Large-scale landslides often result in severe soil displacement and the exposure of bedrock, particularly combined with heavy rainfall. This condition significantly increases the risk of sediment-related disasters. Consequently, vegetation restoration and succession following landslide events are critical strategies for mitigating such hazards and enhancing disaster resilience. In this study, we integrated multi-temporal remote sensing imagery, land use classification, and Markov chain change simulations to evaluate the dynamic restoration of vegetation in a large-scale landslide area. Field surveys were conducted to validate the observed patterns of vegetation recovery. The results showed high accuracy in land use classifications derived from eight temporal images, with overall accuracy surpassing 80% and Kappa coefficients exceeding 0.7. The primary areas of vegetation recovery were identified as forests, followed by grasslands. Spatial change simulations indicated that full vegetation stability is expected to be reached after 2075. We emphasized the efficacy of combining remote sensing and modeling techniques for long-term monitoring of vegetation dynamics and offer critical insights for formulating sustainable strategies for disaster management.
Citation: Chih-Wei Chuang, Hao-Yu Huang, Chun-Wei Tseng. Assessment of vegetation dynamic and its effects in a large-scale landslide in Central Taiwan with multitemporal Landsat images[J]. AIMS Geosciences, 2025, 11(2): 318-342. doi: 10.3934/geosci.2025014
| [1] | Constance J. Jeffery . Intracellular/surface moonlighting proteins that aid in the attachment of gut microbiota to the host. AIMS Microbiology, 2019, 5(1): 77-86. doi: 10.3934/microbiol.2019.1.77 |
| [2] | Laurent Coquet, Antoine Obry, Nabil Borghol, Julie Hardouin, Laurence Mora, Ali Othmane, Thierry Jouenne . Impact of chlorhexidine digluconate and temperature on curli production in Escherichia coli—consequence on its adhesion ability. AIMS Microbiology, 2017, 3(4): 915-937. doi: 10.3934/microbiol.2017.4.915 |
| [3] | Tatyana V. Polyudova, Daria V. Eroshenko, Vladimir P. Korobov . Plasma, serum, albumin, and divalent metal ions inhibit the adhesion and the biofilm formation of Cutibacterium (Propionibacterium) acnes. AIMS Microbiology, 2018, 4(1): 165-172. doi: 10.3934/microbiol.2018.1.165 |
| [4] | Alexandra Soares, Ana Azevedo, Luciana C. Gomes, Filipe J. Mergulhão . Recombinant protein expression in biofilms. AIMS Microbiology, 2019, 5(3): 232-250. doi: 10.3934/microbiol.2019.3.232 |
| [5] | Arsenio M. Fialho, Nuno Bernardes, Ananda M Chakrabarty . Exploring the anticancer potential of the bacterial protein azurin. AIMS Microbiology, 2016, 2(3): 292-303. doi: 10.3934/microbiol.2016.3.292 |
| [6] | Yue Tang, Shaun Cawthraw, Mary C. Bagnall, Adriana J. Gielbert, Martin J. Woodward, Liljana Petrovska . Identification of temperature regulated factors of Campylobacter jejuni and their potential roles in virulence. AIMS Microbiology, 2017, 3(4): 885-898. doi: 10.3934/microbiol.2017.4.885 |
| [7] | Souliphone Sivixay, Gaowa Bai, Takeshi Tsuruta, Naoki Nishino . Cecum microbiota in rats fed soy, milk, meat, fish, and egg proteins with prebiotic oligosaccharides. AIMS Microbiology, 2021, 7(1): 1-12. doi: 10.3934/microbiol.2021001 |
| [8] | Yusuke Morita, Mai Okumura, Issay Narumi, Hiromi Nishida . Sensitivity of Deinococcus grandis rodZ deletion mutant to calcium ions results in enhanced spheroplast size. AIMS Microbiology, 2019, 5(2): 176-185. doi: 10.3934/microbiol.2019.2.176 |
| [9] | Jack C. Leo, Dirk Linke . A unified model for BAM function that takes into account type Vc secretion and species differences in BAM composition. AIMS Microbiology, 2018, 4(3): 455-468. doi: 10.3934/microbiol.2018.3.455 |
| [10] | Jonathan K. Wallis, Volker Krömker, Jan-Hendrik Paduch . Biofilm formation and adhesion to bovine udder epithelium of potentially probiotic lactic acid bacteria. AIMS Microbiology, 2018, 4(2): 209-224. doi: 10.3934/microbiol.2018.2.209 |
Large-scale landslides often result in severe soil displacement and the exposure of bedrock, particularly combined with heavy rainfall. This condition significantly increases the risk of sediment-related disasters. Consequently, vegetation restoration and succession following landslide events are critical strategies for mitigating such hazards and enhancing disaster resilience. In this study, we integrated multi-temporal remote sensing imagery, land use classification, and Markov chain change simulations to evaluate the dynamic restoration of vegetation in a large-scale landslide area. Field surveys were conducted to validate the observed patterns of vegetation recovery. The results showed high accuracy in land use classifications derived from eight temporal images, with overall accuracy surpassing 80% and Kappa coefficients exceeding 0.7. The primary areas of vegetation recovery were identified as forests, followed by grasslands. Spatial change simulations indicated that full vegetation stability is expected to be reached after 2075. We emphasized the efficacy of combining remote sensing and modeling techniques for long-term monitoring of vegetation dynamics and offer critical insights for formulating sustainable strategies for disaster management.
Bacterial adherence factors, also known as adhesins, are proteins on the cell surface that form and maintain physical interactions with host cells and tissues. They are important in both health and disease as they are needed by pathogens for infection and by commensal or “good” bacteria to maintain a symbiotic relationship with the host. Surprisingly, several dozen of these proteins were previously identified as ubiquitous intracellular enzymes that have a canonical function in essential cellular processes and are sometimes referred to as “housekeeping enzymes” [1,2,3,4,5]. The first intracellular/surface moonlighting protein (ISMP) to be identified was an enzyme in glycolysis, glyceraldehyde 3-phosphate dehydrogenase (GAPDH), which has a second role on the surface of pathogenic streptococci [6]. Other intracellular/surface moonlighting proteins include other metabolic enzymes that are also widespread in evolution and function in glycolysis, the citric acid cycle, or DNA and protein metabolism, for example, phosphoglycerate kinase and enolase. Intracellular chaperones (Hsp60/GroEL, Hsp70/DnaK), and protein synthesis elongation factors (EF-Tu, EF-G) have also been found to serve as adhesins in bacteria (Table 1).
In general, moonlighting proteins comprise a subset of multifunctional proteins that perform two or more distinct and physiologically relevant biochemical or biophysical functions that are not due to gene fusions, multiple RNA splice variants, or pleiotropic effects [1]. The MoonProt Database includes information about hundreds of moonlighting proteins for which biochemical or biophysical evidence supports the presence of at least two biochemical functions in one polypeptide chain [7]. Of these, over 30 types of proteins have one function inside the cell and another function as an adhesin on the cell surface. Some are found to moonlight on the surface of multiple species, so there are over 100 ISMPs. The bacterial ISMPs (Table 1) are found in typical Gram-positive and Gram-negative species, as well as mycobacteria, spirochetes, and mycoplasma.
An ISMP can have different extracellular functions in different species. Enolase converts the reversible conversion of 2-phosphoglycerate to phosphoenolpyruvate in the cytoplasm in glycolysis and gluconeogenesis and has been found to have many moonlighting functions in addition to being an adhesin on the bacterial cell surface. As a bacterial adhesin, enolase binds host proteins in the extracellular matrix, mucin, and other proteins and plays an important role in infection of mammalian and avian hosts [8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24] (Figure 1). Some ISMPs also have third (or more) functions as secreted soluble proteins, in many cases with roles in modulation of the immune system [2,3].
In pathogenic bacteria the extracellular function often plays a key role in infection or virulence [2,3]. ISMPs have been found to be involved in aiding the bacteria to bind directly to host cells, including fructose-1,6-bisphosphate aldolase from Neisseria meningitidis [25] and Streptococcus pneumoniae [26] and the Hsp60 chaperone from Clostridium difficile [27], Helicobacter pylori [28], Chlamydia pneumoniae [29], Legionella pneumophila [30] and several other species. In some cases a specific receptor on the host cell surface has been identified. Listeria monocytogenes alcohol acetaldehyde dehydrogenase binds to Hsp60 (another moonlighting protein) on the surface of several human cell lines [31,32]. Streptococcus pyogenes GAPDH binds to the uPAR/CD87 receptor [33]. Streptococcus pneumoniae fructose 1,6-bisphosphate aldolase binds to the flamingo cadherin receptor (FCR) [26]. Haemophilus ducreyi Hsp60 binds to membrane glycosphingolipids [34,35].
Other ISMPs bind to extracellular matrix or secreted mucins in the mucosal layer of the intestines and airway. Mycoplasma pneumoniae EF-Tu and pyruvate dehydrogenase, Mycobacterium tuberculosis malate synthase, and Streptococcus mutans autolysin AltA, Staphylococcus caprae autolysin AltC, and Staphylococcus aureus autolysin Aaa bind to one or more of the extracellular matrix components fibronectin, laminin, and/or collagen [36,37,38,39,40]. Mycoplasma genitalium GAPDH, Salmonella typhimurium Hsp60, and Streptococcus gordonii enolase, EF-Tu, and the beta subunit of the DNA-directed RNA polymerase bind mucin [18,41,42]. Other examples are given in Table 1.
Figure 1. Intracellular enzymes and chaperones can function as adhesins on the bacterial cell surface. An ISMP can function as an enzyme inside of the cell and an adhesin when located on the cell surface. Enolase is found in the cytoplasm in almost all species where it converts 2-phosphoglycerate to phosphoenolpyruvate as the ninth step of glycolysis. In many species of bacteria, it is also found on the cell surface where it can bind to the host's extracellular matrix or airway mucins. For pathogenic bacteria, this attachment can be important for invading host tissues and promoting infection. In most cases, how the intracellular enzyme is transported outside the cell and how it becomes attached to the cell surface are not known (curved arrow). There might be a receptor for the protein on the bacterial cell surface (hexagon), but the nature of the surface attachment is known for only a few ISMPs.The ability of many pathogenic bacteria to use surface proteins to bind to the soluble host protein plasminogen also assists in invasion of host tissues [11,12,13,14,43,44]. Plasminogen is a precursor to plasmin, which is a broad-spectrum serine protease present in blood that helps break down fibrin clots [45]. When an invading pathogen uses a receptor on its surface to bind plasminogen from the host, the plasminogen can be converted to plasmin, the active form of the protease, by using an endogenous protease or subverting the host's tissue-type plasminogen (tPA) activators and urokinase-type plasminogen activators [46]. The active plasmin that is then attached to the surface of the invading organism can be used as a general protease to degrade host extracellular matrix and basement membrane, thereby facilitating migration through tissues. In the case of Mycoplasma hyopenumoniae, a swine-specific pathogen with a reduced genome that lacks genes for building amino acids, having an active protease on the surface enables cleavage of a variety of host proteins to produce peptides and amino acids that can be taken up by the bacterium as nutrients [47,48]. Other ISMPs also aid in infection and virulence by serving as receptors on the bacterial cell surface to acquire nutrients from the host. Staphylococcal GAPDH serves as a transferrin binding protein to acquire iron from the host [49].
The use of moonlighting proteins in adherence to host cells and tissues is not seen only in pathogenic species. Bacterial species that are sometimes referred to as “good” bacteria or probiotics, in other words nonpathogenic symbionts that help promote health and well-being, use ISMP in commensal interactions with host species, especially in the intestines. Lactobacillus plantarum GAPDH and enolase were shown to aid the bacterium in binding to mammalian cells and could play a role for this probiotic species to bind to the lining of the gut [23,50]. Lactobacillus johnsonii EF-Tu and Hsp60 also bind to human cells and to mucin [51,52]. Lactobacillus acidophilus GAPDH also binds mucin [53].
ISMPs may also assist in symbiotic relationships with other species, including a symbiotic relationship between lactic acid bacteria and yeast. The bacteria break down starch and other carbohydrates to produce lactic acid that is used by the yeast. In return they receive nutrients made by the yeast. This symbiotic relationship is found in several kinds of fermented foods like kefir, a drink made from cow's milk. Lactococcus lactis GAPDH, pyruvate kinase, Hsp60/GroEL, DnaK/Hsp70, and 6-phosphofructokinase have been shown to bind to invertase on the surface of the yeast Saccharomyces cerevisiae to help maintain this inter-species symbiotic interaction [54].
One of the benefits of probiotic bacteria has been suggested to be that they compete with pathogens for binding sites or nonspecific binding to the surface of epithelial cells lining the gut. Several moonlighting proteins were found to aid Lactobacillus species in competing with pathogenic species for binding to epithelial cell lines in vitro. Some of the same ISMPs may be involved in the competition of pathogenic and commensal bacteria for binding to epithelial cells. Several of the moonlighting proteins have been found to perform the same combination of enzyme and adhesion functions in both pathogenic and commensal bacteria, for example, enolases from Lactobacillus, Staphylococcus, Streptococcus and several other species bind human plasminogen [14].
The adhesion functions of the ISMPs in Table 1 were mainly found through experiments to identify proteins that bind to a specific molecular target, such as collagen, fibronectin, or other extracellular matrix proteins or through studies of proteins involved in binding to a specific target cell type. In recent years, many more intracellular proteins have been found to have a second location on the cell surface through surface proteomics, or “surfomics”, studies that aimed to identify all the proteins on a cell surface [55]. Surface proteomics studies employ variations of three types of experimental approaches to identify cell surface proteins. The main difference in the methods is in how the candidate proteins are isolated: through fractionating the cells to isolate components of the cell membrane and/or cell wall, surface “shaving” or using proteases to digest proteins on the cell surface without damaging the cell membrane, or labeling proteins on the surface with biotin or O18 before disrupting the cells and isolating the proteins. In each case the surface proteins are then identified using mass spectrometry. Although these methods might incorrectly identify some strictly intracellular proteins as being part of the cell surface proteome due to experimental artifacts inherent in the challenges of cell fractionation, and even some intracellular proteins that are correctly found to have a second location on the cell surface might have a different function other than as adhesins, at least some of the known intracellular/surface adhesins were correctly found to be localized to the cell surface, and it is possible that some of the additional cytoplasmic proteins found in these studies are also moonlighting as adhesins. Additional experiments are needed to determine if the intracellular proteins identified as being on the cell surface through proteomics methods are indeed involved in bacterial adhesion and were not found on the surface because they have another role on the surface or perhaps they were artifacts of the experimental methods.
It might at first seem unlikely that so many intracellular chaperones and enzymes required for central metabolism evolved to function also as cell surface binding proteins. Acquiring the new function required (1) evolution of a new protein-protein interaction site as well as (2) mechanisms for secretion and cell surface attachment, all while maintaining the first function of the protein. Satisfying the first requirement can be surprisingly simple. In general, most of the amino acid residues on a protein's surface are not directly connected to the protein's main function and are therefore not under significant selective pressure during evolution. In fact, surface amino acids vary a great deal even among close homologues. Having just a small number of these surface residues in a correct three-dimensional arrangement can be sufficient for formation of a novel protein-protein interaction site. In fact, Ehinger and coworkers showed that a nine amino acid sequence on the surface of enolase was sufficient for its interaction with plasminogen [56]. In general, for an average protein comprised of 300 or 400 amino acids, there is ample space and material for development of a new protein-protein binding site. In addition, most of these proteins are essential housekeeping proteins that first evolved billions of years ago and are expressed in many species and cell types, providing both the time and variety of cellular conditions for evolution of the protein surface to include a new binding function.
A more difficult question is how most of the ISMPs are secreted and become attached to the cell surface. The ISMPs do not contain a signal sequence or the twin arginine motif found in most proteins secreted by the canonical Sec or TAT secretion systems, respectively. For these reasons, the ISMPs are sometimes referred to as anchorless surface proteins or surface-associated housekeeping enzymes and are said to be secreted through non-classical, noncanonical, or unconventional secretion pathways. It is not clear if any of the known non-canonical secretion systems are involved in the secretion of ICMS, but most still require a kind of secretion signal, and they tend to be involved in the secretion of a few specific proteins [57].
Although it has been suggested that these intracellular proteins could become released from dead or damaged cells, several lines of evidence support the idea that at least some of them do require a secretion system [58,59]. First, the ISMPs are not the most abundant proteins in the cell, and those proteins that are most abundant are not often found on the cell surface. Second, a large portion of the pool of each protein type remains inside the cell while only some of the pool of the protein is partitioned to the cell surface. Why only part of the cytoplasmic pool of these specific proteins become targeted to the cell surface is not known.
For some individual proteins, there is additional evidence that a secretion system is probably involved. Yang and coworkers concluded that the release of GroEL, DnaK, enolase, pyruvate dehydrogenase subunits PdhB and PdhD, and superoxide dismutase SodA, by Bacillus subtilis is not due to gross cell lysis based on observing a constant cell density, no change in secretion in the presence of chloramphenicol, constant cell viability count, negligible amounts of two highly expressed cytoplasmic proteins EF-Tu and SecA in the culture medium, and the lack of effect of deleting lytC and lytD autolysins on the amount of the proteins in the media [60]. They also showed that these proteins were not released into the medium by membrane vesicles and there was no N-terminal cleavage (which might have suggested the presence of a signal sequence). Also, a mutant form of enolase with a hydrophobic helix replaced with a more neutral helix was retained in the cell when the wild type protein was found in the media, which also supports the model that it is not due to cell lysis. They followed up by showing that in Bacillus subtilis enolase, the internal hydrophobic helical domain was essential but not sufficient for export of enolase [61], although a larger portion of the N-terminal domain (residues 1-140) was sufficient for export of GFP in B subtilis and E coli. Boel and colleagues found that Lys341 of E. coli, Enterococcus faecalis, and Bacillus subtilis enolase becomes spontaneously modified with the substrate 2-phosphoglycerate (2PG), and this post-translational modification is required for export form the cell [62]. Substitution of Lys341 with other amino acids (Ala, Arg, Glu, Gln) prevented modification and secretion even though the Lys341Glu mutant enzyme was enzymatically active, showing that enzyme activity was not sufficient for secretion (and also that secretion was not due to cell leakage, because a single amino acid change can cause a decrease in secretion).
Secretion of some ISMPs may involve an as yet unknown secretion pathway, or their secretion might utilize an alternative version of one or more of the known secretion systems. If the latter is true, there are several possibilities, which are not mutually exclusive: One or more of the known secretion systems could be leaky. Post translational modifications (PTMs), possibly transient PTMs, can render some subset of the ISMP to be passable substrates. Alternative versions of the secretion systems might exist that require additional proteins such as a chaperone that have not yet been identified. An alternative system's secretion could be in competition with folding/unfolding or only rare conformations of an ISMP might be competent for secretion. It's possible that some combination of these factors could result in an inefficient secretion process, or that the alternative version requires induction of the expression of an unknown protein component of a known secretion system or an enzyme involved in adding PTMs. A search for shared characteristics might suggest what protein features singled out these intracellular proteins for adoption to play a second role on the cell surface, but a study of 98 ISMPs found that they share physical characteristics typical of intracellular proteins [63]. A couple studies have identified peptides on the cell surface that are the results of proteolytic cleavage of intracellular proteins, including EF-Tu [64,65], and the authors suggested that cleavage might yield peptides that are better at binding to some host proteins than the intact ISMPs. Because intact versions of these proteins are also found on the cell surface, the proteolytic cleavage is likely to take place after transport across the membrane and not as part of the secretion mechanism.
After the intracellular proteins are transported to the extracellular milieu, they become anchored to the surface of the bacterial cells, but in most cases, the mechanism for cell surface anchoring is also not known. For surface proteins in general, known anchoring mechanisms involve an N-terminal signal sequence for secretion and/or a C-terminal sorting motif, such as the LPXTG motif that is recognized by sortase A, for anchoring to the peptidoglycan network on the cell surface [66]. A smaller number of surface proteins have been found to be targeted to the cell surface due to the presence of additional motifs [67,68,69], including the GW repeat, the choline binding motif, and the LysM domain, but these are not found in the majority of the ISMPs in Table 1. Studies with purified proteins have shown that some intracellular/surface moonlighting proteins can adhere to the cell surface by re-association in both Gram-positive and Gram-negative bacteria, so it is possible that some of the ISMPs are secreted and then re-associate with the cell surface of after secretion. An increase in extracellular pH has been shown to cause some Lactobacillus crispatus ISMPs to be released from the cell surface [70]. Some ISMPs may also be released from the surface during cell-wall renewal that occurs during exponential growth phase [71]. In most cases it is not known to which components of the cell surface—proteins, lipids, etc.—the proteins bind, but it was shown recently that extracellular enolase is bound to a rhamnose residue in cell membrane of mycoplasma [72], and enolase and GAPDH bind covalently to lipotectoic acid on Lactobacillus crispatus [73].
With the increasing problem of antibiotic resistance [74,75], new methods for inhibiting bacterial infections and virulence are needed, and studies of ISMPs might provide new targets for the development of novel therapeutics. But it's not the moonlighting proteins themselves that might be the best targets. The catalytic mechanisms of most of these ISMP are conserved between bacteria and their human hosts, which makes sense because they play key roles in central metabolic pathways such as glycolysis. Instead of targeting the ISMPs, elucidating how these proteins are targeted to the bacterial cell surface might identify processes and proteins that are involved in the novel secretion systems (or new versions of known secretion systems) or surface attachment mechanisms and that could serve as novel targets for developing new strategies for controlling infection.
Learning how pathogenic and commensal bacteria adhere to host cells and tissues could also lead to better understanding of how these species colonize host tissues and compete with each other. This information can be important in treatment of diseases that involve an imbalance of pathogenic and probiotic bacterial species, for example ulcerative colitis and Crohn's disease [76], which are autoimmune diseases of the gut that affect over a million people in the US alone [77]. Understanding bacterial adhesion could potentially lead to information about how probiotic species could be used to displace pathogens and improve the balance of bacterial species.
| Protein | Species | UniProt ID | Extracellular function | References |
| 6-phosphofructokinase | Lactococcus lactis | P0DOB5 | yeast invertase | [54] |
| Streptococcus oralis | E6KMA1 | plasminogen | [78] | |
| Aaa autolysin | Staphylococcus aureus | Q2YVT4 | fibronectin | [37] |
| Aae autolysin | Staphylococcus epidermis | Q8CPQ1 | fibrinogen, fibronectin, vitronectin | [79] |
| Aspartase | Haemophilus influenzae | P44324 | plasminogen | [80] |
| Atla autolysin | Streptococcus mutans | U3SW74 | fibronectin | [39] |
| AtlC autolysin | Staphylococcus caprae | Q9AIS0 | fibronectin | [40] |
| Bile salt hydrolase | Bifidobacterium lactis | Q9KK62 | plasminogen | [81] |
| C5a peptidase | Streptococcus agalactiae | Q8E4T9 | fibronectin | [82] |
| DNA-directed RNA polymerase beta subunit | Streptococcus gordonii | A0EKJ1 | Muc7 | [18] |
| DnaK | Bifidobacterium | Q8G6W1 | plasminogen | [81] |
| Lactococcus lactis | P0A3J0 | yeast invertase | [54] | |
| Mycobacterium tuberculosis | A0A0H3L5C8 | plasminogen | [75] | |
| Neisseria meningitidis | A9M296 | plasminogen | [20] | |
| EF-Tu | Lactobacillus johnsonii | Q74JU6 | cells, mucins | [51] |
| Mycoplasma pneumoniae | P23568 | fibronectin, epithelial cells, plasminogen, heparin, fetuin, actin, fibrinogen, vitronectin, laminin | [36,64] | |
| Pseudonomas aeruginosa | P09591 | plasminogen | [43] | |
| Streptococcus gordonii | A8AWA0 | Muc7 | [18] | |
| Elongation factor G | Streptococcus gordonii | A8AUR6 | Muc7 | [18] |
| Endopeptidase O | Streptococcus pneumoniae | Q8DNW9 | plasminogen, fibronectin | [83] |
| Enolase | Aeromonas hydrophila | Q8GE63 | plasminogen | [22] |
| Bacillus anthracis | D8H2L1 | plasminogen, laminin | [8] | |
| Bifidobacterium lactis | B7GTK2 | plasminogen | [11] | |
| Borrelia burgdorferi | B7J1R2 | plasminogen | [16] | |
| Lactobacillus crispatus | Q5K117 | plasminogen, laminin | [14] | |
| Lactobacillus johnsonii | Q74K78 | plasminogen, laminin | [14] | |
| Lactobacillus plantarum | Q88YH3 | fibronectin | [23] | |
| Leishmania mexicana | Q3HL75 | plasminogen | [13] | |
| Mycoplasma fermentans | C4XEI3 | plasminogen | [24] | |
| Mycoplasma suis | F0QRW4 | red blood cells | [84] | |
| Mycoplasma synoviae | Q4A740 | plasminogen, fibronectin | [9] | |
| Neisseria meningitidis | E0N8L2 | plasminogen | [20] | |
| Staphylococcus aureus | Q6GB54 | plasminogen, laminin | [14,21] | |
| Streptococcus canis | I7WI49 | plasminogen | [17] | |
| Streptococcus gordonii | A8AY46 | Muc7 | [18] | |
| Streptococcus mutans | Q8DTS9 | plasminogen | [12] | |
| Streptococcus oralis | A0A1F1EC06 | plasminogen | [19] | |
| Streptococcus pneumoniae | Q97QS2 | plasminogen | [14] | |
| Streptococcus pyogenes | Q1JML5 | plasminogen | [14] | |
| Streptococcus suis | A4W2T1 | fibronectin, plasminogen | [15] | |
| Fructose 1,6-bisphosphate aldolase | Neisseria meningitidis | F0N9L0 | cells | [25] |
| GAPDH | Bacillus anthracis | Q81X74 | plasminogen | [85] |
| Lactobacillus acidophilus | Q5FL51 | mucin | [53] | |
| Lactobacillus plantarum | F9UM10 | mucin, Caco-2 cells | [50] | |
| Lactococcus lactis | P52987 | yeast invertase | [54] | |
| Mycoplasma genitalium | P47543 | mucin | [41] | |
| Staphylococcus aureus | Q6GIL8 | transferrin | [49] | |
| Streptococcus agalactiae | Q9ALW2 | plasminogen | [86] | |
| Streptococcus oralis | A0A0F2E7M6 | plasminogen | [78] | |
| Streptococcus pneumoniae | A0A0H2US80 | plasminogen | [87] | |
| Streptococcus pyogenes | P68777 | uPAR/CD87 receptor on human cells, plasminogen | [33,88] | |
| Streptococcus suis | Q3Y454 | plasminogen | [89] | |
| Glucose 6-phosphate isomerase | Lactobacillus crispatus | K1MKZ7 | laminin, collagen | [90] |
| Glutamine synthetase | Lactobacillus crispatus | D5GYN9 | fibronectin, laminin, collagen I, plasminogen | [90] |
| Mycobacterium tuberculosis | A0A0H3LHU4 | plasminogen, fibronectin | [91] | |
| Bifidobacterium lactis | C2GUH0 | plasminogen | [81] | |
| Hsp60 | Chlamydiae pneumoniae | P31681 | adhesin | [29] |
| Lactococcus lactis | P37282 | yeast invertase | [54] | |
| Legionella pneumophila | Q5X762 | adhesin | [30] | |
| Clostridium difficile | Q9KKF0 | adhesin | [27] | |
| Haemophilus ducreyi | P31294 | glycosphinngolipids | [34,35] | |
| Helicobacter pylori | Q8RNU2 | adhesin | [28] | |
| Lactobacillus johnsonii | F7SCR2 | adhesin | [52] | |
| Listeria | Q8KP52 | adhesin | [32] | |
| Salmonella typhimurium | P0A1D3 | mucus | [42] | |
| Hsp65/Cpn60.2/GroEL2 | Mycobacterium tuberculosis | A0A0H3LCC3 | CD43 on macrophage surface | [92] |
| Leucyl aminopeptidase | Mycoplasma hyopneumoniae | Q4A9M4 | heparin | [93] |
| Malate synthase | Mycobacterium tuberculosis | P9WK17 | fibronectin, laminin, epithelial cells | [38] |
| Glutamyl aminopeptidase | Mycoplasma hyopneumoniae | Q4AAK4 | plasminogen, heparin | [47] |
| Leucyl aminopeptidase | Mycoplasma hyopneumoniae | Q4A9M4 | plasminogen, heparin, DNA | [48] |
| Ornithine carbamoyltransferase | Staphylococcus epidermidis | P0C0N1 | fibronectin | [94] |
| Peroxiredoxin | Neisseria meningitidis | A0A125WDU3 | plasminogen | [20] |
| Streptococcus agalactiae | E7S2A7 | heme | [95] | |
| Phosphoglycerate kinase | Streptococcus oralis | A0A0G7HBY7 | plasminogen | [77] |
| Streptococcus agalactiae | Q8DXT0 | plasminogen, actin | [83,96] | |
| Streptococcus pneumoniae | Q8DQX8 | plasminogen | [97] | |
| Phosphoglycerate mutase | Bifidobacterium lactis | P59159 | plasminogen | [81] |
| Streptococcus oralis | E6IYJ0 | plasminogen | [78] | |
| Pyruvate dehydrogenase | Mycoplasma pneumoniae | P75391 | fibrinogen | [36] |
| Pyruvate kinase | Lactococcus lactis | Q07637 | yeast invertase | [54] |
| Superoxide dismutase | Mycobacterium avium | P53647 | adhesin | [98] |
| Triose phosphate isomerase | Streptococcus oralis | E6J203 | plasminogen | [78] |
DownLoad: CSVThe large number of ISMPs, the variety of bacterial species, and the different host proteins targeted suggests that this phenomenon of intracellular housekeeping proteins moonlighting as adhesins on the bacterial cell surface is widespread. There is still a great deal to learn about these proteins, especially how these intracellular proteins are secreted and attached to the bacterial cell surface. Studies of ISMP that serve as adhesins could help in identifying novel targets for development of therapeutics because their mechanisms of secretion and membrane attachment are likely to involve new proteins and cellular processes.
The author declares no conflict of interest in this paper.
| [1] |
Huang CS, Chen MM, Hsu MI (2002) A Preliminary Report on the Chiufenershan Landslide Triggered by the 921 Chichi Earthquake in Nantou, Central Taiwan. Geology 13: 387–395. https://doi.org/10.3319/TAO.2002.13.3.387(CCE) doi: 10.3319/TAO.2002.13.3.387(CCE)
|
| [2] |
Lin CY, Lo HM, Chou WC, et al. (2004) Vegetation recovery assessment at the Jou-Jou Mountain landslide area caused by the 921 Earthquake in Central Taiwan. Ecol Model 176: 75–81. https://doi.org/https://doi.org/10.1016/j.ecolmodel.2003.12.037 doi: 10.1016/j.ecolmodel.2003.12.037
|
| [3] |
Lin WT (2025) Recovery assessment for earthquake-induced landslides in Central Taiwan: Changes, patterns, and mechanisms. Ecol Eng 212: 107497. https://doi.org/10.1016/j.ecoleng.2024.107497 doi: 10.1016/j.ecoleng.2024.107497
|
| [4] | Chang CH, Liu CH (2015) Action Plan for Prevention and Control of Large-Scale Landslide Disasters. National Science and Technology Center for Disaster Reduction, Taiwan(R.O.C.). |
| [5] | Kokutse N, Fourcaud T, Kokou K, et al. (2006) 3D numerical modelling and analysis of the influence of forest structure on hill slopes stability. Proceedings of the Interpraevent International Symposiu, Disaster Mitig. Debris Flows, Slope Fail. Landslides. 561–567. |
| [6] |
Ji J, Kokutse N, Genet M, et al. (2012) Effect of spatial variation of tree root characteristics on slope stability. A case study on Black Locust (Robinia pseudoacacia) and Arborvitae (Platycladus orientalis) stands on the Loess Plateau, China. Catena 92: 139–154. https://doi.org/10.1016/j.catena.2011.12.008 doi: 10.1016/j.catena.2011.12.008
|
| [7] |
Fan CC, Lai YF (2014) Influence of the spatial layout of vegetation on the stability of slopes. Plant Soil 377: 83–95. https://doi.org/10.1007/s11104-012-1569-9 doi: 10.1007/s11104-012-1569-9
|
| [8] | Mao Z, Bourrier F, Stokes A, et al. (2014) Three-dimensional modelling of slope stability in heterogeneous montane forest ecosystems. Ecol Model 273: 11–22. |
| [9] |
Makoto K, Utsumi S, Zeng R, et al. (2024) Which native legume or non-legume nitrogen-fixing tree is more efficient in restoring post-landslide forests along an environmental gradient? For Ecol Manage 554: 121672. https://doi.org/https://doi.org/10.1016/j.foreco.2023.121672 doi: 10.1016/j.foreco.2023.121672
|
| [10] | Lin SC, Chuang CW, HO SH, et al. (2008) Effect of the Vegetation Index on the Accuracy of Image Classification. J. Soil Water Conserv 40: 181–193. |
| [11] | Xiong P, Li J, Xue G (2023) Research Progress on Remote Sensing Extraction Methods for Fractional Vegetation Cover. Ecol Environ Prot 6: 62–64. |
| [12] |
Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ 8: 127–150. https://doi.org/10.1016/0034-4257(79)90013-0. doi: 10.1016/0034-4257(79)90013-0
|
| [13] |
Tian J, Wang L, Li X, et al. (2017) Comparison of UAV and WorldView-2 imagery for mapping leaf area index of mangrove forest. Int J Appl Earth Obs 61: 22–31. https://doi.org/10.1016/j.jag.2017.05.002 doi: 10.1016/j.jag.2017.05.002
|
| [14] |
Zhumanova M, Mönnig C, Hergarten C, et al. (2018) Assessment of vegetation degradation in mountainous pastures of the Western Tien-Shan, Kyrgyzstan, using eMODIS NDVI. Ecol Indic 95: 527–543. https://doi.org/10.1016/j.ecolind.2018.07.060 doi: 10.1016/j.ecolind.2018.07.060
|
| [15] |
Peng WF, Kuang TT, Taoab S (2019) Quantifying influences of natural factors on vegetation NDVI changes based on geographical detector in Sichuan, western China. J Clean Prod 233: 353–367. https://doi.org/10.1016/j.jclepro.2019.05.355 doi: 10.1016/j.jclepro.2019.05.355
|
| [16] |
Almeida DRA, Broadbent EN, Ferreira MP, et al. (2021) Monitoring restored tropical forest diversity and structure through UAV-borne hyperspectral and lidar fusion. Remote Sens Environ 264: 112582. https://doi.org/10.1016/j.rse.2021.112582 doi: 10.1016/j.rse.2021.112582
|
| [17] |
Fokeng RM, Fogwe ZN (2022) Landsat NDVI-based vegetation degradation dynamics and its response to rainfall variability and anthropogenic stressors in Southern Bui Plateau, Cameroon. Geosy Geoenviron 1: 100075. https://doi.org/10.1016/j.geogeo.2022.100075 doi: 10.1016/j.geogeo.2022.100075
|
| [18] |
Hu X, Wang Z, Zhang Y, et al. (2022) Spatialization method of grazing intensity and its application in Tibetan Plateau. Acta Geographica Sinica 77: 547–558. https://doi.org/10.11821/dlxb202203004. doi: 10.11821/dlxb202203004
|
| [19] |
Sun L, Zhao D, Zhang G, et al. (2022) Using SPOT VEGETATION for analyzing dynamic changes and influencing factors on vegetation restoration in the Three-River Headwaters Region in the last 20 years (2000–2019), China. Ecol Eng 183: 106742. https://doi.org/10.1016/j.ecoleng.2022.106742. doi: 10.1016/j.ecoleng.2022.106742
|
| [20] |
Gu F, Xu G, Wang B, et al. (2023) Vegetation cover change and restoration potential in the Ziwuling Forest Region, China. Ecol Eng 187: 106877. https://doi.org/10.1016/j.ecoleng.2022.106877 doi: 10.1016/j.ecoleng.2022.106877
|
| [21] | Huang KY (2006) Application of geo-spatial information technologies to assessing relationship between debris flow and slope-land for farming use and re-vegetation of landslide scars. J Chinese Soil Water Conserv 37: 305–315. |
| [22] | Chang YH (2010) Estimation Vegetation Recovery and Landslide Potential with Multi-date Satellite Images in Jou-Jou Mountain. Master Thesis. Department of Civil Engineering, National Chung Hsing University, Taiwan (R.O.C.). |
| [23] | Shou KJ, Wu CC, Hsu HY (2010) Analysis of Landslide Behavior after 1999 Chi-Chi Earthquake by SPOT Satellite Images. J Photogramm Remote Sens 15: 17–28. |
| [24] | Chuang CW (2010) Application of Environmental Indices on the Vegetative Restoration of Landslides. Doctoral Dissertation. Department of Soil and Water Conservation, National Chung Hsing University, Taiwan (R.O.C.). |
| [25] | Lu SY, Lin CY, Hwang LS (2011) Spatial relationships between landslides and topographical factors at the Liukuei Experimental Forest, Southwestern Taiwan after Typhoon Morakot. Taiwan J For Sci 26: 399–408. |
| [26] | Tsai PS (2015) Landslide Susceptibility and Conservation Benefit Assessment for Chi-Sun watershed. Master Thesis. Department of Soil and Water Conservation, National Chung Hsing University, Taiwan(R.O.C.). |
| [27] |
Chen M, Tang C, Wang X, et al. (2021) Temporal and spatial differentiation in the surface recovery of post-seismic landslides in Wenchuan earthquake-affected areas. Ecol Inform 64: 101356. https://doi.org/10.1016/j.ecoinf.2021.101356 doi: 10.1016/j.ecoinf.2021.101356
|
| [28] |
Shooshtari SJ, Gholamalifard M (2015) Scenario-based land cover change modeling and its implications for landscape pattern analysis in the Neka Watershed, Iran. Remote Sens. Appl Soc Environ 1: 1–19. https://doi.org/https://doi.org/10.1016/j.rsase.2015.05.001 doi: 10.1016/j.rsase.2015.05.001
|
| [29] |
Verburg PH, Schot PP, Dijst MJ, et al. (2004) Land use change modelling: current practice and research priorities. GeoJournal 61: 309–324. https://doi.org/10.1007/s10708-004-4946-y doi: 10.1007/s10708-004-4946-y
|
| [30] | Forman RTT, Godron M (1986) Landscape ecology. Wiley, New York. |
| [31] |
Aniah P, Bawakyillenuo S, Codjoe SNA, et al. (2023) Land use and land cover change detection and prediction based on CA-Markov chain in the savannah ecological zone of Ghana. Environ Challenges 10: 100664. https://doi.org/10.1016/j.envc.2022.100664. doi: 10.1016/j.envc.2022.100664
|
| [32] |
Devkota P, Dhakal S, Shrestha S, et al. (2023) Land use land cover changes in the major cities of Nepal from 1990 to 2020. Environ Sustain Ind 17: 100227. https://doi.org/10.1016/j.indic.2023.100227 doi: 10.1016/j.indic.2023.100227
|
| [33] |
Temesgen F, Warkineh B, Hailemicael A (2022) Seasonal land use/land cover change and the drivers in Kafta Sheraro national park, Tigray, Ethiopia. Heliyon 8: e12298. https://doi.org/10.1016/j.heliyon.2022.e12298. doi: 10.1016/j.heliyon.2022.e12298
|
| [34] |
Wulder MA, Loveland TR, Roy DP, et al. (2019) Current status of Landsat program, science, and applications. Remote Sensing of Environment 225: 127–147. https://doi.org/10.1016/j.rse.2019.02.015 doi: 10.1016/j.rse.2019.02.015
|
| [35] |
Drusch M, Del Bello U, Carlier S, et al. (2012) Sentinel-2: ESA's optical high-resolution mission for GMES operational services. Remote Sens Environ 120: 25–36. https://doi.org/10.1016/j.rse.2011.11.026 doi: 10.1016/j.rse.2011.11.026
|
| [36] |
Justice CO, Townshend JRG, Vermote EF, et al. (2002) An overview of MODIS Land data processing and product status. Remote Sens Environ 83: 3–15. https://doi.org/10.1016/S0034-4257(02)00084-6 doi: 10.1016/S0034-4257(02)00084-6
|
| [37] |
Lan L, Wang YG, Chen HS, et al. (2024) Improving on mapping long-term surface water with a novel framework based on the Landsat imagery series. J Environ Manage 353: 120202. https://doi.org/10.1016/j.jenvman.2024.120202 doi: 10.1016/j.jenvman.2024.120202
|
| [38] |
Bonney MT, He Y, Vogeler J, et al. (2024) Mapping canopy cover for municipal forestry monitoring: Using free Landsat imagery and machine learning. Urban For Urban Green 100: 128490. https://doi.org/10.1016/j.ufug.2024.128490 doi: 10.1016/j.ufug.2024.128490
|
| [39] |
Green EP, Mumby PJ, Edwards AJ, et al. (1997) Estimating leaf area index of mangroves from satellite data. Aquat Bot 58: 11–19. https://doi.org/10.1016/S0304-3770(97)00013-2 doi: 10.1016/S0304-3770(97)00013-2
|
| [40] |
Price JC, Bausch WC (1995) Leaf area index estimation from visible and near-infrared reflectance data. Remote Sens Environ 52: 55−65. https://doi.org/10.1016/0034-4257(94)00111-Y doi: 10.1016/0034-4257(94)00111-Y
|
| [41] | Rouse JW Jr, Haas RH, Schell JA, et al. (1973) Monitoring vegetation systems in the great plains with ERTS. Third NASA ERTS Symposium, Washington, D.C., NASA SP-351 I, 309–317. |
| [42] |
Elvidge CD, Chen Z (1995) Comparison of broad-band and narrow-band red and near-infrared vegetation indices. Remote Sens Environ 54: 38–48. https://doi.org/10.1016/0034-4257(95)00132-K doi: 10.1016/0034-4257(95)00132-K
|
| [43] |
Huang CL, Li X, Lu L (2008) Retrieving soil temperature profile by assimilation MODIS LST products with ensemble Kalman filter. Remote Sens Environ 112: 1320–1336. https://doi.org/10.1016/j.rse.2007.03.028 doi: 10.1016/j.rse.2007.03.028
|
| [44] | Lin CY (2005) Spatial Distribution and Investigation Analysis of Vegetation Restoration in Chiufenershan, Huashan and Caoling Areas by using Ecological Indices. Soil and Water Conservation Bureau Achievement Report, Committee of Agriculture, Executive Yuan, Taiwan (R.O.C.). |
| [45] | Chou CF, Cheng CC, Chen YC (1991) Application of SPOT data on forest cover type classification. Bull Taiwan For Res Inst 6: 283–297. |
| [46] | Tu WC, Li SC, Chen HH, et al. (2000) Analyze geomorphology changes in mountain regions of Taiwan with remote sensing image. National Land Information System Communications. 36, 22–29. |
| [47] | Lei TC, Chou TY, Wan S, et al. (2007) Space characteristic classifier of Support Vector Machine for satellite image classification. J Photogramm Remote Sens 12: 145–163. |
| [48] |
Rashid N, Alam JAMM, Chowdhury MA, et al. (2022) Impact of landuse change and urbanization on urban heat island effect in Narayanganj city, Bangladesh: A remote sensing-based estimation. Environ Chall 8: 100571. https://doi.org/10.1016/j.envc.2022.100571 doi: 10.1016/j.envc.2022.100571
|
| [49] |
Halder A, Ghosh A, Ghosh S (2011) Supervised and unsupervised landuse map generation from remotely sensed images using ant based systems. Appl Soft Comput 11: 5770–5781. https://doi.org/10.1016/j.asoc.2011.02.030 doi: 10.1016/j.asoc.2011.02.030
|
| [50] |
Lü D, Gao G, Lü Y, et al. (2020) An effective accuracy assessment indicator for credible land use change modelling: Insights from hypothetical and real landscape analyses. Ecol Indic 117: 106552. https://doi.org/10.1016/j.ecolind.2020.106552 doi: 10.1016/j.ecolind.2020.106552
|
| [51] |
Liao J, Shao G, Wang C, et al. (2019) Urban sprawl scenario simulations based on cellular automata and ordered weighted averaging ecological constraints. Ecol Indic 107: 105572. https://doi.org/10.1016/j.ecolind.2019.105572 doi: 10.1016/j.ecolind.2019.105572
|
| [52] |
You W, Ji Z, Wu L, et al. (2017) Modeling changes in land use patterns and ecosystem services to explore a potential solution for meeting the management needs of a heritage site at the landscape level. Ecol Indic 73: 68–78. https://doi.org/10.1016/j.ecolind.2016.09.027 doi: 10.1016/j.ecolind.2016.09.027
|
| [53] |
Peng K, Jiang W, Deng Y, et al. (2020) Simulating wetland changes under different scenarios based on integrating the random forest and CLUE-S models: A case study of Wuhan Urban Agglomeration. Ecol Indic 117: 106671. https://doi.org/10.1016/j.ecolind.2020.106671. doi: 10.1016/j.ecolind.2020.106671
|
| [54] | Janssen LLF, van der Wel FJM (1994) Accuracy assessment of satellite derived land-cover data: a review. Photogramm Eng Rem S 60: 419–426. |
| [55] | Congalton RG, Green K (1991) Assessing the accuracy of remotely sensed data: Principles and practices. Boca Raton, FL, USA: Lewis Publishers. |
| [56] | Lin CY, Wang HF, Chuang CW (2006) A study of vegetation recovery for the landslides in the Chiufenershan using ecological index. J Soil Water Conserv 38: 279–286. |
| [57] | Ahmed SJ, Bramley G, Verburg PH (2014) Key Driving Factors Influencing Urban Growth: Spatial-Statistical Modeling with CLUE-s. Dhaka Megacity. Springer, Netherlands, 123–145. https://doi.org/10.1007/978-94-007-6735-5_7 |
| [58] |
Wu J, Xiang WN, Zhao J (2014) Urban ecology in China: Historical developments and future directions. Landscape Urban Plan 125: 222–233. https://doi.org/10.1016/j.landurbplan.2014.02.010 doi: 10.1016/j.landurbplan.2014.02.010
|
| [59] |
Panda KC, Singh RM, Singh SK (2024) Advanced CMD predictor screening approach coupled with cellular automata-artificial neural network algorithm for efficient land use-land cover change prediction. J Cleaner Prod 449: 141822. https://doi.org/10.1016/j.jclepro.2024.141822 doi: 10.1016/j.jclepro.2024.141822
|
| [60] |
Uwamahoro S, Liu T, Nzabarinda V, et al. (2024) Investigation of Groundwater–Surface water interaction and land use and land cover change in the catchments, A case of Kivu Lake, DRC-Rwanda. Groundwater Sustain Dev 26: 101236. https://doi.org/https://doi.org/10.1016/j.gsd.2024.101236 doi: 10.1016/j.gsd.2024.101236
|
| [61] | Danneels G, Pirard E, Havenith HB (2007) Automatic landslide detection from remote sensing images using supervised classification methods. 2007 IEEE International Geoscience and Remote Sensing Symposium, Barcelona, Spain, 2007, 3014–3017. https://doi.org/10.1109/IGARSS.2007.4423479 |
| [62] |
Quevedo RP, Maciel DA, Reis MS, et al. (2024) Land use and land cover changes without invalid transitions: A case study in a landslide-affected area. Remote Sens Appl Soc Environ 36: 101314. https://doi.org/10.1016/j.rsase.2024.101314 doi: 10.1016/j.rsase.2024.101314
|
| [63] |
Kondum FA, Rowshon MK, Luqman CA, et al. (2024) Change analyses and prediction of land use and land cover changes in Bernam River Basin, Malaysia. Remote Sens Appl Soc Environ 36: 101281. https://doi.org/10.1016/j.rsase.2024.101281 doi: 10.1016/j.rsase.2024.101281
|
| [64] |
Zhao F, Miao F, Wu Y, et al. (2024) Landslide dynamic susceptibility mapping in urban expansion area considering spatiotemporal land use and land cover change. Sci Total Environ 949: 175059. https://doi.org/10.1016/j.scitotenv.2024.175059 doi: 10.1016/j.scitotenv.2024.175059
|
| [65] |
Wang Q, Bai X, Zhang D, et al. (2024) Spatiotemporal characteristics and multi-scenario simulation of land use change and ecological security in the mountainous areas: Implications for supporting sustainable land management and ecological planning. Sustain Futures 8: 100286. https://doi.org/10.1016/j.sftr.2024.100286 doi: 10.1016/j.sftr.2024.100286
|
| [66] |
Yue W, Qin C, Su M, et al. (2024) Simulation and prediction of land use change in Dongguan of China based on ANN cellular automata - Markov chain model. Environ. Sustain Indic 22: 100355. https://doi.org/10.1016/j.indic.2024.100355 doi: 10.1016/j.indic.2024.100355
|
| [67] | Thompson JN (2022) ecological succession. Encyclopedia Britannica. https://www.britannica.com/science/ecological-succession. |
| [68] |
Congalton RG (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ 37: 35–46. https://doi.org/10.1016/0034-4257(91)90048-B doi: 10.1016/0034-4257(91)90048-B
|
| [69] | Lin WT, Huang PH, Lu WH (2023) Long-term Monitoring and Assessment of Restoration for the Earthquake-induced Landslide at the Chiufanershan Area. J Soil Water Conserv 53: 3165–3174. |
| [70] | Lee MH (2023) Vegetation Succession Analysis in Landslide Remediation Areas - Case Study of Landslides in Nantou County. Master's thesis, Department of Soil and Water Conservation, National Chung Hsing University. https://hdl.handle.net/11296/f57y8z |
| [71] |
Lu SY, Wu SW, Sun MY (2023) Soil Depth Estimation and the Influence of Soil Depth and Vegetation Cover on Slope Stability in the Liukuei Experimental Forest. J Chinese Soil Water Conserv 54: 60–69. https://doi.org/10.29417/jcswc.202303_54(1).0006 doi: 10.29417/jcswc.202303_54(1).0006
|
| [72] |
Sartohadi J, Pulungan NAH, Nurudin M, et al. (2018) The Ecological Perspective of Landslides at Soils with High Clay Content in the Middle Bogowonto Watershed, Central Java, Indonesia. Appl Environ Soil Sc. 2018: 2648185. https://doi.org/https://doi.org/10.1155/2018/2648185 doi: 10.1155/2018/2648185
|
| [73] | Ho TC, Chu EL, Wang FT, et al. (2019) Plant resources and conservation in Jiufenfrfhan area. Nat Conserv Q 105: 38–49. |
| 1. | R. Scardaci, F. Varese, M. Manfredi, E. Marengo, R. Mazzoli, E. Pessione, Enterococcus faecium NCIMB10415 responds to norepinephrine by altering protein profiles and phenotypic characters, 2021, 231, 18743919, 104003, 10.1016/j.jprot.2020.104003 | |
| 2. | Beatriz Sabater-Muñoz, Christina Toft, 2020, Chapter 3, 978-3-030-51848-6, 77, 10.1007/978-3-030-51849-3_3 | |
| 3. | Constance J. Jeffery, An enzyme in the test tube, and a transcription factor in the cell: Moonlighting proteins and cellular factors that affect their behavior, 2019, 0961-8368, 10.1002/pro.3645 | |
| 4. | Adriana Espinosa-Cantú, Erika Cruz-Bonilla, Lianet Noda-Garcia, Alexander DeLuna, Multiple Forms of Multifunctional Proteins in Health and Disease, 2020, 8, 2296-634X, 10.3389/fcell.2020.00451 | |
| 5. | Patrick Di Martino, Bacterial adherence: much more than a bond, 2018, 4, 2471-1888, 563, 10.3934/microbiol.2018.3.563 | |
| 6. | Lidia Muscariello, Barbara De Siena, Rosangela Marasco, Lactobacillus Cell Surface Proteins Involved in Interaction with Mucus and Extracellular Matrix Components, 2020, 77, 0343-8651, 3831, 10.1007/s00284-020-02243-5 | |
| 7. | Haipeng Liu, Constance J. Jeffery, Moonlighting Proteins in the Fuzzy Logic of Cellular Metabolism, 2020, 25, 1420-3049, 3440, 10.3390/molecules25153440 | |
| 8. | Dorota Satala, Justyna Karkowska-Kuleta, Aleksandra Zelazna, Maria Rapala-Kozik, Andrzej Kozik, Moonlighting Proteins at the Candidal Cell Surface, 2020, 8, 2076-2607, 1046, 10.3390/microorganisms8071046 | |
| 9. | Constance J. Jeffery, Multitalented actors inside and outside the cell: recent discoveries add to the number of moonlighting proteins, 2019, 47, 0300-5127, 1941, 10.1042/BST20190798 | |
| 10. | Bruna Gonçalves, Nuno Azevedo, Hugo Osório, Mariana Henriques, Sónia Silva, Revealing Candida glabrata biofilm matrix proteome: global characterization and pH response, 2021, 478, 0264-6021, 961, 10.1042/BCJ20200844 | |
| 11. | Amy L. Bottomley, Elizabeth Peterson, Gregory Iosifidis, Adeline Mei Hui Yong, Lauren E. Hartley-Tassell, Shirin Ansari, Chris McKenzie, Catherine Burke, Iain G. Duggin, Kimberly A. Kline, Elizabeth J. Harry, The novel E. coli cell division protein, YtfB, plays a role in eukaryotic cell adhesion, 2020, 10, 2045-2322, 10.1038/s41598-020-63729-7 | |
| 12. | Jana Al Azzaz, Alissar Al Tarraf, Arnaud Heumann, David Da Silva Barreira, Julie Laurent, Ali Assifaoui, Aurélie Rieu, Jean Guzzo, Pierre Lapaquette, Resveratrol Favors Adhesion and Biofilm Formation of Lacticaseibacillus paracasei subsp. paracasei Strain ATCC334, 2020, 21, 1422-0067, 5423, 10.3390/ijms21155423 | |
| 13. | Enrica Pessione, The Russian Doll Model: How Bacteria Shape Successful and Sustainable Inter-Kingdom Relationships, 2020, 11, 1664-302X, 10.3389/fmicb.2020.573759 | |
| 14. | Ying Chen, Lan Yao, Yunsheng Wang, Xiaohan Ji, Zhan Gao, Shicui Zhang, Guangdong Ji, Identification of ribosomal protein L30 as an uncharacterized antimicrobial protein, 2021, 120, 0145305X, 104067, 10.1016/j.dci.2021.104067 | |
| 15. | Marta Bottagisio, Pietro Barbacini, Alessandro Bidossi, Enrica Torretta, Elinor deLancey-Pulcini, Cecilia Gelfi, Garth A. James, Arianna B. Lovati, Daniele Capitanio, Phenotypic Modulation of Biofilm Formation in a Staphylococcus epidermidis Orthopedic Clinical Isolate Grown Under Different Mechanical Stimuli: Contribution From a Combined Proteomic Study, 2020, 11, 1664-302X, 10.3389/fmicb.2020.565914 | |
| 16. | Guolin Cai, Dianhui Wu, Xiaomin Li, Jian Lu, Levan from Bacillus amyloliquefaciens JN4 acts as a prebiotic for enhancing the intestinal adhesion capacity of Lactobacillus reuteri JN101, 2020, 146, 01418130, 482, 10.1016/j.ijbiomac.2019.12.212 | |
| 17. | Chang Chen, Constance Jeffery, 2019, Chapter 13, 978-3-030-23157-6, 269, 10.1007/978-3-030-23158-3_13 | |
| 18. | Dorota Satala, Grzegorz Satala, Justyna Karkowska-Kuleta, Michal Bukowski, Anna Kluza, Maria Rapala-Kozik, Andrzej Kozik, Structural Insights into the Interactions of Candidal Enolase with Human Vitronectin, Fibronectin and Plasminogen, 2020, 21, 1422-0067, 7843, 10.3390/ijms21217843 | |
| 19. | Natayme Rocha Tartaglia, Aurélie Nicolas, Vinícius de Rezende Rodovalho, Brenda Silva Rosa da Luz, Valérie Briard-Bion, Zuzana Krupova, Anne Thierry, François Coste, Agnes Burel, Patrice Martin, Julien Jardin, Vasco Azevedo, Yves Le Loir, Eric Guédon, Extracellular vesicles produced by human and animal Staphylococcus aureus strains share a highly conserved core proteome, 2020, 10, 2045-2322, 10.1038/s41598-020-64952-y | |
| 20. | Yanina Lamberti, Kristin Surmann, The intracellular phase of extracellular respiratory tract bacterial pathogens and its role on pathogen-host interactions during infection, 2021, 34, 0951-7375, 197, 10.1097/QCO.0000000000000727 | |
| 21. | Wanderson Marques da Silva, Nubia Seyffert, Artur Silva, Vasco Azevedo, A journey through the Corynebacterium pseudotuberculosis proteome promotes insights into its functional genome, 2021, 9, 2167-8359, e12456, 10.7717/peerj.12456 | |
| 22. | Mahalingam Srinivasan, Subramanian Muthukumar, Durairaj Rajesh, Vinod Kumar, Rajamanickam Rajakumar, Mohammad Abdulkader Akbarsha, Balázs Gulyás, Parasuraman Padmanabhan, Govindaraju Archunan, The Exoproteome of Staphylococcus pasteuri Isolated from Cervical Mucus during the Estrus Phase in Water Buffalo (Bubalus bubalis), 2022, 12, 2218-273X, 450, 10.3390/biom12030450 | |
| 23. | A. Paula Domínguez Rubio, Cecilia L. D’Antoni, Mariana Piuri, Oscar E. Pérez, Probiotics, Their Extracellular Vesicles and Infectious Diseases, 2022, 13, 1664-302X, 10.3389/fmicb.2022.864720 | |
| 24. | Fei Hao, Xing Xie, Zhixin Feng, Rong Chen, Yanna Wei, Jin Liu, Qiyan Xiong, Guoqing Shao, Johnson Lin, NADH oxidase of Mycoplasma hyopneumoniae functions as a potential mediator of virulence, 2022, 18, 1746-6148, 10.1186/s12917-022-03230-7 | |
| 25. | Xia Liu, Ting Luan, Wanqing Zhou, Lina Yan, Hua Qian, Pengyuan Mao, Lisha Jiang, Jingyan Liu, Can Rui, Xinyan Wang, Ping Li, Xin Zeng, Denise Monack, The Role of 17β-Estrogen in Escherichia coli Adhesion on Human Vaginal Epithelial Cells via FAK Phosphorylation, 2021, 89, 0019-9567, 10.1128/IAI.00219-21 | |
| 26. | Pramod Yadav, Raja Singh, Souvik Sur, Sandhya Bansal, Uma Chaudhry, Vibha Tandon, Moonlighting proteins: beacon of hope in era of drug resistance in bacteria, 2023, 49, 1040-841X, 57, 10.1080/1040841X.2022.2036695 | |
| 27. | Teresa Requena, Gaspar Pérez Martínez, 2022, 9780128220368, 197, 10.1016/B978-0-12-819265-8.00094-2 | |
| 28. | Weng Yu Lai, Zhenpei Wong, Chiat Han Chang, Mohd Razip Samian, Nobumoto Watanabe, Aik-Hong Teh, Rahmah Noordin, Eugene Boon Beng Ong, Identifying Leptospira interrogans putative virulence factors with a yeast protein expression screen, 2022, 106, 0175-7598, 6567, 10.1007/s00253-022-12160-1 | |
| 29. | Cecile El-Chami, Rawshan Choudhury, Walaa Mohammedsaeed, Andrew J. McBain, Veera Kainulainen, Sarah Lebeer, Reetta Satokari, Catherine A. O’Neill, Multiple Proteins of Lacticaseibacillus rhamnosus GG Are Involved in the Protection of Keratinocytes From the Toxic Effects of Staphylococcus aureus, 2022, 13, 1664-302X, 10.3389/fmicb.2022.875542 | |
| 30. | Paola San-Martin-Galindo, Emil Rosqvist, Stiina Tolvanen, Ilkka Miettinen, Kirsi Savijoki, Tuula A. Nyman, Adyary Fallarero, Jouko Peltonen, Modulation of virulence factors of Staphylococcus aureus by nanostructured surfaces, 2021, 208, 02641275, 109879, 10.1016/j.matdes.2021.109879 | |
| 31. | Xing Xie, Fei Hao, Rong Chen, Jingjing Wang, Yanna Wei, Jin Liu, Haiyan Wang, Zhenzhen Zhang, Yun Bai, Guoqing Shao, Qiyan Xiong, Zhixin Feng, Nicotinamide Adenine Dinucleotide-Dependent Flavin Oxidoreductase of Mycoplasma hyopneumoniae Functions as a Potential Novel Virulence Factor and Not Only as a Metabolic Enzyme, 2021, 12, 1664-302X, 10.3389/fmicb.2021.747421 | |
| 32. | Olga S. Savinova, Olga A. Glazunova, Konstantin V. Moiseenko, Anna V. Begunova, Irina V. Rozhkova, Tatyana V. Fedorova, Exoproteome Analysis of Antagonistic Interactions between the Probiotic Bacteria Limosilactobacillus reuteri LR1 and Lacticaseibacillus rhamnosus F and Multidrug Resistant Strain of Klebsiella pneumonia, 2021, 22, 1422-0067, 10999, 10.3390/ijms222010999 | |
| 33. | Natalie A. Harrison, Christopher L. Gardner, Danilo R. da Silva, Claudio F. Gonzalez, Graciela L. Lorca, Identification of Biomarkers for Systemic Distribution of Nanovesicles From Lactobacillus johnsonii N6.2, 2021, 12, 1664-3224, 10.3389/fimmu.2021.723433 | |
| 34. | Keita Nishiyama, Cheng-Chung Yong, Nobuko Moritoki, Haruki Kitazawa, Toshitaka Odamaki, Jin-Zhong Xiao, Takao Mukai, Danilo Ercolini, Sharing of Moonlighting Proteins Mediates the Symbiotic Relationship among Intestinal Commensals, 2023, 0099-2240, 10.1128/aem.02190-22 | |
| 35. | Przemysław Sałański, Magdalena Kowalczyk, Jacek K. Bardowski, Agnieszka K. Szczepankowska, Health-Promoting Nature of Lactococcus lactis IBB109 and Lactococcus lactis IBB417 Strains Exhibiting Proliferation Inhibition and Stimulation of Interleukin-18 Expression in Colorectal Cancer Cells, 2022, 13, 1664-302X, 10.3389/fmicb.2022.822912 | |
| 36. | Jia Wang, Yao Li, Longji Pan, Jun Li, Yanfei Yu, Beibei Liu, Muhammad Zubair, Yanna Wei, Bala Pillay, Ademola Olufolahan Olaniran, Thamsanqa E. Chiliza, Guoqing Shao, Zhixin Feng, Qiyan Xiong, Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) moonlights as an adhesin in Mycoplasma hyorhinis adhesion to epithelial cells as well as a plasminogen receptor mediating extracellular matrix degradation, 2021, 52, 1297-9716, 10.1186/s13567-021-00952-8 | |
| 37. | Tao Shi, Xi Guo, Yuqin Liu, Tingting Zhang, Xiangnan Wang, Zongjun Li, Yu Jiang, Rumen Metaproteomics Highlight the Unique Contributions of Microbe-Derived Extracellular and Intracellular Proteins for In Vitro Ruminal Fermentation, 2022, 8, 2311-5637, 394, 10.3390/fermentation8080394 | |
| 38. | Thomas B. Irving, Burcu Alptekin, Bailey Kleven, Jean‐Michel Ané, A critical review of 25 years of glomalin research: a better mechanical understanding and robust quantification techniques are required, 2021, 232, 0028-646X, 1572, 10.1111/nph.17713 | |
| 39. | Inés Reigada, Paola San-Martin-Galindo, Shella Gilbert-Girard, Jacopo Chiaro, Vincenzo Cerullo, Kirsi Savijoki, Tuula A. Nyman, Adyary Fallarero, Ilkka Miettinen, Surfaceome and Exoproteome Dynamics in Dual-Species Pseudomonas aeruginosa and Staphylococcus aureus Biofilms, 2021, 12, 1664-302X, 10.3389/fmicb.2021.672975 | |
| 40. | Jack A. Doolan, George T. Williams, Kira L. F. Hilton, Rajas Chaudhari, John S. Fossey, Benjamin T. Goult, Jennifer R. Hiscock, Advancements in antimicrobial nanoscale materials and self-assembling systems, 2022, 51, 0306-0012, 8696, 10.1039/D1CS00915J | |
| 41. | Yao Li, Jia Wang, Beibei Liu, Yanfei Yu, Ting Yuan, Yanna Wei, Yuan Gan, Jia Shao, Guoqing Shao, Zhixin Feng, Zhigang Tu, Qiyan Xiong, DnaK Functions as a Moonlighting Protein on the Surface of Mycoplasma hyorhinis Cells, 2022, 13, 1664-302X, 10.3389/fmicb.2022.842058 | |
| 42. | Yu Sun, Xuhang Wang, Qianwen Gong, Jin Li, Haosheng Huang, Feng Xue, Jianjun Dai, Fang Tang, Joanna B. Goldberg, Extraintestinal Pathogenic Escherichia coli Utilizes Surface-Located Elongation Factor G to Acquire Iron from Holo-Transferrin , 2022, 10, 2165-0497, 10.1128/spectrum.01662-21 | |
| 43. | Dorota Satala, Grzegorz Satala, Marcin Zawrotniak, Andrzej Kozik, Candida albicans and Candida glabrata triosephosphate isomerase – a moonlighting protein that can be exposed on the candidal cell surface and bind to human extracellular matrix proteins, 2021, 21, 1471-2180, 10.1186/s12866-021-02235-w | |
| 44. | Teresa Faddetta, Giovanni Renzone, Alberto Vassallo, Emilio Rimini, Giorgio Nasillo, Gianpiero Buscarino, Simonpietro Agnello, Mariano Licciardi, Luigi Botta, Andrea Scaloni, Antonio Palumbo Piccionello, Anna Maria Puglia, Giuseppe Gallo, Gladys Alexandre, Streptomyces coelicolor Vesicles: Many Molecules To Be Delivered, 2022, 88, 0099-2240, 10.1128/AEM.01881-21 | |
| 45. | Alain Filloux, Bacterial protein secretion systems: Game of types , 2022, 168, 1350-0872, 10.1099/mic.0.001193 | |
| 46. | Sébastien Massier, Brandon Robin, Marianne Mégroz, Amy Wright, Marina Harper, Brooke Hayes, Pascal Cosette, Isabelle Broutin, John D. Boyce, Emmanuelle Dé, Julie Hardouin, Phosphorylation of Extracellular Proteins in Acinetobacter baumannii in Sessile Mode of Growth, 2021, 12, 1664-302X, 10.3389/fmicb.2021.738780 | |
| 47. | Ana Luísa Matos, Pedro Curto, Isaura Simões, Moonlighting in Rickettsiales: Expanding Virulence Landscape, 2022, 7, 2414-6366, 32, 10.3390/tropicalmed7020032 | |
| 48. | Atsushi Kurata, Shimpei Takeuchi, Ryo Fujiwara, Kento Tamura, Tomoya Imai, Shino Yamasaki-Yashiki, Hiroki Onuma, Yasuhisa Fukuta, Norifumi Shirasaka, Koichi Uegaki, Activation of the toll-like receptor 2 signaling pathway by GAPDH from bacterial strain RD055328, 2023, 1347-6947, 10.1093/bbb/zbad059 | |
| 49. | Duoyi Hu, Irina Laczkovich, Michael J. Federle, Donald A. Morrison, Tina M. Henkin, Identification and Characterization of Negative Regulators of Rgg1518 Quorum Sensing in Streptococcus pneumoniae, 2023, 0021-9193, 10.1128/jb.00087-23 | |
| 50. | Jiah Yeom, Seongho Ma, Dong Joon Yim, Young-Hee Lim, Surface proteins of Propionibacterium freudenreichii MJ2 inhibit RANKL-induced osteoclast differentiation by lipocalin-2 upregulation and lipocalin-2-mediated NFATc1 inhibition, 2023, 13, 2045-2322, 10.1038/s41598-023-42944-y | |
| 51. | Ariana Casas-Román, María-José Lorite, Juan Sanjuán, María-Trinidad Gallegos, Two glyceraldehyde-3-phosphate dehydrogenases with distinctive roles in Pseudomonas syringae pv. tomato DC3000, 2024, 278, 09445013, 127530, 10.1016/j.micres.2023.127530 | |
| 52. | Dawei Chen, Congcong Guo, Chenyu Ren, Zihan Xia, Haiyan Xu, Hengxian Qu, Yunchao Wa, Chengran Guan, Chenchen Zhang, Jianya Qian, Ruixia Gu, Screening of Lactiplantibacillus plantarum 67 with Strong Adhesion to Caco-2 Cells and the Effects of Protective Agents on Its Adhesion Ability during Vacuum Freeze Drying, 2023, 12, 2304-8158, 3604, 10.3390/foods12193604 | |
| 53. | Rivesh Maharajh, Manormoney Pillay, Sibusiso Senzani, A computational method for the prediction and functional analysis of potential Mycobacterium tuberculosis adhesin-related proteins , 2023, 1478-9450, 1, 10.1080/14789450.2023.2275678 | |
| 54. | Nicole J. Curtis, Krupa J. Patel, Amina Rizwan, Constance J. Jeffery, Moonlighting Proteins: Diverse Functions Found in Fungi, 2023, 9, 2309-608X, 1107, 10.3390/jof9111107 | |
| 55. | Judeng Zeng, Chuan Xie, Ziheng Huang, Chi H. Cho, Hung Chan, Qing Li, Hassan Ashktorab, Duane T. Smoot, Sunny H. Wong, Jun Yu, Wei Gong, Cong Liang, Hongzhi Xu, Huarong Chen, Xiaodong Liu, Justin C. Y. Wu, Margaret Ip, Tony Gin, Lin Zhang, Matthew T. V. Chan, Wei Hu, William K. K. Wu, LOX-1 acts as an N6-methyladenosine-regulated receptor for Helicobacter pylori by binding to the bacterial catalase, 2024, 15, 2041-1723, 10.1038/s41467-024-44860-9 | |
| 56. | Amtul Jamil Sami, Sehrish Bilal, Sadaf Alam, Madeeha Khalid, Hammad Ahmad Mangat, A Method Based on a Modified Fluorescence In Situ Hybridization (FISH) Approach for the Sensing of Staphylococcus aureus from Nasal Samples, 2024, 0273-2289, 10.1007/s12010-024-04892-9 | |
| 57. | Ariana Casas-Román, María-José Lorite, Mariana Werner, Socorro Muñoz, María-Trinidad Gallegos, Juan Sanjuán, The gap gene of Rhizobium etli is required for both free life and symbiosis with common beans., 2024, 09445013, 127737, 10.1016/j.micres.2024.127737 | |
| 58. | Wenqian Liu, Zhen Wang, Shengjia Wang, Minghui Liu, Jian Zhang, Xuepeng Li, Hongye Wang, Jixing Feng, Identification of moonlighting adhesins of highly-adhesive Lactobacillus plantarum PO23 isolated from the intestine of Paralichthys olivaceus, 2024, 590, 00448486, 741044, 10.1016/j.aquaculture.2024.741044 | |
| 59. | Samuel A. Adeleye, Srujana S. Yadavalli, Jue D. Wang, Queuosine biosynthetic enzyme, QueE moonlights as a cell division regulator, 2024, 20, 1553-7404, e1011287, 10.1371/journal.pgen.1011287 | |
| 60. | Susan M. Noh, Jessica Ujczo, Debra C. Alperin, Identification of Anaplasma marginale adhesins for bovine erythrocytes using phage display, 2024, 5, 2673-7515, 10.3389/fitd.2024.1422860 | |
| 61. | Bruna Gonçalves, Diana Priscila Pires, Liliana Fernandes, Miguel Pacheco, Tiago Ferreira, Hugo Osório, Ana Raquel Soares, Mariana Henriques, Sónia Silva, Agostinho Carvalho, Biofilm matrix regulation by Candida glabrata Zap1 under acidic conditions: transcriptomic and proteomic analyses , 2024, 2165-0497, 10.1128/spectrum.01201-24 | |
| 62. | Ke Ma, Lei Deng, Yuanjie Wu, Yuan Gao, Jianhua Fan, Haizhen Wu, Transgenic Schizochytrium as a Promising Oral Vaccine Carrier: Potential Application in the Aquaculture Industry, 2024, 22, 1660-3397, 555, 10.3390/md22120555 | |
| 63. | Ana Carolina Franco Severo Martelli, Beatriz Brambila, Mariana Pegrucci Barcelos, Flávia da Silva Zandonadi, Solange Cristina Antão, André Vessoni Alexandrino, Carlos Henrique Tomich de Paula da Silva, Maria Teresa Marques Novo-Mansur, 2024, Chapter 10, 978-3-031-75983-3, 251, 10.1007/978-3-031-75984-0_10 | |
| 64. | Jizhen Cao, Han Li, Qing Han, Zhicheng Li, Jingyu Zhuang, Chuanfu Dong, Anxing Li, The accessory secretion system in Streptococcus agalactiae regulates protein secretion, stress resistance, adhesion, immune evasion, and virulence, 2025, 158, 10504648, 110172, 10.1016/j.fsi.2025.110172 | |
| 65. | Yinxiao Zhang, Yanchao Wen, Chi Zhang, Yuan Liu, Ran Wang, He Li, Xinqi Liu, Mechanisms of soybean proteins and peptides regulating the adhesion of Lacticaseibacillus rhamnosus GG and Lactiplantibacillus plantarum K25 to intestinal cells: A Comparative Study, 2025, 65, 22124292, 106156, 10.1016/j.fbio.2025.106156 | |
| 66. | Ran Wang, Yuan Liu, Yanchao Wen, Siyu Chen, Xiaohan Zhang, Chi Zhang, Xinqi Liu, Unraveling the secrets of probiotic adhesion: an overview of adhesion-associated cell surface components, adhesion mechanisms, and the effects of food composition, 2025, 09242244, 104945, 10.1016/j.tifs.2025.104945 | |
| 67. | Candelario Vazquez-Cruz, Edmundo Reyes-Malpica, J. Fernando Montes-García, Pamela Bautista-Betancourt, Elena Cobos-Justo, Miguel A. Avalos-Rangel, Erasmo Negrete-Abascal, Actinobacillus seminis DnaK interacts with bovine transferrin, lactoferrin, and hemoglobin as a putative iron acquisition mechanism, 2025, 0015-5632, 10.1007/s12223-025-01271-7 |
Figures(9) / Tables(7)
Chih-Wei Chuang, Hao-Yu Huang, Chun-Wei Tseng. Assessment of vegetation dynamic and its effects in a large-scale landslide in Central Taiwan with multitemporal Landsat images[J]. AIMS Geosciences, 2025, 11(2): 318-342. doi: 10.3934/geosci.2025014
| Protein | Species | UniProt ID | Extracellular function | References |
| 6-phosphofructokinase | Lactococcus lactis | P0DOB5 | yeast invertase | [54] |
| Streptococcus oralis | E6KMA1 | plasminogen | [78] | |
| Aaa autolysin | Staphylococcus aureus | Q2YVT4 | fibronectin | [37] |
| Aae autolysin | Staphylococcus epidermis | Q8CPQ1 | fibrinogen, fibronectin, vitronectin | [79] |
| Aspartase | Haemophilus influenzae | P44324 | plasminogen | [80] |
| Atla autolysin | Streptococcus mutans | U3SW74 | fibronectin | [39] |
| AtlC autolysin | Staphylococcus caprae | Q9AIS0 | fibronectin | [40] |
| Bile salt hydrolase | Bifidobacterium lactis | Q9KK62 | plasminogen | [81] |
| C5a peptidase | Streptococcus agalactiae | Q8E4T9 | fibronectin | [82] |
| DNA-directed RNA polymerase beta subunit | Streptococcus gordonii | A0EKJ1 | Muc7 | [18] |
| DnaK | Bifidobacterium | Q8G6W1 | plasminogen | [81] |
| Lactococcus lactis | P0A3J0 | yeast invertase | [54] | |
| Mycobacterium tuberculosis | A0A0H3L5C8 | plasminogen | [75] | |
| Neisseria meningitidis | A9M296 | plasminogen | [20] | |
| EF-Tu | Lactobacillus johnsonii | Q74JU6 | cells, mucins | [51] |
| Mycoplasma pneumoniae | P23568 | fibronectin, epithelial cells, plasminogen, heparin, fetuin, actin, fibrinogen, vitronectin, laminin | [36,64] | |
| Pseudonomas aeruginosa | P09591 | plasminogen | [43] | |
| Streptococcus gordonii | A8AWA0 | Muc7 | [18] | |
| Elongation factor G | Streptococcus gordonii | A8AUR6 | Muc7 | [18] |
| Endopeptidase O | Streptococcus pneumoniae | Q8DNW9 | plasminogen, fibronectin | [83] |
| Enolase | Aeromonas hydrophila | Q8GE63 | plasminogen | [22] |
| Bacillus anthracis | D8H2L1 | plasminogen, laminin | [8] | |
| Bifidobacterium lactis | B7GTK2 | plasminogen | [11] | |
| Borrelia burgdorferi | B7J1R2 | plasminogen | [16] | |
| Lactobacillus crispatus | Q5K117 | plasminogen, laminin | [14] | |
| Lactobacillus johnsonii | Q74K78 | plasminogen, laminin | [14] | |
| Lactobacillus plantarum | Q88YH3 | fibronectin | [23] | |
| Leishmania mexicana | Q3HL75 | plasminogen | [13] | |
| Mycoplasma fermentans | C4XEI3 | plasminogen | [24] | |
| Mycoplasma suis | F0QRW4 | red blood cells | [84] | |
| Mycoplasma synoviae | Q4A740 | plasminogen, fibronectin | [9] | |
| Neisseria meningitidis | E0N8L2 | plasminogen | [20] | |
| Staphylococcus aureus | Q6GB54 | plasminogen, laminin | [14,21] | |
| Streptococcus canis | I7WI49 | plasminogen | [17] | |
| Streptococcus gordonii | A8AY46 | Muc7 | [18] | |
| Streptococcus mutans | Q8DTS9 | plasminogen | [12] | |
| Streptococcus oralis | A0A1F1EC06 | plasminogen | [19] | |
| Streptococcus pneumoniae | Q97QS2 | plasminogen | [14] | |
| Streptococcus pyogenes | Q1JML5 | plasminogen | [14] | |
| Streptococcus suis | A4W2T1 | fibronectin, plasminogen | [15] | |
| Fructose 1,6-bisphosphate aldolase | Neisseria meningitidis | F0N9L0 | cells | [25] |
| GAPDH | Bacillus anthracis | Q81X74 | plasminogen | [85] |
| Lactobacillus acidophilus | Q5FL51 | mucin | [53] | |
| Lactobacillus plantarum | F9UM10 | mucin, Caco-2 cells | [50] | |
| Lactococcus lactis | P52987 | yeast invertase | [54] | |
| Mycoplasma genitalium | P47543 | mucin | [41] | |
| Staphylococcus aureus | Q6GIL8 | transferrin | [49] | |
| Streptococcus agalactiae | Q9ALW2 | plasminogen | [86] | |
| Streptococcus oralis | A0A0F2E7M6 | plasminogen | [78] | |
| Streptococcus pneumoniae | A0A0H2US80 | plasminogen | [87] | |
| Streptococcus pyogenes | P68777 | uPAR/CD87 receptor on human cells, plasminogen | [33,88] | |
| Streptococcus suis | Q3Y454 | plasminogen | [89] | |
| Glucose 6-phosphate isomerase | Lactobacillus crispatus | K1MKZ7 | laminin, collagen | [90] |
| Glutamine synthetase | Lactobacillus crispatus | D5GYN9 | fibronectin, laminin, collagen I, plasminogen | [90] |
| Mycobacterium tuberculosis | A0A0H3LHU4 | plasminogen, fibronectin | [91] | |
| Bifidobacterium lactis | C2GUH0 | plasminogen | [81] | |
| Hsp60 | Chlamydiae pneumoniae | P31681 | adhesin | [29] |
| Lactococcus lactis | P37282 | yeast invertase | [54] | |
| Legionella pneumophila | Q5X762 | adhesin | [30] | |
| Clostridium difficile | Q9KKF0 | adhesin | [27] | |
| Haemophilus ducreyi | P31294 | glycosphinngolipids | [34,35] | |
| Helicobacter pylori | Q8RNU2 | adhesin | [28] | |
| Lactobacillus johnsonii | F7SCR2 | adhesin | [52] | |
| Listeria | Q8KP52 | adhesin | [32] | |
| Salmonella typhimurium | P0A1D3 | mucus | [42] | |
| Hsp65/Cpn60.2/GroEL2 | Mycobacterium tuberculosis | A0A0H3LCC3 | CD43 on macrophage surface | [92] |
| Leucyl aminopeptidase | Mycoplasma hyopneumoniae | Q4A9M4 | heparin | [93] |
| Malate synthase | Mycobacterium tuberculosis | P9WK17 | fibronectin, laminin, epithelial cells | [38] |
| Glutamyl aminopeptidase | Mycoplasma hyopneumoniae | Q4AAK4 | plasminogen, heparin | [47] |
| Leucyl aminopeptidase | Mycoplasma hyopneumoniae | Q4A9M4 | plasminogen, heparin, DNA | [48] |
| Ornithine carbamoyltransferase | Staphylococcus epidermidis | P0C0N1 | fibronectin | [94] |
| Peroxiredoxin | Neisseria meningitidis | A0A125WDU3 | plasminogen | [20] |
| Streptococcus agalactiae | E7S2A7 | heme | [95] | |
| Phosphoglycerate kinase | Streptococcus oralis | A0A0G7HBY7 | plasminogen | [77] |
| Streptococcus agalactiae | Q8DXT0 | plasminogen, actin | [83,96] | |
| Streptococcus pneumoniae | Q8DQX8 | plasminogen | [97] | |
| Phosphoglycerate mutase | Bifidobacterium lactis | P59159 | plasminogen | [81] |
| Streptococcus oralis | E6IYJ0 | plasminogen | [78] | |
| Pyruvate dehydrogenase | Mycoplasma pneumoniae | P75391 | fibrinogen | [36] |
| Pyruvate kinase | Lactococcus lactis | Q07637 | yeast invertase | [54] |
| Superoxide dismutase | Mycobacterium avium | P53647 | adhesin | [98] |
| Triose phosphate isomerase | Streptococcus oralis | E6J203 | plasminogen | [78] |
DownLoad: CSV| Protein | Species | UniProt ID | Extracellular function | References |
| 6-phosphofructokinase | Lactococcus lactis | P0DOB5 | yeast invertase | [54] |
| Streptococcus oralis | E6KMA1 | plasminogen | [78] | |
| Aaa autolysin | Staphylococcus aureus | Q2YVT4 | fibronectin | [37] |
| Aae autolysin | Staphylococcus epidermis | Q8CPQ1 | fibrinogen, fibronectin, vitronectin | [79] |
| Aspartase | Haemophilus influenzae | P44324 | plasminogen | [80] |
| Atla autolysin | Streptococcus mutans | U3SW74 | fibronectin | [39] |
| AtlC autolysin | Staphylococcus caprae | Q9AIS0 | fibronectin | [40] |
| Bile salt hydrolase | Bifidobacterium lactis | Q9KK62 | plasminogen | [81] |
| C5a peptidase | Streptococcus agalactiae | Q8E4T9 | fibronectin | [82] |
| DNA-directed RNA polymerase beta subunit | Streptococcus gordonii | A0EKJ1 | Muc7 | [18] |
| DnaK | Bifidobacterium | Q8G6W1 | plasminogen | [81] |
| Lactococcus lactis | P0A3J0 | yeast invertase | [54] | |
| Mycobacterium tuberculosis | A0A0H3L5C8 | plasminogen | [75] | |
| Neisseria meningitidis | A9M296 | plasminogen | [20] | |
| EF-Tu | Lactobacillus johnsonii | Q74JU6 | cells, mucins | [51] |
| Mycoplasma pneumoniae | P23568 | fibronectin, epithelial cells, plasminogen, heparin, fetuin, actin, fibrinogen, vitronectin, laminin | [36,64] | |
| Pseudonomas aeruginosa | P09591 | plasminogen | [43] | |
| Streptococcus gordonii | A8AWA0 | Muc7 | [18] | |
| Elongation factor G | Streptococcus gordonii | A8AUR6 | Muc7 | [18] |
| Endopeptidase O | Streptococcus pneumoniae | Q8DNW9 | plasminogen, fibronectin | [83] |
| Enolase | Aeromonas hydrophila | Q8GE63 | plasminogen | [22] |
| Bacillus anthracis | D8H2L1 | plasminogen, laminin | [8] | |
| Bifidobacterium lactis | B7GTK2 | plasminogen | [11] | |
| Borrelia burgdorferi | B7J1R2 | plasminogen | [16] | |
| Lactobacillus crispatus | Q5K117 | plasminogen, laminin | [14] | |
| Lactobacillus johnsonii | Q74K78 | plasminogen, laminin | [14] | |
| Lactobacillus plantarum | Q88YH3 | fibronectin | [23] | |
| Leishmania mexicana | Q3HL75 | plasminogen | [13] | |
| Mycoplasma fermentans | C4XEI3 | plasminogen | [24] | |
| Mycoplasma suis | F0QRW4 | red blood cells | [84] | |
| Mycoplasma synoviae | Q4A740 | plasminogen, fibronectin | [9] | |
| Neisseria meningitidis | E0N8L2 | plasminogen | [20] | |
| Staphylococcus aureus | Q6GB54 | plasminogen, laminin | [14,21] | |
| Streptococcus canis | I7WI49 | plasminogen | [17] | |
| Streptococcus gordonii | A8AY46 | Muc7 | [18] | |
| Streptococcus mutans | Q8DTS9 | plasminogen | [12] | |
| Streptococcus oralis | A0A1F1EC06 | plasminogen | [19] | |
| Streptococcus pneumoniae | Q97QS2 | plasminogen | [14] | |
| Streptococcus pyogenes | Q1JML5 | plasminogen | [14] | |
| Streptococcus suis | A4W2T1 | fibronectin, plasminogen | [15] | |
| Fructose 1,6-bisphosphate aldolase | Neisseria meningitidis | F0N9L0 | cells | [25] |
| GAPDH | Bacillus anthracis | Q81X74 | plasminogen | [85] |
| Lactobacillus acidophilus | Q5FL51 | mucin | [53] | |
| Lactobacillus plantarum | F9UM10 | mucin, Caco-2 cells | [50] | |
| Lactococcus lactis | P52987 | yeast invertase | [54] | |
| Mycoplasma genitalium | P47543 | mucin | [41] | |
| Staphylococcus aureus | Q6GIL8 | transferrin | [49] | |
| Streptococcus agalactiae | Q9ALW2 | plasminogen | [86] | |
| Streptococcus oralis | A0A0F2E7M6 | plasminogen | [78] | |
| Streptococcus pneumoniae | A0A0H2US80 | plasminogen | [87] | |
| Streptococcus pyogenes | P68777 | uPAR/CD87 receptor on human cells, plasminogen | [33,88] | |
| Streptococcus suis | Q3Y454 | plasminogen | [89] | |
| Glucose 6-phosphate isomerase | Lactobacillus crispatus | K1MKZ7 | laminin, collagen | [90] |
| Glutamine synthetase | Lactobacillus crispatus | D5GYN9 | fibronectin, laminin, collagen I, plasminogen | [90] |
| Mycobacterium tuberculosis | A0A0H3LHU4 | plasminogen, fibronectin | [91] | |
| Bifidobacterium lactis | C2GUH0 | plasminogen | [81] | |
| Hsp60 | Chlamydiae pneumoniae | P31681 | adhesin | [29] |
| Lactococcus lactis | P37282 | yeast invertase | [54] | |
| Legionella pneumophila | Q5X762 | adhesin | [30] | |
| Clostridium difficile | Q9KKF0 | adhesin | [27] | |
| Haemophilus ducreyi | P31294 | glycosphinngolipids | [34,35] | |
| Helicobacter pylori | Q8RNU2 | adhesin | [28] | |
| Lactobacillus johnsonii | F7SCR2 | adhesin | [52] | |
| Listeria | Q8KP52 | adhesin | [32] | |
| Salmonella typhimurium | P0A1D3 | mucus | [42] | |
| Hsp65/Cpn60.2/GroEL2 | Mycobacterium tuberculosis | A0A0H3LCC3 | CD43 on macrophage surface | [92] |
| Leucyl aminopeptidase | Mycoplasma hyopneumoniae | Q4A9M4 | heparin | [93] |
| Malate synthase | Mycobacterium tuberculosis | P9WK17 | fibronectin, laminin, epithelial cells | [38] |
| Glutamyl aminopeptidase | Mycoplasma hyopneumoniae | Q4AAK4 | plasminogen, heparin | [47] |
| Leucyl aminopeptidase | Mycoplasma hyopneumoniae | Q4A9M4 | plasminogen, heparin, DNA | [48] |
| Ornithine carbamoyltransferase | Staphylococcus epidermidis | P0C0N1 | fibronectin | [94] |
| Peroxiredoxin | Neisseria meningitidis | A0A125WDU3 | plasminogen | [20] |
| Streptococcus agalactiae | E7S2A7 | heme | [95] | |
| Phosphoglycerate kinase | Streptococcus oralis | A0A0G7HBY7 | plasminogen | [77] |
| Streptococcus agalactiae | Q8DXT0 | plasminogen, actin | [83,96] | |
| Streptococcus pneumoniae | Q8DQX8 | plasminogen | [97] | |
| Phosphoglycerate mutase | Bifidobacterium lactis | P59159 | plasminogen | [81] |
| Streptococcus oralis | E6IYJ0 | plasminogen | [78] | |
| Pyruvate dehydrogenase | Mycoplasma pneumoniae | P75391 | fibrinogen | [36] |
| Pyruvate kinase | Lactococcus lactis | Q07637 | yeast invertase | [54] |
| Superoxide dismutase | Mycobacterium avium | P53647 | adhesin | [98] |
| Triose phosphate isomerase | Streptococcus oralis | E6J203 | plasminogen | [78] |
