS sample buffer (Thermo Scientific) and boiled at 100 °C for 10 min. Samples were loaded into NuPage SDS-PAGE gels (Thermo Scientific) and transferred to nitrocellulose membranes using the XCell Sure-Lock mini-cell electrophoresis system (Thermo Scientific). Nitrocellulose membranes were blocked overnight in 5% NFDM and treated with primary antibody against SP_1505 at a concentration of 1:500. After washing, membranes were treated with secondary antibody goat anti-rabbit IgG-HRP  at a concentration of 1:3000. Membranes were developed using the SuperSignal West Dura Extended Duration Substrate (Thermo Scientific) and were visualized using a BioRad ChemiDoc MP imaging system.Strains were grown in Todd-Hewitt broth to OD 0.4. Following this, cells were fractionated as previously described in ref. Bacteria were inoculated from TSA plates into C + Y media, at OD 0.4, culture was split into 1 mL aliquots and treated with vancomycin (0.25 μg/mL) or daptomycin (0.5 μg/mL). For antibody treatment, strains were grown in C + Y media until OD 0.3. At this time, samples were treated with SP_1505 antibody or control rabbit IgG antibody (Sigma) at concentrations indicated in figure legends, incubated for 30 min, followed by antibiotic treatment. At 4 h post-antibiotic addition samples were plated for bacterial enumeration.6 CFU of wild-type pneumococcal cells in a volume of 100 µL. Eight hours following the challenge mice were treated with vehicle , vancomycin (0.25 mg/kg), daptomycin (2.5 mg/kg), a-SP_1505 antibody (100 uL), and control rabbit IgG. At 16 h following antibody/antibiotic treatment  mice were euthanized, and lungs and chest cavity blood were removed for quantification of bacteria. Whole lungs were washed twice in PBS, and lung tissue was subsequently homogenized in 1 mL PBS. Homogenized lung samples were centrifuged at 300xg, and bacteria-containing supernatant was plated onto Neomycin-containing blood agar plates for CFU titers. Mouse experiments were approved under St. Jude Children’s Research Hospital IACUC approved protocol #538 and Boston College IACUC approved protocol #2019-007-01. Mice were housed with a 12 h/12 h:dark/light cycle. The room temperature set point was 71 degrees F (±2 degrees) and the humidity setpoint was 40%.Isoflurane-anesthetized 7-week-old female BALB/c mice were inoculated intranasally with 10Peptide P1  and peptide P2 (Ala-Lys-Thr-Ile-Lys-Ile-Thr-Gln-Thr-Arg) were synthesized on a preloaded Wang resin using the standard Fmoc/tBu chemistry for peptide synthesis. All coupling reactions were carried out in DMF using HBTU as the coupling reagent, 0.4 N-Methyl Morpholine in DMF as base. After each coupling, deprotection of the Fmoc group was done by using 20% piperidine in DMF. After completion of synthesis, peptides were cleaved from resin using TFA and purified using RP-HPLC. The integrity and purity of the peptides were confirmed using LC–MS.82. S. pneumoniae were grown in THY to OD 0.6. Cells were pelleted, washed twice in PBS and resuspended in 3.5 mL PBS. 1 mL of cells were incubated with 50 μM antibiotic for 10 min at 37 °C. Following incubation, 800 μL of drugged cells were spun  through 700 μL of a 9:1 mix of AR20 and high temperature silicon oils (cooled to −80 °C), after which the supernatant of silicone oil and free compound were carefully removed. For lysis, pelleted cells were resuspended in 200 μL dH2O and lysed via bead beating (3x 15 s at 5 m/s). Debris was pelleted  and 100 μL of supernatant was removed and saved. Cell debris was resuspended in the remaining 50 μL dH2O and mixed with 200 μL methanol. Potential cell debris was pellet again and 150 μL of the methanol extract was mixed with the 200 μL dH2O supernatant from the previous step. The extract was pelleted one final time  before being filtered (0.22 μm).Antibiotic accumulation was determined as previously described−1 with the following gradient: 0−4 min, 99.9% solvent A and 0.1% solvent B; 4–5 min, 10% solvent A and 90% solvent B; 5–6 min, 99.9% solvent A and 0.1% solvent B. Tandem mass spectra were acquired with the following conditions: Ciprofloxacin: CV:20, CE:25, m/z ion: 333.14.→245.11; Kanamycin: CV:40, CE:20, m/z ion: 485.25→163.11. High-resolution spectra were calibrated by co-infusion of 2 ng mL−1 leucine enkephalin lockspray (Waters). Data were quantified using Waters MassLynx software v4.2 where the AUC was determined by integrating the corresponding daughter peak of the parent compound. Concentrations of the unknown compounds were determined by the linear fit of the corresponding standard curve. Concentrations are reported as the average of three biological replicates.Samples were analyzed with a Waters Acquity M Class series UPLC system and Xevo G2 QTOF tandem MS/MS with Zspray. Hundred nanoliters of extract was separated using a Phenomenex Kinetex 2.6 μm XB-C18, 100 Å (300 μm × 150 mm) column with solvent A, 0.1% formic acid in water, and solvent B, 0.1% formic acid in acetonitrile. The inlet method utilized a flow rate of 8 μL minS. pneumoniae strains were grown at 37 °C in 10 mL ThyB to an OD of ~0.5. Cultures were split into 5 mL aliquots for mupirocin-treated versus untreated controls. To induce the stringent response and ppGpp production, mupirocin was added in a final concentration of 25 µg/mL and incubated at 37 °C for 30 min. Cells were centrifuged at 6000 × g for 5′, supernatant was discarded and cell pellets were frozen at −80 °C. For LC/MS analysis cell pellets were resuspended in 2 mL cold methanol, and 150 pmol of [13C10]-GTP (Sigma) was added and incubated at −80 °C for 30 min. Samples were centrifuged at 4000 × g for 10′, and the supernatant was removed and dried overnight in a Savant Speedvac Concentrator SPD 1010 (Thermo Fisher). Samples were analyzed using a Shimadzu Prominence UFLC attached to a QTrap 4500 equipped with a Turbo V ion source (Sciex). Samples (5 μL) were injected onto a SeQuant ZIC-cHILIC, 3 μm, 2.1 × 150 mm column at 30 °C (Millipore) using a flow rate of 0.3 mL/min. Solvent A was 25 mM ammonium acetate, and Solvent B was 75% acetonitrile + 25 mM ammonium acetate. The HPLC program was the following: starting solvent mixture of 0% A/100% B, 0 to 2 min isocratic with 100% B; 2 to 4 min linear gradient to 85% B; 4 to 17 min linear gradient to 65% B; 17 to 22 min isocratic with 65% B; 22 to 25 min linear gradient to 100% B; 25 to 30 min isocratic with 100% B. The QTrap 4500 was operated in the negative mode, and the ion source parameters were: ion spray voltage, −4500 V; curtain gas, 30 psi; temperature, 400 °C; collision gas, medium; ion source gas 1, 20 psi; ion source gas 2, 35 psi; declustering potential, −40 V; and collision energy, −40 V. The MRM transitions are: ppGpp, 602.0/159.0; pppGpp, 682.0/159.0, and [13C10]-GTP, 522.0/159.0. [13C10]-GTP was used as the internal standard. The system was controlled by the Analyst softwarev1.7 (Sciex) and analyzed with MultiQuant™ 3.0.2 software (Sciex). Peaks corresponding to ppGpp and pppGpp were quantified relative to the internal standard. The limit of detection for ppGpp and pppGpp is 5 pmol, and for GTP, GDP, ATP and ADP 0.05pmol.7 CFU, bacterial suspension in 1× PBS. Each bacterial suspension contained a 1:1 mixture of S. pneumoniae TIGR4 wild type and ΔSP_0829 or ΔSP_1396. The challenge dose was always confirmed by serial dilution and plating on blood agar plates. Infected mice receiving antibiotic treatment were administered either 1 mg/kg cefepime (WTvsΔSP_0829) or 10 mg/kg meropenem (WTvsΔSP_1396) 16 h post-bacterial challenge by intraperitoneal (i.p.) injection. Antibiotic dosing was previously determined to reduce bacterial loads 10-100-fold in vivo. Mice were euthanized by CO2 asphyxiation at 6 h post-antibiotic administration . Blood by cardiac puncture, nasopharynx lavage, and total homogenized lungs were collected from each animal to determine bacterial burden by serial dilution and plating blood agar plates as previously described18. Mouse experiments were approved under St. Jude Children’s Research Hospital IACUC approved protocol #538 and Boston College IACUC approved protocol #2019-007-01. Mice were housed with a 12 h/12 h:dark/light cycle. The room temperature set point was 71 degrees F (±22 degrees) and the humidity setpoint was 40%.In vivo competition experiments were essentially performed as previously described (van Opijnen and Camilli 2012). Specifically, groups of at least 12 outbred 4-6-week-old Swiss Webster mice  were anesthetized by isoflurane inhalation and challenged intranasally (i.n.) with 50 µL, ~1.5 × 10https://www.patricbrc.org/) was screened for antibiotic-resistant S. pneumoniae isolates. There is a potential risk that isolates in the database are clonally related, which could mean that multiple isolates would contain exactly the same sequence and for instance the same stop codon, which could bias the analysis. To reduce this potential bias candidate isolates were limited to those belonging to a different MLST type. While this considerably reduced the number of potential isolates, we were able to collect 533 β-lactam resistant and 1147 co-trimoxazole resistant strains. Moreover, an equal number of non-resistant strains were compiled. From each genome, gene sequences were extracted that match those from each of the 4 gene sets. Each gene was scanned for premature stop codons occurring in the first 90% of a gene. For each gene set the number of strains with at least one stop codon in the gene set were recorded, as well as the total number of stop codons in all genes in a set. To test for differences in the number of isolates containing a stop codon within (susceptible vs. resistant) and between sets a Fisher’s exact test was performed.Four gene sets were compiled to test for the differential occurrence of stop codons in patient samples. Each gene set consists of 34 genes and are defined as: Set 1 consists of genes that when disrupted lead to a significant decrease in antibiotic sensitivity in the presence of at least one antibiotic (in vitro ABx fitness positive), and have no fitness defect in lung and nasopharynx ; Set 2 consists of genes that when disrupted lead to a significant decrease in antibiotic sensitivity in the presence of at least one antibiotic (in vitro ABx fitness positive), and have a significant fitness defect in lung and nasopharynx (in vivo fitness negative); Set 3 consists of genes that when disrupted have no fitness benefit in any of the antibiotics , but with a significant fitness benefit in lung and nasopharynx (in vivo fitness positive); Set 4 consists of genes that have decreased fitness in the presence of antibiotics (in vitro ABx fitness negative), and that have a significant fitness defect of >15% in lung and nasopharynx (in vivo fitness negative). The PATRIC database (Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Reporting Summary


To compare the response and safety of Ixazomib/Lenalidomide/Dexamethasone (IRd) and Bortezomib/Lenalidomide/Dexamethasone (VRd) treatment in newly diagnosed multiple myeloma (MM).n = 21) and VRd (n = 31) groups. After 4 cycles of chemotherapy, the best response and adverse events were recorded. Moreover, the progression‐free survival (PFS) and overall survival (OS) were calculated.This was a single‐center retrospective analysis in Xiangtan Central Hospital. A total of 52 newly diagnosed MM patients from June 2019 to June 2021 were enrolled and divided into the IRd (p = 0.70), OS (Log‐rank p = 0.61) and overall response rate . In addition, patients in VRd group showed lower Eastern Cooperative Oncology Group scores (p = 0.047), and higher incidence of peripheral sensory neuropathy  than that of patients in IRd group.Patients in IRd group and VRd group showed similar PFS (Log‐rank Compared to VRd regimen, IRd had the similar efficacy, better safety, and may be more convenient for patients with poor basic condition for newly diagnosed MM. This study provides an insight for physicians to use IRd as first‐line treatment in MM. This study indicats the similar efficacy, PFS, and OS in patients treated by VRd and IRd. Moreover, IRd shows better safety than VRd, and it may be more convenient for patients with poor basic condition. This study provides an insight for physicians to use IRd as first‐line treatment in MM. Patients in combination with other hematological disease, malignant tumor, or with an expected survival of less than 3 months and chemotherapy using current regimens for less than 4 cycles were excluded. All the enrolled MM patients provided informed consent.2.2n = 21) and VRd (n = 31) groups. The collected data of patients included gender, age, International Staging System (ISS) stage, Eastern Cooperative Oncology Group (ECOG) performance status, results of blood test  concentration, lactic dehydrogenase (LDH)), urine test , imaging modality , and bone marrow examination .This was a retrospective, single‐center study in Xiangtan Central Hospital from June 2019 to June 2021, and approved by the Medical Ethics Committee of Xiangtan Central Hospital . A total of 52 newly diagnosed MM patients were enrolled and divided into the IRd  on 1–21 days and dexamethasone at the dose of 20–40 mg (dose regulated according to the age) on days of 1, 8, 15, and 22, with one cycle of 28 days. After treatment the best response, adverse events, and concentration of hemoglobin (HGB), β2‐MG, white blood cells (WBC), platelets (PLT), Creatinine (CRE), LDH, globulin (GLB), and serum calcium (Ca) were recorded.VRd is considered as the standard first‐line therapeutic strategy for newly diagnosed MM. IRd is an oral regimen, which is convenient for elderly patients with poor basic condition, especially in the case of COVID‐19. IRd or VRd regimen is recommended according to the comprehensive evaluation of the patient's condition, and patients make informed choices. Patients in IRd group received oral ixazomib at the dose of 3 mg or 4 mg (dose selected according to the kidney and liver state of patients) on the days of 1, 8, and 15, lenalidomide at the dose of 10 mg or 20 mg (dose regulated depending on the creatinine clearance rate) on 1–21 days, dexamethasone at the dose of 20–40 mg (dose regulated according to the age) on the days of 1, 8, 15, and 22 with one cycle of 28 days. Patients in VRd group received subcutaneous injection of bortezomib at dose of 1.3 mg/m2.3The primary endpoint was overall response rate (ORR), and the secondary endpoints included progression‐free survival (PFS), overall survival (OS), and adverse events incidence.Treatment efficiency was evaluated according to the IMWG criteria (2016). Determining the concentration of immune globulin before each cycle of therapy, combined with other examination such as marrow cell, bone marrow flow cytometry immunophenotyping, serum and urine light chain, M protein, serum protein electrophoresis, and serum and urine immunofixation electrophoresis after 4 cycles of treatment. Thereafter, the best responses of each patient were recorded, including complete remission (CR), stringent complete response (sCR), partial response (PR), very good partial response (VGPR), stable disease (SD), progressive disease (PD). The overall response rate (ORR) was calculated using formula as follow: /cases total number × 100%.PFS was calculated from diagnosis to progression of disease, death, or the last follow‐up, whereas OS was calculated from diagnosis to death or the last follow‐up.Adverse events were graded according to the data derived from the Common Terminology Criteria for Adverse Events.2.42 and rank sum test. Measurement data were expressed as mean ± standard deviation (SD), and tested using Student's t‐test. OS and PFS were analyzed using GraphPad Prism, version 7.00 , and statistical analysis was performed using the Kaplan–Meier method followed by the log‐rank test. p < 0.05 was considered to be statistically significant.Statistical analysis was performed using SPSS, version 20.0 . Enumeration data were represented as frequency and percentage (%), and texted using x33.1p > 0.05). However, there was significant statistical difference in ECOG scores between IRd group and VRd group at initial diagnosis (p = 0.047) . Of them, 8 patients had high‐risk cytogenetics, 8 had standard‐risk cytogenetics, the remaining 6 patients did not undergo cytogenetic risk stratification due to the lack of fluorescence in situ hybridization. Whereas, among 31 patients in VRd group, 15 were male and 16 were female with a mean age of 67 . Of them, 12 patients had high‐risk cytogenetics, 11 had standard‐risk cytogenetics, the remaining 8 patients did not undergo cytogenetics risk stratification due to the lack of fluorescence in situ hybridization. In addition, 13 patients in the IRd group and 19 patients in VRd showed bone lesion. The clinical characteristics of MM patients were shown in Table 3.2p > 0.05)  Table .3.3p = 0.61, 95%CI, 0.18–17.63) and PFS  between patients in IRD and VRD groups. To better evaluate the PFS and OS in these two groups, longer follow‐up is needed.The longest follow‐up was 21.5 months. Due to the short follow‐up period, the median OS and PFS were not achieved. As shown in Figure 3.4p > 0.05). Diarrhea, peripheral sensory neuropathy, and limb numbness were the main non‐hematologic toxicities in the patients. And two patients had diarrhea in IRd group, of which one was grade 1–2, another was grade 3–4. Six patients had peripheral sensory neuropathy in VRd group, of which 1 was grade 1–2, and 5 were grade 3–4. And three patients in VRd group had grade 1–2 limb numbness. No intergroup differences were observed in the occurrence of the Diarrhea and limb numbness. Whereas, patients who suffered from peripheral sensory neuropathy showed intergroup difference between IRd and VRd groups .The adverse events that occurred in patients are shown in Table 4Proteinase is the vital constituent of the ubiquitin‐proteasome pathway, playing an important function in protein homeostasis, cellular growth, and apoptosis regulation.n = 21) and VRd (n = 31) groups. The patients treated with IRd received 90.48% of ORR including 28.57% of CR, which was similar to previous study.Ixazomib is a first FDA‐approved oral second‐generation proteasome inhibitor that selectively binds to the β5 subunit of the 20S proteasome and inhibits its chymotrypsin‐like activity, leading to apoptosis of multiple myeloma cells.Previous study showed that the adverse reactions of IRd were mainly reflected in peripheral neuropathy, rashes, neutropenia, thrombocytopenia, diarrhea, and fatigue.As we know that the incidence of MM is increasing with the aging of population, and about 65% of patients diagnosed with MM are over 65 years. The elderly patients often have more basic diseases and poor physical status. In our retrospective study of 52 newly diagnosed MM, 13 patients with ECOG score of 3, accounted for 25%. These patients received low intensity of chemotherapy due to poor tolerance to adverse reactions of chemotherapy, which affected the treatment of the disease, thereby leading to disease progression and seriously reduced the quality of life of the patients. In addition, we found that the basic state of patients in IRd groups was worse than that in the VRd treatment group, as demonstrated by the higher ECOG scores of patients in IRd group. This may be associated with the way of administration, patients with poor condition prefer to choose the oral regimen (IRd), which is more convenient. In addition, oral regimen has more advantage under the condition of COVID‐19 that patients can take medication at home, which also relieves the pressure on doctors to some extent. The TOURMALINE‐MM2 trial reported by Thierry et al. also demonstrated that IRd is a feasible strategy for certain transplant‐ineligible in newly diagnosed MM patients who often have limited mobility and reduced ability to frequently attend hospital.A limitation of the current study was that this was a retrospective, single‐center study and the sample size was too small to obtain the accurate conclusions. In addition, the short duration of follow‐up may confuse the conclusion of prognosis. Collectively, the present data demonstrate the efficacy and safety of the oral regimen IRd for newly diagnosed MM after 4 cycles of chemotherapy. However, this study lacks transplant correlation analysis due to small sample size and the tolerability of prolonged IRd treatment was unclear. We will continue to expand the sample size and extend follow‐up time to verify the conclusions of this study and uncover the tolerability of newly diagnosed MM patients to IRd.5In conclusion, this study retrospectively analyzed the efficacy, safety, PFS, and OS of different treatments in 52 newly diagnosed patients with MM, indicating the similar efficacy, PFS, and OS in patients treated by VRd and IRd. Moreover, IRd showed better safety than VRd, and it may be more convenient for patients with poor basic condition. Collectively, the present work indicated the efficacy and safety of IRd for newly diagnosed MM in the real‐world clinical setting, providing an insight for physicians to use IRd as first‐line treatment.Z. J. Huo, F. Chen, and Z. M. Luo designed this study. Z. J. Huo, F. Chen, and P. Liu collected and analyzed the data. Z. J. Huo and F. Chen searched the literature and drafted the manuscript. Z. M. Luo acquired the funding and revised the manuscript. All authors read and approved the final manuscript.The study was supported by China Hunan Provincial Science & technology Department (No. 2018SK52108).Authors have no potential conflict of interest to declare.This study was approved by the Medical Ethics Committee of Xiangtan Central Hospital .


Early onset drusen maculopathy (EODM) can lead to advanced macular degeneration at a young age, affecting quality of life. However, the genetic causes of EODM are not well studied. We performed whole genome sequencing in 49 EODM patients. Common genetic variants were analysed by calculating genetic risk scores based on 52 age‐related macular generation (AMD)‐associated variants, and we analysed rare variants in candidate genes to identify potential deleterious variants that might contribute to EODM development. We demonstrate that the 52 AMD‐associated variants contributed to EODM, especially variants located in the complement pathway. Furthermore, we identified 26 rare genetic variants predicted to be pathogenic based on in silico prediction tools or based on reported pathogenicity in literature. These variants are located predominantly in the complement and lipid metabolism pathways. Last, evaluation of 18 genes causing inherited retinal dystrophies that can mimic AMD characteristics, revealed 11 potential deleterious variants in eight EODM patients. However, phenotypic characteristics did not point towards a retinal dystrophy in these patients. In conclusion, this study reports new insights into rare variants that are potentially involved in EODM development, and which are relevant for future studies unravelling the aetiology of EODM.  In addition, common AMD‐associated variants may also contribute to EODM, as we hypothesise that EODM is an early manifestation of AMD, caused by a combination of common variants and highly penetrant rare variants. Caution is warranted when studying EODM, as phenotypically some inherited retinal dystrophies (IRDs) resemble EODM, for example, Stargardt disease  and Sorsby fundus dystrophy .Although AMD mainly affects the elderly, some individuals develop AMD characteristics, including drusen, which are deposits that accumulate underneath the retinal pigment epithelium (RPE), RPE alterations, geographic atrophy (GA) and choroidal neovascularization (CNV) at a much younger age. Consequently, these patients with early onset drusen maculopathy (EODM) often develop the vision‐threatening end stages of the disease, including GA and CNV, earlier in life. Its early onset suggests a prominent role for genetic factors rather than environmental factors in EODM development. In particular rare, highly penetrant variants may contribute to an early onset of AMD characteristics.The aim of this study is to determine the contribution of common AMD‐associated variants, and to identify potential disease‐causing rare variants in patients with EODM.22.1CFH, CFI, C3, C9 and CFB were identified.We collected a cohort of 89 unrelated patients with EODM from the European Genetic Database (EUGENDA), as described previously,2.2https://gnomad.broadinstitute.org], GoNL, Wellderly, 1000genomes, in‐house variant frequencies database containing WES‐data of 15 576 individuals), in silico prediction , predicted protein effect, gene and disease OMIM description, gene regulation and expression data.WGS was performed on a BGISeq500 using 2 × 150 bp paired‐end reads and a 30‐fold minimal median coverage per genome by BGI Genomics. The sequencing reads were mapped to the human reference genome (hg19) using the Burrows‐Wheeler Aligner (BWA) v0.7.13. Qualimap V.2.2.1 was applied to assess quality of sequencing alignment data based on insert size, percentage mapped reads, percentage duplicated mapped reads, coverage, bases with >20× coverage and error rate. Variant calling was performed using xAtlas V.0.1 and thereafter annotated using the Variant Effect Predictor (VEP V.91) and Gencode V.34lift37 basic gene annotations. Single nucleotide variants were annotated using an in‐house developed pipeline which provides further information including different population allele frequency databases  based on the GWAS of the International Age‐related Macular Degeneration Genomics Consortium.The 52 AMD‐associated variants, as reported previously,2.4We performed multiple filtering steps to identify potential disease‐causing variants in EODM patients. As a first step, WGS data were filtered on SNVs with a minor allele frequency (MAF) <1%, based on the in‐house database, and on the Genome Aggregation Database (gnomAD). Next, we extracted rare variants present in four candidate gene lists, including genes involved in the complement pathway, the lipid pathway, genes evaluated in mouse models that developed drusen or other AMD characteristics, and genes located in the 34 AMD‐associated loci.2.5www.graphpad.com.Continuous variables that were normally distributed were displayed as means with corresponding standard deviations (SD), and medians with corresponding interquartile ranges (IQR) were used for skewed distributions. Categorical variables were displayed as proportions with corresponding percentages. GRS, complement GRS and lipid GRS among disease stages were compared using Kruskal–Wallis tests for skewed data and one‐way analysis of variance for normally distributed data. Statistical analysis and data visualisation were performed with GraphPad Prism version 5.03 for Windows, GraphPad Software, San Diego, California USA, 33.1n = 13), CNV (n = 8), or a combination of GA and CNV (n = 1) (Table ).The 49 EOMD patients in this current study had a mean (SD) age of 51.8 (9.2) years, and 67.3% were female. 55.1% showed signs of early/intermediate AMD (RC 1–3), and 44.9% were affected by advanced disease stages, including GA (3.2p < 0.001), indicating that the AMD‐associated variants contribute to EODM development. Considering that the overall GRS consists of genetic variants present in different disease pathways, we extracted the complement and lipid variants to assess pathway specific GRSs. We observed a higher mean (SD) complement GRS in EODM cases compared to control individuals (0.57 (1.01) vs. 0.03 (0.86), p < 0.001). In contrast to the complement GRS, the lipid GRS did not differ between EODM cases and control individuals (−0.12 (0.31) vs. −0.19 (0.27), p = 0.08)  GRS was higher in EODM compared to control individuals (1.03 (1.29) vs 0.20 (1.13), p < 0.001 for all). In EODM, overall GRS and complement GRS showed a trend of increasing GRS and complement GRS, with increasing severity of disease stages . The lipid GRS showed a trend of decreasing lipid GRS, with increasing severity of disease stages (p = 0.32)  were present in two candidate gene lists.Next, we studied the contribution of rare variants in EODM. We first focused on candidate genes present in the complement gene list, lipid gene list and list of genes tested in mouse models that developed drusen or other AMD characteristics. In total, 97 unique candidate genes were present in those three candidate gene lists Table , and we C3, CFH, CFHR2, C8A, C8B, C9, CSMD1, CSMD2, FCN1, MASP1, MASP2). Two of these variants were located in C3: c.1661A > G (p.Asp554Gly) is predicted to be damaging based on all three in silico prediction tools, and c.4992A > G (p.Ter1664TrpextTer24) results in a stop‐loss. One frameshift variant (c.454del [p.Ala152Glnfs*8]) and two nonsense variants (c.865A > T (p.Lys289*) and c.162C > A (p.Cys54*)) in C8A, C8B and C9, respectively, were identified in three different EODM patients. Furthermore, a rare splice‐site variant in CFH (c.350 + 6 T > G), reported to be pathogenic in ClinVar, and a rare nonsense variant in CFHR2 (c.595G > T [p.Glu199*]) were identified in two different EODM patients. The other potential deleterious rare variants in complement‐related genes CSMD1, CSMD2, FCN1, MASP1 and MASP2 had either a high CADD score, or were potentially deleterious based on variant type, including a nonsense variant in FCN1 (c.868C > T [p.Arg290*]) and a frameshift variant in MASP1 (c.1770del [p.Lys591Serfs*11]). However, these variants were not reported in ClinVar, except for the variant in MASP2, which was reported as (likely) benign. The other potential deleterious rare variants were identified in lipid‐related genes  or in genes that were evaluated in mouse models .Out of all variants in those 51 genes, 26 rare variants in 19 genes were considered to be potentially deleterious based on variant type , in silico prediction tools  or pathogenicity category according to ClinVar (pathogenic or likely pathogenic) . Of these 93 overlapping rare variants, the majority (84/93) was identified in genes in the AMD‐associated loci, whereas the remaining nine overlapping variants were identified in complement‐related genes , lipid‐related genes  or in genes evaluated in mouse models that developed drusen or other AMD characteristics . Based on variant type, in silico prediction tools and pathogenicity category according to ClinVar, we identified 20 rare variants that are potentially damaging  variant  variant presented with cuticular drusen, and in the left eye of patient B1 a pseudovitelliform lesion was observed  pathogenic in ClinVar or in literature , which was identified in two EODM patients. The low‐density lipoprotein receptor (LDLR) is expressed in the RPE, and is localised in the basement membrane of the RPE. It is involved in the uptake of LDL particles by RPE cells from the circulation, and thereby belongs to the lipid pathway.Ldlr deficient mice, emphasising the potential role of LDLR in AMD.LDLR, including the c.47 T > C (p.Leu16Pro) variant, are associated with familial hypercholesterolemia.LDLR might play a role in EODM. Further research is needed to elucidate the role of rare LDLR variants, e.g., evaluating lipid deposition in iPSc‐based AMD/EODM model systems, or measuring cholesterol levels in blood of patients with rare variants in LDLR. Although two EODM patients carried the same rare variant in LDLR, their phenotypes did not show similarities. Of interest, one of those patients suffered from C3 glomerulopathy. It is described before that patients with C3 glomerulopathy can develop retinal drusen, and part of the patients with C3 glomerulopathy carry risk alleles in complement‐related genes.With respect to rare variants, we did not identify specific rare variants that were present in a large proportion of EODM patients or in one specific gene. Most of the potential deleterious rare variants were found in a single or in two EODM patients. Several potential deleterious rare variants in specific genes identified in this current study are of interest. One of these variants includes CD36 and CX3CR1. CD36 is expressed in the RPE and involved in the uptake of lipoproteins. One EODM patient carried a rare nonsense variant (c.1144C > T [p.Gln382*]) and one a rare missense variant (c.31G > A [p.Ala11Thr]) CD36. Although this latter variant was not predicted to be deleterious, the first one results in a premature stop codon and is predicted to be damaging . Moreover, a previous study showed age‐related photoreceptor degeneration and choroidal involution in Cd36 deficient mice, pointing towards a potential important role of CD36 in the pathogenesis of EODM.CX3CR1 in two EODM patients. Cx3CR1 c.74A > G (p.Asp25Gly) is predicted to be pathogenic based on all three in silico prediction tools. The functional effect of the other CX3CR1 variant (c.169A > G [p.Thr57Ala]) is unclear. The CX3C chemokine receptor 1 (CX3CR1) is expressed in retinal microglial cells. In a previous study, lipid‐bloated microglial cells accumulated in the subretinal space of Cx3cr1 knockout mice. Furthermore, microglial cell accumulation in these mice was associated with an exacerbation of laser‐induced CNV. In addition, other AMD characteristics, including complement deposition and photoreceptor atrophy, were observed in Cx3cr1 knockout mice, emphasising the potential role of this gene in EODM.We also identified potential deleterious variants in two genes evaluated in mouse models that developed AMD characteristics, including CFHR genes are considered to be protective for AMD, rather than risk increasing. This is in line with a recent study, reporting that low‐frequency variants resulting in low or absent levels of FHR‐2 and FHR‐5 are protective for AMD.C3 (c.4992A > G (p.Ter1664TrpextTer24)), was identified in one EODM patient, and was not reported in AMD before. This variant is predicted to add 24 erroneous amino acids to the C‐terminus of C3, and could potentially interfere with correct folding of C3, and likely results in degradation within the cell. However, functional data for this variant is lacking. Noteworthy, this particular patient has a high GRS, and carries multiple risk alleles in complement‐related genes, which may contribute to the patients' phenotype. Furthermore, two nonsense variants and one frameshift variant in C8A, C8B and C9 were identified. These three genes encode components of the membrane attack complex, which plays an important role in targeting pathogens. In general, mutations  in the C5–C9 genes are associated with different types of C5–C9 deficiencies (e.g. C9 deficiency in the Japanese population).C5–C9 are protective for AMD, and presumably also for EOMD, considering the same underlying disease mechanisms. Nevertheless, it is important to note that EODM is likely due to a combination of risk increasing and risk decreasing genetic variants. Last, in one patient we identified a splice‐site variant in CFH (c.350 + 6 T > G). In a previous study it was suggested that monogenic inheritance of CFH variants could result in drusen at a young age.CFH. However, the GRS in this patient was high (2.03). The high genetic load together with the rare variant in CFH fits with the EODM phenotype is this patient.Furthermore, multiple nonsense, stop‐loss and frameshift variants were identified in EODM patients in genes of the alternative complement pathway, a pathway that has firmly been confirmed to play an important role in AMD pathogenesis. Based on variant type, these variants are considered to be pathogenic, however, one needs to take into account the effect of the variants on the specific proteins. For example, the factor H related (FHR) proteins compete with factor H (FH) in binding to C3b, thereby counteracting complement inhibition by FH. Less inhibition by FH results in increased complement activation, which in turn is a risk factor for AMD. Therefore, nonsense variants in TIMP3 c.70 T > G (p.Cys24Gly) mutation. Mutations in TIMP3 are linked to SFD, an IRD characterised by CNV with or without the presence of drusen. It is reported that most of the disease‐causing mutations in TIMP3 involve cysteine residues.TIMP3 in this current study, presented with large drusen and small (nascent) GA lesions, which was not suspect for SFD. The GRS was high (2.53), suggesting that the disease may be primarily driven by AMD‐associated variants. However, the TIMP3 variant might additionally contribute to the patients' phenotype. Phenotypic characteristics of the other EODM patients in whom we identified a rare variant associated with an IRD did not point towards an IRD. We cannot confirm nor rule out that the identified variants contribute to the patients' phenotype. For future studies it can be helpful to include family members, since this could provide information about segregation of specific genetic variants.Awareness for overlap in phenotypic features between AMD/EODM and IRDs is warranted, especially in patients presenting with drusen or other AMD characteristics at an early age. In this study we identified one patient with a To our knowledge, this is the first study using WGS in a large cohort of EODM patients, and allowed for the evaluation of both common and rare variants, and revealed insights into biological disease pathways that may contribute to EODM. This study also has its limitations. The limited sample size and the design of the study did not allow to detect significant associations of rare variants with EODM. However, we identified several rare variants in EODM patients that are potentially involved in EODM. These findings should be replicated in a larger cohort of EODM patients, to elucidate their contribution to EODM. Furthermore, many variants were not reported previously, were of uncertain clinical significance, or were predicted to be (likely) pathogenic based on in silico prediction tools. Testing the effect of these variants in AMD/EODM model systems (e.g. mouse or iPSc‐based models), or by measuring protein or metabolite levels in patients with specific rare variants, will improve the interpretation of these variants and may result in new insights in the EODM pathogenesis. In the current study family members of EODM patients were unfortunately not available, therefore, we recommend ascertaining family members for segregation analysis in a follow‐up study. We consider EODM as an early manifestation of AMD, which is a multifactorial disease. In some EODM cases a high GRS based on the AMD‐associated variants may already explain the disease. In other cases where the GRS is low and where we did not find potential deleterious rare variants in the studied candidate genes, the cause may lie in other genomic regions or in genes that were not evaluated in this study. In addition, we focused solely on genetic factors, and did not analyse non‐genetic risk factors such as age, smoking and diet, as we hypothesised that EODM is more genetically driven compared to AMD due to the early age of onset.5CFH variants in a large proportion of EODM patients,In this current study we showed that common AMD‐associated variants contribute to EODM development, and we identified rare, potential deleterious variants that might play a role in EODM pathogenesis. Together with the identification of rare Anita de Breuk, Carel B. Hoyng, and Anneke I. den Hollander: Concept and design; All authors: acquisition, analysis, or data interpretation; Anita de Breuk and Anneke I. den Hollander: Manuscript drafting; All authors: critical revision of the manuscript for important intellectual content; Anita de Breuk and Anneke I. den Hollander: statistical analysis; Caroline C. W. Klaver, Carel B. Hoyng, and Anneke I. den Hollander, and Anita de Breuk: Obtained funding; Yara T. E. Lechanteur, Caroline C. W. Klaver, Carel B. Hoyng, and Anneke I. den Hollander: Supervision.This research was supported by the Dutch Research Council (016.Vici.170.024 to Anneke I. den Hollander) and by the Bayer Ophthalmology Research Award (BORA). The funding organizations had no role in the design or conduct of the study and were not involved in interpretation of the data or writing of the manuscript.Anita de Breuk, Yara T. E. Lechanteur, Galuh Astuti, Jordi Corominas Galbany, Caroline C. W. Klaver, and Carel B. Hoyng declare no conflict of interest. Anneke I. den Hollander is a consultant for Gemini Therapeutics, Gyroscope Therapeutics, Ionis Pharmaceuticals and Roche.This research was approved by ethical committees at the Radboud university medical centre, Nijmegen, The Netherlands, the University Hospital of Cologne, Cologne, Germany, and the Institut de la Màcula, Barcelona, Spain, and adhered to the tenets of the Declaration of Helsinki, and all study participants provided written informed consent. Approval code 2007‐158. Approval date whole genome sequencing, August 21st, 2018.Figure S1 Colour Fundus Photographs of EODM Patients Carrying Rare Variants in IRD GenesClick here for additional data file.Table S1 Overview of 52 AMD‐associated variants identified in a large GWAS study Click here for additional data file.Table S2 Candidate Gene ListsClick here for additional data file.Table S3 Genetic risk scores per patientClick here for additional data file.Table S4 Candidate genes and variantsClick here for additional data file.Table S5 Rare variants in inherited retinal dystrophy genesClick here for additional data file.Table S6 Rare Variants in IRD genes predicted to be damaging by three prediction algorithms or reported as (likely) pathogenic by ClinVarClick here for additional data file.Table S7 All rare variants in candidate genes in the 49 EODM patientsClick here for additional data file.Table S8 Nonsense, frameshift, splice‐site and missense variants with high prediction scoreClick here for additional data file.


Gastric cancer liver metastasis (GCLM) patients usually accompany by abnormal serum liver function tests (LFTs) more or less; however, the prognostic value of LFTs is not fully understood. This study aimed to develop a liver chemistry score (LCS) based on LFTs and incorporate it into prognosis determination for GCLM patients who received palliative chemotherapy.Data were derived from hospitalized GCLM patients in two general hospitals in China. LCS was generated based on the results of LFTs by LASSO regression. Cutoff value of the score was determined by restricted cubic spline. The score was then incorporated into Cox regression analysis to construct a predictive nomogram; the model was then evaluated internally and externally by AUC of time‐dependent receiver operating characteristic curves (ROC) and calibration curves.p < 0.001). A nomogram including LCS and other clinical parameters was constructed and showed superior performance than model not including LCS. AUC of 6‐month ROC improved from 0.647 (95% CI: 0.584–0.711) to 0.699 (0.638–0.759) in internal validation, and 0.837 (0.734–0.940) to 0.875 (0.784–0.966) in external validation.Three hundred and thirty‐six and 72 patients were included in development and validation cohort, respectively. LASSO regression analysis in development cohort finally reached a two‐parametric LCS calculated on AST and ALP levels as 0.03343515 × ln  + 0.02687997 × ln , and 0.232 was set as optimal cutoff value. Patients in low (LCS < 0.232) or high (LCS ≥ 0.232) score group experienced different survival times; median OS was 13.54 (95% CI: 11.1–15.6) months in the low LCS group and 7.3 (6.6–9.3) months in the high LCS group  were analyzed by lasso regression; a liver chemistry score (LCS) was generated as 0.03343515 × ln  + 0.02687997 × ln . Patients in low (LCS < 0.232) or high (LCS = 0.232) score group experienced different survival times. The score was also demonstrated as an independent prognostic factor for GCLM patients and incorporated into predictive nomogram model. A panel of serum chemistry test, namely liver function tests (LFTs), has been clinically employed to reflect organ function. Elevation of liver enzymes was seen in cancer patients with or without liver metastasis.We hereby hypothesize that the survival outcome of liver metastatic gastric cancer could be determined by profiles of serum liver chemical indicators at liver metastasis. By using Lasso‐penalized analysis of a cluster of 12 common indicators of LFTs, a prognostic liver chemistry scoring system was developed in a retrospective gastric cancer cohort and was then introduced into prognostic nomogram with other clinical and laboratory parameters.22.1This two‐center retrospective study consisted of a development cohort and a validation cohort. The development cohort included consecutive patients who were diagnosed as advanced gastric cancer with liver metastasis (GCLM) and received palliative chemotherapy in the First Affiliated Hospital of Anhui Medical University (China) from September 2010 to December 2020; the validation cohort included eligible patients in Ma'anshan Municipal People's Hospital from August 2009 to December 2020. The study protocol conforms to the ethical guidelines of the 1975 Declaration of Helsinki  and was approved by the Ethics Committee of the First Affiliated Hospital of Anhui Medical University (reference number: Quick‐PJ 2021‐05‐19). Due to retrospective design, informed consent was waived.The inclusion criteria were as follows: 1) pathologically diagnosed gastric adenocarcinoma; (2) radiologically or pathologically confirmed liver metastasis; (3) clinical information and results of LFTs at the diagnosis of liver metastasis were available; (4) palliative chemotherapy was received. Patients with multiple primary cancers, or incomplete clinical information, or missing value(s) in LFTs were excluded. The flowchart of patients' selection was shown in Figure  patholog2.2The endpoint event was death due to any reason. Outcomes of patients were matched in the population death register information system of Anhui province (China), and the death date was recorded. For those unmatched individuals, survival status was obtained by regular telephonic contact to the patients or their relatives. Overall survival (OS) was defined as interval months between the onset of liver metastasis and the death date or the last follow‐up date.2.3Hospital‐stored medical files were accessed to obtain essential clinical and laboratory data. The following clinical variables were documented: (1) patient‐related variables, such as birth date, sex, history of alcohol drinking and cytotoxic drug exposure before liver metastasis, comorbidities of chronic liver diseases including hepatitis virus B or C infection, liver cirrhosis, fatty liver disease, body mass index (BMI), and Eastern Cooperative Oncology Group (ECOG) performance status score at liver metastasis; (2) tumor‐related parameters, including tumor grade, date of liver metastasis, number of liver metastases, carcinoembryonic antigen (CEA) and carbohydrate antigen 19‐9 (CA19‐9) level at liver metastasis, existence of extrahepatic metastasis or not, local treatment aimed to hepatic lesions such as chemotherapy, radiotherapy, surgical resection, and hepatic vascular intervention therapy. The serum LFTs consisted of 12 markers: total protein, albumin, globulin, total bilirubin, direct bilirubin, indirect bilirubin, alanine transaminase, aspartate aminotransferase (AST), alkaline phosphatase (ALP), gamma‐glutamyl transpeptidase, lactate dehydrogenase, and total bile acid. Only those tested within 4 weeks to radiological confirmation of liver metastasis but before commencing of new round of antitumor therapy were eligible in this study.2.4λ. A liver chemistry score (LCS) based on the selected indicators was the sum of the regression coefficients (β) derived from the Lasso Cox regression model multiplied with its ln‐transformed level. The analysis was performed in R software 4.1.1  with “glmnet” and “caret” packages.Lasso‐penalized Cox regression analysis was employed to reduce dimensionality of LFTs cluster data as to their impact on survival outcomes. All the values of LFTs were ln‐transformed to reduce skewed distribution and to accord with proportional hazard (PH) hypothesis. Tenfold cross‐validations were used to determine the optimal values of 2.5p values <0.05, and excluding those with p values >0.10. The Cox regression results were described as hazard ratios (HRs) and 95% confidence intervals (CIs). All P values were two‐tailed. p values less than 0.05 were considered statistically significant.Before performing Cox regression, numerical variables (CEA and CA19‐9) were transformed into categorical variables; RCS was used to determine the cutoff values as to their impact on survival. The univariate Cox regression was used in the primary screening of prognostic factors in the development cohort. Statistically significant variables were included in the multivariate Cox hazard model analysis. In addition, a multivariate Cox regression without LCS was also performed with aims of comparison with above model. Cox regression was performed in “R” statistics using the “cph” function in “rms” package with. The forward stepwise method was used to select prognostic predictive variables, including parameters with 2.6The nomogram score was calculated using the β coefficient of the Cox regression. The nomogram was plotted using the “rms” and “survival packages” in “R” version 4.1.1. The model was validated both internally in the development cohort and externally in the validation cohort. The discrimination ability was evaluated by investigation of area under time‐dependent ROC curve (“timeROC” package).2.7p‐value less than 0.05 was considered statistical significant.All statistical description and analysis were performed using R software 4.1.1. Continuous variables were described as median with interquartile range and compared using Wilcoxon test between two groups. Categorized variables were described as number (%) and compared using chi‐square test or Fisher exact test. Kaplan–Meier method was used to plot survival curves and log‐rank test was used to detect statistical significance. A 33.1In the development set, a total of 454 GCLM patients were filtered out from 1333 advanced gastric cancer patients. After excluding 118 ineligible individuals, 336 patients were finally included into analysis. The flow diagram of participant enrollment was illustrated in Figure plog‐rank = 0.377). The median BMI was lower in development cohort (p = 0.002). Local treatment for hepatic lesion and history of alcohol drinking were more frequently observed in the validation set (p < 0.05).Baseline characteristics of patients in the two cohorts were shown in Table 3.2p < 0.001); in the validation cohort, patients with low score also experienced longer survival than patients with high score , and the same result was achieved after PSM . Kaplan–Meier survival curves for low and high liver chemistry score patients in difference development and validation cohort is shown in Figure The 12 indicators are globally ubiquitous in biochemical tests; therefore, it is clinically feasible to implement a prognostic tool incorporating these markers. Figure n Figure  and had n Figure . The med3.3p values <0.05). These significant variables were included into multivariate Cox analysis; after running forward stepwise procedure, ECOG, extrahepatic metastasis, number of liver metastasis, CA19‐9 level, and LCS were finally retained in the equation (model incl. LCS), although multiple liver metastasis marginally increased death risk . As comparison, we also performed a new round of multivariate stepwise Cox regression analysis which excluded LCS (model not incl. LCS); the result was showed in Table First, RCS analysis in development dataset resulted “10 ng/mL” and “35 U/mL” as the thresholds for CEA and CA19‐9 level in consideration of their relationship with survival outcome Figure ; patient3.4Based on the multivariate Cox regression result, a prognostic nomogram which incorporated ECOG, number of liver metastases, extrahepatic metastasis, CA19‐9 level, and LCS was built, the 6‐ and 12‐month survival rates were predicted as shown in Figure 4In the current study, we generated a prognostic LCS based on profiles of serum LFTs in GCLM patients and validated its ability in distinguishing patients with different prognosis in an independent cohort. By using LASSO regression, two predictive indicators (AST and ALP) were filtered out from a panel of LFTs and total score for each patient was calculated with the equation 0.03343515 × ln  + 0.02687997 × ln . While incorporating the score into prognostic models for GCLM patients, the predictive ability was higher than conventional model (not incl. LCS). This study built a bridge between patients' serum LFTs at liver metastasis and their long‐term survival, the results would be helpful to clinicians for prognosis prediction and decision‐making.LASSO regression, the short for least absolute shrinkage and selection operator regression, is a method for both feature selection and regularization to improve the accuracy and regularization of statistical models. It is suitable for survival data of high dimension, strong correlation, and small sample.In previous models,The prognostic values of liver enzymes in gastric cancer patients have been explored by previous studies. Elevated ALP,The C‐indies of nomogram was moderate in this study. We attributed this to the following reasons: (1) the retrospective nature of this study: some of the patient‐related characteristics which may influence expression of liver enzymes, such as fatty liver disease and medication history, was incompletely documented in the patients' files and resulting in bias on the results; (2) other factors that may have impacted on survival of patients were difficult to traceback, such as Her‐2 status of the tumor, latter lines of treatment after liver metastasis, and so on. In addition, this is model employed data from two medical centers; multicentral data are needed to further verify the performance of the model.5A liver chemistry score based on AST and ALP was developed and shown ability in separating survival of GCLM patients, nomogram involving poor performance status (ECOG 2~), existence of extrahepatic metastasis, multiple liver metastasis, high level of CA19‐9 (≥ 35 U/mL) and LCS (≥0.232) can be used as tools for individually prognostic predicting.Y.F., C.Z., Z.W., and T.M. designed the study. Y.F., Z.W., M.X., Y.Y., and Y.Z. referred to case files and collected patients' data. H.X., X.Z., and C.F. accomplished follow‐up and vital status determination. Y.F., C.Z., C.F., and J.S. performed the statistical analysis and interpreted the results. C.Z., Y.F., and T.M. prepared the manuscript. All authors reviewed and approved the final manuscript.This study was funded by grants from the Anhui Provincial Key Research and Development Program (1804b06020351), the First Affiliated Hospital of Anhui Medical University Clinical Research Project (LCYJ2021YB015), and the Youth Research Foundation of Ma'anshan Municipal People's Hospital (YQ‐2022‐07).The authors declare that they have no competing interest.All procedures performed in studies involving human participants were in accordance with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The protocol was approved by the Ethics Committee of the First Affiliated Hospital of Anhui Medical University (reference number: Quick‐PJ 2021‐05‐19). Due to the retrospective nature of this study, informed consent was waived by the Ethics Committee of the First Affiliated Hospital of Anhui Medical University.Figure S1Click here for additional data file.Table S1Click here for additional data file.